How to kick-start your data governance program
Data governance is all about how a business handles its data. It involves processes, policies, standards and metrics – as well as clearly defined roles and responsibilities – to make sure that the information is used securely, effectively and efficiently.
Evangelos Matadakis, lead quality and regulatory engineer, discusses how to plan, set up and execute a successful data governance strategy.
Why is data governance important?
Having in place a data governance program is especially important for businesses and institutes that operate in a highly regulated environment, regardless of their size or turnover. If applied correctly, good data governance can become a company’s most valuable business strength – the tool that powers your business intelligence.
Data governance allows a business to make informed decisions based on historical, current and predictive operational insights. Its impact underpins and benefits the whole business, and here we consider here three main operational areas that can be targeted:
Tailoring marketing programmes to maximise resource allocation
Comparing the effectiveness of different marketing campaigns within a single user-friendly dashboard visualisation.
Improving customer loyalty
Combining consumer data to offer personalised deals, identify any areas of weakness and spot emerging trends.
Increasing employee performance
Analysing workforce behaviour and evaluating variables such as performance, absenteeism and overtime.
Supporting data governance with the PDCA model
The plan-do-check-adjust (PDCA) model is one of the most popular methods for the control and continuous improvement of business processes.
Modelling a data governance program
This section demonstrates how to apply the principles of PDCA to data governance by using our six-step plan.
1. Initiate project
- The first step is to build the right team for the project, and assign roles and responsibilities. Identify the key shareholders, data owners and system administrators.
- Set out your project’s vision, scope, objectives and key performance indicators (KPIs).
- Identify the key areas, such as governance, security or modelling, for example, of data management that you would like to improve.
- Allocate resources:
- Key roles for particular tasks
- Finance for support materials
- Timeline and project milestones.
2. Assess current maturity level
Based on the selected criteria, conduct an internal audit to evaluate both quantitatively and qualitatively your current performance:
- Hold interviews with the relevant teams, such as data scientists, project managers, quality engineers and system administrators.
- Review your company practices (both documented or undocumented) such as standard operating procedures (SOPs), architectures and tools.
Review the findings with the team and define the current maturity level for each key area.
3. Analyse gaps and create roadmap
Define your next maturity level and set KPIs to prioritise the steps needed to achieve your goals.
Bring the shareholders together and develop your roadmap, identifying how you could fill the gaps.
Finalise the roadmap and agree on the high-level delivery plan. This may involve updating existed processes, introducing new tools, creating new roles or extending responsibilities, and identifying training needs.
The final roadmap from step three can be used as a project management checklist to monitor the project progress and identify and resolve any barriers to success.
5. Assess new maturity level
Begin a new maturity assessment and measure progress, assessing whether you met the initial objectives.
This phase is the time to try to resolve any missed objective through identifying the reason and taking corrective action. More opportunities for improvement could be identified at this stage.
Don’t go it alone
At Whitehat Analytics, we are well versed in data governance maturity planning, and all aspects of data management. Get in touch to learn how our expert team can help you make the most of your data.
About the author
Evangelos Matadakis, lead quality and regulatory engineer at Whitehat Analytics, brings extensive experience in quality assurance and regulatory affairs to clients, developing and delivering regulatory plans to help bring products to market throughout the UK, EU and USA. Since gaining his MSc in Biomedical Engineering from Ghent University nearly 10 years ago, Evangelos has developed data governance programs to help small and medium-sized enterprises grow their business.