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“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore, author and consultant.
A firm data governance process is required to keep and optimise the health of the data of any business, big or small, in any industry. It is the only way to make sure that you get all the correct answers each time. Data is the foundation of a company’s growth and success. In this blog, we shall look at data governance steps to enable a company to make the right decisions based on accurate data and measure against the right KPIs.
Here we look at the 7 steps for good data governance process:
Choose a data governance owner/team - If your company is a small lean and mean organisation then select one person who will be the data governance owner as its term for the analytics implementation, It’s also good to have a backup person in case the main person is not available for some reason. For larger firms, a team which comprises of members/lead/heads of different departments such as marketing, data, etc are represented. So everyone is on board and not working on cross purposes.
Create centralised implementation spec – To document new events or properties, a business needs to create a centralised implementation spec.
Document new events or properties – Before the release of a new product feature, the person concerned known as “the product manager” must lay the foundation of the right metrics to hold everyone accountable. Plus he/she would need to submit a request to the data governance owner/team. This would lead to the creation of the events and properties needed to measure the progress against the established metrics.
Review the new events or properties – After all the documentation is sorted out, it’s time to review the new events or properties by the data governance owner/team along with the product manager. Get everyone to discuss and give the green signal to back the quantifiable business metrics.
Execute the novel phase – Next to the data governance owner/team can inform the technical head to proceed with the development, leading to the events and properties in the implementation spec into triggers within the product’s source code. The process varies as it is dependent on the internal processes.
Conduct proper Quality Assurance – The next step is to get involved in quality assurance processes that look at whether the data collated is accurate as well as in synch with the new events and properties noted in your implementation spec.
Note the event and property definitions – If the business is doing mixpanel, the data governance owner/team should add details of all new events and properties as well as organise the data for clarity. This enables all employees to understand what data is being collated, which will help them make data-backed decisions.
We hope the above steps listed are of use to you in the daily running of your firm/business. A sound data governance process is the key to the success of your business.
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