mobile big data
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MOBILE BIG DATA THE RIGHT INTELLIGENCE, RIGHT NOW !
Intelligence: The knowledge of an event, circumstance
- received or imparted
Brian Blackmarr
Fusion Labs, Inc.
April 2014
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CONTENTS
I. EXECUTIVE SUMMARY………………………………………………………………………....05
II. INTRODUCTION………………………………………………………...................................06
III. “WHY” IS MOBILE BIG DATA A TRUE BUSINESS ADVANTAGE?................................... 09
A. Empowering Better Decisions……………………………………………….….. 10
High Knowledge Intensity : The Three Vs of Data……………………….….. 11
Liquid Data, Free Range Data and Data Combo……………………….… 12
Customer Facing: Living at the Edge…………………………………………..13
B. Minimizing Decision Latency, Speeding Time to Value…………………… 14
The Need for Speed and the Race to Zero…………………………………. 14
Real Time is Money: Halting Fraud and Enabling Mobile Marketing…... 16
Democratizing Intelligence…………………………………………………...….16
C. Leveraging Market Trends and User Preferences………………………….. 17
Petabyte is the New Terrabyte and Gigaflop Blow-Out………………... 18
Crowdsourced Intel: Likes, Tweets and Twerks………………………….…. 20
BYOE Phenomenon………………………………………………………………. 21
D. “Why” Mobile Big Data Takeaways…………………………………………… 21
IV. “WHAT” ARE THE CORE CAPABILITIES OF MOBILE BIG DATA ?...............................23
A. Industrial Strength Processing of Big Data……………………………………..23
MPP, Cloud Option and HAL Jr……………………………………………….. 24
Speed Dating Data: Hadoop/MapReduce………………….……………. 25
Dark Data, Active Archive and High Density Processing………………... 27
Fabric-Like InfiniBand Topology……………………………………………….. 27
B. Mobile Based Interfaces, Access and Applications………………………. 28
User Mobility with Preferred Platform…………………………………………. 28
Provisioning Actionable Intelligence…………………………………………. 29
Mobile Data Capture and Platform Transparency……………………….....30
C. Avanced Analytics Derrived Intelligence……………………………………...31
Strategic Analytics and Analytics in Motion…………………………………..31
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Complex Event Processing……………………………………………………… 32
Soft Science: Market Listening and Data Visualization…………………… 33
Self-Serve Analytics………………………………………………………………. 33
D. “What” Is Mobile Big Data Takeaways…………………………………………35
V. “WHERE” DOES MOBILE BIG DATA FIT BEST? .............................................................36
A. ROI Rich Environments Profile…………………………………………………….37
Big Data Extension……………………………………………………………….. 37
Financially Valued Actions……………………………………………………… 38
Complex Event Response………………………………………………………. 38
Customer Facing Edge………………………………………………………….. 39
B. Real World Examples ……………………………………………………………. 39
Minimizing Disruption in Complex Transportation Network……………….. 39
Edge Worker Communication and Citizen Mobilization………………….. 40
Improved Customer Retention and Cross Selling…………………………...40
Accelerating Product Claims Response……………………………………... 41
VI. RECOMMENDED MOBILE BIG DATA BEST PRACTICES……………………………….42
A. Organizational Recommendations…………………………………………… 42
Management: Stand and Deliver…………………………………………….. 42
Empiricists vs. Analysts: MIA Skills……………………………………………. 43
User and Tech Staff Preparation………………………………………………. 44
B. Implementation Best practice…………………………………………………. 44
Make vs. Buy vs. Both…………………………………………………………….. 45
Interative Piloting with Metrics…………………………………………………. 45
Step-Wise Extension……………………………………………………………… 45
GLOSSARY OF TERMS AND ABBREVIATIONS………………………………………………… 47
©2014 by Fusion Labs, Inc. All rights reserved. Reproductions in whole or in part are prohibited except by written
permission. Email requests or feedback to [email protected] Product and company names mentioned
herein may be trademarks and/or registered trademarks of their respective companies.
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ABOUT THE AUTHOR
Brian Blackmarr is the Chairman and a co-founder of Fusion Laboratories Inc. Brian
is a former IBM senior engineer and has authored more than 200 technical articles
and publications. He is a Registered Engineer in the State of Texas and has more
than 20 years experience serving as a NASDAQ company Director and a Trustee of
the A+M Research Foundation. He’s personally designed and developed
numerous statistically based decision models addressing complex situations in a
variety of industries (banking, electronics manufacturing, airlines, aerospace, oil
and gas, federal and state agencies, U.S. military, etc.), has authored technical
journal reports and taught senior IT management seminars internationally. Brian has
an MS in Operations Research from the Mechanical Engineering School of the
University of Texas at Austin and has received awards and nominations including
High Tech Exporter of the Year, E+Y Entrepreneur of the Year, etc.
ABOUT FUSION LABORATORIES INC.
Fusion Laboratories Inc, Fusion Labs, is headquartered in Dallas, Texas with offices in
Charleston, SC. and Houston, Tx. Fusion Labs is focused on the development and
support of a variety of specialized application software. Fusion Labs provides a
suite of proprietary software to the large non-profit foundation market and
previously provided proprietary advanced supply chain software through its former
subsidiary, RFID Systems Inc. Fusion Labs and its predecessor BRBA ( became
Brightstar Technologies, a NASDAQ company ) have consulted internationally
regarding complex large scale information systems design, development and
operation and have provided ongoing hosting and managed services support for
major international applications.
Fusion Labs has developed numerous mobile platform commercial applications for
retail, medical, insurance and financial sectors and recently celebrated
exceeding 100,000 downloads of its ChromeRDP utility, a Mobile application cross-
platform ulitily developed by Fusion Labs in partnership with Google Inc.
Additionally, Fusion Labs has several major Mobile platform development efforts in
process directed at medical and health care specific environments. Fusion Labs
also has channel partner relationships with major Big Data related suppliers
including SGI., Silver Peak, Solid Fire, Centerity, etc. and is regularly a joint sponsor
of Big Data related events and conferences. In the U.S. Fusion Labs also supplies
and supports Fuzed, a high security oriented social media type system for
enterprise employee interconnection and communication. The website
fusionlabs.net provides an overview of the Fusion Labs proprietary software and
service offerings
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I. EXECUTIVE SUMMARY
Mobile Big Data is above all a practical business tool having a unique capacity to
provision easy to use and up to the minute business intelligence to decision makers
at all levels and all locations within an enterprise. Although Mobile Big Data
continues to rapidly evolve and improve, it’s based on the consolidation of solidly
mature technologies and its functional capabilities have been deployed to
address a number of genuine real world, and fully ROI driven, business needs.
The clear objectives of Mobile Big Data are to (1) enable better quality decisions,
especially in dynamic and complex environments and (2) reduce the decision
latency and thus reduce the “Time to Value “ of business decisions and optimize
outcomes with time critical opportunities. Mobile Big Data meets these objectives
by effectively discerning and delivering multi-type multi-source derived business
intelligence from internal files, IoT liquid data and social media. These strong
Mobile Big Data capabilities represent a convergence of the massive data
handling capabilities of Big Data, the user preferred intuitive interfaces and
delivery of pervasive Mobile platforms and the sophisticated data analysis of
Advanced Analytics tools. The functional confluence of these established
technologies provides the proven capabilities of Mobile Big Data. The business
advantage of using those capabilities will be a 20% improvement in all financial
metrics per the Gartner Group.
The sweet spot for Mobile Big Data deployment starts with enterprises having Big
Data in place, those with customer facing edge workers (knowledge workers at
the organization’s physical and logical perimeter) and complex event based
environments with ever changing variables. ROI rich deployment opportunities
may also include financial transaction based organizations with high value
activities, fashion oriented retail, data driven medical, complex airline/rail
networks, public utilities, multi-level manufacturing and major public sector
organizations. In each case the ability to provision timely and fully actionable
business intelligence directly to the enterprise knowledge workers is the business
advantage of Mobile Big Data.
Certainly “ the winds of change are sweeping across the land “ is a gross over
statement, but just as certainly, a variety of practical needs and technology
factors now make Mobile Big Data, and the business intelligence it can cost
effectively provision, a valuable business tool and potentially as a true competitive
edge.
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II. INTRODUCTION
The mission statement of this white paper is to introduce and outline the currently
available and ever evolving capabilities of Mobile Big Data. Taken separately, the
three primary component elements of Mobile Big Data are significant in
themselves. Equally impressive are (1) Big Data’s ability to efficiently handle
today’s immense and disparate data files, (2) Mobile platform’s ease of use for
directly delivering user information and (3) the ability of Advanced Analytics to
efficiently discern valuable business intelligence. Taken together, these distinct
technologies combine to provision seamless and timely business intelligence in
support of the targeted user’s decision making. Facilitating a more effective user
decision making process is especially important with the complex and time critical
decisions common to rapidly changing enterprise environments. The combined
Mobile Big Data technical capabilities can also assure that users receive cross
referenced information derived from multiple disparate input data sources,
including unstructured, and providing a solid contextual point of reference. The
net Mobile Big Data objective is better quality decision making with significantly
reduced decision latency.
This white paper will introduce the Mobile Big Data features and functions
including a general overview of the business case rationale and potential ROI
available through enterprise level implementation. The white paper’s enterprise
level orientation is primarily due Mobile Big Data always needing to show a valid
ROI, so implementation costs today will likely require a degree of scaling. To better
illustrate the practicality of Mobile Big Data concepts a set of enterprise level
examples are included. Mobile Big Data is continuing to evolve rapidly, both from
a technical capabilities standpoint and from a business case/ROI perspective.
Because Mobile Big Data continues to evolve, the contents of this white paper
should not be considered as fully definitive. A comprehensive tutorial for Mobile Big
Data implementation would be premature. This white paper serves as an
introduction to Mobile Big Data concepts and as a general guideline for its
potential enterprise level deployment.
