Data Management, Governance And Stewardship — II

Continuing the discussion from the last post, let’s understand the relationship between Data management, governance and stewardship.
 
 You have just built a world class analytics platform that will cater to the data needs of your analysts and business managers so that they can slice and dice it and bring you insights that will improve operational efficiency and give you a cutting edge over your competitors. But what happens when after spending millions of dollars your business managers are not confident about the data they are using? Can your organization rely on the decisions they make? How do you ensure that the system maintains the same level of quality with which you built the system and it is not deteriorating over time? How do you ensure that your data and its handling is compliant and in agreement with various internal and external regulations?
 
 If you are asking these questions you are probably questioning the quality of your data or information. Some people argue that data and information are two separate entities with data representing the ‘numbers’ and information providing the ‘context around those numbers’. Since both are incomplete without each other, for the purpose of this blog we will treat them as the same.
 
 
 International Association for Information and Data Quality defines Information quality as: “consistently meeting all knowledge worker and end-customer expectations.”
 
 With data growing exponentially year on year, establishing and maintaining data quality is a major headache for most organizations. The answer to the problem lies in a well-defined and effective data governance, stewardship and management program. Being an evolving area in its infancy there is still an ongoing debate on what each term means and encompasses what functions. More often than not all three are used interchangeably or with blurring boundaries. In this article we will not only try to understand what each means at a very high level, but also understand their relationship with each other and the role they play in influencing the data quality.
 
 Data Management is the widest of the three in terms of both meaning as well as scope. Data Management Association International (DAMA) in their Data Management Body of Knowledge (DAMA-DMBOK) define Data Management as: 
 
 “the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.”
 
 This means that Data Management manages all aspects of data from planning to laying down policies to putting them into practice for all aspects from development to execution to supervision to not only deliver and enhance but to control and protect as well.
 
 This also means that Data Governance is a sub-function of Data Management. As defined by the Data Governance Institute:
 
 “Data Governance is the exercise of decision -making and authority for data-related matters.”
 
 Not only governance but Data Stewardship is a also sub-function of management. Data Governance Institute defines Data Stewardship as: 
 
 “the set of activities that ensure data-related work is performed according to policies and practices as established through governance.”
 
 This means at a very high level data management is the overarching umbrella under which governance and stewardship reside; where governance defines, controls and stewardship executes.

Now that the initial relationship has been established, in subsequent posts we will explore the role of governance and stewardship in influencing the data quality.


Originally published at theobservinganalyst.blogspot.com on May 17, 2016.