the roadmap for successful big data adoption · milestones of big data adoption . 8 . unaware ....

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Svetlana Sicular

Twitter: @Sve_Sic

Blog: http://blogs.gartner.com/svetlana-sicular

The Roadmap for Successful Big Data Adoption

http://blogs.gartner.com/svetlana-sicular






Gartner Definition of Big Data: High-volume, velocity and variety

information assets that demand cost-effective, innovative forms of

information processing for enhanced insight and decision making.

Gartner Research Circle 2013 Big Data Survey

720 Respondents

Worldwide

$3.2B Mean

Company Size

5,100 Mean

Employees

60% Mainstream

Adopters

18% Focused on

Running/Maintaining

Are They Investing?

30% Have

31% No plans

at this time

19% Plan to within

the next year

15% Plan to

within two years

5% Don't know

How Does That Compare to Last Year?

Note — Survey base increased from 473 in 2012 to 720 in 2013

27

15

16

11

30

19

15

31

5

Have invested

Within next year

Within two years

No plans

Don't know

2013 2012

0 10 20 30 40

Things Are Done Differently in Silicon Valley

Traditional IM • Requirements based

• Top-down design

• Integration and reuse

• Technology consolidation

• World of DW and ECM

• Competence centers

• Better decisions

• Commercial software

"Big Data" Style

• Opportunity oriented

• Bottom-up experimentation

• Immediate use

• Tool proliferation

• "World of Hadoop"

• Hackathons

• Better business

• Open source

Business Process: Information Centric

Information-centered tomorrow

Info. & Content,

Analytics, Metadata

Structured Process

Resource Optimization

Case Management

Business Applications

Analytics

Meta data

Information &

Content

Applications-centered today

Build bridges between the world in

information and the world of process

Process Application Information Information Process Application

The Data-Driven Enterprise

7

The Road Map: Typical Stages and Milestones of Big Data Adoption

8

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time

Value

Initial implementation of the LDW

Traditional technology cannot meet all needs

Data-Driven Enterprise

Big data initiative is justified

Big data strategy planned

Stabilized big data infrastructure

Information governance is a must

Data products emerge

Big data is becoming the new normal

Ramp up (investments outstrip returns)

A milestone

9

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time

Value









“Something about the way we do work today is

either broken or is about to break."

Stage 1: Unaware

Simultaneous Shifts Brought About by the Nexus of Forces

Customer

Borderless

Physical

Ubiquitous

Mobile

Enterprise

Highly Structured

Digital

Handful

Desktop

Business Focus

Organization

Scope

Devices

Workstation

Shifts

Stage 1: Unaware

11

Organizational Inertia Data

Growth

Traditional Technology

Cannot Meet All Needs

BI Immaturity

Outsourced IT

Changed Customer

Expectations

Actions to Progress to the Next Stage

Start information governance

Evaluate your BI maturity and plan BI improvements

Educate IT and businesspeople about big data opportunities to spark their interest in big data adoption

Stage 1: Big Data Myths

A Common Myth

Reality

12

Big data is about volume

Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making

Big data = Hadoop

Big data is a technology stack that satisfies the definition of big data. A logical data warehouse (LDW) is the target architecture for combining data warehousing and big data technologies

Our current EA and EDW solutions will work for a long time without architecture changes

Enterprise architecture (and the EDW as part of it) is a process not a goal, it reflects the organizational evolution necessary for the business survival under the forces of the nexus.

13

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time









Value

"An understanding of when to use big data is

lacking right now."

Stage 2: Aware

In Search of Solutions

14

Doctor, I have a big problem

You have a BIG DATA problem!

Do You Have a Big Data Problem?

Stage 2: Aware

15

No Business Case for Big

Data Adoption

Skunkworks With New

Technologies

Big Data Strategy

Limited Information

About Actual Big Data

Deployments

Business Does Not

Understand Big Data

Opportunities

Digital Marketing in

the Cloud


Select big data use cases that represent best business opportunities

Assess the state of your information governance and plan its improvements

Get educated about big data technology, people and process.