Largely for practical reasons, the truly accurate white paper title of “Mobile Big
Data - Provisioning Vital Decision Support Information to the Appropriate Enterprise
Level Persons in a Timely and Actionable Manner “ has been greatly shortened.”
Nonetheless, the focus of this white paper is the ability of Mobile Big Data to
improve and speed the decision processes typical of an enterprise and to provide
a significant ROI as a result. In this context a serious effort has been made to
minimize the hype seemingly attendant with any such emerging technology. This
white paper is primarily directed at addressing the following key questions;
(1) Why is the deployment of Mobile Big Data a true business advantage?
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(2) What are the principle components and capabilities of Mobile Big Data?
(3) Where does Mobile Big Data likely fit best and provide a solid ROI?
(4) What Best Practice recommendations apply to Mobile Big Data?
The white paper’s ROI based focus derives from the fact that the rationale for
significant technology investment should be driven by its ability to effectively
achieve specific and well defined business requirements (with measurable results
metrics and thus calculable ROI) rather than being an implementation based on
technical capabilities. Mobile Big Data is also best considered as being highly
individualized and customized, definitely not a generic “plug and run” type
technical offering. It should be noted that successful to-date Mobile Big Data
examples (including described herein) have been specific business needs driven
and have avoided the generic “ hammer in search of a nail “ type approach.
In moving ahead with a Mobile Big Data implementation the white paper’s
recommended methodology outlines a fairly cautious sequential stepwise effort
rather than being a “Big Bang Theory “approach. The typical implementation
gating factors for Mobile Big Data are not unique to the technology and clearly
include (a) senior management buy-in and visible support (b) end-user
management buy-in and visible participation (c) significant technical staff skills
ramp up and (d) solid user start-up support with full ROI based feedback. Properly
addressing the organizational related issues of a Mobile Big Data deployment is at
least as important as properly addressing the technical concerns. Technically
compounding Mobile Big Data implementation, however, is likelihood for the
inclusion of unstructured data (sensors, etc.) and the potential usage of somewhat
irregular external data (social media, etc.). A variety of Mobile Big Data
implementation alternatives exists (e.g. buy vs. build, etc.) to mitigate possible
technical issues and these are also included.
A general overview of Mobile Big Data concepts best begins with a visualization of
the confluence of the three primary component elements as shown by Figure 2.1.
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Figure 2.1: Component elements of Mobile Big Data
Figure 2.1 visualizes the confluence of the primary Mobile Big Data components of
Big Data, Mobile platforms and Advanced Analytics to enable the seamless
provisioning of fully actionable business intelligence to the decision making
knowledge workers at all levels and locations of the organization.
The primary sourcing of the information contained in this white paper includes
direct experience, a variety of technical publications and the specific capabilities
of current product offerings.
Big Data
Advanced Analytics
Mobile Platform
MOBILE BIG
DATA
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III. “WHY” IS MOBILE BIG DATA A TRUE BUSINESS ADVANTAGE?
The clear focus of Mobile Big Data is intelligence provisioning for decision making
employees, especially those in complex environments. The business value of
providing effective decision support intelligence is always dependent on the
situation but can be quite considerable. To emphasize the need to evaluate the
deployment of Mobile Big Data as a business advantage for intelligence
provisioning, versus just continuing with a “ business as usual “ approach, consider
these findings from several recent studies;
(1) Knowledge workers, persons who regularly make some form of business
decisions, currently represent approximately 80% of all enterprise level
employees and less than half believe they’re appropriately trained and
properly supported.
(2) Approximately 80% of knowledge workers regularly access social media
and many use the information obtained in their business related decision
making.
(3) About 70% of knowledge workers utilize informal data analysis tools
(spreadsheets, ad hoc reports, etc.) to define their own intelligence, which
is then used in decision making.
(4) Over 50% of knowledge workers consider the decision support intelligence
formally supplied to them by their IT function not to be complete, timely or
actionable.
Clearly, knowledge workers aren’t that satisfied with the business intelligence they
now receive formally so have moved on to informally obtain and analyze their
own decision support information. Under this scenario complex business decisions,
some having critical customer facing and bottom line implications, are regularly
being influenced by, and perhaps even based on, disparate information typically
taken out of context and of indeterminate accuracy.
The business advantage “Why“ of Mobile Big Data derives directly from this serious
situation and its ability to provide timely, easy to use and fully actionable business
intelligence to decision makers at all levels and locations of the organization. These
Mobile Big Data capabilities are directed at (1) empowering higher QUALITY
decision making (especially in complex environments) and (2) reducing the
LATENCY of the decision making process to take better advantage of time critical
opportunities and thus speed the “ Time to Value”.
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To quote David Boyle, SVP of Insight at EMI Music, regarding provisioning business
intelligence to their knowledge worker employee base;
“I want quick, but I want complex.”
Delivering such business intelligence is exactly the focus of Mobile Big Data. The
fairly extensive range of likely enterprise benefits that could result from improved
business intelligence provisioning, in the opinion of the actual decision makers, is
displayed in Figure 3.1.
Figure 3.1: Potential Benefits of Better Business Intelligence
Source: The Economist
It’s key to note from Figure 3.1 that better decision quality and faster decision
making are the primary drivers for most of the majority of these important potential
benefits.
A. EMPOWERING BETTER DECISIONS
The interest in improving decision quality frequently relates directly to the poor
business intelligence now being supplied to decision makers. Today’s business
intelligence may be (1) restricted by being based on limited data types and/or
sourced only from the internal data of the Enterprise Data Warehouse (EDW), (2)
based on stale information that’s become more historical than actionable or (3)
delivered in a confusing and hard to use format. The foundational capabilities
intrinsic to Mobile Big Data focus totally on the provisioning of timely intelligence
derived from multi-source multi-type data and delivered in an intuitive and
actionable format.
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High Knowledge Intensity: the Three Vs of Data
Business intelligence must be knowledge intense, have solid content, to be
valuable and achieving that goal begins with the raw input data being analyzed.
In producing effective business intelligence it’s key to utilize the best possible input
data for analysis. To do this it’s essential to understand and effectively deal with all
“three Vs “of source data.
Volume - How much source data is available and appropriate for analysis?
Velocity – How fast is the source data flowing and what is its useful shelf
life?
Variety – How many distinctly different types of source data are
appropriate?
The ability to successfully account for and use these three Vs of source data can
have a huge impact on usability of the business intelligence in support of critical
decisions. Clearly there will always be compromises and tradeoffs to the general
rule that the more distinctly different data types/sources, and the larger the
sample size, the better will be the resultant intel. The judicious use of front end data
filtering, compression and sampling techniques may also seriously lessen the
processing burden by reducing storage and analysis volumes as much as possible.
Producing knowledge intense intelligence for an affordable cost is at the heart of
Mobile Big Data. Figure 3.2 displays the various data types that intelligence
analysts consider should be used by them in defining business intelligence.
Figure 3.2: Preferred Business Intelligence Input Data Types
Source: Several Recent Surveys
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As displayed, decision support intelligence should be derived from a wide variety
of distinct data types and data sources.
Liquid Data, Free Range Data and Data Combo
The term “liquid data“ refers to the streaming data available as a result of the
emerging “Internet of Things” (IoT). Recall that today the internet connects far
more “ things“, devices of all types, than people and this IoT trend is accelerating.
The available streaming data is often derived from sensors, video feeds, etc. and
typically consists of largely unstructured bit streams. The processing implications for
effectively analyzing the IoT streaming data, with the huge data volumes involved,
have recently driven many Big Data initiatives. Liquid data is an ever flowing
current of semi-structured or unstructured bits as opposed to the neat stacks of
discrete data elements (often relational in nature) typically found in an enterprise’s
Master Data Base (MDB). For process control, etc. this is important data, frequently
with a short shelf life, and the use of periodic sampling and range limit monitoring
can be appropriate. Using these methodologies to analyze steaming data to
derive business intelligence, in support of and real time decision support, is
practical, however, the set up may be a non-trivial effort. The detection of a liquid
data analomy typically requires an algorithmic based analysis to determine if the
inconsistency was likely random or the beginning indicator of an important
pattern. This type analysis of liquid data may require considerable real time (and
in-memory) processing capability using fairly specialized analytical tools. With such
provisions Mobile Big Data can fully support liquid data as a data type.
The term “free range data“ describes most of what may be obtained directly from
social media sources. The good news is that, unlike liquid data, social media data
typically has some degree of structure; the bad news is that this structure is likely to
be in the form of textual files bordering on gibberish. Along with understanding the
anticipated variances and inflections of text data (similar to voice recognition)
there’s also an embedded amalgam of one-off abbreviations, emoticons, etc.
Free range data can appear to wonder all over the language landscape and
serious efforts to categorize it, boil it down or filter it run the risk of reducing or losing
the spontaneity and raw meaning of the data - often the main value proposition
for using a social media data. However, the advanced social media analytics
available with Mobile Big Data do a reasonable job of decoding a garbled stream
of free range type data to identify nuggets (consumer sentiment, competitor
negatives comparisons, etc.) of true market intelligence. Social media data is
another distinct data type that can be included in business intelligence through
using specialized Mobile Big Data Advanced Analytics tools.
To improve decision quality often requires providing users with cross referenced
“combo data“ intelligence derived from multiple data types and independent
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sources. For example, complete and actionable provisioned intelligence could be
derived from customer demographic files, competitor pricing and market trending,
transaction history, sensor driven data (temperature, etc.) and public sourced
information (interest rate fluctuations, etc.). Decision makers often require the
convergence, or at least general agreement, of data from multiple sources in
order to feel comfortable making complex decisions. Mobile Big Data is oriented
to processing combined and cross referenced data types from multiple and
sources to generate intelligence a fully contextual manner (inclusive of associated
time references, etc.).
Customer Facing: Living at the Edge
To further accelerate business processes of all types, enterprises have increasingly “
pushed “ key decision making out to the edges of their organization. “Edge
workers “now include many types of remote location employees, field sales
persons, work from home staff, off-site call centers, etc. Many edge workers are
customer facing, and thus required to make on the spot decisions that may be
vital to generating or retaining customers, preserving enterprise assets, etc.
