Why You Need a Data Governance Framework
Data governance, as you might have guessed, is the governance of data. However, while data governance as a concept is fairly easy to define, it’s not quite as simple as it sounds when you really start to look into it.
It is “a collection of practices and processes which help to ensure the formal management of data assets within an organization.” (Source: DATAVERSITY)
Included under this ‘formal management’ umbrella are aspects such as managing the availability and security of data that it is valuable to the wider organisation.
To make a success of your data governance policy, it’s crucial that you establish a governing body that draws in people from across the business to help you avoid any potential blackspots or omissions.
What are the benefits of a data governance framework?
While it’s quite straightforward to provide a definition of data governance, actually putting such a policy in place requires much more effort.
However, there are many benefits that stem from the establishment of data governance best practices. Below are a few examples:
- it increases the overall consistency and trustworthiness of your data
- access to better data and more consistent information makes it easier to make informed decisions at all levels
- data governance processes allow for greater transparency and justification for any data-related processes
- on a practical level, having these policies in place makes the training of employees easier, quicker and means that the language is the same across the business
- if the data is more useful and usable, it automatically becomes more valuable to the organisation
- having a clearer idea of what information is accessible and by whom, both within the organisation and outside it, makes it easier to put in place security measures
If you’re interested in getting a detailed insight into data governance best practices, we have two white papers on this topic which you can find here ->
The pillars that support a data governance framework
This piece by TechTarget proposes that there are four pillars that play a vital role in the success of any data governance initiative. They are:
- data stewardship
- data quality
- master data management
- use cases
Data stewardship is clearly a fundamental part of any data governance policy, because this involves defining who the owners, or custodians, of information within your organisation will be. These stewardship teams will often be made up of or include database administrators and business analysts, alongside other business personnel who are familiar with the organisation’s data practices.
Data stewardship naturally leads on to data quality, which is described as “the driving force behind most data governance activities”, and master data governance.
How to write a data governance framework definition
For any data governance project to be successful, data managers and stewards need to agree on clear definitions that will be used across the organisation.
As education and research charity JISC puts it, “once you have an overarching data model you can begin to look at definitions of the data down to field level”, allowing you to create a data glossary.
“This level of information is essential to support an effective data management policy.”
But how do you create a good data governance definition glossary, and what needs to be included?
The below video by data governance coach Nicola Askham outlines some of the steps you can take (or things to consider) when creating definitions to make them useful for other people in your organisation.
She suggests that data governance definitions should:
- be unique and distinguishable from other definitions
- be written as descriptive phrases or sentences
- avoid using acronyms or abbreviations that require prior knowledge or can cause confusion
- state what the concept is – not what it isn’t
- be clear, concise and unambiguous
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