big data maturity model a practical application cookbook

Big Data Maturity Model

A PRACTICAL APPLICATION COOKBOOK

September 2013

© 2013 SAP AG. All rights reserved. 2

However you look at it, Big Data is critically relevant

across industry

20%

15%

Improvement in all financial metrics

for those that leverage Big Data

(McKinsey & Co)

Organizations that will have

successfully achieved value from

Big Data by 2015 (Gartner)

90% Organizations that consider Big

Data an Opportunity to be mined,

not an IT Problem to be overcome

(TDWI)


On the radar of Leadership

Big Data isn’t just one more technology

initiative. In fact, Big Data isn’t a

technology initiative at all; it’s a business

program that requires technical savvy


Practical questions that must be addressed

How am I supposed to build this

business program?

What are the practical steps my

organization needs to take?

Who should lead this / is my

CIO’s team capable of delivering

on this or do I need to task

someone else?

When should I expect to start

seeing returns on this and where

will they come from?

How do I ensure we don’t boil the

ocean on this and turn it into

another Y2K?

What do we want to build as a

core capability and what do we

want to outsource to technology

vendors? What is the plan to

build the core capability?

Questions Executives are asking now

Comprehending the Vision


The Vision: Turn Big Data into Intelligent Data, in real time

Answer &

analyze any

question

in real-time

Build a

photographic

memory of your

enterprise

Simplify your IT

landscape to transact,

analyze and act

instantly

Answer questions at

the speed of thought

Store and analyze

all data within your

enterprise

Access detailed

records from any

point in time

Personalized

Insights to

Everyone,

Everywhere

Build a culture of data-

driven decision making

Give all business

stakeholders access to

information wherever

they are

100101 011010 100101

Unlock

business value

from Diverse

Data Sources

Identify high-value

information from

mountains of data

Drive insights to

where they deliver

business value

Action 2

Action 1

Action 3


Big data challenges IT departments are challenged with …

Action 2

Action 1

Action 3

Quick Insight,

AND

Best Action,

AND All Data,

AND

Long delays between

transaction, analysis,

and action

Complex queries can

take hours to answer;

and sometimes never

complete

Challenge identifying

high value insights

from mountains of

data

Difficulty providing

complete and

accurate information

for best decision

making

Complex architectures

to overcome current

DBMS limitations

Inability to effectively

store and analyze all

types of data

Right Audience,

BUT

Power users are a

bottleneck to getting

data to the right people

quickly

Difficult to drive insights

into business processes

and to mobile workers

100101 011010 100101


Big Data high level process

The Big Data Process

Signal Processing: Focuses on connecting to relevant sources, monitoring changes, filtering which signals should be simply

noted, and which signals and data elements should be acquired into the architecture, either as a replication or an abstract.

Staging and Modeling: Information that is known in the platform requires analysis to identify which signals and patterns are

worth retaining and in what format, which sources are noise, or require additional processing. This can be a highly complex

area ranging from Data Quality improvements and auto enrichment all the way through to Predictive models to identify the

value of a source.

Analyze: Pattern finding, and understanding which Attributes are critical to understand and influence business outcome lies at

the heart of creating value through a big data program or initiative. Complex Analysis, pattern finding and driving true insights

too often is however the end of the road for big data projects.

Act & Automate: Value realization depends on being able to translate insights from Analysis into Action. These are either

behavior changes or moves to automate actions. It is here that scalable change is achieved and where business models are

effected.

Signal Processing

Staging & Modeling

Analyze Act & Automate


Central Battle for “Big Data” – Data vs. Knowledge

Source: Martin Decision Sciences

Understanding the Nuances


Big Data: A natural evolution of Analytics, one that we

only now have the ability to take advantage of

Business Intelligence Big Data

Raw Data

Cleaned Data

Standard

Reports

Ad Hoc

Reports

& OLAP

Generic

Predictive

Analytics

Predictive

Modeling

Prescriptive Modeling

What happened?

Why did it happen?

What will happen?

What is the best that

could happen?