Customer facing service concerns and challenges are important for every
enterprise; the difference comes in how this activity is handled. A competitive
business advantage may come by empowering the customer facing employee to
be able to say “ here’s specifically what we will do right now to resolve this matter
“ as opposed to “ let me check on this matter and get back with you about what
we may be able to do ”. The typically HQ based marketing, product
development, etc. staff also require decision support intelligence but historically
are more likely to have situation targeted support than the edge workers.
Customer facing edge workers obviously need intelligence provided in a rapid
but, just as important, in an easy to understand and actionable manner. Edge
workers are already familiar with mobile platforms (ubiquitous smart phones and
tablets) so these devices are an excellent means to deliver intelligence. Figure 3.3
displays where today’s knowledge workers are typically located.
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Figure 3.3: Typical Knowledge Worker Location
Source: Several Recent Surveys
As can be noted in Figure 3.3 more than half of today’s knowledge workers are
regularly making their business decisions outside an organization facility, employee
mobility is a fact. With regard to the Mobile Big Data associated cost of providing
edge worker support, most enterprises should note that major elements of the
required infrastructure are very likely already in place. The needed mobile Wi-Fi
connectivity is fairly common and in many cases an available Big Data capacity
of sufficient size to get started exists. The required additional elements likely consist
mostly of interfaces and activity specific applications that may be sufficiently
inexpensive to get started with an initial Proof of Concept (POC) or pilot phase.
The POC phase should then provide feedback sufficient to better define and
evaluate the ROI for moving to Mobile Big Data based edge worker support.
B. MINIMIZING DECISION LATENCY, SPEEDING TIME TO VALUE
In dynamic and complex in environments the ability to immediately make a
decision (real time or near-real time) may be highly important. In many cases a
very accurate decision, but one made after the opportunity has expired, can
have little to no value. There are numerous examples where a late decision is in
effect, no decision at all. In addition to avoiding a lost opportunity, accelerating
the decision cycle can reduce the “time to value “(often expressed as when it can
be booked), typically the sooner the better. The blindingly fast intelligence delivery
of Mobile Big Data (close to instantaneous) can be critical to its real world value
proposition.
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The Need for Speed and the Race to Zero
Largely as a result of the Great Recession enterprises of all types found that in order
to be competitive they required much faster decision cycles for issues like pricing
determination, spot promotion definitions, etc. The mantra of reduced time to
market now has traction well beyond the marketing department and affects all
areas of business. Product development cycles may now be halved and customer
facing employee responses have become close to real time. In this context any
enterprise action underlying the timing of business decisions, including intelligence
development, must be accelerated or opportunities will be lost to a more agile
competitor.
The “Race to Zero “is another time to value related concept and term. It basically
states a desire by equity/commodity traders to reduce the latency of their trades
to zero. Already measured in nanoseconds this trading latency reduction has been
the subject of numerous projects and major Big Data investments. Certainly, the
process of actually deciding to make the trade (often based on near real time
situational analysis) is also a valuable element of the overall trade process cycle
and another area for acceleration. Time to Value is a common concern for all
types of financially valued activities and the overall trade cycle is another key
target for near real time intelligence provisioning with Mobile Big Data.
As an emphasis of the need for improving the timeliness of current data analysis,
and thus improving the “ freshness “ provisioned business intelligence, Figure 3.4
displays current business intelligence analysis cycles.
Figure 3.4: Current Business Intelligence Refresh Cycle
Source: Several Recent Surveys
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Sure, real time analytics (less than 5%) can be costly but it’s fairly incredulous that
anyone could consider a monthly refreshed (35% incidence) analysis as
actionable business intelligence. Perhaps these analyses are status reports, but
even in long cycle manufacturing and construction all such information is strictly a
historical record by that point. It’s a small wonder the majority of today’s
knowledge workers use their own resources and informal means (smart phones,
internet access, spreadsheets, etc.) to supplement or replace the stale business
intelligence they now receive.
Real Time is Money: Halting Fraud and Enabling Mobile Marketing
In many real world situations, fraud being a good example, to be effective
decisions must be made while the actual transaction is in process. In a variety of
credit card and other financial transaction related processes (credit application
processing, claims adjusting, etc.) semi-automation is common, but such systems
are frequently too slow to be of assistance while the transaction is in process or are
based on limited data sources and ill-defined criteria (location zip code,
transaction total amount etc.) likely to be ineffective. With Mobile Big Data based
connectivity, specialized analytics and near real time delivery, it’s possible to
provide in-process transaction specific intelligence (tailored to the specific
situation and cross referenced from multiple data sources) for well informed
decision making, again while the transaction is in process. This Mobile Big Data
ability is especially valuable for halting in-process fraudulent transactions to
minimize the associated losses.
Conversely, there are transactional circumstances where real time intelligence
may be valuable not for the purpose of halting in-process fraud but rather for the
purpose of improving sales revenues. These upside transaction based situations are
often available to customer facing edge employees (call center staff, field sales
account reps., etc.) were the situation frequently includes temporary time-
dependent opportunities, either close the deal now or it’s gone for good. In such
situations Mobile Big Data can provide real time knowledgeable guidance ( based
on individualized intelligence from past preferences, usage behaviors, click
through history, market trending, etc. ) in-process during help desk calls, pricing
quote determinations , product feature and availability queries, etc. The ability of
the employee to knowledgeably offer a personally tailored promotion, a unique
“one-off “ pricing quote, etc. can improve close rates and increase business
volume, all with existing staff. An ROI based on increasing business revenues may
even be better (for its potential future impact) than one based entirely on cost
reduction. The referenced real world success examples include the Mobile Big
Data enablement of real time cross selling and mobile marketing.
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Democratizing Intelligence
Post great recession enterprises have pushed decision making out to the
organizational edges and significantly flattened themselves, reducing
organizational layers to get “lean and mean”. In this context it’s appropriate to
strongly consider democratizing business intelligence. This intelligence
democratization includes flattening and speeding all intelligence associated
processes and especially provisioning intelligence directly to first level
management. In many organizations the effectiveness of first level management is
critical to the organization hitting targeted goals. First level management
(especially on the customer facing edge) often has the highest customer impact
with a major opportunity to gain or retain business. Continuing to provision
intelligence to the organization’s senior management and marketing teams, often
within the corporate headquarters monolith, is as important as ever, but the SAME
type intelligence provisioned to them should also go to first level management at
the SAME time. Traditionally business intelligence may be initially routed through a
delay ridden management review (and unfortunately filtering) process prior to
being released. The associated adjustments and slowed delivery can make the
intelligence far less actionable where it may count the most.
The screening, filtering and senior management approval associated with business
intelligence should occur during the analytics algorithm and recommended
response definition stages. The experienced intelligence analysts should be
thoroughly instructed, given the analytics tools needed to do their job properly
and then trusted to produce and distribute actionable intelligence. Once the key
experience based business rules and alert/action definitions are in place and
working well, the same resultant intelligence should go directly to all appropriate
persons, at all levels of the organization, without further review and “ aging “.
Unfortunately the old saw that “knowledge is power“ is sometimes used by the HQ
staff as leverage over the field staff in an unproductive philosophy of “share
nothing “or share only bits of data. For maximum effectiveness, and to provide the
best ROI, the business intelligence produced by Mobile Big Data (without filtering
and adjustment) should be shared equally and shared ASAP. The powerful
processing, analytics and delivery capabilities of Mobile Big Data should be
applied to (1) develop the best business intelligence possible and (2) the
democratize this intelligence through immediate delivery, thus providing a true
business advantage.
C. LEVERAGING MARKET TRENDS AND USER PREFERENCES
In addition to the functional capability reasons to deploy Mobile Big Data, several
current market trends and general user preferences also contribute to its potential
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as a true business advantage. Instead of ignoring, or working around, these
external environmental issues, Mobile Big Data takes full advantage of them to
help gather data, lower deployment costs and improve user acceptance. For
example, per recent IDC reports, almost a billion smart phones were sold during
2013 and their growth is expected to continue with at least a 15% CAGR. Given this
general environment it’s totally logical for Mobile Big Data to ultilize smart phones
(and tablets) to deliver near real time business intelligence to knowledge workers.
This current market trend and several others encourage and facilitate the cost
effective deployment of Mobile Big Data.
Petabyte is the New Terrabyte and Gigaflop Blow-Out
Almost without exception, the huge amount of data that enterprises regularly store
and process for all purposes, including business intelligence, would have been
unimaginable only a few years ago. For years annual IT budgets have included
major CAPX increases just to accommodate these volumes and the associated
capacity for ever expanding storage, processing and communication. The
increased demand now means that for many enterprises the required data
storage capacity is increasingly being described in petabytes (Note; 1 PB = 1000
TB) with required processing capacity edging ever closer to petaflops (Note: 1 PF =
1000 TF). The huge capacity related costs have been a backbreaker for many IT
shops and a practical gating factor for advanced information system deployment
of all types, including business intelligence.
Fortunately to address this serious situation Moore’s Law (i.e. due largely to ever
greater chip densities, and other associated ongoing technical improvements, the
cost per unit of capacity will halve about every two years) has come into play and
has helped considerably. There has lately almost been a blow-out sale for
gigaflops (1000 floating point processor transactions per second), with associated
storage unit cost drops, and this combined with increasingly efficient chip level
hardware architectures and improved software have greatly helped the situation.
Figure 3.5 illustrates the ever lower cost gigaflop.
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Figure 3.5: GIGAFLOP Cost per Unit
Source: Wikipedia
This cost per unit lowering process is forecast to continue with the future
implementation of advanced processor designs combined with the potential
move to non-silicon based technologies. As with other processing intense activities
these continued cost per unit reductions are critical to future Mobile Big Data
upgrades and in many cases the issue will come down to “ how much is
affordable? “Rather than “ can we do it?”
