Data governance

Bridging the governance gap – the rise of data governance as a service

In recent years there has been an increasing focus on data governance, with increasing levels of regulation accompanied by a widespread awareness that data is an asset with – potentially – enormous latent value.

But, with experience and skills being paramount, many organizations apply an inaccurate set of methodologies and tools in an attempt to manage and operate their data assets while remaining compliant.

As a result, it’s safe to say that the results are mixed. For example, many organizations decide they need to take steps to check the box for data governance, but don’t know where to start. Then there are others who, in an effort to get ahead, purchase data governance tools but are then unable to deliver meaningful business value and results.

A third group are organizations that invest large sums in tools, but then struggle with persistent and unoptimized weaknesses in their approach. And finally, some choose to initiate a data governance strategy via an internal team. The problem here is that it can be very difficult to acquire the expertise and skills necessary to deliver usable results, let alone implement a complex strategy.

For organizations focused on delivering win-win, compliant data governance that also unlocks better business performance, Data Governance as a Service (DGaaS) has emerged as a compelling option. In particular, the outsourced “as a service” model allows organizations to approach planning, designing and implementing a data governance strategy that is more clearly focused on their key objectives.

How does DGaaS work?

The role of DGaaS is to bridge the gap between data governance goals and outcomes, taking the risk out of investments and providing the experience, skills and proven technologies needed to ensure the success of these data governance initiatives. more and more important. Instead of defining and limiting their capabilities, technology becomes the enabler of a service-centric approach to data governance.

In doing so, it emphasizes four fundamental components that are critical to developing strategies that avoid common roadblocks while delivering tangible results and ROI:

  1. Discovery and classification

In any successful data governance strategy, discovery and classification must be ubiquitous. Without this holistic view, organizations are not only unable to identify the data they own, manage and share, but they also cannot hope to understand if they are mismanaging data and, by definition, assess their level of risk. .

For example, data governance teams often assume that there are tools that can access data sources to instantly analyze and identify governance violations. In reality, this process is impossible without first understanding what is being sought in the first place. The net result is that governance initiatives are unlikely to produce tangible results or benefits.

  1. Creation and documentation of processes

Creating and documenting processes provides organizations with the guidance and expertise they need to carry their plans through to execution and impact. For example, if an organization needs to implement a data minimization project, it needs to understand what it involves, what people and processes are needed, and the choice of technology tools to ensure success. In many cases, this is simply not possible with existing internal resources, but without them there is a very real risk that projects will suffer from inertia or fail altogether.

  1. Operational implementation

With processes and reliable documentation in place, governance strategies can then focus on developing the policies and standards that need to be enforced, rather than having to worry about how to deploy and manage them. . This is especially important given the general shortage of data governance talent that currently exists. Additionally, few organizations have a management hierarchy in place to support those responsible for data governance, let alone embed it effectively into the wider organization.

But, by removing the overhead of operational and expertise requirements, organizations are freed to focus on creating value from their data.

  1. Using data governance to drive business intelligence

While the emphasis on business intelligence is understandable, legacy approaches mean that the time to value can simply get longer, with senior executives asking for updates, results and dashboards to see progress that simply do not exist. In many cases, organizations realistically talk about an 18-24 month deployment of a data governance program and toolset before they start to see the value. In today’s agile business environment, that’s just too slow.

Instead, DGaaS gives organizations the ability to take the raw results of their governance tools and turn them into tangible business outcomes, enabling them to achieve even the most ambitious data governance goals.

These are critical considerations, because organizations today are increasingly faced with a binary choice: to seize the opportunity that effective governance presents to ensure business growth, innovation and compliance, or risk competitive advantage and the penalties imposed by powerful regulators for failure.

image credit: nialowwa/

Michael Queenan is the co-founder and CEO of consultant-led data services integrator, Nephos Technologies. Ten years ago, Michael and his business partner Lee Biggenden identified a gap in the data market for a service-led integrator to guide larger organizations through the complex process of strategy, governance and analytics. Datas. They believed this expertise would enable their customers to drive growth, compliance, and insights from their data assets. In 2012, he founded Nephos Technologies with Lee, to provide real expertise and value around data integrity and inspire organizations to think differently about valuable assets.