dama webinar: what does "manage data assets" really mean?

/Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016 T.C. Redman,

What Does “Manage Data Assets”

Really Mean?

Dataversity Webinar March 15, 2016

Thomas C. Redman, Ph.D. Data Quality Solutions

www.dataqualitysolutions.com

Objectives

!  Provide a simple way of thinking about what it takes for data to qualify as assets.

!  Re-examine the management system for data quality, calling out the essential roles of data creators and data customers.

!  Show how improving data quality is a legitimate strategy in its own right.

!  Ask that everyone make a “Friday Afternoon Measurement” to baseline data quality in their area.

/Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016 T. C. Redman,

But first, a bit of context !  Circa 2005: “Manage data as assets” enters the lexicon, without

any content. "  An odd choice of phrase.

!  2008: wrote Data Driven, partly in response. !  Soon thereafter: “Data driven” enters the lexicon, though usually in

a narrower context. !  In parallel: “Big data,” “Data scientist,” “Chief Data Officer” enter

the lexicon. !  Recently: Witness the disruption caused by Uber, simply by

connecting “I need a ride” with “I’m looking for a fare.” !  Looking forward: For most, the future depends on data.

BIGGEST CHANGE: THE URGENCY OF RECOGNIZING THAT, “PROPERLY MANAGED, DATA ARE ASSETS OF GREAT

POWER”


But all is far from well in the data space

The future may depend on data, but:

!  Increasingly apparent that data management has failed most companies.

!  Fear has replaced apathy as the number one enemy of all things data.

!  Most organizations can only be judged as “unfit for data.”



What Does “Manage Data Assets” Really Mean?

Generally recognized as assets: !  Capital, in its various forms !  People, including the knowledge in

their heads. When one recognizes anything as an

asset, they: !  Take care of it. !  Put it to work, in the private sector,

usually to make money. !  Advance the management system

in recognition of the ways the asset is different from other assets.

Suppose You’re Good With Numbers….

To advance quantitative skill to the level of an asset, you would:

Take Care of that Skill: !  Do the KenKen. !  Build that skill by getting a technical degree. Put that Skill to Work: !  Become a data scientist. !  Build a career based on those growing skills. Advance your personal management of the skill: !  Manage yourself to take full advantage of the skill.



For Data, “taking care” is about quality, security and privacy

Prescription 1: !  Possess and acquire the right kinds of data. !  Make sure people can access, understand, and trust

them. !  They are of high enough quality to withstand market

scrutiny. !  Improve/build on the most important data. !  Keep them safe from loss or theft.


Putting data to work

Prescription 2: Use data to create new revenue !  Sell them directly in the market. !  Build them into other products and services. !  Uncover and exploit the “hidden nuggets” that lie therein

(big data/analytics). !  Create and exploit an “information asymmetry.” !  Make better decisions/improve the day-in, day-out

running of the business. Critical point: Management must think through how they

will put data to work to create new value.

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

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

Asymmetries !  Closing Asymmetries

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

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

technologies !  Information appliances ! Tools

Working out “what’s right for us” is the key challenge for senior leadership! And “forcing the conversation” is the number one priority of the “Top Data Job.”

Internally !  Improve

operational efficiency

!  360°-view ! Data-Driven

Culture

/Redman-DataAssets-DV-March2016 T. C. Redman, ©Data Quality Solutions, 2000-2016

Four basic strategies for competing with data (with dozens of variants)

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

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

!  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!


Uber: Informationalizing the Taxi Business


I need a ride

I’m looking for a fare

Witness the disruption caused by connecting with


Advance the management system

Prescription 3: !  Recognize that data have unique properties:

"  Example: Unlike other assets, data can be shared "  Data may be the “ultimate proprietary asset.”

!  Craft a “data friendly organization:” "  Not led out of IT. "  Top Data Job. In time, true C-band Chief Data

Officers. "  Federated structure. "  Many, many, more people!

!  To improve quality, recognize data customers and creators with specific responsibilities to behave as such.

Data possess properties unlike any other asset. We are just beginning to understand these properties

Property Example Implication for DQ Data multiplies Sheer rate of growth strains ability to manage them

Data are more complex than they appear

Much (data model, correct values, presentation) has to go right

Data can be digitized They are (technically) easy to share. And steal.

Data create value “on the move”

Data sitting in a database are profoundly uninteresting

Data are intangible They have no physical properties, complicating measurement

Data are organic Data morph as they move to suit different needs

Data are the means by which organizations encode knowledge

They have very long lifetimes.

Data are subtle and nuanced. They have become the organization’s lingua franca

The distinction between the “right data” and “almost right data” is akin to the distinction between “lightning” and a “lightning bug” (after Twain)

Each organization’s data are uniquely its own

They are the “ultimate proprietary technology” /Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016 T. C. Redman,

Data Quality

1.  What are the implications of “data are organic?”

2.  Does Improving Data Quality present a legitimate strategic choice?

3.  First Step: Perform a Friday Afternoon Measurement


For Data, Only Two Moments Really Matter

©Data Quality Solutions, 2000-2016

The Moment of Use The Moment of Creation

PATH FROM CREATOR TO CUSTOMER

DATA CREATOR DATA CUSTOMER

The whole point of data quality

management is to connect the two!

Note that they DO NOT occur

in IT

T. C. Redman, /Redman-DataAssets-DV-March2016

Implications The “management system” must explicitly call out the

responsibilities of: !  Data customers to clarify their needs. !  Data creators, to understand those needs and meet

them. !  A data quality team and leadership to connect the two

for the organizations most important data. A Note on Tech: While active data customers and data

creators are ravenous consumer of technology, Tech is singularly ill-positioned to lead the effort.