In addition to cost concerns is the issue that the significant processing requirements
of performing business intelligence analytics can’t take precedence over an IT
operation’s ability to perform its other responsibilities, namely keeping the lights on
(i.e. accounting, supply chain, etc.). A general fact of life is that there will always
be a shortage of available IT resources; further emphasizing the importance of a
metrics based ROI approach for all Mobile Big Data deployments. Of note with
regard to IT resources issues are (1) increasingly the business intelligence functions
may NOT report through the IT organization but rather through functional user
group management who are closer to its value proposition and (2) cloud services
are increasingly available to at least partially offset potentially disruptive
processing loads by handling Mobile Big Data associated processing off site. Of
course, with a Cloud supported approach the source data (which can be
extremely proprietary) and the resultant business intelligence (which can be highly
confidential) are not in total control and thus become subject to significant
security and reliability concerns.
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Crowd sourced Data: Likes, Tweets and Twerks
When considering anything to do with consumer related business intelligence it’s
important to consider the following from USA Today.
- 96% of the U.S. internet connected population access social media.
- Mobile devices now outnumber the earth’s human population.
- Facebook has over 200 billion friends and does 6 billion likes/day.
- Facebook has processed over 8 trillion messages.
If those statistics don’t get your attention, nothing will. And yet it has been difficult
and slow for most enterprises to obtain much value from this virtual landslide of
consumer information. Sure, regular market trend summaries are available for
purchase and subscriptions to general analysis services are available, but cutting
edge business usage of social media data types for business intelligence still isn’t
common. Capabilities intrinsic to Mobile Big Data may be the key to facilitating
the practical business usage of social media derived intelligence.
As previously reviewed most social media data is only semi-structured and quite
often mostly text based. Admittedly the meaningful analysis of text information is
perhaps more an art form than a science and certainly best addressed by
experienced analysts. While key word and phrase identification is fairly simple,
unless care is taken to retain the context of such information any business
importance may be lost. Without the proper context mere reference occurrences
and count numbers may be misleading. To retain the context of social media data
can unfortunately entail retaining relatively large amounts of support data.
However, it’s definitely possible to produce meaningful social media sourced
intelligence (consumer sentiment trends, competitor trending comparisons, etc.)
with specialized analytics tools.
Practical Mobile Big Data enterprise usage of social media derived intelligence
has included the quick identification of emerging customer service/relationship
issues and proactively initiating responsive actions to address them. Some
organizations have used social media based information to trigger quality actions
and pricing adjustment decisions without waiting on direct customer feedback.
Other enterprises have gone to the extent of immediately sending a responsive
reply to customers expressing problems or concerns (hopefully, not in an
anonymous or derogatory manner). In some cases an attached file or address is
included to further explain correct product usage or appropriate service options.
In effect, the enterprise uses social media to take proactive actions in an attempt
to make real time customer situation improvements. Please recall that Mobile Big
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Data can be used to communicate directly with customers should such action be
warranted by recalls, alerts, etc.
BYOE Phenomenon
Please also consider the below phenomenon when reviewing market trends
having a significant effect on many enterprises.
- 2/3 of U.S. citizens own a smart phone and 1/3 own a tablet.
- 60% of U.S. employees now use a BYOD platform for business.
The term Bring Your Own Everything (BYOE) originated with employees being
allowed to Bring Your Own Device (BYOD), typically smart phones or tablets, to use
in their business related activities – the real world situation has now gone way
beyond that point. It seems most employees prefer to have their own familiar easy
to use personal device and don’t much care if they, rather than the employer,
pay for it. They like doing this so much so that the Yankee group found that 60% of
U.S. employees currently use BYOD platforms including a significant number of folks
doing so covertly because their employer specifically prohibits BYOD (typically for
security reasons). At a surface level BYOD seems fairly logical and is quickly
becoming the rule rather than the exception. More problematical is the BYOD
extension to BYOE where employees are increasingly using their favorite smart
phone applications for business and tapping into whatever information sources
they feel like to obtain business related information. Other than monitoring
employees closely (through Wi-Fi, etc.) and enforcing penalties for employees
caught doing BYOE there’s not much an employer can do. Yet employers are
typically considered responsible for the actions of their employees (especially
when performing job related duties) – when this BYOE situation causes security
breaches, incorrect actions, etc. the resultant liability and potential customer
problems will fall on the employer.
In many cases, BYOE is occurring because employees find it easier, and possibly
more timely, to go around their enterprise provided systems, where they even exist.
The best BYOE response may therefore reside in doing a better job of providing the
employee with true intelligence they can’t readily obtain elsewhere, in a timely
and easy to use manner. To do this means enterprise support systems require well
developed (not merely converted) applications that operate uniformly across the
various disparate BYOD Mobile platforms that are likely to be encountered. Once
the enterprise provides such applications experience has shown users will be quite
willing to use well targeted mobile applications. By incorporating providing mobile
BYOD enterprise applications through the overall Mobile Big Data system these
become a key element of effective end to end user intelligence support.
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D. “WHY” MOBILE BIG DATA TAKEAWAYS
The primary takeaway points from the WHY Mobile Big Data section include;
1. Over half of today’s employees are knowledge workers regularly making
business decisions and more than half of them don’t believe they receive
adequate decision support intelligence.
2. Many knowledge workers are customer facing edge employees regularly
making key decisions and more than half require a high degree of mobility.
3. About 60% of knowledge workers use their BYOD platforms (even where
prohibited), often using ad hoc applications to locate external data and
generate their own business intelligence.
4. Time to Decision and Time to Value are often synonymous so accelerating
all aspects of the decision making cycle is essential and doing so can have
a measurably value. The ROI metrics for enabling better quality and faster
decision making may include significant revenue increases as well as cost
reductions.
5. The knowledge intensity of business intelligence is directly dependent on
using the best available input data. Solid business intelligence typically
derives from multiple data sources with multiple data types and may
potentially include social media data and IoT liquid data input.
6. The analysis of social media sourced data and streaming IoT liquid data can
be challenging but is practical through the use of Advanced Analytics.
7. Mobile Big Data is a solid business advantage with the objectives of (1)
minimizing decision latency and (2) enabling better quality decisions.
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IV. WHAT ARE THE CORE CAPABILITIES OF MOBILE BIG DATA?
Because Mobile Big Data combines three largely distinct elements (Mobile
platforms, Big Data and Advanced Analytics) it’s appropriate to review each
component separately and in the context of their convergence. The Mobile
platform element provides intuitive and familiar user interfaces to deliver business
specific intelligence and applications. The Big Data element is directed at
efficiently performing large volume data handling and processing. The Advanced
Analytics components perform complex analyses on various data types to discern
significant patterns and cross-referenced business intelligence. The combined
Mobile Big Data system delivers on its dual goal of (1) significantly improving the
quality of complex business decisions and (2) reducing the delay, or latency, of the
decision making process.
A. INDUSTRIAL STRENGTH PROCESSING OF BIG DATA
Obviously, to handle the huge data files appropriate to Mobile Big Data and
perform almost real time complex analysis requires a major processing throughput
capacity. The required capacity is available in a reasonably economic manner
with a combination of Big Data derived technologies including large memory
footprint platforms, MPP, etc. Figure 4.1 displays the amount of “analytics only”
data (internally sourced data) that business analysts typically expect to be
regularly required for their organization in 2014.
Figure 4.1 : Estimated 2014 “Analytics Only Required Data” Volume
Source: Several Recent Surveys
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This volume of data handling and processing strictly in support of business
intelligence related analytics activities represents a significant requirement and
these volumes are anticipated to continue their rapid growth.
MPP, Cloud Option and HAL Jr.
Gone are the “Big Iron “processor days and in its place Massive Parallel Processing
(MPP) has become the future path of most processing intensive environments.
Where in the past huge transaction loads (banking, insurance, etc) drove large
scale processing requirements, now the liquid data stream handling and analytics,
all done in near real time, is a key factor for the move to MPP. MPP essentially
provides a huge collective throughput capacity derived from a massive array
(potentially several hundred) smaller and commodity type processor platforms. In
this manner each platform handles a portion of the processing with their resulting
output then being collected and coordinated into the final results. Efficiently
coordinating and managing these parallel processor arrays is an ever improving
technical process but when scaled they can provide a net processing throughput
definable in petaflops. Some specialized processor platforms can now quite
practically provide capacities approaching 2.5 petaflops. The processor
throughput cost per unit generally follows Moore’s Law but because of their size
MPP based systems can come with a hefty price tag. Mobile Big Data today, and
even more so in the future as it continues to ramp its analysis volumes, frequently
depends heavily on the MPP processors to perform complex analysis of large data
files in a timely and cost effective manner. These significant MPP costs again
emphasize the need for Mobile Big Data to specifically target essential business
requirements, rather than technical capability, as its deployment rationale.
A key concern inherent to the MPP approach is a significant potential for individual
processor platform failure. By using a large array of smaller and cheaper processor
platforms it is to be expected that the failure of one or more of these commodity
processor units may be a relatively “common “occurrence. To address this issue,
and eliminate the potential of a catastrophic single point of failure, a set of
advanced processor array operating systems and non-hierarchical component
architectures are utilized. Along these same lines data storage, and application
execution, is done in a redundant parallel manner. Processing overhead could
obviously be an issue with such complex synchronization and control methods but
near native instruction sets allow for net array efficiencies.
Of course Mobile Big Data can be initiated, and scaled in a limited manner on
existing non-MPP enterprise processing platforms. The concern here is that the
other tasks, essentially keeping the enterprise lights on, can’t suffer from the major
additional processing load required to support advanced data analytics, etc. In
many ways the Mobile Big Data processing decisions again come to a very
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practical issue of “how much can you afford ?”. In the future different processor
types (non-silicon) will continue to make multi-petaflop level processing ever more
affordable.
Also coming is improved machine learning or artificial intelligence, almost to the
level of the HAL 6000 processor of Stanley Kubrick’s “2001, A Space Odyssey “.
