Managing Data as a Strategic Asset: How is that Accomplished? Tuesday, April 28, 2015

Transcription

1 Managing Data as a Strategic Asset: How is that Accomplished? Tuesday, April 28, 2015

2 Data Management Practices Hierarchy 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 greater risk (with thanks to Tom DeMarco) Advanced Data Practices MDM Mining Big Data Analytics Warehousing SOA Foundational Data Management Practices Data Governance Data Quality Data Management Strategy Data Platform/Architecture Data Operations

3 DMM Structure 3

4 DMM Capability Maturity Model Levels DM is strategic organizational capability, most importantly we have a process for improving our DM capabilities Optimized (5) We manage our data as a asset using advantageous data governance practices/structures Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices Defined (3) Measured (4) 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. Managed (2) Our DM practices are defined and documented processes performed at the business unit level Performed (1) Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts

5 \ Assessment Components Data Management Practice Areas Data Management Strategy Data Quality Data Governance Data Platform/Architecture Data Operations DM is practiced as a coherent and coordinated set of activities Delivery of data is support of organizational objectives the currency of DM Designating specific individuals caretakers for certain data Efficient delivery of data via appropriate channels Ensuring reliable access to data Capability Maturity Model Levels 1 Performed 2 Managed 3 Defined 4 Measured 5 Optimized Examples of practice maturity Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts We have DM experience and have the ability to implement disciplined processes We have standardized DM practices so that all in the organization can perform it with uniform quality We manage our DM processes so that the whole organization can follow our standard DM guidance We have a process for improving our DM capabilities 5

6 Comparative Assessment Results Data Management Strategy Challenge Data Governance Challenge Data Platform & Architecture Data Quality Data Operations Challenge Client Industry Competition All Respondents

7 Confusion 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?" 7

8 Common Organizational Data (and corresponding data needs requirements) Future State Evolve (Version +1) Evolving Data is Different than Creating New Systems Systems Development Activities Create Data evolution is separate from, external to, and precedes system development life cycle activities! New Organizational Capabilities 8

9 Top Data Job Top Job Top IT Job Top Operations Job Top Data Job Top Finance Job Top Marketing Job Data Governance Organization 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, id/ ? 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)

10 Joseph W. Grubbs, Ph.D., AICP, GISP Modis, Inc. Health Information Technology Mobile: (804)

11 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

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

13 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

14 Asset Management Asset management strategies need to include information assets Information should be managed, maintained and secured as a critical intangible asset

15 Data Asset Management Metadata systems o Searchable o Structured o Standardized Discovery, reuse, reduced redundancy, standardization, ROI

16 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

17 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

18 ENTERPRISE INFORMATION ARCHITECTURE AN OPEN APPROACH

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20 2015 OIR TDOT TDH Infrastructure Development Open Data Mark Bengel, TN CIO Mike Newman, TDH CIO David Reagan, TDH CMO Environmental Scan Collaboration Partner Agencies Local Health Departments Central Office 2014

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22 JK1 Transformed Analytical Data Marts Analytics for adaptive applications Normalized (OLAP) Hadoop DW Security Public Health Data Resting mongodb (Store) (Prep ETL) Transactional (OLTP) Integration Engines Structured Data Reference Data

23 Slide 22 JK1 Jeffrey Kriseman, 3/31/2015

24 Hadoop (DW) MSSQL (MERGE) (Prep ETL) mongodb (Store) Integration Engines Structured Data Reference Data

25 Analytics for adaptive applications Hadoop DW Public Health Data

26 Interpretation Ownership Easily Digestible Access Manipulation Limited Legwork

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31 Source Code Available Source Integrity No Upfront Cost Technology Neutral Licensing Derivative Works Collaborative Governing Body

32 Source Code Available Source Integrity No Upfront Cost Technology Neutral Licensing Derivative Works Collaborative Governing Body

33 Source Code Available Source Integrity No Upfront Cost Technology Neutral Licensing Derivative Works Collaborative Governing Body

34 If you want to go fast, go alone. If you want to go far, go Source No Upfront together. Integrity Cost Source Code Available African proverb (American cliché) Technology Neutral Licensing Derivative Works Collaborative Governing Body