The Hidden Data Factory

/Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016

Data ok?

Input data

Fix errors

Complete our Value-added

work

no

yes

Said differently, if the “straight-through path,” costs a dollar,

then the “fix errors path” costs ten.

the Data Doc’s Rule of Ten: “It costs ten times as much to complete a unit of work when the data is

flawed in any way as it does when they are perfect!

T. C. Redman,

Rule of Ten Illustrated The costs associated with this “fix errors path” can

mount up very quickly. Consider “our data is 99% accurate.” Sounds pretty good! And suppose: !  It takes twelve pieces of data to complete a simple operation (many

simple operations require 10-25 pieces of data). !  Further suppose the organization completes 100 work items/day !  Recall the straight-thru path costs $1.00

Then: !  The “value-added cost” is $100 (100 items at $1.00 each) !  Twelve percent (12%) of the work items have defective data.

These cost $10 each, or $120 total.


Data Quality as the means to cost reduction

Cost-of-Poor-Data Quality (COPDQ) Calculator

Cost to complete 88 defect-free ($1 each) = $88.00 Cost to complete 12 defectives ($10 each) = $120.00 Total cost = $208.00 Non-value-added cost = $108.00 %COPDQ ($108/$208) = 52%

This is any extremely important point: Even a “seemingly low” error rate can lead to an enormous increase in operational costs.

IMPROVING DATA QUALITY CAN REDUCE SUCH COSTS BY TWO-THIRDS OR MORE! /Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016 T. C. Redman,

Bad data adds other costs as well !  Some errors leak through and making good for

customers or undoing bad decisions is even more costly.

!  Data customers spend excessive time looking for what they need.

!  “Systems don’t talk”/shadow systems


IMPROVING DATA QUALITY (e.g., CLOSING THE HIDDEN DATA FACTORY) MAY BE THE SIMPLEST, MOST-EFFECTIVE WAY TO PURSUE A STRATEGY OF COST REDUCTION.

A powerful first step: The Friday Afternoon Measurement

The Friday afternoon measurement aims to help answer the question,

“Do I need to worry about data quality?”


Friday Afternoon Measurement Protocol

Assemble “last 100 records”

Assemble 2-3 experts

Mark “obviously-erred” data

in red

Summarize and

interpret results

©Data Quality Solutions, 2000-2016 /Redman-DataAssets-DV-March2016 T. C. Redman,

Assemble the last 100 records

After Fig 18.2, Redman, Data Quality: The Field Guide ©Data Quality Solutions, 2000-2016

Attribute 1: Attribute 2: Size

Attribute 3: Amount etc

Record A Jane Doe Null $472.13 Record B John Smith Medium $126.93

C Stuart Madnick XXXL Null

D Thoams Jones

Record 100 James Olsen

One Locked Place $76.24

/Redman-DataAssets-DV-March2016 T. C. Redman,

Select 10 – 15 most important attributes

Mark the obvious errors




Record A Jane Doe Null $472.13 Record B John Smith Medium $126.93

C Stuart Madnick XXXL Null

D Thoams Jones


One Locked Place $76.24


Rate the record as “perfect” or not




record perfect?

(y/n) Record A Jane Doe Null $472.13 n Record B John Smith Medium $126.93 y

C Stuart Madnick XXXL Null n

D Thoams Jones n


One Locked Place $76.24 n


Count the “perfects”




record perfect? (y/

n) Record A Jane Doe Null $472.13 n Record B John Smith Medium $126.93 y

C Stuart Madnick XXXL Null n

D Thoams Jones n

Record 100 James Olsen One Locked Place $76.24 n

Count perfect 67


Data Quality = 67%

Here the interpretation is a full third of recent customer orders had a serious DQ issue.

A worry indeed!

©Data Quality Solutions, 2000-2016 /Redman-DataAssets-DV-March2016 T. C. Redman,

Summary Against Objectives !  To qualify as assets, data must pass three, deliberately-

demanding (yet common sense) criteria: "  You must take care of them "  You must put them to work "  You must advance the management system

!  Re-examined the management system for data quality, emphasizing the essential roles of data creators and data customers.

!  Showed how improving data quality is a legitimate “low-cost provider” strategy in its own right.

!  Explained the simple elegance of the “Friday Afternoon Measurement” and asked everyone to make one.


©Data Quality Solutions, 2000-2016 T. C. Redman,

Questions?

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

+1 732-933-4669 [email protected]

www.navesinkconsultinggroup.com

/Redman-DataAssets-DV-March2016

/Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016 T.C. Redman,

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 Data Quality Solutions in 1996.

!  Latest and greatest: “Data’s Credibility Problem,” HBR, Dec, 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 unfit for data. Further, despite enormous potential, few are yet considering how they will compete with data. Finally, high-quality data is pre-requisite. These crystallize THE management challenges of the 21st century.”

Picture

©Data Quality Solutions, 2000-2016 T. C. Redman,

Data Quality: The Non-delegatable Choice

/Redman-DataAssets-DV-March2016

Unm

anag

ed

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

Typical Result

/Redman-DataAssets-DV-March2016 ©Data Quality Solutions, 2000-2016

Each error not made saves an average of $500. Quickly millions!

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ords

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First-time, on-time results

Accuracy Rate ave lower control limit upper control limit target

2. First Meas

Program start

1. Rqmts defined

3. Improvements 4. Control

T.C. Redman,