Today an almost HAL Junior type processor platform exists in IBM’s Watson, a huge
capacity MPP system, which is designed to proactively learn from its mistakes and
to minimize or totally negate structured programming instruction of any type.
Certainly an AI based learning machine with advanced self-correcting capabilities
could be of key long term importance to the continued evolution of complex
event Mobile Big Data.
Speed Dating Data: Hadoop/MapReduce
Somewhat analogous to the rapid fire speed dating process of learning a lot
during a short time period, the Big Data analysis of truly huge data files had to be
accelerated. It also quickly became apparent, even with high throughput MPP
platforms, that a more efficient data handling methodology was required for
unstructured data types. As a result Hadoop and Map Reduce were developed
initially to improve the efficiencies of data handling and processing for addressing
Google’s huge data bases. These tools were later generalized and made
available as open source software to provide very specialized environments. As
such they are important potential elements of Mobile Big Data.
With Apache Hadoop (the open source variant) as a data file is being entered
into the system it’s fed through a server that breaks it into user definable blocks,
replicates it and feeds it to 3 separate storage locations (ideally on 3 separate
processors) for batch analysis. These multiple data block locations are indexed
and records kept as to the correct sequence, etc. When it comes time to act on
the data blocks the application is also segmented (according to specifically what
is being performed) with replicated instruction elements being sent to the server
platforms containing the appropriate data. The various processing platform
processing results are then verified, cross-checked and may be summarized,
reprocessed again, etc. according to the specific requirements. This segmentation
of the data stream and the programming functions takes good advantage of the
MPP array architecture to drive throughput and minimize the impact of device
failure. In this manner the failure of a specific physical device is immediately
detected and there is no single point of failure for the overall processing. Due to
the fast processing speeds and the device specific instruction set the overhead
involved with Hadoop becomes less a factor and the array’s net throughput is
immense.
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Apache Hadoop can run on Linux or M/S platforms and has been available since
2005 but due largely to the nature of its open source license (without supplier
support, contracted upgrades and enhancements, dynamic security patches,
etc.) Apache Hadoop has been somewhat slow to achieve widespread enterprise
acceptance. To better address typical enterprise system needs the overall
Hadoop concepts ( major design elements, etc. ) have spawned a number of
proprietary varietal offerings, add-ons and lookalikes, all suitably “ hardened “ to
better meet commercial requirements. Whatever specific variant is used, the
Hadoop data handling concepts are an important MPP enabler for Mobile Big
Data. Its not perfect but for the right batch processing environment it may be the
best alternative.
MapReduce is a Hadoop paired operating environment and, when dealing with
distributed file systems (HDFS, etc), further reduces network traffic to significantly
improve overall throughputs. The Map portion optimally filters and sorts data being
entered and the Reduce element efficiently collects the various sub problem
results and defined final processing results. These MapReduce capabilities
automatically optimize the assigned location and processing sequence to speed
the overall processing. Although such improvements are measured in
nanoseconds, with the volumes involved this may be the only practical approach.
As an example MapReduce has been paired with the Apache Hive by Amazon
(using an Elastic MapReduce variant) on a more than 10,000 Linux processor array
and Facebook uses a Hadoop based system to operate on it’s massive 100+ PB
database. Other specialized and valuable Big Data related tools exist (SAP HANA,
Cloudera, NoSQL, etc.) but Hadoop, or a variant, can be key for the highly
scalable processing environment essential for certain varieties of business
intelligence analysis.
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Figure 4.2: Analyst’s Preferred Analytics Processing Platform(s)
Source: Several Recent Surveys
As shown in Figure 4.2 analytics users prefer their analytics processing be
performed in various locations with a growing number preferring that Hadoop be
included. The group indicating a preference for a Hadoop based analysis
platform typically prefer this approach for effectively handling disparate data
types. Also note that many of today’s intelligence analysts consider having
multiple analytics processing platforms to be acceptable approach.
Dark Data, Active Archive and High Density Processing
Most enterprises now have a vast amount of available data located at various
points of the organization and kept on a variety of storage systems. The issue is that
storing all this data is on Tier1 devices can be cost prohibitive and may lead to
totally off line storage (inaccessible for short-term requirements). Unfortunately this
valuable data resource then becomes “dark data” that for most purposes is dead.
The other concern is that of the Tier1 stored data today, approximately 80% hasn’t
been accessed in the last six months.Without an effective means to use Tier 2
(secondary disk based) or Tier 3 (typically active tape) the ever growing data
storage investment CAPX issues quickly lead to ever increasing dark data. The Big
Data answer has increasingly become active archive.
With an active archive approach data is actively managed and assigned to the
most appropriate storage type and location. Interestingly, none of the active
archive managed locations are truly off line, although some clearly have a longer
recovery cycle, so nothing ever goes dark and thus becomes of no value.
Preserving the value of data may be the 4th V of data.
Another characteristic of major data volumes is that in order to process them, for
analytics or whatever, they may have to be segmented due to processor platform
data size limitations. This data segmentation can be highly inefficient by requiring
multiple combinational processing, etc. The Big Data response is through high
density processing (actually high data density) platforms. Such processing
platforms now approach 100 TB of data for single pass under one operating
system, true “first time final” type processing. In effect this high density approach
enables valuable application consolidation that can greatly facilitate big load
analytics (huge image files, etc.) A good example of the value of these advanced
Big Data capabilities was the recent case where the enterprise was able able to
reduce a major 200 hour processing job to about 20 minutes, indeed a clear
business advantage.
Fabric-Like InfiniBand Topology
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The linkage or interconnection methodogy of processors is another focus for Big
Data throughput improvement. To address speeding these linkages InfiniBand was
developed to better support nanosecond cycle processing with active congestion
management and has resulted in further latency reduction and reliability
improvements. There are alternative connectivity methods, but InfiniBand is the
connectivity approach of choice for petaflop level processors when reducing
overall latency. InfiniBand logically allows no single point of failure while eliminating
the typical mesh based device connectivity latency (again measured in
nanoseconds) associated with their typically complex coordination and control.
InfiniBand methodology also reduces potential failure recovery impact on
throughput. InfiniBand design provides a chip level dynamic single pathing
approach for an intelligent fabric-like device interconnection. With this logical
(rather than physical) multiple interconnection pathway fabric, each logical path
is continually optimized for throughput and the net reliability of guaranteed
delivery is possible, a serious consideration for MPP based processing.
B. MOBILE BASED INTERFACES, ACCESS AND APPLICATIONS
The vital user interface of Mobile Big Data comes through the usage of today’s all
pervasive mobile devices, Wi-Fi connected smart phones and tablets. To be
effective, especially considering the time critical nature of many supported
decisions, these devices need to be running purpose built mobile applications.
Clearly the implied display limitations are critical as is the issue of having the mobile
applications work and operate in a similar manner regardless of the specific
mobile platform in use. In addition mobile devices themselves are also continuing
to grow and evolve with the addition of wearable watch based units and glasses
type display and data collection.
User Mobility with Preferred Platform
From numerous recent surveys it’s been shown decision making employees,
knowledge workers, now greatly value the mobility provided to them through their
smart phones and tablets above all other types of technology. Employees no
longer consider it appropriate to be required to utilize “standardized “devices and
to do so in a controlled environment. They want, and are determined to have, the
mobility to work from home, a Starbucks or any other site that’s convenient.
Supporting the often informal technical environment (frequently with minimal
security) has become a major enterprise concern.
Key among the employees specific mobility supported capabilities is their having a
direct, and easy to use, access to decision support information (including
proprietary pricing metrics, etc.) through their totally familiar personal BYOE
platform. They’re willing to use employer provided applications and information
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just so long as this remains as user friendly and intuitive as commercially available
alternatives (most with trivial pricing). The message here being an employer must
either offer its employees (especially edge workers) full mobility , with easy access
to enterprise provided business intelligence, or they will use their own BYOE
platforms and applications to access whatever data they feel appropriate.
Provisioning Actionable Intelligence
Current mobile platforms provide high resolution, but physically size limited, displays
and fairly HD image capture with audio. Also available are image projection
capabilities and logical keyboard input methodologies. Key for employee
acceptance and usage is developing applications specifically designed to work
well on such devices with their size and operational limitations. Again as a result
the mobile platform applications provided to employees must be purpose built
and specific to mobile platform use. Truncated standard device applications
(desktop, laptop, etc.) won’t compare well to commercially available apps and
will be a point of contention.
Practical guidelines for developing effective mobile apps include;
- Limit display information but provide numerous drill down features
- Design for touch screen user interface with user familiar icons, etc.
- Use display color and audio to emphasize alerts and/or key facts
- Avoid detailed small diagrams and dark on dark or light on light displays
- Minimize streaming volumes to speed downloads
- Require minimal user input with easy abbreviations, etc. limit keyed data
- Provide pop ups for key contextual information
- Include available reference links for follow-up (esp. with large files)
- Provide a “master long-term retention “option and offer a print option
- Always include proprietary data instructions and security warnings
- Include likely FAQ help instructions for any potentially confusing
functionality
- Don’t be dramatic but don’t be gameslike or cartoonlike
- Be as conversational as possible, using familiar slang, abbreviations, etc.
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The lessons learned from years of effective mobile app. development all serve to
make Mobile Big Data delivery processes highly intuitive and extremely user
friendly. Figure 4.3 displays the preferences of active users of business intelligence
as to what type decisions they believe are best supported by business intelligence
provisioning.
Figure 4.3: Business Decision Types Likely to Benefit from Improved Intelligence
Provisioning
Source: The Economist
From Figure 4.3 it’s obvious that most current users believe a number of different
type business decisions would benefit from improved intelligence provisioning.
Foremost among these opportunities are key business issues dealing with
identifying new markets, retaining current customers and responding to
competitive pressures.
