By implementing your Data Governance program, you’ve taken the first step to improving the quality and consistency of your data and your data practices – but how can you make sure your program is robust enough to give you full confidence in your data?
In this page, we break down the seven Data Governance best practices when it comes to managing and maintaining your governance framework.
Read on to discover:
- How attainable goals can show value
- The benefits of standardization
- Why communication is key
- How to change your mindset on governance
- Why you should never leave any part of your process to trust
It is estimated that 463 exabytes of data will be created daily by 2025, so there has never been a more urgent time to commit to installing a governance framework. However, you will not be able to tame and glean insight into your vast amounts of information overnight. Data governance is a marathon, not a sprint.
By setting small, attainable goals, you will:
- Show the value of your Data Governance tools
- Help measure the success of your framework
- Be able to set the standard for larger goals
- Identify both good and bad elements of your data
Once you start to develop small goals, you can set metrics for a measurable, established, organization-wide goal. Later, we discuss how these small steps will lay the foundations for more benefits further along your informational journey.
Develop and standardize definitions
Governance that is enforced is rarely as effective as governance that is communicative and collaborative. Many will resist change if they do not understand your framework.
That is why it’s considered best practice to sit with your teams to develop and standardize definitions for your Data Governance policy across your business.
Standardization provides a structure for creating and maintaining data quality, and through consistent formatting, you will be able to identify and extract errors and eliminate extraneous data.
This can cover anything from capitalization to punctuation, the formatting of sentences, acronyms, and more. Anything that can disrupt or prevent your framework from allowing you to create clean data needs to be brought under this umbrella.
Communicate and educate
Communication is seen as the key to any governance success - and for good reason.
Anyone beyond your core team may struggle to understand Data Governance, and effective communication will help them to learn - even if it is just basic knowledge to allow them to carry out their roles effectively.
Analytics, governance, estimates, frameworks, insight - the language surrounding data causes confusion, and confusion leads to fear. From the top down, you need to ensure productive conversations are happening to dispel this unease - and we mean the very top.
67% of C-Suite executives stated they’re uncomfortable accessing information from their management tools and resources. By actively involving those who many look towards for leadership, you can instil confidence.
Adding to their ranks is an option that is gaining popularity. A study from Strategy& reveals that 21% of companies have appointed a Chief Data Officer since their last study in 2015 - an increase from just 6%.
Educational programs should also be on your agenda when implementing Data Governance. They should be pushed into every level of your organization - and as 93% of executives identify their people as their biggest obstacle towards becoming Data Driven, it is vital that every person is on board.
Maintaining your Data Governance framework takes a lot of skilled hands, and there are numerous data roles that can be brought in to collect, analyze, and organize your data.
When a company has decided to invest in their data, this is usually the first hire. A Data Engineer is responsible for discovering how to gather, organize, and maintain the unmanaged information. They may create this alone, or follow the strategic guidelines set out by the Architect. Done well, the later roles will be able to extract exactly what they need with ease.
Responsible for planning the architecture in which your information will be stored, a Data Architect holds many of the same roles as an Engineer, except for this one separate distinction. They will do this all while adapting to users needs - and once a streamlined plan has been created, the Engineer will follow it.
Once the framework is in place, a Data Analyst will be sought after to mine through the collected information to perform cleaning operations and transformation. They will pinpoint areas for deeper analysis, and report their findings back to others in a visual, accessible way.
The role of gleaning statistics and insight from the findings of the Analysts is in the hands of the Data Scientists.
It is assumed that these two roles exist on a junior and senior plane - and though it can work that way, it isn't necessarily the case for all businesses. The Scientist and Analyst are as vital as each other, and need a specialized skillset to do the job.
The easiest way to discover which role you need to hire for is to pinpoint where you are in your journey. Engineers and Architects are ideal in the before implementation stages, whereas Analysts and Scientists are for once the framework is in place.
Account for everything
Your Data Governance framework not only needs to handle the information you wish to manage directly, but the information you house in and around your IT infrastructure.
Sensitive files, folders, and shares account for some of the most valuable data to your business - and are therefore at the most risk.
When discussing goals for your data governance, ensure to include everything that’s beyond the remit of analysis. In 93% of cyber attack cases, an external attacker can breach an organization's network perimeter and gain access to local files.
From acquisition to retiring, your framework must govern the entire lifespan of your data. This can help you determine how to make your attained knowledge useful at every point in the cycle.
For any data to be useful it needs to be relevant, of high quality, and easy to understand.
Your Data Governance framework works in parallel to your company goals, and when one changes, the other should too. Effectiveness leads to accuracy, and when left without management, you could find your policy is quickly outdated and missing valuable collections.
Or worse - intaking attained knowledge that is of no use.
Both internal and external circumstances will enforce a change, and this is all linked to your company data goals. This is why starting small is so effective.
As we mentioned earlier, by setting attainable goals you can discover why you need these policy changes, and how to upscale them for future, much larger goals.
Banish the ‘project’ mindset
The most important best practice feature is to remember that Data Governance is a practice and not a project. This is not something that can be crossed from a to-do list.
Once the framework is implemented, there should be no end date - for as long as your business remains invested in collecting, organizing, and analyzing clean, accessible information, your Data Governance framework remains robust.
Like any new practice, it will need amendments and over time will develop into something that can power your business toward making informed decisions.
Final thoughts on Data Governance best practices
Almost 90% of Data Governance implementations fail. Why? Because they do not consider best practices. Agile Solutions can help you at every stage of your governance journey - utilizing our expertise to defend your framework from becoming another statistic in failure. Book a discovery call to discuss your plans to become data-driven.