The CDO Agenda: Competing with Data - Strategy and Organization

Competing with Data:
Strategy and Organization

Thomas C. Redman, Ph.D.
“the Data Doc,”
Navesink Consulting Group
www.navesinkconsultinggroup.com
Dataversity Webinar, March 2014

/Redman-Competing-March2014

© Navesink Consulting Group LLC, 2000-2014

T.C. Redman, Page 1

Data at Grandma’s
We find some real nuggets—that
lead to fundamental innovations
and create new industries.
We use them to
Improve product
and service

People, top-to-bottom,
bring more data to the
decision-making table

Good things follow:
• The economy grows
• Health care is better
and less costly.
• We’re freer and
safer.

FOUNDATION: The data are high-quality—
we know we can trust them.


T. C. Redman, Page 2

Junior Executive

VS




Family Practitioner

VS




Rising Middle Manager

VS




CEO Capital Request
Our future…
I better get
involved!


VS

More tech
b#@S%!



“Data come up in every single
conversation”
POINT
Individually, nothing more
than tough social and
organizational issues.

COUNTERPOINT
Collectively, something
deeper.

The successful have always Data, especially big data,
sought and taken advantage are exploding everywhere.
of superior data.
Impressive “big data”
successes, from all over.


Viewed through the “data
lens,” the financial crisis is a
colossal failure of data.



Opinion:
A full-strength data revolution is brewing!









It is not “big data,” it is all data.
Revolutions are chaotic, messy and inherently
unpredictable.
No one will remain untouched.
There are no roadmaps. By the time they
appear it will be too late.
The technological challenges are tall. These
pale in comparison to the organizational
challenges.




The Four Most Common Things I Hear








“We’re data rich and information poor.” (e.g., “We’ve not
thought through how we’ll compete with data
“I’ve been in this industry twenty-five years. Trust me.
These data are as good as they can possibly be.”
“Tom, you’ve got to keep in mind that we are much
more siloed than the other companies (industries, etc)
you work with.”
“If its in the computer, it must be IT’s responsibility.”




Today’s organizations are unfit for data








Don’t know how to compete with data, nor gained
enough experience to do so in a sensible fashion.
Lack talent, up and down the organization chart.
Silos impede data sharing.
Quality is essentially unmanaged.
Responsibility for data buried in the bowels of IT. Step
one: Move it out!

WORKING THROUGH THESE ISSUES IS THE
MANAGEMENT CHALLENGE OF OUR
GENERATION



Bottom Line
The leadership challenge in a nutshell:

A full-strength data revolution is brewing

Today’s organizations are stunningly unfit for data
So… what to do?

Sort out how to compete with data

Build organizational capability:
 Get responsibility for data out of IT
 Quality is pre-requisite (and we know what to do!)
 Think end-to-end.
 Develop and exploit data that are uniquely your own.
 Recognize this will take a lot of people spread throughout



So far, I’ve identified eighteen distinct
ways to “put data to work”
Internally
 Improve
operational
efficiency
 360°-view
 Data-Driven
Culture
•

Provide (Sell) Content

New Content

Re-package

Informationalization

Unbundling

Exploiting
Asymmetries

Closing Asymmetries

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

Facilitators
 Own the Identifiers
 Infomediation
 Big Data/Advanced
Analytics
 Privacy and security
 Training
 New Marketplaces
 Infrastructure
technologies
 Information appliances
 Tools

*Every organization must think
through the four in bold



Four Basic Strategies








Innovation (Big Data/Advanced Analytics):
Find hidden nuggets in the data and,…
Content: Provide or exploit content that others
don’t have.
Build a Data-Driven Culture: Make better
decisions, bottom-to-top and across the
company.
Be the low-cost provider: Superior data
quality keeps costs down!




I’m Excited About
Informationalization








Eisner: “Content is king”
Basic idea: Make existing products and services
more valuable by building in more data and
information
Ubiquity: e.g., The hospital gown.
Available to all: Doesn’t require massive
quantities of data, people with advanced degrees,
or capital investment.
Caution: Customers already in information
overload.




High Quality Data is Pre-requisite


Poor quality the norm.



Enormous, mostly hidden, costs.



Decision-makers discount data they don’t trust.
And analyses based on them. Wisely so.



In advanced analytics, data are highly
leveraged. Recall the the financial crisis.




For Data, Only Two Moments Really Matter
The moment of
creation

Note that they do
not occur in IT

The moment of use

The whole point of
data quality
management is to
connect the two!



Data Quality Done Properly
Fraction Perfect Records

First-time, on-time results
1

0.9
0.8
0.7
0.6
0.5
0

5

10

Accuracy Rate

mean

15
control limits

Month 20
target

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



It is so easy for accountability to shift
downstream!!!
Here’s how
you do
number 3!




Where does analytics fit?
Analytical “sophistication”
Basic
Process
Improvement

New,
sophisticated
algorithms

“One-time”
opportunity

Series of
Fundamental
Discoveries

In the line:
Everyone
involved

Really close
to, but not in
the line

Project team
wo/line

Permanent
“lab”

responsibilities

“Home” for Analytics
/Redman-IDQ-Nov2013



Think End-to-End
Whatever strategy you select, you need a D4-Process:
Data: High-quality, well-understood, potentiallyinteresting data is pre-requisite.
Discovery: Finding something truly interesting in the
data
Delivery: Getting the results to a decision-maker, into
an ongoing process, into a new product/service,
etc.
Dollars: Making money from the data, discovery and
delivery
Expect none to be easy!



It helps to have something others don’t!


Your data are uniquely your own.
 And you make more each day.
 Subtle and nuanced.



Some, maybe most data become standardized to
facilitate communications.



A small fraction offer opportunity for sustained
advantage.
These data merit special attention!




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:

For every good+ analyst, need dozens of good+ managers.

In every “clever analysis” that actually bears fruit, the “unsung
hero” is a manager who took a chance!
Executive Leadership:

Stone-cold, sober evaluation of “what we can actually pull off.”

Sooner or later, all change is top-down.
/Redman-IDQ-Nov2013



Federated Structure for Managing Data
People Management

Data Assets

Regular people and
managers: Day-in, day-out
people management.

Regular people and
managers: Create high-quality
data. Put data to use in novel
ways.

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

Departmental DG: Facilitate
DQ, analytics, delivery, day-in,
day-out innovation.

Corporate HR: Succession
planning, pay scales, etc

Corporate DG: “metadata, ”
unique data
Data Lab: innovation via big
data, advanced analytics




I hope I’ve excited, and scared, you!
The leadership challenge in a nutshell:

A full-strength data revolution is brewing

Today’s organizations are stunningly unfit for data
For most, it is too soon to set strategy. But it is time to get
moving

Quality is pre-requisite. Move responsibility out of IT!

Experiment with ways of competing with data

Think end-to-end

Sort out which data are strategic.

Build organizational capability.
And above all BE COURAGEOUS!



Questions?

Thomas C. Redman, Ph.D.
“the Data Doc”
+1 732-933-4669
tomredman@dataqualitysolutions.com
www.navesinkconsultinggroup.com




Thomas C. Redman, “the Data Doc”


Ph.D., Statistics, Florida State, 1980.



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



Formed Navesink Consulting Group in 1996.



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



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



Latest and greatest: “Data’s Credibility Problem,” Harvard Business Review,
December, 2013.



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



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 learning to compete with and organizing for data is THE management challenge
of the 21st century.”



NCG, Page 26

The CDO Agenda: Competing with Data - Strategy and Organization

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