Cognitive, Cloud, Big Data: A New Beginning for Data Quality? · and Master Data Management •Towards single view of a customer and product data Integrating data for business intelligence

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© 2014 IBM Corporation

IBM Strategy & Analytics

24 June 2014

Cognitive, Cloud, Big Data:

A New Beginning for Data Quality? Closing Ceremony Keynote Information and Data Quality Summit 2014 Dr. Alexander Borek IBM Center of Competence for Advanced Analytics


@bigdatarisk

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“Change is the law of life. And those who look only to the past or present are certain to miss the future.”

John F Kennedy

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24 June 2014

We look at a half century of enterprise data management as it has evolved - and the practices of data quality management alongside

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Separating data from applications in data bases • Address validation

Introduction of ERP systems • Towards a single

overarching operational system

Dominance of e-commerce • External data

becomes increasingly important

• Digital channels become essential

CRM Systems and Master Data Management • Towards single

view of a customer and product data

Integrating data for business intelligence in data warehouses • Make use of data

beyond their operational use

• First enterprise analytics: Slicing and dicing, querying, reporting of data

Digital Transformation & Big Data Analytics • Enablers: Cloud,

Analytics, Mobile, Social, Smart

My own view and very simplified interpretation on how some key developments in data management. This is no real timeline, but rather of illustrative nature.

Data as a resource

Reinvention: digital processes

Analytics as differentiator

Standardization of systems

Reinvention: digital enterprise

Modularization

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This is a historic moment

“For the first time in human history, mainstream media, marketing, and government are directly addressing using data and information to make society better, optimize companies, and answer unasked questions”

John Ladley, President, IMCue Solutions

Organization & Governance 4

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What do I want to talk about today?

I. Companies entered a race of digital transformation making data a top priority for executives

II. The driving forces behind those changes are technological progress and adoption in fields such as Cloud Computing, Big Data Analytics, Mobile Communication, Social Networking, Smart Things, and Cognitive Computing

III. We discuss what these developments could mean for the future of data quality management

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PART 1

Companies are changing to embrace digital

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“The first step toward change is awareness. The second step is acceptance.” Nathaniel Branden

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The new enterprise is made with data: Examples of the transformation of front office and operations

Digital Front Office Digital Operations

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digital lab

predict demand

one to one marketing

operational efficiency and risk reduction

optimized assets

digitally enabled workforce

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24 June 2014 Commerzbank AG | RDA | Workshop | June 16th, 2014

Data gets a lot of senior executive attention today

Many companies are embarking on a journey to transform their core business processes with data

New investments in technology are accompanied with major changes in the business and new business models

Chief Data Officers are the fastest growing new data role associated with big data and analytics

“By 2017, 50% of all companies

in regulated industries will have a Chief Data Officer.”

– Gartner

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Source: The New Intelligent Enterprise, a joint MIT Sloan Management Review and IBM Institute of Business Value analytics research partnership. Copyright © Massachusetts Institute of Technology 2011.

Information management

proficiency

Analytic skills and tools proficiency

Data-oriented culture

Aspirational

High

Low

High Low

Experienced

Transformed Collaborative path

Specialised path

What is Required

Organizations travel one of two paths to move to Transformation

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Centralised Quasi De-Centralised Hub & Domain

We observe three types of organization models for transformed firms

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UK Telecom South African Bank

Australian Bank

US Investment Firm

First Tennessee Bank

Global Credit Card Provider

US Bank

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24 June 2014

Funding

Source of Value

Sponsorship

Data

Platform

Trust

Culture

Measurement

Strategy Technology Organisation

Expertise

Establish a common vision to guide actions and deliver value

Governance and security creates confidence in the data

Ensure alignment between analytic focus and value creation

Rigor and collaboration create value

Measure impact and model the future

Make decision based on facts

Development and access to skills and capabilities

Integrate hardware and software to manage data

Governance and security creates confidence in the data

Rigor and collaboration create value

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There are nine levers of differentiation for data analytics transformation when we compare successful leaders to followers

Create trustworthy relationships across business functions

Source: Analytics: A blueprint for value, IBM Institute for Business Value Study 2013

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24 June 2014

PART 2

Driving forces behind those changes

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“I can't change the direction of the wind, but I can adjust my sails to always reach my destination.” Jimmy Dean

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24 June 2014

Six forces create disruption in the IT and organizational landscape and form a catalyst for company wide digital transformation

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Cloud • Delivery of

computing as a service rather than a product as a utility over a network

Analytics • Examining large

amounts of data in an effort to uncover hidden insight

Mobile • Mobile computing

enables new ways of interaction between humans and computers in the real world (e.g. in retail stores, at work place)

Social • Platforms to build

social networks or social relations among people are growing and change the way people interact between each other

Smart • The world

becomes more instrumented, interconnected and intelligent

• Internet of Things and Smart Products (e.g. Connected Car)

Cognitive • Computer takes a

question expressed in natural language, seeks to understand it in much greater detail, and returns a precise answer to the question

