Managing the Data Deluge and

IoT Data Integration

Peter DeNagy, President

Acommence Advisors, Inc,

November 29, 2017

Acommence Advisors, Peter DeNagy, President

➢ 35+ years in Technology – Strategy consulting for industry

➢ North American Mobile and M2M/IoT Practice Leader – Capgemini

➢ US GM Enterprise Mobility / M2M – Samsung

➢Mobility/M2M GTM/Alliances/Solutions Leader, The Americas – Accenture

➢Microsoft Partner Board Member (MPEB) - Windows Phone/Mobile

➢ Board of multiple start-ups

➢ Entrepreneurship advisor/mentor University of Texas, Austin / Tech Wildcatters / NEX

➢ Co-Chair Tech Titans IoT Forum /the North Texas Assoc. for Technology BoD

➢ IoT Board Member / The Telecommunications Industry Association (TIA)

➢ Globally Renowned Speaker on Technology Innovation / Mobile / IoT

➢ Book Collaborator “Managing the Mobile Workforce” 2010

• Launched dozens of products

• Ideated mobile API structure (SAFE) and Security schema (KNOX) for

Samsung

• Almost $ 4,000,000,000.00 in lifetime sales

• Started organizations within 4 separate companies (Serial Intrapreneur)

• Participated in 3 successful exits (Entrepreneur)

IoT Data Explosion

IDC predicts there will be 30 billion connected things worldwideby 2020, McKinsey predicts 50 B by 2025.

IPv4 uses 32-bit address, allows only 2^32 (just over 4 billion)addresses or so, which requires some devices to share addresses.With more and more sensors embedded in more and morethings—each requiring an IP address—this state of affairs isunsustainable

IPv6 uses 128-bit system, solves this problem by bumping up theuniverse of available addresses to a number that’s hard tocomprehend —something like 340 trillion trillion trillion)

IoT and Data Volume

• The volume of data produced with IoT is HUGE and getting bigger.

• Examples

– 1 GE or RR Jet Engine LA to NYC

– Parking Lot of 200 cars

– Manufacturing Plant

IoT Data Explosion

As IoT grows, the amount of data generated by proliferatingsensors embedded in connected things will grow as well. And fororganizations deploying IoT devices to move all this data back andforth via the cloud is simply untenable.

Hence the idea of Edge Computing. Edge Computing, is the idea ofprocessing data on the “edge” where IoT devices are deployed—rather than sending all sensor-generated data back to missioncentral over the cloud.

Without edge processing, IoT will not be a reality.

The Data Deluge

The information that businesses collect and store, but that has

traditionally remained relatively stagnant because it isn’t used for

analytical purposes.

Most of the IoT data collected today are not used at all, and data

that are used are not fully exploited.

When used selectively, such as to better understand customers,

data is invaluable to businesses, as it allows them to uncover

additional insights more efficiently.

Edge Computing addresses most of the high volume data issues.

Data Collection & Analysis

Considerations in Data collection should consider:

✓ Data collection and analysis methods should be chosen to

match the particular evaluation in terms of its key evaluation

questions and the resources available.

✓ Evaluations should make maximum use of existing data and

then fill gaps with new data.

✓ Data collection and analysis methods should be chosen to

complement each other’s strengths and weaknesses.

Collect and/or Retrieve Data

Your company needs to focus on ways to collect and/or retrieve

data as well as the activities, results, context and other factors.

You need to also consider triangulating your options in order to

ensure multiple data sources and perspectives.

There are five clusters of options to be considered:

✓ Information from individuals

✓ Information from groups

✓ Observation

✓ Physical measurements

✓ Reviewing existing records and data

Data Quality

✓ Validity: Data measure what they are intended to measure.

✓ Reliability: Data is measured and collected consistently

according to standard definitions and methodologies; the results

are the same when measurements are repeated.

✓ Completeness: All data elements are included (as per the

definitions and methodologies specified).

✓ Precision: Data have sufficient detail.

✓ Integrity: Data is protected from deliberate bias or manipulation.

✓ Timeliness: Data is up to date (current) and information is

available on time.

Combining Data Sources

✓ Enriching Using qualitative data to identify issues or obtain

information about variables that cannot be obtained by

quantitative approaches

✓ Examining Generating hypotheses from qualitative data to be

tested through the quantitative data (such as identifying

subgroups that should be analyzed separately in the quantitative

data, e.g., to investigate differential impact)

✓ Explaining Using qualitative data to understand unanticipated

results from quantitative data

✓ Triangulating (confirming or rejecting) Verifying or rejecting

results from quantitative data using qualitative data (or vice versa)

Edge Computing

Edge computing is a method of optimizing cloud computing systems

by performing data processing at the edge of the network, near the

source of the data. This reduces the communications bandwidth

needed between sensors and the central datacenter by performing

analytics and knowledge generation at or near the source of the

data.

Possible advantages of Edge Computing are:

✓ Edge application services significantly decrease the volumes of

data that must be moved, the consequent traffic, and the

distance the data must travel, thereby reducing transmission

costs, shrinking latency, and improving quality of service (QoS).

Edge Computing

✓ Edge computing eliminates, or at least de-emphasizes, the corecomputing environment, limiting or removing a major bottleneckand a potential point of failure.

✓ Security improves as encrypted data moves further in, toward thenetwork core. As it approaches the enterprise, data is checked as itpasses through protected firewalls and other security points, whereviruses, compromised data, and active hackers can be caught earlyon.

✓ The ability to "virtualize" (i.e., logically group CPU capabilities on anas-needed, real-time basis) extends scalability. The edge-computingmarket generally operates basically on a "charge for networkservices" model, and it could be argued[original research?] thattypical customers for edge services are organizations desiring linearscale of business application performance to the growth of, e.g., asubscriber base.

Edge Computing

Analytics

Data Collection & Analysis

✓ Descriptive Questions require data analysis methods that

involve both quantitative data and qualitative data

✓ Causal Questions require a research design to address

attribution and contribution

✓ Evaluative Questions require strategies for synthesis that

apply the evaluative criteria to the data to answer the key

evaluation questions

Analytics

Different analytic types are used according to the requirementsof IoT applications

✓ Real-time analytics

✓ Off-line analytics

✓ Memory-level analytics

✓ BI analytics

✓ Massive analytics

Questions?