2017 CIGO Summit - Information Governance · Emerging Technologies: cloud and big data, FUD, risk...

2017 CIGO Summit

LEARN TO BE AN INFORMATION LEADER Chicago, IL | May 10-11, 2017

2017 CIGO Summit Welcome to Day One

Who Is the IGI?

The Information Governance Initiative is a think tank and community dedicated to advancing the adoption of Information Governance practices and technologies through research, events, advocacy and peer-to-peer networking. We are dedicated to the professionalization of IG and have called for the creation of a new kind of information leader called the Chief Information Governance Officer. The IGI Community is where thousands of practitioners from cybersecurity, IT, analytics, privacy, legal, records management, and the other facets of IG come together and learn from each other. The IGI was founded by recognized leaders in the field of IG, and is supported by leading providers of IG products and services.

IGI Supporters

2017 CIGO Summit Sponsors

ENERGY BREAKS

PLATINUM SPONSOR

GOLD SPONSORS

SILVER SPONSORS

PARTNERS

LUNCH SPONSOR

Bennett Borden, IGI Jason R. Baron, IGI

Welcome Exercise: What Do We Want to Accomplish at the 2017 CIGO Summit?

Welcome Exercise What Do We Want to Accomplish

at the 2017 CIGO Summit?

Bennett B. Borden Jason R. Baron IGI

One. Information Valuation: Let’s (finally?) get serious about it. Two. Pragmatism as doctrine: a common thread for IG success? Three. Let’s find out . . .

Themes for the 2017 CIGO

Summit

How Information Governance Is Helping Boeing to Become a Data-Driven Company in Its Second Century

Mark Milone Boeing

Copyright © 2017 Boeing. All rights reserved.

Information Technology & Data Analytics

Digital Transformation: From Disruption to Intelligence

CIGO Summit 2017


Enterprise Architecture | Information Valuation Information Technology & Data Analytics

Personal Transformation: Wall Street to Aerospace

Boeing: Senior Counsel to Information & Analytics, CIO is my primary internal client Practice Areas: cyber, privacy, IP, governance, investigations, transactions, M&A Ch-Ch-Ch-Changes: transformations experienced by myself and my clients Publications: intersection of tech and law, technology always wins, tension between privacy and security, Information Security Law (American Lawyer Media 2006) Intelligent Transformation: how do we plan for inevitable change and continuous improvement?



Personal Transformation: Wall Street to Aerospace

Early Career: pre-Internet Bubble, start ups, law firms Commodities Exchange: physical trading floor, cards in a ring, electronic trading, loss of market share, platform development, late to the game, acquired by competitor Bloomberg: finance in the 80s, internal politics, relegated to IT, rise of the modem, Bloomberg Terminal, leverage user data to offer new services Life Lesson: change is inevitable, how we react will determine whether we remain relevant, plan for it and make the most out of a crisis



IT Transformation: Cost-center to Value-driver

Boeing Network: large corporate network, over 6k IT employees, supports 160k employees, 21k suppliers, 70 countries, 190 international sites Prior Boeing IT: functions distributed across enterprise, 2008 core up, CIO reports to CTO Great Recession: cost-cutting, reductions in force, cyber security Reactive Mindset: back office order taking, risk identification Emerging Technologies: cloud and big data, FUD, risk aversion Death by Diligence: all requests for external cloud subject to strenuous review, no one is allowed in to or out of the building….



IT Transformation: Cost-center to Value-driver

Information & Analytics: 2016 CTO retires, CIO promoted to ExCo, IT becomes “Info & Analytics”, closer integration with business for data enabled services Value Mindset: shifting from managing risk to adding value, run IT like a business Priorities: analytics, cloud, dev ops, additive manufacturing Goals: trust, standardization, transparency Challenges: mindsets, shadow IT, customized legacy systems, data silos



Company Transformation: Things to Data

First 100 Years: we made things, complex things that last a long time Next 100 Years: we understand things to make them better, faster, cheaper Networked Things: everything creates data, everything is connected, products inextricably linked to data, no difference between info and product, we sell flying networks Platform: get into data stream, analytics, insights, service creation, rinse, repeat New Competitors: everyone wants to be a platform, suppliers, customers, etc. Customer Engagement: how do we empower customers to get the most out of products?



