organizational imperatives in the unfolding data revolution · the “viewpoint” taken here: a...

Organizational Imperatives in the Unfolding Data Revolution

Thomas C. Redman, Ph.D. “the Data Doc”

Navesink Consulting Group www.navesinkconsultinggroup.com

Information and Data Quality Conference, Little Rock, AR, November 4-7, 2013

/Redman-IDQ-Nov2013 T.C. Redman, © Navesink Consulting Group LLC, 2000-2013

Junior Executive

/Redman-IDQ-Nov2013 © Navesink Consulting Group LLC, 2000-2013 T. C. Redman,

VS

Family Practitioner


VS

Rising Middle Manager


VS

CEO Capital Request


Our future… I better get involved!

More tech b#@S%! VS

Synthesis o Individually, nothing more than tough social and organizational

issues. o Collectively, suggest something deeper. o Since the dawn of time, the successful have sought and taken

advantage of superior data. o Data, especially big data, are exploding everywhere.

o Some impressive successes, from all over.

o At the same time, viewed through the “data lens,” the financial

crisis is a colossal failure of data.


The “viewpoint” taken here: A full-strength data revolution is brewing!

o It is not “big data,” it is all data. o Revolutions are chaotic, messy and inherently

unpredictable. o Sooner or later every industry, every company, every

department, and every job will be impacted. o They move slowly, then at dizzying speed. o There are no roadmaps. By the time they appear it will

be too late. o The technological challenges are tall. These pale in

comparison to the organizational challenges.


Five of the Most Common Things I Hear

o “We’re data rich and information poor.” o “I’ve been in this industry twenty-five years. Trust me.

These data are as good as they can possibly be.” o “Tom, you’ve got to keep in mind that we are much

more siloed than the other companies (industries, etc) you work with.”

o “Of course my customers like what I give them. I’ve still got a job, don’t I?”

o “If its in the computer, it must be IT’s responsibility.”


Today’s organizations are unfit for data

o Lack talent, up and down the organization chart. o Silos impede data sharing. o Quality is essentially unmanaged. o Organizations have not thought through how to

compete with data, nor gained enough experience to do so in a sensible fashion.

o Responsibility for data buried in the bowels of IT. Step one: Move it out!


Bottom Line

The leadership challenge in a nutshell: o A full-strength data revolution is brewing o Today’s organizations are stunningly unfit for data So… what to do? o You’re going to need a strategy for competing with data o Quality is pre-requisite (and we know what to do!) o Where does Analytics fit? o You need an end-to-end D4-process o Some data are uniquely your own o Revolutions stand or fall on people


So far, I’ve identified eighteen distinct ways to “put data to work”

Provide (Sell) Content o New Content o Re-package o Informationalization o Unbundling o Exploiting

Asymmetries o Closing Asymmetries

Facilitators o Own the Identifiers o Infomediation o Big Data/Advanced

Analytics o Privacy and security o Training o New Marketplaces o Infrastructure

technologies o Information appliances o Tools

*Working out “what’s right for us” is the key challenge for senior leadership!

*Every organization must think through the five in bold

• Internally o Improve

operational efficiency

o 360°-view o Data-Driven

Culture

/Redman-IDQ-Nov2013 T. C. Redman, © Navesink Consulting Group LLC, 2000-2013

Four Basic Strategies

o Innovation (Big Data/Advanced Analytics): Find hidden nuggets in the data and,…

o Content: Provide or exploit content that others don’t have. n Informationalization n Infomediation (e.g., Google) n Asymmetry (e.g., Hedge fund)

o Build a Data-Driven Culture: Make better decisions, bottom-to-top and across the company.

o Be the low-cost provider: Superior data quality keeps costs down!


I’m Excited About Informationalization

o Eisner: “Content is king” o Basic idea: Make existing products and services more

valuable by building in more data and information o Ubiquity: So far, haven’t come across a product or

service that couldn’t be informationalized. o Available to all: Doesn’t require massive quantities of

data, people with advanced degrees, or capital investment.

o Caution: Customers already in information overload.


High Quality Data is Pre-requisite

o Poor quality is the norm. o The internal costs to accommodate bad data are

enormous. o You can’t (really) sell bad data. o Decision-makers discount data they don’t trust. And

analyses based on them. Wisely so. o In advanced analytics, data are highly leveraged. The

risk of using bad data is high. o Witness the financial crisis.


Data Quality Done Properly


Each error not made saves an average of $500. This amounts to millions quickly!

