The way many organisations are currently addressing data governance is frequently incompatible with their objectives or their ability to define, implement and evaluate a strategy that can deliver meaningful impact.
The result is that data governance strategies can hit some critical (and expensive) roadblocks. For example, some organisations decide they need to act on data governance, but don’t know how, while others buy data governance tools, but are unsure how to drive business value and outputs, despite spending substantial sums.
In other scenarios, and despite investment in tools, organisations are hampered by unoptimised gaps in their approach to governance. Then there are those that initiate a data governance strategy with the intention of running it in-house but struggle with expertise, skills shortages and delivering usable outputs.
Whatever the situation, the downsides of directing time and resources into data governance strategies that don’t work can be serious. Instead, today’s data-centric organisations should be freed from the strategic and operational restrictions imposed by the current inadequate data governance ecosystem.
The emergence of Data Governance-as-a-Service (DGaaS) is offering a way to overcome these obstacles. It’s an approach that brings together objectives and results, taking the risk away from investments and delivering the experience, skillsets and proven technologies required to ensure data governance projects succeed.
DGaaS allows organisations to approach the planning, design and delivery of a data governance strategy focused more clearly on their key objectives. And instead of defining and limiting their capabilities, technology becomes the enabler in a service-led approach to data governance.
DGaaS Building Blocks
An effective approach focuses on a number of foundational components. For instance, having taken the decision to focus on data governance, the most common mistake organisations make is to immediately prioritise outcomes without first addressing the need for effective data discovery and classification.
In many cases, teams with the responsibility for delivering data governance will often assume that there are tools out there that can be given access to data sources to analyse and identify governance violations instantaneously. In reality, this process is impossible without first understanding what is being looked for in the first place. The net result is that it’s unlikely governance initiatives will deliver any tangible results or benefits.
Therefore, it’s vital that data governance best practices should first define what data classification looks like for each unique situation. Customer data, for instance, will be held in different locations and databases in every organisation. What level of classification of that
data is down to each individual company, but whatever the situation, good governance is only possible if this data is correctly identified and classified. From that point onwards, it becomes practical to apply gap analysis to understand whether there are violations, such as misclassified data or residency issues. Without it, any attempts at data governance are going to be made much harder from day one.
Enabling organisations to operationalise data governance, and in the process, give them the ability to create and design the policies and processes they want to enforce rather than having to worry about how to deploy and manage them is key to effective DGaaS. In practice, this takes all the operational and expertise requirement overhead away from customers and leaves them free to focus on value creation from data.
Furthermore, the delivery of consultancy-focused process creation and documentation enables organisations to drive their ideas and objectives through to execution. For example, if an organisation needs to implement a data minimisation project, DGaaS consultancy enables them to understand what that entails, what people and processes are required, alongside the choice of technology tools to ensure success.
Bridging the governance gap
This plays a vital role in bridging the gap that frequently exists between objectives and the skillsets, experience and resources required to deliver on them. For example, organisations often suffer from a data governance skills shortage, with a dearth of recruitment candidates to choose from. What’s more, few organisations have a management hierarchy in place to support those given responsibility for data governance, let alone effectively integrate it into the wider organisation.
This generally results in a major disconnect between senior executives and those with ‘data governance’ in their job title, job description or on their to-do list. As a result, governance projects stall because the people given the jobs don’t know what to do. Many are put into the impossible position of being given broad objectives with no insight into the processes, documents or people they need to achieve them - let alone how this maps against the toolsets they have bought.
Giving organisations the ability to take the raw outputs from the toolsets and turn them into tangible business outputs is the next phase in the evolution of data governance. DGaaS brings these capabilities together, allowing businesses to deliver on even the most ambitious data governance objectives.
The bottom line is that effective data governance empowers organisations to make major improvements across a wide range of key operational and performance issues. These can range from data integrity and accuracy to compliance, decision-making and bottom-line growth.
Done well, the impact can be truly transformative, enabling leaders to act with new levels of insight and confidence. As a result, organisations are increasingly investing in technologies and processes in an effort to balance compliance with performance and unlock the power of their data. This is where DGaaS is set to play a key role in ensuring that governance becomes part of the standard business toolkit that delivers a win-win of effective compliance and business innovation.