Mobile Data Capture and Platform Transparency
In addition to mobile platform provided user interfaces they can also collect and
input some limited forms of data. This mobile data can include user activity logs,
but also extends to video and audio streams and spatial location specific
information. Retailers have already conducted tests with mobile spatial data to
determine user apparent interests (what display did the user pause to look at and
for how long, etc.) and a variety of independent subscription services can provide
mobile usage data (what books were read and when, etc.). The image and video
capture capabilities of smart phones are regularly being used for business related
purposes, including accident and adjusting records, in-store display set up, etc. In
addition, although the various internal features of mobile platforms are not
uniform, those equipped with accelerometers can deliver data on driving patterns
66%
55%
46% 41%
35%
29%
29%
26%
19%
New Market
OpportunityCustomer Satisfaction
Product and Service
ChangesCompetior Evaluation
Financial Review
CAPX Investment
Risk Analysis
Market Segmentation
Fraud Prevention
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(tendency to weave in traffic, frequency of sudden stops or rapid acceleration,
etc.) that may have significance for consumer needs or even law enforcement.
The use of mobile platforms for data collection continues to develop with the
continued enhancement of wearable devices.
In the process of providing easy to use business intelligence another key item must
be accounted for, the various non-standard, and quite specific, product
differences likely to be encountered in a BYOD device landscape. The mobile
applications must be able to look and work the same on an Android based mobile
device as they do on an iO/S platform. To address this cross-platform application
issue a variety of established utilities (including those provided by Fusion Labs) exist
to assist with development. Again, this potentially troublesome technical issue has
already been addressed by the established mobile platform support resources and
is thus available to help speed Mobile Big Data deployment.
C. ADVANCED ANALYTICS DERIVED INTELLIGENCE
Fairly advanced analytics in one form or another have been with us for some time,
the differential with Mobile Big Data is the speed at which analytics have to work
efficiently and the irregularity of the data being analyzed. Surely, given sufficient
time and attention analytics have long been able to discern the hidden patterns
and subtle meaning in even the most motley of data, the trick with Mobile Big
Data is doing this on the fly in nanoseconds. Liquid data streams can vary
continuously, even in never seen before, or ever to be seen again, manner without
a significant pattern. Determining what represents actionable information that
needs to be immediately delivered to users on their mobile devices is the
challenge.
Strategic Analytics and Analytics in Motion
Strategic Analytics largely include statistically based data analysis, primarily
analyzing existing static data though packaged routines, has been available for
decades and is accurate to determine the statistical significance of data
variances, identify variable correlations, identify change inflection points, etc.
That’s the good news, the not so good news is that these strategic long standing
statistical methodologies work better with well understood and structured input
data and that with large files they can be computationally intensive (multiple non-
linear regression analysis of a huge file, etc.). They wouldn’t be the analysis
analytics approach of choice for working on IoT streaming data or textual analysis
but can be effective for reviewing existing internal data files to identify significant
business intelligence. Statistically based modeling and analysis tools can also be
used for predictive trend definition and are the basis for stochastic models of
automated decision tools (including providing confidence levels). What they
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don’t do really well is discern the subtle significances of random appearing
consumer information typical to social media data streams. Statistical analytic
tools are available off the shelf and can thus provide economic analyses of MDB
structured data. For the purpose of contributing to actionable business
intelligence, the statistical analysis results should be taken out of their typical
statistical jargon (large correlation coefficient, etc.) and put into terms (high
confidence trend, etc.) better understood by decision makers.
Analytics in Motion typically refers to the best analytics for understanding the data
types more typical to IoT data streams and social media sources. To discern
significant business intelligence from text a combing type review is done based
primarily on the ability of the analytic tool being used to (1) identify critical words
or strings of words and (2) define the likely “ sentiment “ being expressed in the
tweets and (3) tally these according to predefined query limitations. For practical
reasons the input data being analyzed is typically defined to focus on most likely
sources of needed intelligence (the tweets from a geographic area for a specific
period of time instead of the whole U.S. for a year). This type of sentiment analysis
can be effective for quickly spotting customer problems with products,
understanding the general public perceptions from institutional advertising, etc.
Frequently consumer sentiment trending verses that of direct competitors is used to
evaluate advertising campaigns, pricing and promotions, etc. Where impractically
large scale background is desirable, a statistical sampling technique may be
applied.
The strategic and in motion analytics can be visualized as being centered on
different type data (static, liquid, text, etc.) so it’s critical to be able to specify
what data is to be pulled in, what query or analytics tools are to be applied and
what the blended output intelligence is to consist of. Recall the analytics are
focused on identifying early-on consumer preferences, non-random sensor
deviations, purposely disguised programmed trading alert algorithms, etc. In many
cases the results are presented to users with general text statements, graphical
plots, etc., care must be taken however not to imply a degree of confidence that
doesn’t actually exist. Certainly, the analytics derived results represent valid
business intelligence but the decision makers should always fully understand the
intelligence, and its reliability, and use their best judgment prior to acting.
Complex Event Processing
Complex events processing could almost be viewed as a hyper drive type real
time IoT, liquid data sampling and monitoring. Complex Event Processing (CEP)
analytics typically extend the input data monitoring into real time (or near real
time) as a result of the importance of the situation being monitored. CEP
simultaneously monitors several data inputs (often including liquid data) to
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continuously verify they individually and collectively are within predefined
boundaries. It may well be that each variable is within an acceptable range but
that a certain combination or grouping of the monitored variables is not within an
acceptable range. The complex event situation being monitored for is typically
made up of a fairly complicated layering of contributing smaller events and
activities, often with each occurring in very rapid fire manner. Quickly recognizing
the compounding of a complex event and properly responding in a timely
manner can be a major challenge. However, specialized analytics tools can help
to recognize and address these serious and time urgent situations. Complex event
analytics typically include;
- Situational experience based monitoring and evaluation rules
- Multiple live data feeds of potential CE situation contributing factors
- Automatically adjusted data stream sampling and filtering may be used to
focus on key variables
- Event correlation analysis used to evaluate significance of developing
situation
- Includes alerts as to unexpected trending and “ out of bounds “ situations
- Full real time in-memory processing may be required
- Algorithm derived complex event responses are predefined
- Alerts and recommended response actions are immediately transmitted
- Feedback for potential rules adjustments is provided for permanent variable
shifts
Complex event processing analytics are obviously a resource intensive approach
but are justified where there’s a requirement to respond to time urgent and
continually changing environments having a high value. Typified in this type
solution set deployment are financial market situations, critical process
environments (rocket fuel blending comes to mind) or life and death medical
situations. CEP may indeed be a part of overall business intelligence, but is often
handled separately by dedicated specialists.
Soft Science: Market Listening and Data Visualization
Market Listening refers to the use of a regular ongoing monitoring of market
sentiments and preferences. As described, social media listening can be done as
a part of Mobile Big Data analytics through text based tools that discern the
sentiment of customers or prospects and are also able retain the apparent
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context. This type market intelligence may also come from a variety of subscription
services but in that situation the same business intelligence is likely to be available
to others, including key competitors. The intelligence from regular market listening
is primarily intended to provide early on alerts for market shifts or newly developing
trends. For example such intelligence could be triggered by warranty claim
related tweets that would result in the system checking recent field service logs, a
review of related in-process engineering changes, etc. In addition to external,
social media based, market listening, some enterprises “listen” to internal text data
for alerts and may include text from field reports submitted by their edge worker
employees. As an example a major insurance firm automatically reviews recent
field adjuster reports and may use this intelligence for account negotiating. For
cases where social media trending can be key, such as fashion dependent retail,
relevant portions of social media data (RSS feeds filtered for color comments, etc.)
may be appropriate for inclusion. The point being that Mobile Big Data is ideally
suited to provide employees making complex decisions with the timely support
intelligence they need as derived from multiple sources and disparate data types.
Data Visualization is another seemingly soft science pattern identification analytics
method where apparently random data points are displayed or printed (typically
on a colorful 2 axis plot or other highly graphic format) and then manually
examined to identity patterns leading to potential intelligence. Although quite
basic, this type analytics approach can be very helpful as a first pass to define
following,and more rigorous, analysis. The data visualization process obviously
requires trained and experienced analysts in order to be highly effective. Data
visualization can, however, be quite effective in explaining the basis for derived
intelliegence and once properly identified and verified these data patterns can
be quite striking.
Self-Serve Analytics
In many cases the effective use of advanced analytics is not a common skill of the
recipients of business intelligence. While the users are often capable of making
normal queries, etc. fully understanding the usage and output of complex
analytics is likely best left to experienced analysts. In situations where intelligence
users are highly skilled there exists a variety of packaged off the shelf analytics
tools that can be used from remote locations. This may be especially important to
those enterprises where the business intelligence function itself reports to
operational management and/or is being outsourced to cloud based resources
with minimal involvement of IT support. Although self-serve analytics are frequently
promoted, it’s doubtful this is appropriate to highly complex environments or those
with extensive edge worker populations. It’s a trend to watch but should be
approached with caution. Not having knowledgeable and experienced analysts
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involved in the business intelligence process may be false economy resulting in
costly mistakes and a loss of confidence by the users.
D. “WHAT” IS BIG DATA TAKEAWAYS
“What” are the Mobile Big Data capabilities section takeaways include;
1. Mobile Big Data is a business advantage tool that provisions business
intelligence to user employees at all levels and locations of the
organization and which is based on existing and proven technology.
2. The Mobile Big Data functional capabilities derive from a seamless
convergence of Mobile platforms, Big Data processing environments and
Advanced Analytics.
3. The Big Data processing capabilities often derive from fast and reliable
MPP processors operating in specialized high throughput environments.
4. The Mobile platform based features provide user mobility and include
intuitive interfaces, business specific applications and data collection.
5. Advanced Analytics capabilities include the efficient analysis of multiple
data types (including liquid data and social media) and the blending of
resultant intelligence into a fully actionable format.
6. The Mobile Big Data deployment rationale should be based on
addressing defined business needs rather than technical capability.
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V. “WHERE” DOES MOBILE BIG DATA FIT BEST?
