The benefits of data and analytics can be easy to grasp when you’ve seen it first-hand, but the importance of data governance and cleansing can be harder to wrap your head around.
In short: data governance and cleansing are key to accurate and reliable data and analytics.
By providing a formal, standardised process for good data handling and processing across your company, you can ensure that the data that you end up analysing to make important corporate decisions is the best it can possibly be.
What is Data Governance and why should you do it?
Data governance is the procedure of setting rules and regulations for your data in place. These rules and regulations guide the handling and management of your data so it can comply with governmental standards as well as ensuring data quality. This is particularly important as bad data governance is a dangerous business, and poses significant security risks as well as breaches of GDPR legal regulations. It doesn’t matter how big or small your business is, your data is still at risk of being used incorrectly when not governed properly.
Good data governance should cover multiple different facets of data, including:1
- Data catalogue – Knowing what data you have and where it came from
- Data dictionary and metadata management – What the data actually means
- Data lineage – How the data is calculated and transformed
- Data logging and audit – Who is accessing the data·
- Master data management – Ensuring data is consistent and correctly conforms
Data should be valuable to all businesses no matter of size, with data governance underpinning the quality, integrity and compliance of said business data. Different businesses will have different priorities for their data, so applying governance to your priority categories such as data lineage or data logging is a great way to personalise your own data governance journey.
Data cleansing is the physical act of preparing whatever data you need for data and analytics. Clean data is vital as it ensures that whatever outcome your analytics provides, you can be sure that it’s reliable and accurate information. It’s important to note that different types of data need different levels of cleansing, so prioritising cleansing to the types of data that you use the most is an excellent way to assign resources within your company.
Organisation-wide useful data is the best place to start when thinking about centralised data cleansing. This can be part of a universal, coordinated data strategy to bring data from across different departments into one centralised data source such as a data warehouse. This also ensures that the data within your warehouse remains clean and ready to analyse even on an ad-hoc basis.
What must Business Leader’s consider when thinking about data governance and cleansing?
As a Business Leader, overseeing the company as a whole, it’s important to recognise that data governance is only one pillar supporting an overall solid data and analytics strategy. Without the other pillars, it would be impossible to deliver aspects such as data literacy, technology or an overall data and analytics roadmap to your company. However, as someone with many more responsibilities for the company beyond data, it’s also important to understand that data delegation beyond assigning analysts in IT is also a critical factor in the success of overall data and analytics maturity.
Accountability and responsibility with regards to data must be crystal clear. Having a hierarchy of responsibility so everyone knows who to answer to and what to answer for is the best way to ensure that the rules and regulations set out by your data governance policies are followed exactly. This can be done in a multi-stage process by first appointing ‘Data Owners’ at the executive level to take accountability for their data sets. Beneath these data owners, they can appoint ‘Data Stewards’ to take on the responsibility of the data itself and all of the governance and cleansing that goes with it. The IT department shouldn’t be something separate to the company as a whole, and its critical part in data governance and cleansing can be re-enforced by appointing data owners and stewards within other areas of the company.
By installing a data hierarchy into your company data quality, integrity and compliance should follow in its wake. By having people at every level of business taking accountability for and enforcing data governance rules and cleansing, you should never have to worry about the reliability of your data or compliance with governmental laws such as GDPR ever again, ensuring a smoother and more worry-free journey to full data and analytics maturity.
This article is part of our Data-Driven SMB series. For more information, advice and resources on how to accelerate your organisation’s data and analytics maturity, click here or contact us today.
Nick Finch is currently CTO at TrueCue leading the engineering effort behind the TrueCue Platform as well as CIO across Concentra. A specialist in building and leading teams focused on delivering scalable, bespoke cloud based solutions and products with over 19 years' experience in technical and leadership roles across data analytics, development, infrastructure, information security and QA.