Market Transformation: 21st Century Manufacturing

Pillars: revenue, margin, ecosystems are knowledge sharing platforms, rate of innovation Leadership: identified a shift in the market, provided strategic/role clarity I&A Initiative: provide biz with enterprise-wide access to info and analytics for increased efficiency, business intelligence, and profitable growth Analytics: unlock the power of data, strengthen ability to develop and market new services IT Differentiation: integrate IT with biz processes, seat at the table, data-driven service



Technical Transformation: Intelligent Systems

Bedrock Systems: systems that design, procure, manufacture products Standardize: highly customized legacy systems, interoperability, limit technical debt Digital Thread: product and operational info representing full value stream and lifecycle Digital Twin: virtualization, a common language allows you to model products and operations, planes, manufacturing floors, IT systems, etc. Simulation: create the right model and capture the right data, travel through time to better understand how that part or system works, simulate new conditions, optimize, predict failure Intelligence: when systems and people speak the same language, they learn from each other, they see patterns, they become smarter



Technical Transformation: Intelligent Systems

Machine Learning: programming is dead (soon), we train computers to learn Human-to-Machine: workers and robots on the factory floor, pilots on a plane, enable our products and systems to learn from interactions with users Machine-to-Machine: systems understand and learn from each other, human-to-human? Predictions: first train ML with examples, then find correlations with the trained model, ML is an alternative to logic and reasoning Challenge: ML needs HUGE data sets, need a data collection strategy, APIs for vision, speech, translation, etc.



Role Transformation: Lawyer to IT Guy (Sort Of)

Shifting Market: continuous capability improvement, legacy systems are fragile, manual provisioning processes are slow New Platforms: hyper scaling, self-provisioning, dynamically assign storage/compute, quickly stand up development environments….secure? New Services: troves of product data, can we make sense to find patterns, can we build capabilities from these patterns, how do we price these services? Caution: senior leaders do not trust these systems, loss of control…better security? Informed Risk Decisions: how can we accept risk when we don’t really know the full scope of our exposure?



Role Transformation: Lawyer to IT Guy (Sort Of)

Cloud: biz appetite, risk aversion, shadow IT, no liability for data loss, insurer of last resort Analytics: data sets, domain expertise, prognostics, apps, rapid capability development, service pricing Value At Risk: how do we understand the value at risk as we place information in external systems or share information with strategic partners? Service Pricing: can we use info value for pricing or capability development partnerships? Project: determine best practices for quantifying the value of information



Information Value

Best Practices: no clear industry leader for info valuation, few mature methodologies, PII-per-record value, IP valuation remains mysterious Information Assets: most businesses do not consistently treat info as an asset with value, multiple competing internal valuation models Valuation Formulas: cost-based, market-based, script for interviewing data owners Problems: manual process, no automation, not scalable, difficult to operationalize Increased Interest: other groups became interested in findings including Finance, Data Analytics, Intellectual Property Management, Business Architecture, Portfolio Management



Information Governance

Vision: support business transformation and enable IT differentiation in service creation Unified Information Model: universal translator, provides “grammar” to established lexicon, dynamically assign attributes to data, business architecture alignment, value streams Future State: Natural Language Processing to interpret data, Deep Learning to identify insights, Artificial Intelligence to enable real-time decision making Systems Integrators: evaluation criteria, boundary conditions, no “company-paid learning”, expose design principals, commitment to success, knowledge transfer Challenge: incorporate intelligence while maintaining integrity of data rich environments, must trust data, governance model must push right data to right people



Lessons Learned

Disruption: transformation is painful, internal COI, cultural aversion, mindsets vs. operations Strategic/Role Clarity: transformation is led from above with help from outside (maybe), new initiatives need authority, no one can say “yes”, everyone can say “no” Mindsets: thinking in terms of value rather than risk, not everyone wants transparency, access to internal data can be a challenge, who really “owns” data? Engagement: get the right people involved early, words matter, engage for buy-in Simplify: take away complexity, provide info to leaders at right level, find simple analogies My Role: universal translator enabling human-to-human interaction and learning

An Introduction to Infonomics

Doug Laney

VP and Distinguished Analyst Gartner

CONFIDENTIAL AND PROPRIETARY This presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other intended recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates. © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

Doug Laney (@doug_laney)

Infonomics Overview

26 © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

? Where is the value of

an organization's information

represented on the its balance sheets?


What Is an Asset Anyway?

A single item of ownership having exchange value or convertible into cash. Total resources of a person or business such as cash, notes and goodwill. Webster

Any economic resources (tangible/intangible) that can be owned or produce value. Assets have a positive economic value. American Institute of CPAs

A resource controlled by the enterprise as a result of past events and from which future economic benefits are expected to flow to the enterprise.

International Accounting Standards Board

A probable future economic benefit obtained or controlled by a particular entity as a result of past transactions or events.