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Cloud computing changes the game for enterprise data management: Many processes are outsourced together with infrastructure

Enterprise IT is becoming more flexible and allows rapid new business models and cheap processing of Big Data

Companies are replacing legacy systems with modern applications designed and built for cloud, analytics and mobile computing

IT infrastructure is moving out of company bounds, which enables application and data quality process outsourcing incl. BI and analytics as a service

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Cloud remakes Enterprise IT

Source: The Power of Cloud. IBM Institute for Business Value Study 2012

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Big Data Analytics is key to create leveraged value out of data and all the other technological advancements mentioned

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Analytics is transforming enterprises: – how strategies are made – how business models are designed – how operational processes work – the way companies communicate with

customers – how products and services are developed – how companies act in supplier networks

Analytics is needed to create value out of cloud, mobile, smart products, social and cognitive

Analytics makes data a strategic asset and differentiator

Source: Analytics: A Blueprint for Value. IBM Institute for Business Value Study 2012

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The double revolution of Mobile and Analytics sparks the Individual Enterprise

Mobility compresses time between identifying situations and taking action

Mobility drives a step change in productivity growth

Mobility allows fundamental redesign of the enterprise

Mobility dynamically reconfigures workflow around every individual

Mobility stimulates skill acquisition and sharpens career focus

Mobility empowers individuals to create their own work experience

Mobility amplifies analytics value through real-time situational understanding

Mobile analytics brings intelligence to every action in the moment

Mobile analytics accelerates the return on investment of information

Create new business

value

Mobility redefines operating models

Mobility is the foundation of new models of engagement

Mobility shifts design to optimize for the end-user

Designed first for mobile

Unleash empowered employees

Powered by analytics

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Integrated ecosystems - Insight at the point of engagement - Contextual actions in the moment

Source: The Individual Enterprise - How mobility redefines business. IBM Institute for Business Value Study 2014

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Online social networks change the way companies communicate with consumers and with employees

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Enable the social organization

Engage and listen Build the community Shift towards sales and

service

Increase knowledge transparency and velocity

Find and build expertise Leverage capabilities beyond

organizational boundaries

Capture new ideas from anyone Use internal communities

to innovate Enable structured innovation

efforts

Address risk Measure results Manage the change

Social business

Create valued customer

experiences

Drive workforce

productivity and effectiveness

Accelerate innovation

62% of companies are likely to invest next years

Source: The Business of Social Business. IBM Institute for Business Value Study 2012

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The digital industrial economy is reshaping industries: The world becomes more instrumented, interconnected and intelligent

By 2020, The Internet of Things will add $1.9 trillion of economic value across industries (Gartner)

The verticals that are leading its adoption are manufacturing, healthcare and insurance (IDC)

Thousands of Domains, e.g.: – RFID for Logistics and Transportation – Connected Car – Connected Home – Mobile Health Monitoring – Aset Performance Management

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Now we can see the world...

...and make smart interventions Source: Bluemine tools and solution group, data from IDC study 2014 IoT Top 10 Predictions

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A new breed of computing: Cognitive technology processes information more like a human than a computer…

…by understanding natural language, generating hypotheses based on evidence, and learning as it goes

Natural Language: Watson can read and understand natural language, important in analyzing unstructured data that make up as much as 80 percent of data today.

Hypothesis Generation: When asked a question, Watson relies on hypothesis generation and evaluation to rapidly parse relevant evidence and evaluate responses from disparate data.

Dynamic Learning: Through repeated use, Watson literally gets smarter by tracking feedback from its users and learning from both successes and failures.

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Watson “gets smarter” in three ways:

1. by being taught by its users

2. by learning from prior interactions

3. and by being presented with new information

Organizations can more fully understand data that surrounds them and make better decisions.

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Examples of cognitive applications: Ask your phone for shopping advise and pose business questions to a machine

Watson could be your next shopping partner Watson provides precise custom answers to your business questions using ALL data

Imagine having IBM’s artificially intelligent robot, Watson -- the same robot that made even the smartest of us feel like bumbling fools on Jeopardy -- help you decide on just what pair of heels to buy.

“By tapping into IBM Watson’s cognitive intelligence, Fluid is infusing the personalized, interactive feel of an in-store conversation into every digital shopping interaction,” said Mike Rhodin, a senior vice president at IBM’s Watson Group

http://www.entrepreneur.com/article/233317

Watson Explorer provides users with a unified view displaying all of their data-driven information

Deliver a comprehensive, contextually-relevant view of any topic for users ranging form business users to data scientists

Uncover and share data-driven insights more easily

Launch big data initiatives more quickly

Example question:

Should I invest in company?

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http://www.entrepreneur.com/article/233317

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24 June 2014

Examples of cognitive applications: IBM’s Watson is now a cooking app with infinite recipes

Cooking with Watson The cooks from Bon Appetit tried cooking with Watson and here´s what they say:

“Working with Watson forces you to consider ingredients you never would on your own: Like the Chinese spicy mustard on the ribs. When was the last time I thought about that? Or the marjoram in the berry cobbler.”

