What is Data Governance?

Data governance is a discipline that exists to ensure the effective, efficient and responsible use of information to allow an organisation to achieve its goals. It is a subject that encompasses the practices, processes, standards, and metrics required in meeting this challenge. Not surprisingly, it can be quite an imposing and at times foreboding subject, conjuring up images of red tape, bureaucracy and obstacles for those who work in data. Data governance does however exist for the benefit of all. Despite common perceptions to the contrary, its real purpose is not to complicate matters. Its aims are to provide a structured framework for managing data assets across an organisation, ensuring that data is accurate, available, consistent, and secure.

At its core, data governance involves the coordination of people, processes, and technology to manage, protect and capitalise on data assets. The practice defines who can take what action, upon what data, in what situations, using what methods. It’s a critical component of an organisational data strategy, including data quality, data management and policies, as well as aspects of business process management and risk management. With all this in mind, given the importance of data to just about every organisation, data governance is not something that can really be kicked down the road for too long.

Traditional Top-Down Data Governance

Historically, data governance has been a top-down initiative. The ‘command and control’ approach and accompanying rules are often perceived by those subject to them as obstructive and arduous. For a long time, data governance was about compliance and data security only. Both these are of course incredibly important to any organisation working with data, with reputations being frequently thrown to the wall in their absence. Organisations of all sizes often make mistakes in these areas as we are all aware of with regular media coverage of breaches and abuse of rules around customer/user data. There is however much more to the remit of what we now refer to as data governance.

Modern Bottom-Up Data Governance

A more recent view of data governance includes more elements that are focused on the benefits to employees who work with data. Any data consumers from business analysts and C-suite executives to data engineers and data scientists could be said to have the same fundamental needs of their data. If they are to capitalise on the potential of their data it needs to be trustworthy, adaptable, well understood and available. This is in essence what makes data valuable. If any of these are lacking, the value decreases as the consumers of that data wrestle with the issues that result.

More and more data is being consumed by businesses who also seek more agility in working with it. This moves the responsibilities and ownership to the early stages of this data’s journey within the organisation. ‘Self-service’ data analytics brings greater freedom to work with data at a departmental level. This requires a shift of responsibility regarding the ownership of that data. If data governance is to scale to satisfy the modern organisation’s appetite for data, there will be a need to extend it beyond the traditional central team of stewards and steering committees. The onus should be broadened to include data subject owners at the departmental level, or perhaps even lower. The role of central organisational elements of the program are still critical in defining company-wide requirements and standards. They are however only part of the whole program, serving a number of roles that generally do not require an intimate understanding of business data.

Key Components of Data Governance

There are differing opinions on what really ‘defines’ a data governance program. A good starting list that covers most aspects is given below:

Data Quality

This focuses on ensuring that data is accurate, complete, and reliable. If data is to be trusted, this is a key aspect of building that trust.

Data Management

The processes and policies for handling data throughout its lifecycle. Data should be well managed, shaped to fit the consumer, and provided for consumption. Ease of use and availability are key considerations. This is traditionally where organisations will invest a large part of their resources in data engineers and systems administrators. To complement these, there needs to be the understanding of cataloguing, describing and communicating these data assets across the organisation.

Data Policies

The rules and regulations that govern data usage within an organisation. These traditional elements aren’t going away and form the bedrock on which teams can build their own initiatives. From there they can expand upon and decentralise responsibility for data ownership.

Business Process Management

Aligns data governance with business processes to ensure that data supports business objectives and capabilities. The true value in data is in providing the insight and understanding required by the business to be successful. This alignment of the ‘behaviour of data’ within a business with the various drivers of success is essential more today than ever. Careful consideration is required throughout all aspects of an organisation’s operations. All areas are fueled by the availability of the right data, from logistics to marketing and beyond. If the flow of that data is stifled, so too are the opportunities within the business.

Risk Management

Identifies and mitigates risks related to data privacy, security, and compliance. The risks associated with an organisation’s data are reduced only by having a firm understanding of this information. Where it resides, what it includes, how it is being accessed and how is should be safeguarded are all paramount. Penalties for not managing this risk are not just financial in the form of hefty fines but perhaps more damagingly reputational. Customers have suffered a slew of data breaches and misappropriation of their data over the last decade or so. Organisations need to do more to address these very real concerns.

The above components highlight key drivers behind programs on data governance. For more formal and in-depth definitions there are various frameworks available. We’ll discuss in our series post Data Governance Frameworks.

Summing Up

We are all very aware of the rapid increase in the need for data and its importance within the organisation. The availability of affordable platforms for generating value have placed data front and centre of businesses of all sizes.  Given the perceived overhead of the list above it is no surprise that many organisations are late to the game. Forming a robust data governance program or discipline is an involved undertaking. However, for those that truly understand the value of their data and the responsibilities of ownership, data governance is a subject that should be embedded in all areas of the business.

During this series we’ll be taking a look at some of the challenges of data governance. We’ll also discuss strategies and approaches for overcoming what may at first appear to be a Herculean task. Once we understand the art of the possible we will then discuss how this can be employed to maximise business benefit.

Helping You Apply Effective Data Governance

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