Data Governance – why it matters?
Updated: Dec 5, 2019
In the words of William Deming – ‘Without data, you are just another person with an opinion.’
More and more organisations are making key strategic decisions backed by corporate data. Data is slowly being identified as a key strategic asset which organisations are leveraging to achieve competitive advantage. However, like any other asset, data needs to be governed.
A huge amount of data is already generated in Customer Relationship Management (CRM), Supply Chain Management (SCM), HR, Finance and many other enterprise systems. But it is not enough to just capture and store data. This data has to be managed and governed because leaving it unmanaged can lead to poor data quality and increase the cost of business transactions. Good data governance can lead to immediate revenue increases and cost cuts, which in turn increases shareholder value.
Many organisations view data governance as a ‘technology problem’ and at times also as an overhead. However, the two most important moments in a piece of data’s lifetime are the moment it is created and the moment it is used – and both these points lie within the business. Therefore, business and IT have to jointly address this problem.
What is Data Governance?
In simple words, data governance is how an organisation gets its data right, keeps it right and takes full advantage of it. The data governance framework includes people, processes, and technology that is required to create a consistent and proper handling of an organisation’s data across the business enterprise.
Some objectives of data governance:
Increase consistency and confidence in decision making and maximize value of the enterprise data
Designate accountability for information quality
Decrease the risk of regulatory compliance issues
Typically, these are the problems found in an enterprise without data governance:
There is no data ownership: Owners of key data domains (e.g. Product, Customer, Supplier etc.) have not been assigned. People are unsure of who to ask for the right answers and incorrect information could be used to make key business decisions
Data definitions are incomplete and not confirmed: Data definitions have not been agreed. Consequently the terminology, rules for usage of data is inconsistent across the business
No data standards and policies: Supporting policies and standards for data do not exist. Without these it becomes impossible to inform, measure and report on the compliance and quality of data captured in the operational systems
How can you start?
Establish a data governance program that encompass people, process and technology
Set objectives, decision-making bodies and decision rights that fit the organizational culture and staffing
Designate data stewards to serve as go-to data experts in their respective domains
Conduct a data audit to discover all the data sources in use, how current they are, and how many variants and versions of the same data are in existence and in use
Define a process for on boarding new data sources to make sure they meet quality standards and availability criteria. During this process, business users will usually identify and suggest new valuable sources of data, and IT will manage operationalizing it
Build a data infrastructure that allows for consolidation and guide users to a shared data platform that is current and accurate