Stating that data is an asset is misleading unless there is clarity of definition. It may be a useful analogy, but there are dangers in making the claim. An asset, per the International Financial Reporting Standards (IFRS), is “a resource controlled by the entity as a result of past events and from which future economic benefits are expected to flow to the entity (IASB Framework).”* To meet the requirements for recognition as an asset, data’s future economic benefits would have to be reliably measurable. To date few, if any businesses, recognize the value of their data on their financial statements. For this reason, it is difficult to get the level of executive attention implied by the analogy that data is (valuable as) an asset.
 
There is simply no line item or artifact that shows the value of data to an Executive. As a result, as reported by the CMMI website, “52% of C-Suite executives have dismissed data because they couldn’t understand it.”** Irrespective of being an asset or not, data must be managed.
 
 
To successfully manage anything, it must be defined. Data is difficult to conceptualize and define apart from the processes that give it context. To most people, data is merely the input or output of a process. From a recent data governance (DG) survey by erwin, inc. when asked, “How do you define DG?,” sixty-seven (67%) of respondents answered, “Understanding the data flows across the organization.”***
 
However, when we look at the term governance from a systems audit perspective, (ISACA, COBIT 5), governance is the activities of evaluating, directing, and monitoring (EDM) applied to a management process. With respect to Data Governance, EDM is the high-level authority which sets the objectives that respond to business needs that result in a set of policies that are implemented by the Data Management (DM) capability.
 
The DM capability as defined by the CMMI DMM consists of the following process areas:
 

  • Data Management Strategy (DMS)
  • Data Governance (DG)
  • Data Quality (DQ)
  • Data Operations (DO)
  • Platform & Architecture (P&A)
 
Essentially, the capability consists of independently describing data by its form (DG), content (DQ), and lifecycle (DO), which conform to a set of standards (P&A).
 
COBIT 5 describes management as a set of processes to plan, build, run, and monitor (PBRM) a business activity. To differentiate Data Governance (COBIT 5 EDM) and Data Governance Management (PBRM) we will introduce a framework to manage the form, or structure, that is the management process to govern data.
 
 
SANDHILL DATA GOVERNANCE MANAGEMENT FRAMEWORK
 

                                                                                                                     © Sandhill Consultants 2018
 
 
PLAN:
Consists of the evaluation of the current maturity of an org’s data governance management (DGM) process. The output of this assessment forms a roadmap that identifies the requirements for DGM process improvement and a set of DGM strategic initiatives.
 
BUILD:
The output of the DGM requirements and timelines, facilitates the development of the policies and resources required to support the configuration of the Business Term Glossary and Technical Metadata Management Inventory.
 
RUN:
Delivery of the DGM Framework requires the integration with the major data governance/ data management practices for DMS, DQ, DO, P&A. Resource deployment defines the responsibilities and accountabilities with related skills and knowledge. Automation opportunities for DG technologies must be leveraged for the collection and maintenance of Business Terms and Technical Metadata. Metrics are identified to drive the feedback process.
 
MONITOR:
The three elements of monitoring the DGM process are the delivery of feedback to the DG (EDM) process, the performance measurement of the observation of the process against a set of standards, and the identification of opportunities for further process improvement.
 
Call to Action:
 
One size does not fit all. Data Governance Management is not one-dimensional, therefore a simple application of a framework fits none!  
 
Contact Sandhill Consultants to find out how to adapt the Sandhill Data Governance Management Framework to support your:
  • Organization’s industry
  • Regulatory environment
  • Culture and size
  • Data management maturity level.

 
Be on the lookout for further discussion of the segments of the DG Management Framework.
 
Contact Robert Lutton  at Robert.Lutton@sandhillconsultants.com  to review your current Data Governance needs or visit Sandhill Consultants at
https://www.sandhillconsultants.com/offerings/data-governance-capability-delivery-system-dg-cds/
 
References:
*https://www.iasplus.com/en/standards/other/framework
**https://cmmiinstitute.com/data-management-maturity
***https://erwin.com/white-papers/2018-state-data-governance-report/