Leveraging Big Data requires capabilities beyond

traditional BI

Use Cases

People & Skills

Standards & Processes

Governance

Architecture


Big Data Maturity Model is driven by Use Cases

Us

e c

as

e s

op

his

tica

tio

n

Business Value extraction through data insight

No Big Data Capabilities

Search for a significant item in big data

Understand the big picture from all available data

Generate change in response to shifts in data automatically or manually

Use big Data to predict outcomes and adjust processes accordingly

Often a single step

Coping Understanding Managing Innovating Ignorance


Maturing from BI to Big Data is an iterative process

Use case

• Utilizing design thinking to identify Use case approach in line with Strategy

• Define the use case by Outcome

• Establish clear targets, SLAs, KPIs, for core use case

• Establish creative “free Friday” approach to Edge issues

Discovery

• Identify potential data sources and their quality

• Evaluate Technical requirements for connectivity (capture vs index)

• Create rapid prototype (visualization, model, Exploitation)

• Check End-User acceptance of prototype

First Evaluation

• Did the data meet the requirements for the outcome

• What is the likely cost vs benefit calculation

• How many core issues and edge issues remain and are their in your control to fix

• What is the realistic timeline to deliver the first benefits

• Does the maturity you encountered match the solution

Information Architecture Design

• Explicit definitions of information profiling, Source analysis, intervals, security, rules

• Explicit definition of component architecture and re-use (Integration, Staging, Transformation,….)

• Load management – explicit design of work-load management on functions such as NLP, Predictive models,….

• ILM policies, Information ownership, governance, master data control, meta data,…

Implementation

• Technical implementation

• Information flow testing

• Analysis

• Automation,

• Feedback loops, controls, change control,…

Steps to Get There


Step 1: Define / Refine your Use Cases

Look at what your organization is already doing that could be improved - or what

others in your peer group are doing that could be emulated

What could be done better with Big Data techniques, such as mashing up social data

with product data, or developing new cost models based on consumer profiles?

Apply ‘Design Thinking’ to surface potential new use cases

This approach can help you to identify brand new, previously unconsidered use cases

(e.g., http://www.sap.com/campaigns/2013_05_design_thinking/index.epx)

Categorize use cases by outcome

These may be cost-focused rather than revenue-focused, e.g. by better predicting crime

hot spots you could redirect police patrols

Prioritize and validate the use cases

Rank them in order of anticipated benefits. If the goals are valid, what decisions need

to be made that would move the needle on those goals?

Establish clear targets, SLAs and KPIs by Stakeholder

If you don’t define success, how will you know what success looks like? Definition of

success can vary by stakeholder: be clear on how the Use Case will help each

http://www.sap.com/campaigns/2013_05_design_thinking/index.epx

http://www.sap.com/campaigns/2013_05_design_thinking/index.epx


Step 2: Data Discovery

What data exists, what is its size and composition, where is it located,

how it is managed, what are its dependencies, how does it integrate

with other systems?

Check under the bed

Don’t overlook the ordinary

Mine opportunities in machine data

Confidentiality and compliance

Pay attention to neglected data

Look outside

Read the signs

See the forest for the trees

?


Step 3: Initial Evaluation. Get started, experiment, and

learn along the way

Did the data meet the requirements for the outcome?

What is the likely cost vs benefit calculation?

How many core issues and edge issues remain and are they in

your control to fix?

What is the realistic timeline to deliver the first benefits?

Does the maturity you encountered match the solution?


Step 4: Information Architecture Design

First, understand the

complete Logical Architecture

possible…

…then layer in Component

architecture to determine

where there are gaps


Step 5: Implementation

Get executive buy-in, support and sponsorship

Allow business managers to translate the goals into

objectives and define success metrics

Put the necessary IT infrastructure, applications and governance processes in

place

• Technical implementation

• Information flow testing

• Analysis

• Automation

• Feedback

Learnings and Support


Early Learnings from Big Data projects

• Form follows function: Start with “Business” not with Technology

• Prioritize on 2 dimensions: Business Value and Feasibility

• Determine which capabilities are core for you to build and which can you

outsource


Help is at Hand

big data maturity model a practical application cookbook

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