Financial Accounting Standards Board

ü Owned and controlled

ü Exchangeable for cash

ü Probable future economic benefit


Information-Savvy Organizations Receive Higher Market Valuations

Market vs. Tangible Asset Value ("Tobin's q" ratio)

Average Company Info-Savvy Companies Info-Product Companies

2-3x

4-5x


Infonomics is the economic theory of recognizing information as new asset class, and the discipline of measuring, managing and monetizing information just as any other enterprise asset.


Infonomics: 3-Dimensional Challenges and Opportunities

Monetizing Information

Generating measurable

economic benefits from or

attributable to available

information assets

Managing Information

Applying traditional asset

management principles and practices to information

Measuring Information

Gauging and improving

information’s economic

characteristics


Monetizing Information Assets

INDIRECT DATA MONETIZATION

§  Using data to improve efficiencies

§  Using data to develop new products, markets

§  Using data to build and solidify partner relationships

§  Branded indices

DIRECT MONETIZATION

§  Bartering/trading with information

§  Information-enhanced products or services

§  Selling raw data through brokers

§  Offering data/report subscriptions

100

100

100

100 or


Information Monetization Examples

Monetizing Sales & Inventory Data

Monetizing Social Media

Monetizing Project Content

Monetizing Genealogy Data

Monetizing Customer Data


Managing Information as an Asset: Borrowing From Traditional Asset Management Practices

Material

Financial

Workforce

Inventory Maintenance

Security

Disposal

Investment Leverage

Portfolio Management Volatility

Credit

Training

Roles

Hiring

Teams


Measuring Information Assets: Gartner Information Valuation Models Foundational Measures

How correct, complete and exclusive is this data?

Intrinsic Value of Information

(IVI)

How good and relevant is this data for specific purposes?

Business Value of Information

(BVI)

How does this data affect key business drivers?

Performance Value of Information

(PVI)

Financial Measures What would it cost us if

we lost this data?

Cost Value of Information

(CVI)

What could we get from selling or trading this data?

Market Value of Information

(MVI)

How does this data contribute to our bottom line?

Economic Value of Information

(EVI)

Focused on improving

information's economic benefits

What is your objective for

valuing information?

Focused on improving

information management discipline

Leading Indicator

Lagging Indicator

Source: "Why and How to Measure the Value of Your Information Assets" (G00277972)


Major global retailer

§  Evolved from "squeaky wheel" IT funding model

§  Focused on data accessibility to benefit the enterprise

§  Quantified and reported on data quality issues

Information Valuation Models in Practice

We prioritized analytics initiatives

Healthcare services company

§  Move beyond copy-cat budgeting and "blunderfunding"

§  Measure information risks and liability

§  Treat information as an asset

We are validating our data protection investments


Software-as-a-service company

§  Quantifying the value of software usage data

§  Identifying ways to generate economic value from data

§  Improving customer experience, attracting partners

Global financial services firm

§  Improving information management practices

§  "Putting dollar signs on data"

§  Redefining information-related roles

Information Valuation Models in Practice (Continued)

We're changing employee behavior

We identified ways to monetize data


Security system company

§  Measured the business relevancy of various data

§  Measured the economic value generated by this data

§  Innovated with data having high potential and low value

Utility company

§  Identified and measured the cost of "dark data"

§  Measured the economic value of this data

§  Made a defensible disposal decision to delete the data

Information Valuation Models in Practice (Continued)

We improved our market valuation by $300M via information innovation

We reduced infrastructure costs by

over $1M/year


Infonomics Book

u  Available September 2017 –  Foreword by Dr. Thomas H. Davenport –  Part I — Monetizing Information as an Asset:

§  Why Monetize Information

§  Top Ways to Monetize Information

§  Methods for Monetizing Information

§  Analytics: The Engine of Information Monetization

–  Part II — Managing Information as an Asset: §  Information Management Maturity and Principles

§  Information Supply Chains and Ecosystems

§  Leveraging Asset Management Principles and Practices

§  Applied Asset Management for Improved Information Maturity

–  Part III — Measuring Information as an Asset: §  Is Information Really an Asset?

§  Who Owns the Information?

§  Quantifying and Accounting for Information's Value

§  Adapting Economics Principles for Information

§  Infonomics Trends

© 2017 — Taylor & Francis Publishing

Welcome Reception

2017 CIGO Summit

LEARN TO BE AN INFORMATION LEADER Chicago, IL | May 10-11, 2017

2017 CIGO Summit Welcome to Day Two

CONTACT US Information Governance Initiative 1271 Avenue of the Americas Suite 4300 New York, NY 10020 (866) 626-2917 http://iginitiative.com https://twitter.com/IGInitiative

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2017 CIGO Summit - Information Governance · Emerging Technologies: cloud and big data, FUD, risk...

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