A Common Myth

Reality

16

All our unresolved problems are big data problems

Big data technologies are best for solving business problems where questions are not known in advance and the data types to answer these questions could vary and might need unstructured data

Everybody is implementing big data technologies and we are behind our peers

The majority of enterprises, including those touted by the media as successful adopters of big data technologies, are at Stages 2, 3

Big data is created outside our enterprise

“Dark data” — an underutilized data in existing data stores — is of greatest interest to all enterprises who investigate big data opportunities.

17

Unaware

Opportunistic

Strategic

Transformative

Time







Experimental Aware



Value

"Being highly focused on the business driver

is fundamental to success in big data."

Stage 3: Experimental

The Internet of

People

18

Opportunities are in Interconnectedness

The Internet of

Things The Internet of

Data

The Internet of

Ideas

@

Data Scientist Is Just a Part of the Solution….

A Multi-disciplinary Team:

• Mix of business and technology is necessary

• Techies are predominantly introverts but big data requires extroverted understanding of customers, partners and competitors

• The need for specialized skills that are non-traditional for the enterprise (such as linguists or anthropologists)

• Team members inspire and enrich each other.

Increase Maturity:

• Team and teamwork

• Elements of big data analytics

• Big data analytics process

• IT

• Team members can later represent specific functional groups or sub teams

The Goal Is to Share the Big Data Experience

Stage 3: Experimental

20

Wrong Evaluation

of a Big Data Initiative

Business Leadership

Justification of a Big Data Initiative

Lack of Skills

Absence of Information Governance

Identified Big Data

Use Cases

POCs


Start forming a multidisciplinary big data team of business and technology professionals

Filter out best business ideas for big data, prove their value, select appropriate technologies and justify big data adoption in the well-informed business case.


A Common Myth

Reality

21

All big data opportunities are evaluated by the same criteria

Big data opportunities are of two types: game-changing and business extension. Evaluate them differently.

Big data technology is mature for the enterprise needs

Many technology gaps exist. They require workarounds or might not possible to mitigate.

A data scientist will implement a big data solution for our enterprise.

A multidisciplinary team with diverse skills can meet technology challenges and solve complex business problems of big data adoption.

What's Still Needed?

Security

Data Warehousing Tools

Governance

Distributed Optimization

Subproject Optimization Skills

23

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time









Value

"When you integrate disparate data together,

it's not clear what the exposure is: We're afraid

of what we might discover and not having

control of it."

Stage 4: Opportunistic

Big Data Governance: Value, Reuse, Risk and Compliance

USE CASE

& Data

25

The Heat Map of Big Data Governance Priorities

What From To Business

Value Reusability Compliance Risk

Business Focus

Enterprise Consumer

Organization Highly Structured

Borderless

Data Confined Liberated

Devices Handful Ubiquitous

Scope Digital Physical

Workstation Desktop Mobile

Software Proprietary Open Source

Solutions Deterministic Probabilistic

Applications Monolithic "Fit for purpose" in App Stores

Technology Adoption

IT Business

High Medium Low

12 Dimensions of Big Data

• Volume — sources x produced data

• Variety — different types of structures

• Velocity — timing and streams

• Complexity — standards, rules, formats

Classification Contracts

Technology Pervasive Use

Perishability Fidelity

Validation Linking

Velocity Volume

Variety Complexity

• Perishability — shelf life

• Fidelity — reusability of data and results

• Validation — understanding of use case

• Linking — relationships between elements

• Classification — access controls

• Contracts — SLA

• Technology — fragmented tools

• Pervasive Use — usage levels

Stage 4: Opportunistic

27

Siloed Big Data Implementations

Business Extension Use Cases

Stabilized Big Data

Infrastructure

Lack of Specific Skills

Reactive Information Governance

Information Governance

Additional Use Cases Amplify the Value of Big

Data


Revisit your initial assumptions and adjust your big data strategy

Make information governance an integral part of big data adoption

Stabilize infrastructure and operations for big data technologies

Start building an LDW to transform your business strategy and operations for the data-driven economy


A Common Myth

Reality

28

Hadoop is cheap to implement

Initial implementation is more expensive than expected. It involves a lot of unanticipated technical and non-technical difficulties.

Big data infrastructure usually takes advantage of commodity hardware.

Enterprises buy high-end hardware.

