Setting up efficient data governance in an organisation should focus on people, data development and goals. It is equally important to accept that the path toward a data-driven organisation requires change.
Our last blog discussed and defined data governance. Modern and productive organisations are data-driven, thus data is at the core of their business processes. As we noted, to become data-driven, an organisation needs to set up efficient data governance.
We argued data governance consists of the actual management and utilisation of data assets and the governance of said assets. Data management is about HOW to handle and process the data, the governance side tells us WHAT the organisation is doing (related to data) and WHY. It also defines WHO does the governing and management.
Efficient and comprehensive data governance is closely tied to the organisation’s strategy, defines clear responsibilities and roles, and actively coordinates the data-related development of an organisation. Since most processes nowadays are either dependent on, or at the least connected to IT-systems, most development in an organisation is in fact data-related.
To summarise, efficient data governance takes into account:
- Data development
Naturally, transparency and communication are crucial, too, especially between key stakeholders.
Prepare for change
Setting up efficient data governance in an organisation typically requires some changes, be it in roles, responsibilities, processes, organisational structure etc. Thus, a crucial part of setting up data governance is efficient change management and willingness to change.
Depending on the maturity level, data governance might require quite a few changes, especially if the roles and responsibilities required are non-existent, loosely defined or misunderstood. An organisation might interpret data governance solely as ‘document management and archiving’ or ‘just IT’ for example, which usually indicates a change is required in both thinking and organisation. The former interpretation seems to be mind-bogglingly common in the public sector while the ‘just IT’ approach is quite common regardless of sector.
It is critical to understand data governance as a wider enterprise level concept, requiring coordination and co-operation between. A continuous dialogue should exist not just between business and IT but also between business, IT, and the entity responsible for data governance in your organisation. Defining clear roles and responsibilities tends to help.
The stakeholders of data-related activities should be represented in a data governance steering group, office, or any similar organ responsible for data governance in an organisation. Ideally, this should include representatives from the organisation’s IT, business development, data management and basically any other agent representing stakeholders in the development activities and strategic goals related to data.
While it might be challenging, especially in larger organisations, to fit everyone into a huge steering group, a way to co-operation and communicating data-related issues within an organisation is vital. It enables enterprise-level steering of data-related development. This brings us to roles and responsibilities.
Efficient data governance requires the definition of data and process ownership, and roles. In some cases, these might be completely new roles, in others, existing roles might require either some redefinition or simply more clout.
Implementing data governance without a sufficient level of influence, or power if you will, within the organisational hierarchy, is doomed to fail. The main point is to understand and identify the critical roles required by data governance and find and define the equivalent roles within the organisational structure. And ensure they have enough authority to actually govern the data.
2. Data development
Fully analogic processes are gradually becoming quite rare, if not extinct. Hybrid processes, by which we mean processes with the digital part of the process intermingled with one or more manual phases, however, are not.
One of the goals of efficient data governance and a characteristic of a data-driven organisation is the development of these processes towards fully digital ones, to get the most out of the data, and processes. This quite obviously requires an understanding of various development activities in an organisation to affect the way the organisation utilises data. Not just ‘data development’.
The larger the organisation, the more challenging it is to get an accurate overall picture of development activities. Start with the fact that most processes in an organisation are either implemented by or at least connected to one or more IT systems. To make matters interesting, making changes to one usually affects another.
Understanding the dependencies between various IT-systems is critical if data development is to be led and coordinated efficiently. Dependencies are not just about understanding how certain technological solutions are related to each other, but also about understanding various roles and responsibilities, like data and process ownerships, in an organisation.
A sole definition of roles and responsibilities is just a start and enables the most critical aspect of any development activity: better communication between various experts and stakeholders.
One useful tool for understanding and depicting these dependencies and the organisation’s operational environment is enterprise architecture, which should be closely tied to data governance operations.
Organisations typically use enterprise architecture as a tool to visualise and depict the operating environment in an ordered and holistic manner, to manage the environment and changes to it in a meaningful way. It is one of the tools used for an efficient management of data assets in an organisation but the architecture itself needs management, or rather, governance, too.
Data is one of the most important assets of an organisation and typically functions as a catalyst for the organisation’s strategic goals. Building digital services for example, is rather challenging without viable data and interoperability to access and utilise that data. Instead of complex and all-encompassing approaches and policies, the focus should be on delivering real business value.
If a data-driven organisation is the desired strategic goal, data governance should support organisation’s strategy and also be an integral part of it. To put it bluntly, the main function of data governance in an organisation is to ensure the business gets the data it requires to achieve strategic objectives. Thus, a lack of efficient data governance massively raises the risks of failing to achieve strategic objectives.
Metrics should be developed to measure the return on the value of data governance. These metrics can be used to demonstrate to a stakeholder audience the benefits and return of investment in data governance. Or, alternatively, to describe the costs and risks of not adopting the new approach.
We suggest keeping it simple. Monitoring the amount of development projects in the organisation ‘influenced’ by Data Governance office might work, provided the organisation has some common guidelines and practices for steering data development in a coordinated and business oriented way. Getting those guidelines and practices might, however, require people, data development and perhaps some changes in the immediate goals.