Managing Data as a Strategic Asset: How is that Accomplished?

Tuesday, April 28, 2015

You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will:• Take longer• Cost more• Deliver less• Present

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy

Advanced Data

Practices• MDM• Mining

• Big Data• Analytics

• Warehousing• SOA

Foundational Data Management Practices

Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

DMM℠ Structure

3

One concept for process improvement, others include:• Norton Stage Theory• TQM• TQdM• TDQM• ISO 9000

and focus on understanding current processes and determining where to make improvements.

DMM℠ Capability Maturity Model Levels

Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts

Performed(1)

Managed(2)

Our DM practices are defined and documented processes performed at the

business unit level

Our DM efforts remain aligned with business strategy using standardizedand consistently implemented practices

Defined(3)

Measured(4)

We manage our data as a asset using advantageous data governance practices/structures

Optimized(5)

DM is strategic organizational capability, most importantly we have a process for

improving our DM capabilities

Assessment Components

• \

Data Management Practice Areas

Data Management Strategy

DM is practiced as a coherent and coordinated set of activities

Data Quality

Delivery of data is support of organizational objectives – the currency of DM

Data GovernanceDesignating specific individuals caretakers for certain data

Data Platform/Architecture

Efficient delivery of data via appropriate channels

Data Operations Ensuring reliable access to data

Capability Maturity Model Levels Examples of practice maturity

1 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts

2 – ManagedWe have DM experience and have the ability to implement disciplined processes

3 – DefinedWe have standardized DM practices so that all in the organization can perform it with uniform quality

4 – MeasuredWe manage our DM processes so that the whole organization can follow our standard DM guidance

5 – Optimized We have a process for improving our DM capabilities

5

0 1 2 3 4 5

Data Management Strategy

Data Governance

Data Platform & Architecture

Data Quality

Data Operations

Client

Industry Competition

All Respondents

Comparative Assessment ResultsChallenge

Challenge

Challenge

Confusion

7

• IT thinks data is a business problem– "If they can connect to the server, then my job is done!"

• The business thinks IT is managing data adequately– "Who else would be taking care of it?"

Evolving Data is Different than Creating New

Systems

8

Common Organizational Data (and corresponding data needs requirements)

New Organizational Capabilities

Systems Development Activities

Create

Evolve

Future State

(Version +1)

Data evolution is separate from, external to, and precedes system development life cycle activities!

Top Operations

Job

Top Data Job Top Job

TopIT

Job

Top Marketing Job

Data Governance Organization

TopData Job

• Dedicated solely to data asset leveraging

• Unconstrained by an IT project mindset• Reporting to the business• There is enough work to justify the

function and not much talent• The CDO provides significant input to

the Top Information Technology Job

• 25 Percent of Large Global Organizations Will Have Appointed Chief Data Officers By 2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, 2014. http://www.gartner.com/newsroom/ id/2659215?

• By 2020, 60% of CIOs in global organizations will be supplanted by the Chief Digital Officer (CDO) for the delivery of IT-enabled products and digital services (IDC)

Top Finance

Job

Joseph W. Grubbs, Ph.D., AICP, GISPModis, Inc. Health Information Technology

Mobile: (804) 467-7729Email: joseph.grubbs@outlook.com

Value of Enterprise Data• Data has been called the “currency” of government

(NASCIO, 2008)• This currency must be valued and managed as an

enterprise asset• Not all data are created equal• Data value will vary depending on content, format,

timeliness, quality and utility

Asset Management: Systems, infrastructure and processes for monitoring and maintaining an entity’s assets through the entire lifecycle

Asset Management• Asset management has become a priority at all

levels of government and across government domains

• However, the focus remains mostly on infrastructure, IT, physical plant, fleet and other “fixed” assets

Asset Management• Asset management strategies need to include

information assets• Information should be managed, maintained and

secured as a critical intangible asset

Data Asset Management• Metadata systems

o Searchableo Structuredo Standardized

• Discovery, reuse, reduced redundancy,standardization, ROI

Data Asset Management• Inventory data systems across the enterprise to

identify the array of information assets• Data profiling of enterprise systems to assess the

architecture, data elements, definitions and specifications

• Organize enterprise data systems into a taxonomy with subject areas and information classes

Data Asset Management• Compile metadata for enterprise systems, including

refresh frequency, maintenance, security, standards and exchanges

• Publish metadata in a searchable metadata registry or repository

• Establish data monitoring and data stewardship as key roles in the organization’s enterprise information architecture program

ENTERPRISE INFORMATION ARCHITECTURE

AN OPEN APPROACH

OIRTDOTTDH

Mark Bengel, TN CIO

2014

Collaboration

2015

InfrastructureDevelopment

Environmental Scan

Central Office

Local Health Departments

Partner Agencies

Open Data

David Reagan, TDH CMO

Mike Newman, TDH CIO

Integration Engines Structured Data Reference Data

(Prep ‐ ETL)

mongoDB (Store)

Hadoop DW

Analytical Data Marts

Transactional(OLTP)

Resting

Normalized(OLAP)

Transformed

Security

Public Health Data

Analytics for adaptive applications

JK1

Slide 22

JK1 Jeffrey Kriseman, 3/31/2015

(Prep ‐ ETL)

Integration Engines Structured Data Reference Data

mongoDB (Store)

Hadoop (DW)

MSSQL (MERGE)

Hadoop DW

Public Health Data

Analytics for adaptive applications

Manipulation

Interpretation

Ownership

Access

Limited Legwork

Easily Digestible

Source Code Available

Source Integrity

No Upfront Cost

Collaborative

Technology Neutral

Licensing Governing Body

Derivative Works

Source Code Available

Source Integrity

No Upfront Cost

Collaborative

Technology Neutral

Licensing Governing Body

Derivative Works

Licensing Derivative Works

Collaborative Governing Body

Source Code Available

Source Integrity

No Upfront Cost

Technology Neutral

Licensing Derivative Works

Collaborative Governing Body

Source Code Available

Source Integrity

No Upfront Cost

Technology Neutral

“If you want to gofast, go alone. If youwant to go far, gotogether.”‐ African proverb(American cliché)