Managing Data as a Strategic Asset: How is that Accomplished? Tuesday, April 28, 2015 Data Management Practices Hierarchy You can accomplish Advanced Data Practices without becoming proficient in Advanced the Foundational Data Management Data Practices however this will: Practices • MDM • Take longer • Mining • Cost more • Big Data • Analytics • Deliver less • Warehousing • SOA • Present greater risk (with thanks to Tom DeMarco) Foundational Data Management Practices Data Governance Data Quality Data Management Strategy Data Platform/Architecture Data Operations DMM℠ Structure 3 DMM℠ Capability Maturity Model Levels DM is strategic organizational capability, most importantly we have a process for improving our DM capabilities 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 Managed (2) Performed (1) Defined (3) Measured (4) Optimized (5) 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. Our DM practices are defined and documented processes performed at the business unit level Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts • \ Assessment Components Data Management Practice Areas Capability Maturity Model Levels Data Management Strategy DM is practiced as a coherent and coordinated set of activities 1 – Performed Data Quality Delivery of data is support of organizational objectives – the currency 2 – Managed of DM Data Governance Designating specific individuals caretakers for certain data Data Efficient delivery of data Platform/Architecture via appropriate channels Data Operations 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 3 – Defined We have standardized DM practices so that all in the organization can perform it with uniform quality 4 – Measured We manage our DM processes so that the whole organization can follow our standard DM guidance Ensuring reliable access to 5 – Optimized data We have a process for improving our DM capabilities 5 Comparative Assessment Results Data Management Strategy Challenge Data Governance Challenge Data Platform & Architecture Client Industry Competition Data Quality All Respondents Challenge Data Operations 0 1 2 3 4 5 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 Future State Common Organizational Data (and corresponding data needs requirements) Evolve Evolving Data is Different than Creating New Systems (Version +1) Data evolution is separate from, external to, and precedes system development life cycle activities! Systems Development Activities Create New Organizational Capabilities 8 Top Data Job Top IT Job Top 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, 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) Joseph W. Grubbs, Ph.D., AICP, GISP Modis, Inc. Health Information Technology Mobile: (804) 467-7729 Email: [email protected] 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 o o Searchable Structured 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 2015 OIR TDOT TDH Infrastructure Development Open Data Mark Bengel, TN CIO Collaboration Partner Agencies Mike Newman, TDH CIO Environmental Scan David Reagan, TDH CMO Local Health Departments Central Office 2014 JK1 Transformed Analytical Data Marts Analytics for adaptive applications Normalized (OLAP) Security Hadoop DW Public Health Data Resting mongoDB (Store) (Prep ‐ ETL) Transactional (OLTP) Integration Engines Structured Data Reference Data Slide 22 JK1 Jeffrey Kriseman, 3/31/2015 Hadoop (DW) MSSQL (MERGE) mongoDB (Store) (Prep ‐ ETL) Integration Engines Structured Data Reference Data Analytics for adaptive applications Hadoop DW Public Health Data Interpretation Ownership Easily Digestible Access Limited Legwork Manipulation Source Code Available Licensing Source Integrity No Upfront Cost Technology Neutral Derivative Works Collaborative Governing Body Source Code Available Licensing Source Integrity No Upfront Cost Technology Neutral Derivative Works Collaborative Governing Body Source Code Available Licensing Source Integrity No Upfront Cost Technology Neutral Derivative Works Collaborative Governing Body “If you want to go fast, go alone. If you want to go far, go together.” Source Code Available Source Integrity No Upfront Cost Technology Neutral Collaborative Governing Body ‐ African proverb (American cliché) Licensing Derivative Works