What is Data Governance and Why Is It Important?

Data drives successful businesses — that is a fundamental fact well-known by insight teams and stakeholders across industries. But data is a tricky topic, stakeholders can’t just go about gathering whatever data they wish without a second thought, mainly because of the myriad of data protection laws that have passed in multiple countries.

Ethics and morality have a place in data generation and usage, so much so that information security teams around the world have extensive processes and practices in line with the data protection laws necessary whenever data is generated from insight teams or the stakeholders themselves. Building on these information security practices, some successful organisations even have a comprehensive data governance strategy to ensure that no data is being misused or generated without the full consent of those who are supplying said data.

What is Data Governance?

Simply put, data governance is the action of keeping all the data an organisation has access to safe. During it’s lifecycle, data is passed through the hands of many people, from consumer to researcher to many stakeholders until it has lost its usefulness, a data governance strategy is the key to making sure that the data is not misused, manipulated or misplaced.

Data governance is the action of keeping all data in an organisation safe through dedicated processes and policies — how do organisations make a dedicated data governance framework?

A lot of businesses are already practicing great data governance without fully recognising it. There are a lot of crossovers with data security practices, as data governance is used to give accountability and responsibility for the data is it goes through it’s lifecycle, making sure it’s secure from generation to erasure.

The Importance of Data Governance

There are many reasons to why data governance is important to businesses, but some of the more compelling arguments for those in market research centre on that of better data quality, and stakeholder and respondent trust.

Data governance will help insight professionals generate only the most trustworthy data for stakeholders to make accurately and timely decisions. This in and of itself provides a myriad of benefits for both insight experts and client stakeholders — for stakeholders, the benefits are pretty obvious, gaining better quality data will ensure that they make more successful decisions each time a decision has to be made. Their strategies will pan out a lot more like they need, and their actions will ensure that their role is a credit to their organisation. Because of this, the benefits for insight teams are two-fold:

  • More stakeholders will learn the value of market research and insights.
  • Thus, more stakeholders will reach out to them and fund more research in the future.

Everything from this reason actually goes on to inform the next reason, which is that data governance earns greater trust from both research respondents and stakeholders. If stakeholders are sure that their data-driven decisions are achieving greater success then they are more likely to trust the data that research teams generate in the future. For research participants, if they feel that their data and they themselves are protected and not abused in anyway, then they are more likely to open up and provide more detail than ever before — therefore helping insight experts produce higher quality data than ever before.

Frameworks and Key Methods

A solid framework for data governance can take a few forms, but because it is a collection of policies, best practices and processes that enforce data security and privacy measures in an organisation, there will be some staples that feature no matter what organisation and industry a stakeholder is in.

These data security rules will come under the form of laws, the EU’s General Data Protection Act, the British Data Protection Act of 1988 and the California Consumer Privacy Act will all set the standard for what a data governance strategy should measure up to.

The rules set out by these laws cover aspects such as:

  • Protecting the rights of users and their data
  • Ensuring privacy laws keep up with changing technological landscape

As mentioned in a previous blog, under GDPR, companies now have to be clear and informative about the purpose for gathering data, who the data will be shared with, how long the data will be stored for, and what the participant’s rights are in terms of opting-out.

Starting off with these laws to outline the boundaries and aims of a data governance programme will make for a great starting framework, but the best frameworks come about when they’re personalised to the business they serve — to do this, stakeholders need to answer questions including: where does personal data exist within the organisation? How is data used within the organisation? How is data kept secure when stakeholders are handling it? What tools are being used to ensure the security and privacy of data?

From this information, businesses can put together an effective data governance framework built with policies that can protect the rights of sensitive personal data and those that provide it.

When it comes to implementing this framework, there are a couple of popular framework methods to note, the first is a top-down method, which is a more centralised approach and relies on a team of data and insight professionals to employ and enforce data governance policies. This team is responsible for the research into how data is currently used in the organisation and then formulate a plan to make sure that any revolutionising of data security policies and best practices are done to an impeccable standard.

How do stakeholders know if their data governance policies work? There will be a distinct reduction in data security breaches, as well as more tangible insights activation across the organisation.

The second method is a bottom-up method, which starts with stakeholders using data as they need to and then data quality controls and measures implemented as tailored data governance structures are created based on that usage. While this is a more iterative agile approach, there is a lot more risk to the security of the data until those measures are in place.

A third collaborative method is good to note as it attempts to balance the first two strategies. Working with a data governance team is essential for success, but organisations need to make it a sustainably scalable approach too. This method is all about creating well-defined principles to securely introduce and manage an increasing number of data sources to an increasing number of stakeholders within the organisation, thus building an ever-greater level of trust and a conscientious approach to handling data within the organisation all at the same time.

The key measurement to success that stakeholders will actively notice will be a reduction in data security and policy breaches, and more recorded and tangible insights activation across all teams within the organisation.

This article was originally published on the FlexMR Insights Blog and can be accessed here.

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