5 Good Data Governance Principles

5 Good Data Governance Principles

Companies are, more than ever, aiming to be “data driven” and for good reason. According to a PwC survey companies that implement a successful data strategy are three times more likely to report a significant improvement in decision making. However, it is important to understand every aspect of what this kind of transformation entails. Actual, on-the-ground implementation can vary greatly in quality and effectiveness. A poorly executed enterprise data system upgrade can generate new problems and cause unanticipated future headaches. 

A way to minimise the chance of these issues arising is to aim, from the very beginning, to achieve good data governance. This means not expecting a single fire-and-forget event but instead more of an ongoing commitment to good practice as well as a permanent shift in organisational mindset. To begin thinking about how you can achieve this in your organisation, consider these 5 good data governance principles which can help you get your bearings in these rough seas. 

1. Treat data like a vital business asset 

It is important not to treat your data as an afterthought. It is not something which exists parallel or concurrent to business processes, nor is it merely a trail of crumbs that gets left in the wake of “normal” operations. Regardless of your industry, your business data is your lifeblood. When your organisation has made the decision to commit to acting on analytics insights, data should be treated like any other resource which actively contributes to generating revenue.

This involves properly investing in the necessary infrastructure and creating new roles (such as a data steward) within the business to be solely responsible for data governance related tasks and projects. It also involves making the link between poor data quality management and poor business outcomes.

an executive in a suit sitting in front of a laptop working on implementing data governance

2. Know your data ecosystem 

It is important for everyone within the organisation to have at least some awareness of where their actions and processes sit within the greater data governance landscape. In the past, access to databases and analytics tools would have been restricted to certain technical gatekeepers and administrators. Within modern businesses, with the rise of self-serve analytics and advanced business tools, every individual has more power and more responsibility. Devoting proper time and resources to education across all roles and departments is important to any successful data system rollout. 

While there might be some specific regulatory requirements for data siloing, having separate data environments that have their own schema, terminology, formats and rules is becoming less common. Data governance is made much simpler when every department follows the same set of standards and there is a shared vocabulary that can be understood by a business user in any part of the organisation. For example, implementing a single customer view goes a long way towards preventing double-handling, sifting through junk data and miscommunications between departments.

3. Don’t expect a magic bullet 

The time and cost to set up a data warehouse has decreased over the years. A data lake style warehouse can be set up and be used to store enterprise data within a matter of hours. However, it is important to not let quick and easy successes go to your head. Without creating and implementing a plan for the actual transformation, visualisation and disposal of data, your data in storage becomes idle, stagnant and eventually even a liability. This situation is called a data swamp and causes a range of problems for any business. 

The proper way to approach setting up a data warehouse is to commit the time and resources to proper planning and preparation. Knowing in advance how the source systems will be integrated and what kind of reporting will be required at the presentation layer will create purposeful data pipelines with a traceable lineage and even save money on storage and compute costs. 

4. Work towards building organisation-wide cultural change 

The process of building a business with a solid data governance foundation isn’t exclusively technocratic. Unfortunately, it’s not as easy as setting up a new software suite. In order for it to be an effective transformation, change has to occur to the very DNA of the organisation. This means establishing roles and empowering leaders to drive this change as well as creating penalties for failure to do so. 

We have previously written about data stewardship and how, specifically in relation to data breaches:

anyone who writes and email or creates a document is responsible for recognising the sensitivity and value of the information it contains.

Although innovations have made it much easier to access and alter databases on the fly, this has also meant that sloppiness and carelessness on the part of users can have more dramatic repercussions.

a group of people having a meeting to discuss organisation wide implementation of good data governance principles

5. Know what tools you need for data governance

While sometimes it may seem that introducing more tools into your arsenal can add more moving parts and further complicate an enterprise data situation, this is not always the case. With the increased focus on the importance of data governance, there are ever more tools available that can help specifically with introducing and maintaining a well-governed enterprise data landscape.

It is also important to keep up to date with latest developments and conversations around platforms and technologies so you don't fall into self-inflicted problems such as operating with very dated ideas about cloud platform risk.

Data task automation and orchestration tools can give you clear access to every step of how your data sources are extracted, loaded and transformed (ELT). Data catalogues and glossaries can help ensure that data lineage is always easily traceable and there are no mysteries about sources, transformations or how any element relates to a business process. Data quality monitors measure the real-time pulse of your data pipelines, allow the creation of custom alerts and ensure that any anomalies are flagged to be immediately scrutinised further. 

If you want to know more about Data Governance and how you can start directly implementing it in your organisation, download our free eBook, written by one of our directors with over 15 years of experience in the field.