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20Frac

tion

Perf

ect R

ecor

ds

Month

First-time, on-time results

Accuracy Rate mean control limits target

For Data, Only Two Moments Really Matter


The moment of use The moment of

creation

The whole point of data quality

management is to connect the two!

© Navesink Consulting Group LLC, 2000-2013 T. C. Redman,

Data Quality: The Non-delegatable Choice

/Redman-IDQ-Nov2013

Unm

anag

ed

Eliminate The Sources Of Pollutant To Clean Up The Lake, One Must First

It is so easy for accountability to shift downstream!!!


Here’s how you do

number 3!

Where does analytics fit?

Basic Process

Improvement

New, sophisticated

algorithms

Series of Fundamental Discoveries

In the line: Everyone involved

Permanent “lab”

Analytical “sophistication”

“Home” for Analytics /Redman-IDQ-Nov2013 T. C. Redman, © Navesink Consulting Group LLC, 2000-2013

Project team wo/line

responsibilities

“One-time” opportunity

Really close to, but not in

the line

The D4-Process Whatever strategy you select, you need an end-to-end

process: Data: High-quality, well-understood, potentially-interesting data

is pre-requisite. o In short supply and much more difficult than it may first

appear. Discovery: Finding something truly interesting in the data o Where most of the attention is focused today Delivery: Getting the results to a decision-maker, into an

ongoing process, into a new product/service, etc. o Easy to underestimate how difficult this is. Dollars: Making money from the data, discovery and delivery o “Marketplace” and “scientific” standards are not the same. /Redman-IDQ-Nov2013 T. C. Redman, © Navesink Consulting Group LLC, 2000-2013


Data are the Organization’s Ultimate

Proprietary Technology!

o Difficult to sustain an advantage from publicly-available data.

o Data you create are uniquely yours. And you make more each day.

o Data are subtle and nuanced (e.g., define “customer” in your own way).

o Some, maybe most data become standardized to facilitate communications.

o Unique data offer opportunity for sustained advantage. o These data merit special attention!

/Redman-IDQ-Nov2013

Putting data to work requires new skills and

talent, up the organization chart Everyone/Culture: So far, Information Technology has not

delivered on its promise to make everyone smarter. Analysts: The truly great ones are in short supply. Managers: o For every good+ analyst, need dozens of good+ managers. o In every “clever analysis” that actually bears fruit, the “unsung

hero” is a manager who took a chance! Executive Leadership: o Stone-cold, sober evaluation of “what we can actually pull off.” o Sooner or later, all change is top-down.

/Redman-IDQ-Nov2013 T. C. Redman, © Navesink Consulting Group LLC, 2000-2013

A Federated Organization Model for Data

People Management Data Assets Day-in, day-out: “Regular” people and managers. HR role: Policy setting and admin

Day-in, day-out: High-quality data creation and novel use of data is the responsibility of people, processes and departments. DG role: Policy setting and admin

Departmental HR: Help their units find and advance the talent they need

Departmental DG: Help their units find and/or create the high-quality data they need. Home for quality facilitators, analysts

Corporate HR: Succession planning, pay scales, etc

Corporate DG: “metadata” processes, special provision for unique data


I hope I’ve excited, and scared, you! The leadership challenge in a nutshell: o A full-strength data revolution is brewing o Today’s organizations are stunningly unfit for data For most, it is too soon to set strategy. But it is time to get

moving o Quality is pre-requisite. Move responsibility out of IT! o Experiment with ways of competing with data o Explore analytics, at all levels. o Think end-to-end (e.g., D4-process) o Sort out which data are strategic. And above all BE COURAGEOUS!



Questions?

Thomas C. Redman, Ph.D. “the Data Doc”

+1 732-933-4669 [email protected]

www.navesinkconsultinggroup.com

/Redman-IDQ-Nov2013

/Redman-IDQ-Nov2013 © Navesink Consulting Group LLC, 2000-2013 NCG,

Thomas C. Redman, “the Data Doc” o Ph.D., Statistics, Florida State, 1980.

o Conceived and led the Data Quality Lab at AT&T Bell Labs.

o Formed Navesink Consulting Group in 1996.

o Helped dozens of companies think through, define, and advance their data and data quality programs.

o Led development of most of today’s best-practice data quality management methods & techniques.

o Latest and greatest: Data Driven: Profiting from Your Most Important Business Asset, Harvard Business School Press, 2008.

o Known bias: “Data are quite obviously the key asset of the Information Age. Yet today’s organizations are singularly ill-designed for data. This leads me to conclude that organizing for data is THE management challenge of the 21st century.”

o Much current work focuses on “organizing for data.”

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