Programming for big data technologies is difficult.

Programming for big data technologies is easy for programmers.

29

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time









Value

"When mining big data, you'll find unexpected

(but real) results. Don't start a project if you're

unwilling to deal with the findings."

Stage 5: Strategic

Logical Data Warehouse

Which Big Data characteristic is the

biggest issue for your organization?

Stage 5: Strategic

Technology Immaturity Leads

to Custom Solutions

Game-Changing Business Ideas

Logical Data Warehouse

Talent Attrition

Variety of New

Technologies


Treat big data as the new normal.

Operationalize infrastructure like for any ongoing initiative. Develop support, provisioning and capacity planning.

Complete the initial stages of the LDW by implementing a variety of integration and virtualization tools.


A Common Myth

Reality

32

Organizations can complete building out their LDW in several years

The LDW will keep evolving. It is being built for extensibility, not for completion. Implement it in incremental releases that deliver capabilities for your roadmap toward a data-driven enterprise.

The ratio between unstructured and structured data is 80:20. Unstructured data contains 80% of information value.

80% represents share of the quantity of unstructured data, not necessarily value of it. Structured data has been refined and its density and quality are much greater than comparable amounts of unstructured data.

Companies can completely migrate off RDBMS

RDBMS technologies serve many purposes better that big data technologies.

33

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time









Value

“During this transition, some leaders left the

company, some people were forced out, and

some people changed their mind.”

Stage 6: Transformative

Big Data (not to scale)

Fish Grows Bigger in a Bigger Tank

Big Data Causes a Mindset Shift

• Asking bigger questions

• Finding new answers

• New ideas

• Openness

• No return to small tank

The Emergence of Collaborative-decision-making Platforms

CDM Environment

• Reliable and secure

• Integrated with systems of record

• Complex decision support (workflow)

• Capture best practices Social Networking

• Examine relationships of decision makers

• Map information flows

• Intelligent social profiles

Decision Tools

• Simulations

• Optimization tools

• Scenario planning

• Mind mapping

• Brainstorming

• SWOT

• Predictive analytics

• Prediction markets

• Decision methodologies

Collaboration

• Shared workspace

• Communication (email, IM, phone, etc.)

• Web conferencing

People

• Involve right people to inform the decision

• Incorporate expert and diverse opinion

• Minimize bias

Information

Decision Tools Collaboration

Social Networking People

CDM Components

Information

• Access to any data source and decision input

• Search, content analytics, visualization tools

• Business intelligence content

• Assumptions and pattern detection and monitoring

Stage 6: Transformative

36

Technology Inability to Meet Leaders' Needs

Collaborative Decision-Making

Platform

Information Products

Non-Data-Driven Employees

Information Trust Models


A Common Myth

Reality

37

Only internet companies can be at this level

Any enterprise can become data driven. In fact, there are already such organizations virtually in every industry.

We will sure find a solution for all our problems.

There are problems which inherently do not have solutions. Do not persist if a solution does not seem to be possible. In other words, fail fast.

Our company will remain a leader for a long time

The business environment and technology change fast. Continuously monitor these changes and adjust your strategy to stay ahead of competition.

38

The Data-Driven Enterprise

Recommendations

Develop or adjust your big data strategy by using the big data adoption roadmap

An initial goal of big data adoption must be learning and growing big data expertise

Build your big data strategy by planning improvement targets for four dimensions at each stage: business, people, process and technology

Avoid common big data fallacies

It is extremely important to understand not only the stages but how to move from one stage to the next.

Recommended Gartner Research

The Road Map for Successful Big Data Adoption Svetlana Sicular (G00254965)

Decision Point for Practical Big Data Use Cases Svetlana Sicular (G00239633)

A Framework for Evaluating Big Data Initiatives Svetlana Sicular (G00246250)

Information Governance in the Age of Big Data Svetlana Sicular (G00251071)

How to Design and Implement the Next-Generation Data Warehouse Mei Selvage (G00219658)

To learn more about how Gartner for Technical Professionals can assist you, visit [email protected] or contact your

Account Executive.

mailto:[email protected]

the roadmap for successful big data adoption · milestones of big data adoption . 8 . unaware ....

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