As to be expected, the cost effective application of Mobile Big Data is highly
dependent on the organization’s specific business requirements and the targeted
user’s typical activities and capabilities. In enterprises today a considerable
proportion of employee are “knowledge workers”, who on a regular basis are
using the best available information to make vital decisions. This situation is partially
due to the post great-recession economic pressure that causing many enterprises
to push decision making out to the organizational edges and greatly empower
their remote knowledge workers. Always recall that a key Mobile Big Data usage
rationale is its ability to facilitate better quality decision making while speeding the
decision making process, thus reducing decision latency.
Clearly the intrinsic value of intelligence is enhanced through the use of the best
available source data. Figure 5.1 displays the rather considerable estimated global
value still available to major vertical market segment enterprises by expanding
their business input data types to include available international market data.
Figure 5.1: Estimated Upper End Annual Value of Improving Global Intelligence by
Including Available Multiple International Data Sources
Source:McKinsey and Co.
It should be noted from Figure 5.1 that the upper range of overall potential benefit
from “globalizing” business intelligence exceeds $5.3 Trillion per year. Certainly
such global benefit estimates are difficult to substantiate but nonetheless indicate
a quite significant potential ROI through improved intelligence provisioning.
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To better focus on the types of enterprise environments most likely to be a good
Mobile Big Data application point (e.g. those environments most likely to provide a
solid measurable ROI) the “Where “discussion will first briefly categorize the typical
high ROI potential type environments followed by a few real world examples.
These early on success examples best serve to emphasize typical Mobile Big Data
benefits and to generally point out its likely direction. There will obviously be many
other types of successful Mobile Big Data implementations as it matures technically
and becomes increasingly cost effective.
A. ROI RICH ENVIRONMENTS PROFILE
Just as the Periodic Table of Elements categorizes materials according to their
basic atomic structure so does this ranking of likely enterprise environment related
ROI based on typical decision making processes. It should be recognized,
however, that the following categorization of ROI opportunities for Mobile Big Data
is not nearly so precise or scientific, and is based on the best currently available
information and the results obtained from a fairly limited number of to-date
implementations. The potential Mobile Big Data ROI ranking is in reality a set of
empirically based general guidelines. The identification of where Mobile Big Data
fits best begins and ends with the defined business needs and specific objectives
of each enterprise. There are indeed some emerging “best fit “characteristics
where user needs are similar, however, the specific business requirements of the
situation take clear precedence over any such general application guidelines.
Big Data Extension
Clearly a likely place to identify highly cost effective Mobile Big Data deployment
opportunities are those environments already invested in its biggest CAPX intense
element, Big Data. A solid ROI is likely when extending these existing Big Data
capabilities (out to the edge worker decision makers) where the primary cost
elements are likely limited to upgrades of existing infrastructure and new
application development. Even where a reasonable low improvement may be
projected for outcome metrics (3% customer contract renewal rate increase, etc.)
such environments can find a good rate of return for Mobile Big Data deployment.
Of course, from the Mobile Big Data implementation side those enterprises with
well-established Big Data (often, energy sector, medical, R+D, etc.) also tend to
have significant in house technical skills that can accelerate the deployment and
support of Mobile Big Data. Where new skills are required these typically can be
outsourced by an internal staff experienced in managing such efforts. The largest
skills shortage for such organizations is likely regarding Advanced Analytics
implementation and specific Mobile platform application development. In many
ways the ROI driven rationale for deploying Mobile Big Data is based on the fact
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that so much of what is needed already exists. With some fairly minor cost
upgrades and additions, it’s ready to be deployed.
Financially Valued Decisions
As experienced trading floor folks express so well in their appropriately labeled
“Race to Zero” efforts seeking absolutely no trading transaction latency, time is
money. Nanoseconds in that environment can have a readily defined value,
perhaps small individually but collectively huge due to massive trade volumes.
Other financial decision based organizations, including all varieties of trading, but
also banking, credit card transaction based, various types of underwriting, etc.
also have a clearly assignable value to concluding all their employee’s required
background decisions as rapidly as possible. Certainly speeding such decisions is
an intrinsic result of Mobile Big Data. Where as the time value calculation may be
somewhat obtuse for being able to accelerate marketing decisions for corn snack
foods, the value calculation for reducing the latency of a corn commodity trade is
fairly direct. Also, financial transaction based enterprises are information based
and thus typically have the ROI metrics in place needed to justify their often
massive investments in IT – Mobile Big Data is no exception.
Complex Event Response
Complex event driven enterprises that require employees to regularly make real
time complex decisions are another spot where Mobile Big Data can produce
solid ROI. Put yourself in the position of having to make time critical operating
decisions when directing the large and complex operational network of a major
airline, huge rail system, ocean going freight forwarder, etc. This complex event
driven situation is affected by many significant and ever changing variables
including weather, equipment and employee availability, etc. Providing the real-
time multi-source and fully actionable intelligence is a perfect application of
Mobile Big Data. Improving the operational metrics by just a few percentage
points for these high volume and low margin enterprises offers all the ROI needed.
Visualize an airline’s personnel being able to (1) have the support intelligence to
proactively make complex passenger rerouting decisions in response to a
developing weather event and (2) contact the passenger’s mobile phone to
inform them of the developing situation, and the proactive draft decision made for
their rerouting, and asking them to approve this plan – all without anyone talking
on the phone or passengers standing in line at an airport desk. The same type big
data based analytics driven complex network management with proactive
rerouting/notification delivered to employee and customer mobile devices is now
being done by Canada Rail.
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Mobile Big Data has a huge ROI potential for these difficult decision making
environments and indeed an entire set of off the shelf analytic tools is specific to
efficiently supporting complex event decisions. Complex event driven
environments may be where Mobile Big Data does best, especially with customer
facing edge employees. The ROI may not have the easy or direct metrics of
financial organizations, but the business results (lower cost and operational
improvements) can be quite solid.
Customer Facing Edge
Where employees regularly deal directly with customers in a remote location
(customer site, accident or disaster scene, kiosk location, etc.) it is appropriate to
consider a Mobile Big Data approach to enable better informed decisions. In this
situation the employee may be provided with competitor information, such as their
recent pricing and promotions, active product recalls, new services offerings, etc.
The end result is an edge worker with the information to be more effective in
working one on one with a customer or prospect, The practical business value from
better empowering edge worker employees may include higher sales close rates,
up-selling an increased ticket size, better contract renewal rates, etc. Without
increasing the expensive edge worker (and possibly reducing them) a revenue
increase is possible that may be accompanied by an improved customer
satisfaction rating, all due to enabling better informed and more responsive
representatives. Edge workers may also include emergency responders (EMTs, etc.)
where being able to receive immediate assistance, possibly based on interactive
information exchange, can be critical. Of course, the recipients of edge support
can also be the customers themselves or the citizens affected by governmental
agencies. Given the pervasive mobile platform availability (in some cases the only
available linkage) this type edge support is likely to expand dramatically.
B. REAL WORLD EXAMPLES
The following real world enterprise level Mobile Big Data examples are highlighted
for their results in successfully addressing business needs and producing
measurable ROI. They should be considered as reasonably representative but are
subject to continued refinement. These Mobile Big Data examples have mostly
been documented in various media so additional background information is
generally available.
Minimizing Disruption in Complex Transportation Network
ENVIRONMENT: Major Commercial Airline
ORGANIZATION; Delta Airlines Inc.
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The management of an airline network is a perfect example of a complex event
driven enterprise with many critical and highly changeable variables including
hard to maintain and expensive equipment assets, weather, skilled employee
resources, etc. Overlaid on this situation are regulatory constraints, union rules and
the obvious safety considerations, etc. Many airlines, Including Delta, have
implemented Big Data based CEP analytics to define up to the minute, and best
practices based actionable intelligence and recommendations. These outputs are
typically near real time and may be delivered to a variety of devices including
mobile platforms. The information may include schedule changes, routing revision,
equipment and crew assignment revision, baggage problem alerts, etc. A portion
of this CEP produced data may be routed to edge worker devices and even to
the customer’s mobile phones. The results have included reduced delays,
operational efficiency improvement driven savings, and better customer relations.
Edge Worker Communication and Citizen Mobilization
ENVIRONMENT: Large Metropolitan Government
ORGANIZATION; City Of Minneapolis, Minn.
Many governmental agencies at all levels use a Mobile Big Data approach to
more cost effectively communicate with and instruct their dispersed edge worker
employees and to alert and mobilize citizens. This specific example uses CEP type
analytics based on a wide variety of input data types and sources (including EDW,
external data feeds, sensor type “liquid data”, weather service input, etc.) along
with a set of best practices based algorithm decision rules and recommendations
to optimize event response. Alerts and recommended action instructions are sent
to both edge worker employees and to the mobile devices of likely to be affected
citizens. The potential event situations addressed include public safety issues
(weather, terrorist threats, etc.) traffic management, time critical municipal
permitting, and management of entertainment venues. To optimize ease of use a
variety of custom tailored dashboards (including pie charts, etc.) and intuitive
displays (including mapping, etc.) are utilized along with reference links and
accessible portals. The results have included savings from improved operational
efficiencies, significantly fewer public safety disruptions and increased citizen
participation and assistance.
Improved Customer Retention and Cross Selling
ENVIRONMENT: Major Insurance Carrier
ORGANIZATION: Metropolitan Life Ins. Co.
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As a part of its ongoing push for improved customer service and higher insurance
renewal rates MetLife introduced a social media inclusive analytics approach to
identify potentially dissatisfied customers (without any formal complaint) and
proactively approach them with proposed improvement proposals including cross
selling for appropriate additional products. The potential customer satisfaction
analytics use a combination of internally sourced data (EDW based) along with
social media input to prepare recommended actions. This information is forwarded
to the appropriate edge worker account manager who then contacts the
customer (as a routine satisfaction survey) and then takes the opportunity to
propose improvements. The customers have typically appreciated this positive
approach and as a result policy renewal rates have climbed and product cross
selling results have raised about four fold. The Mobile Big Data ROI basis of
increasing revenues has proven to be correct.
