Getting Started with Data Governance

Organizations need to have a clear stand on safeguarding its most important asset — data. And as for any other asset this means to define the processes and procedures by which their data will be managed.

The goal of data governance is to ensure that an organization’s business objectives are accomplished, by guaranteeing that data is available as needed for business purposes, but also secure, private and in compliance with regulatory requirements.

There is no one-size-fits-all approach to data governance and specially in the present moment — when organizations are being pressed for results or to quickly adapt to cope with a rapidly changing business environment — a more pragmatic and agile approach is paramount.

Start with a vision

A common view of what the organization wants data governance to accomplish and how it plans to reach, a vision that mustn’t be static and that continues to shape as capabilities and goals evolve with the close involvement of the key data stakeholders, be it data owners, IT leaders, data privacy officers, or analytics and security experts).

Leadership buy-in and commitment

Data governance is a process that needs buy-in from every level of an organization, otherwise, it will constantly stumble into resistance pouches within the organization. Clear leadership is critical, and it should be in the hands of a senior business leader who can easily coordinate with the business and IT and be able to connect data governance with business impact.

He can play the role of a chief data officer (CDO) or not, but the important is that it must be someone that can understand, and communicate, business needs, technical capabilities, and limitations, and prioritize the most strategic data.

Focus on strategic data

Approaching data governance in a holistic perspective will inevitably lead to a lack of focus, resulting on a misalignment with the business objectives and incapability to deliver value.

As any other asset in the organization data’s purpose is to create value, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives.

Data governance must be supported on strong business cases, anchored on clear and measurable business objectives, otherwise it will risk being viewed as another siloed IT project with no perceived value from the business side.

Business Cases

From here it is possible to identify how data may be used to deliver those priorities and objectives. These will be the business cases for the data strategy.

In an early stage, for effectiveness purposes, there should not be more than five business cases, depending on the size of the organization, and the scope and complexity of these cases.

But all, with clear, achievable objectives and stakeholders that are aware of the importance and impact of data. Essentially, the purpose is to take a business problem or opportunity and show how better data quality brings better results through better data governance.

Starting the right business cases is crucial, for reaching the objective of building a robust data governance framework.

Choose business cases where it’s clear for senior managers that data is deterring business opportunities or creating a significant pain points or risks, where data quality has a high impact for the business, and where remediation is not too complex.

Start small, think big

Always aligned with the data strategy start with a small, targeted initiative, where the impact and value of data can be clearly identified and working with a business stakeholder that can passionately and effectively articulate the impacts of data in their business processes and that will be eager to defend the project.

Selecting the right business case kicks-off this process in the right direction. There is not much effort or resources and the are not too little effort or resources. Data governance activities are specifically sized and customized for the business case at hand.

Data governance is growing, maturing, and gaining traction in the organization, while business is seeing value being generated and the initial data governance capabilities are put in place.

A success history will create the necessary momentum to move forward, identifying and prioritizing new business cases and consequentially more governance capabilities.

This is a compounding, ongoing, cyclical process where data governance is progressively expanding and developing, and while keeping the business cases closely tied to with clear, achievable business objectives, more and more business value keeps being delivered.

Measure and communicate

Setting up a set of metrics that can be linked to data governance and communicating them across the organization, a success story, that even at a small scale will create the awareness and act as a motor to leverage the replication of that story in other business units.

Measuring permanently data quality and the data governance capabilities maturity and persistently convey that information across the different stakeholders, allowing the organization to align on how, where, and when to adjust, improve or accelerate.

Business on the driver seat

All the program and initiatives must be driven and oriented by the business units. Data governance is not an IT function, it is a business function, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.

Agile mindset

Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow that visible results are presented in short time lapses.

Data governance implementation can be expensive, time and resource consuming and span through long time frames and take some time to deliver ROI, so it is critical to develop the capability to deliver results within short time frames.

It’s key to deliver quick outcomes, the focus must be on keeping things simple at this early stage and keep away from complex processes and tools.

This approach brings multiple benefits. It delivers value. It triggers business buy-in for data governance. It builds data governance capabilities gradually, building only the capabilities the initial business cases require.

Data Minimalism

All the data being collected and processed in the organization within a specific context, either operational, regulatory, and its collected and analysed with an end in mind, sustained by a business case and aligned with the business objectives.

Growing from within

This approach will produce long-term benefits, creating traction and increasing the awareness across the organization and will end-up acting as the motor from within the organization for a Data Governance structure that will grow organically out of the initial iteration.



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