Organisations are waking up to the transformative benefits that arise when they implement effective data governance programmes that deliver measurable business outcomes.
No longer a ‘nice to have’, in recent years the emergence of regulatory requirements such as GDPR means it’s now imperative to maintain a strong handle on what data is where – and how it is used. But that’s not the only trend that’s driving organisations to achieve effective data governance.
As organisations look to unleash the enterprise-wide data analytics and BI reporting that generates value-add business insights and optimises operations, they need to be certain they can harness data in any way that’s needed. Which means that the availability, usability, integrity and security of data is becoming a must-have if organisations are to productively unlock business intelligence from their data sets.
According to McKinsey, good data governance is a key factor that differentiates those firms that are able to capture value from their data, and those that are not.
For those that master data governance, the rewards are significant. In addition to eliminating the costly overheads associated with managing their data ecosystems, they’re able to implement digital and analytics programmes that generate measurable competitive advantage. Meanwhile, data governance laggards are still on the starting blocks where realising their data ambitions are concerned. Worse still, underinvestment in governance efforts means their organisations may be exposed to significant regulatory risk.
Building an effective foundation for data governance is not without its challenges. Having invested in a plethora of data governance technologies, many firms hit roadblocks when these tools fail to deliver the required controls needed to underpin new digital initiatives or maintain a compliant posture. Indeed, a recent Gartner report found that 25% of firms had failed to accomplish anything with their data as a consequence of poor data governance.
The how and why that motivates organisations to pursue good data governance are many and varied. These range from undertaking a targeted effort to improve compliance standards, to implementing a ground-up strategy that supports new data-driven initiatives that will deliver measurable business benefits to the enterprise.
These objectives, combined with the level of experience and available resources, determine which persona an organisation will typically assume. And it’s these personas that will ultimately determine whether a data governance programme is a success or failure.
1 – The Slow Starter: We want to do more around data governance, but don’t know where to begin.
Driven by regulatory pressure, management edict or a desire to promote a commitment to good governance, Slow Starters often struggle to move beyond the initial decision to begin. Key questions like who will be responsible for design and implementation or what resource commitment is required remain unanswered. As a result, governance projects fail before they’ve even got off the ground.
2 – The Inexperienced Enthusiast: We’ve bought some tools but can’t operationalise our efforts because we lack the right skills or people.
Many organisations see buying tech as the solution to cracking the data governance nut, but many of today’s governance solutions are narrowly focused and can’t be integrated into the wider business strategy or processes without specialist knowledge and experience. However, the scarcity of data governance skills and know-how in the marketplace means this talent is hard to find and recruit.
3 – The Frustrated Investor: We’ve purchased tools but business leaders now want outputs that support better business outcomes – and we don’t know how to get these.
Significant investment in data governance tools may have ticked all the boxes in relation to issues like data classification, but organisational leaders now want to see what this all adds up to in terms of tangible returns and benefits. Problem is, the data governance strategy was never designed with this in mind.
In all these scenarios, when data governance initiatives struggle to meet their objectives, the missing link that organisations run up against is access to relevant experience alongside proven, integrated tools that can support complex strategies, not inhibit them.
The advent of the data governance-as-a-service (DGaaS) model is enabling organisations to overcome gaps in data governance capabilities, experience or technologies so they can move their data governance projects forward.
Addressing everything from data quality to master data management, DGaaS frees organisations to plan, design and deliver a data governance strategy and drive their core governance objectives through to execution.
From the initial data discovery and classification that makes it possible to identify data assets, assess gaps and any associated risks, to the creation of documentation and effective processes that streamline execution, DGaaS takes away resource and technical limitations that so frequently cause projects to fail.
Whatever their experience or organisational persona, DGaaS eliminates the roadblocks that can get in the way of making data governance part and parcel of their business toolkit. For organisations looking to leverage effective governance to deliver business growth, innovation and compliance, the ‘as-a-service’ approach is proving its worth when it comes to both establishing and maintaining good governance – and evolving data governance programmes so they can deliver tangible business outputs.
Michael Queenan is co-founder and CEO at Nephos Technologies
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