Accelerating Product Claims Response
ENVIRONMENT: Large Pharmaceutical Maker
ORGANIZATION: Confidential
A large pharmaceutical firm’s customers when making various types of product
related claims (in-transit damage, incorrect amounts, wrong SKU, etc.) were
displeased with the claim resolution delays they experienced, which they
considered as indifferent customer service, and this situation negatively affected
customer reordering and cross selling. The root cause of the claims resolution delay
problems was the claim processing employee’s typical difficulty in accessing and
understanding the sometimes complex information required for them to make a
claim resolution decision. The decision support information often had to be
obtained from a variety of external and internal sources, often without cross
referencing or contextual content. To address these decision delay issues an
advanced self-serve analytics system was established and a set of best practices
algorithms based recommendations defined. In this manner employees trying to
resolve open product claims (whether edge worker or central employees) are
able to efficiently access the data they need for their specific product claim and
receive guidance on recommended resolution actions. As a result the typical
product claim resolution period has dropped from a little over a month to less than
a week. The resultant positive customer service experience had a significant halo
effect on increasing customer reorders and cross selling success.
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VI. RECOMMENDED MOBILE BIG DATA BEST PRACTICES
As stated, Mobile Big data remains in its early stages and continues to evolve at a
rapid pace so as yet there is no definitive manual of best practices. The
experience gained from initial Mobile Big Data deployments and the operation of
its primary component elements has, however, been adequate to identify some
initial best practice recommendations. Although preliminary in nature these
suggested general recommendations are worth serious consideration.
A. ORGANIZATIONAL RECOMMENDATIONS
In that the technical foundation of Mobile Big Data is based on three solid and
proven components there are limited technical factors to be considered as true
best practices. In fact, most Mobile Big Data deployment concerns and
recommendations generally relate more to the preparation of the organization
than to technical factors. The root cause of these organizational issues is the fact
that the business intelligence prepared and delivered by Mobile Big Data, is often
a new concept. As a result the implementers and technical support teams have to
understand their role and the recipients need to know how to best utilize this new
resource and what is expected of them.
Management: Stand and Deliver
The biggest initial organizational hurdle is the need for visible senior management
sponsorship. As with any major business practices change, senior management
must lead the way and be very clear about why this effort is being done, the
expected impact and the roles of all involved. In the eyes of business intelligence
recipients this issue may be the most important deployment success factor. The
sponsoring senior management needs to fully understand and support the value
proposition and leave nothing vague or ill defined. The kick off needs to include a
senior management “here’s why we’re doing this and here’s is what expected
from each of you “type introduction. Obviously, the clear definition of solid
business driven ROI metrics for evaluating results will go a long way toward taking
this Mobile Big Data business advantage initiative out of any potential “science
project “type status.
The next issue might well be exactly WHO will be the sponsoring senior executive in
charge of the Mobile Big Data initiative. In many organizations, largely due to its
technical nature, Big Data logically fell under the purview of the CIO. The CIO was
responsible for the EDW so extending the user management responsibility to all
aspects of Big Data made sense. Mobile Big Data isn’t quite so clear. The mobile
device delivery platform is frequently BYOD (if not actually BYOE) based and the
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users, and their management, deal directly with those issues. Security is always a
concern but that seems to have been sidestepped and can be minimized at the
Big Data interface levels. The business intelligence user management
(sales/marketing, claims processing, etc.) may be closer to the Mobile Big Data
value proposition and thus better able to see that the provisioned business
intelligence is used as intended and that results occur as planned. Figure 6.1
displays that largely for these reasons, in many enterprises today the actual
business intelligence analytics operation (as distinct from the processing platforms)
may report outside the IT organization.
Figure 6.1: Location of Business Intelligence Analytics Management
Source: Several Recent Surveys
It’s of significance to note that a specialized separate analytics management
function is currently the most common business intelligence analytics
management location.
Empiricists vs. Analysts: MIA Skills
The second key organizationally related issue for Mobile Big Data deployment that
often needs to be addressed is the fact that experienced intelligence analysts
may not exist. Frequently there has been little to no reason previously to have fully
knowledgeable analysts to perform analytics processes or intelligence gathering.
Those are distinct skills from typical EDW management and may include dealing
with different data types (social media information, liquid data, etc.) many of
which are semi-structured or unstructured. In addition, the ability to define business
needs driven heuristics and decision model algorithms may not have been
encountered and the interpretation of social media derived output can resemble
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a “soft science”. Given the importance of the business decisions that may be
based on the intelligence provided, this analyst work may be something best not
left to amateurs.
Directly related to the Mobile Big Data requirement for specialized analysts is the
difference between data empiricists (often DBA specialists) and true analytics
experts. An empiricist may tend to defer to statistically proven methodologies and
analysis outcomes, even when such approaches are impractical. Whereas a true
analyst in such a situation may cross reference the information obtained from
multiple data sources and types to produce actionable recommendations.
Effective intelligence development often requires an experienced interpretation
going well beyond professorial types of theorem proofs. A clear example of the
value of these analyst interpretive skills is the use of data visualization analytics
where the analyst first examines the visual pattern of a huge data array to identify
possible patterns. These are the valuable personal skills of the flexible analyst versus
the more rigid approach of a empiricist.
User and Tech Staff Preparation
Another organizational issue regarding Mobile Big data deployment is user and
technical staff training and preparation. An early on effort to evaluate user needs,
capabilities and skills should drive the definition of a well thought through training
experience for these employees. In large and spread out user organizations it may
be necessary to adopt a train the trainer type approach but there’s no substitute
for having a knowledgeable person of some variety in close proximity to users
during start up. The training involved must closely resemble the real world
environment and include decision situations that users will likely encounter and not
be theoretical or overly technically based. The users need to understand enough
about how the intelligence was derived that they can believe in its validity and be
comfortable using it (more than they apparently do today) but they don’t need a
PhD in statistics to reach this comfort level. They need to use good judgment in
applying business intelligence, but they don’t require a copy of all the algorithms
being used.
With regard to tech staff preparation it’s often possible to send the operational
staff, and analysts as required, through training classes as specific to the technical
tools included. Most suppliers of such software and hardware components offer
canned training programs and supplemental webinars on a regular basis. The
technical staff’s full familiarity with these systems likely will come mostly through
actual set up and rigorous testing.
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B. IMPLEMENTATION BEST PRACTICE
As with organizational Best practices the implementation related
recommendations for technically related issues remain a work in process. For the
most part the key point to Mobile Big Data implementation is spending the time
and effort needed for very thorough pre go-live testing and adjustment. Identifying
the appropriate input data types and sources and defining the detailed analytics
decision rules and algorithms can be time consuming but can’t be short cut, these
steps are critical to the accuracy of the resulting intelligence.
Make vs. Buy vs. Both
As a general rule the ability to license proven off the shelf analytics components
typically outweighs the development of such routines. The same applies to the Big
Data related elements. These two key Mobile Big Data elements can be obtained
off the shelf in a fairly easy to set up, integrate, and use form. The mobile platform
apps specific to a business situation may be another matter. In some cases the
required mobile applications may be available, but most likely these will require
significant customization to better match specific conditions and to be totally user
friendly. The user interfaces must be as understandable and intuitive as possible so
should be almost conversational. This means including the business terminology,
and jargon, already familiar to the user set. Please recall the intelligence now
received is not felt complete or easy to use and for that reason is often ignored. It’s
likely in a major Mobile Big Data installation to encounter both standard off the
shelf type elements as well as custom developed or heavily modified components.
Iterative Piloting with Metrics
Iterative piloting with metrics relates to the best practice of starting small, then
growing, when introducing a new technology. That doesn’t mean the starting
point won’t be an important situation, it may be just the opposite and the
beginning point may well address a critical business need. The iterative piloting
process allows for careful front end analysis and solid testing prior to entering
production status. Especially important during this effort are user perceptions and
feedback and close metrics monitoring. Mobile Big Data represents a new
concept and can be disruptive – rigorous front end testing and fine tuning is
absolutely warranted to be sure it’s working as planned and expected.
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Stepwise Extension
To avoid deadly Big Bang Theory start up issues, it’s always best to start with a
limited scope and then expand as appropriate. This means beginning with the
most likely to succeed sub-set of users – perhaps those with the most related
experience or those most convenient to training and start-up support. The less
skilled or less interested users, and possible those most remote, can be added
once things are running well. Obviously, that approach may be difficult to do
where the prime target Mobile Big Data opportunity is with edge workers - in that
case a region by region roll out plan may be appropiate. Starting with a limited,
and success likely user set, then expanding with a step-wise deployment including
published schedules is the preferred Mobile Big Data deployment approach.
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GLOSSARY OF TERMS AND ABBREVIATIONS
A glossary definition of the specialized abbreviations and terminology contained in
this white paper includes.
- IoT; Internet of Things is a term referring to the internet connection of
devices rather than people.
- MPP: Massive Parallel Processing as performed by an array of smaller
platforms aligned and controlled to operate as a single huge throughput
system.
- NoSQL : Not Only SQL is an indication that several data base types and
structures will be used ( in addition to SQL format DBs )
- CEP: Complex Event Processing, the typically real time (or near real time)
processing of several data streams or variables that contribute to the
situation being managed.
- EDW : The Enterprise Data Warehouse primarily refers to the internal data
repository of the enterprise and the data is typically kept in a hierarchical
and/or relational manner
- Apache Hadoop; The open source and licensable version of Hadoop.
- MDB; Master Data Base typically refers to the structured data contained in
the EDW.
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For further information please contact:
Brian R. Blackmarr Martin Ward
Chairman Vice President of Sales
Fusion Laboratories Inc. Technical Services Division
214.217.9783 (office) 214.217.9763 (office)
214.435.4433 (cell) 214. 909.7709 (cell)
Two Lincoln Centre
5420 LBJ Frwy. Ste. 850
Dallas, Texas 75240
214.739.5454
www.fusionlabs.net