That berry cobbler ended up being one of the favorites of the batch, and Perry’s still thinking of ways to make it better.

If you don’t use every single ingredient, Watson’s not going to come yell at you.”

“Something we’ve started to explore is that Watson can help people be creative and come up with never before seen ideas,” explains Steve Abrams, Director, IBM Watson Group. “We’ve been using the culinary arts to explore that activity.”

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http://www.fastcompany.com/person/steve-abrams

http://www.fastcodesign.com/company/ibm-watson-group

http://www.fastcodesign.com/company/ibm-watson-group

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24 June 2014

Cooking with Watson

22 http://www.fastcodesign.com/3032501/ibms-watson-is-now-a-cooking-app-with-infinite-recipes

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24 June 2014

PART 3

A new beginning for data quality management

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“They always say time changes things, but you actually have to change them yourself.” Andy Warhol

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Once upon a time, data was an IT topic. Today, it is on the top of executive agendas

Executive´s attention shifts towards digital transformation and rediscover data quality as side effect

This is a tremendous chance to revive the importance of our discipline

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How can it be that data is on the top of the executive agenda, but data quality and governance get overall less attention?

1. As a discipline, we have failed so far to promote the importance of data quality for Big Data Innovation and Digital Transformation

2. We need to update our methods and skills to a world of data flows rather than data stocks

3. We might need to consider changing the organizational setup for data quality management

All three things are tightly interconnected. 25

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Action No.1: We have to promote the importance of data quality for Big Data Innovation and Digital Transformation more aggressively

We always stress the importance of data quality for business success

Yet, data quality is perceived as a slow and long journey in an ever-changing world that needs fast results

We have not done a great job so far at demonstrating that data quality management skills can contribute to Big Data innovation quick wins

Data quality managers can be agents to speed up digital transformation

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Action No.2: We may have to upgrade our data quality professional curriculum to stay on top of the game

We need to better integrate the data scientist´s toolkit and Big Data transformation

A more agile approach to data quality management is required in a world of data flows rather than data stocks

Start using analytics to identify and solve data quality problems in a Big Data environment

The new data landscape requires data quality adapted to different zones of trust

Data quality management needs to be extended to monitoring analytics quality and also to unstructured and external data

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Different zones of trust in a Big Data Analytics World

External data

Extended internal and supplier data

Insights

Data for operations

Some key considerations:

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Action No.3: We might need to consider changing the organizational setup for data quality management

Data quality professionals should embrace the changes through Big Data innovation and Digital Transformation rather than seeing it as a threat. After all, data becomes more important as a result

Analytics is usually business driven, data quality was supposed to be, but often was part of IT

Data quality and governance business units should aim to become closer integrated into teams that are responsible for digital transformation, data science, analytics and Big Data innovation, which are perceived more value adding by the business

There are several options, in particular, the rising importance of the Chief Data Officer and the setup of Centers of Excellence for Big Data and Analytics provide an enormous opportunity

Organization & Governance 28

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Chief Data Officer

The Chief Data Officers could become the new home for data quality management and data governance

Finding ways to use existing data assets to advance the cause of the organization

Data leverage

Finding new avenues of earnings and revenue opportunities outside existing processes and functions

Data monetization

Augmenting existing datasets through the combination of - fragmented internal data sources - the acquisition of external data from government feeds or social media sources - and the integration of a business partner’s data

Data enrichment

Managing the health of the data under governance

Data upkeep

Protect data as an asset

Data protection

29 Source: The new hero of big data and analytics – The Chief Data Officer. IBM Institute for Business Value Study 2014

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Data quality managers should be part of every Big Data or Analytics team to ensure that analytics results meet customer requirements

Center of Excellence

Data quality managers should be an integral part of a new Center of Excellence that is built for Big Data and Analytics

Currently, data scientist often do their work without involving the data quality manager in a company

The quality of analytics is often not standardized

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24 June 2014

PART 4

Summary and conclusions

“Hell, there are no rules here - we're trying to accomplish something.” Thomas A. Edison

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24 June 2014

Summary and conclusions

Companies entered a race of digital transformation making data a top priority for executives

The driving forces behind those changes are technological progress and adoption in fields such as Cloud Computing, Big Data Analytics, Mobile Communication, Social Networking, Smart Things, and Cognitive Computing

We discussed that the changes provide an enormous opportunity, a new beginning, for the data quality profession as a whole, which has not been taken advantage of so far.

There are three potential actions to bring our profession back to the top of the game: – Action No.1: Promote the importance of data quality for Big Data Innovation and Digital

Transformation more aggressively – Action No.2: Upgrade our data quality professional curriculum – Action No.3: Consider changing the organizational setup for data quality management

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“The best thing about the future is that it comes one day at a time.”

Abraham Lincoln

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24 June 2014

Documents

Cognitive, Cloud, Big Data: A New Beginning for Data Quality? · and Master Data Management •Towards single view of a customer and product data Integrating data for business intelligence