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The trouble with tools - Overcoming the frustration of failed data governance technologies

data woman
(Image credit: Future)

Effective data governance empowers organizations 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, organizations are increasingly investing in technologies in an effort to balance compliance with performance and unlock the power of their data.

Indeed, understanding the potential data governance has for improving business performance is becoming vital. According to a report by McKinsey, “Leading firms have eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. Data governance is one of the top three differences between firms that capture this value and firms that don’t. In addition, firms that have underinvested in governance have exposed their organizations to real regulatory risk, which can be costly.”

The problem is, building an effective data governance strategy can be challenging, with organizations suffering from the assumption that technology investment offers a guaranteed route to success. All too common, however, are the experiences of organizations and their data governance teams who see their efforts frustrated by software tools that promise much but deliver relatively little.

The result can be delayed or even failed projects that waste budget, resources and don’t deliver on core objectives. This can also translate into future reluctance to reinvest in the process for fear of repeating the same mistakes. As a result, organizations experience a knock-on effect on their ability to effectively address data governance and derive tangible business benefits from their efforts.

As McKinsey also points out, “Without quality-assuring governance, companies not only miss out on data-driven opportunities; they waste resources. Data processing and cleanup can consume more than half of an analytics team’s time, including that of highly paid data scientists, which limits scalability and frustrates employees. Indeed, the productivity of employees across the organization can suffer.”

The role of technology and the choices made by businesses in what tools to apply to their data governance strategy play a huge role in determining the success or failure of their efforts. Many of the issues faced by businesses trying to modernize their approach to data governance stem from the difference between legacy and contemporary tools.

Legacy governance technologies were designed for a data governance environment where organizations hosted all of their data within their own data center. They were also implemented in an era when there was far less data residing on far fewer servers. They simply aren’t built for modern architectures where, for instance, multiple authentication systems reside in multiple places.

Today, data is stored and processed across an increasingly broad range of execution venues. Many organizations store multiple data types across a complex strategy of on-premise, in-cloud and SaaS locations, making compliance both complex and costly. What’s more, the average company has dozens or even hundreds of apps, each generating, manipulating and storing data volumes that are growing exponentially.

The challenges don’t end there. To an extent, many businesses have also lost control of their IT estate. It’s now so easy for almost any department or team to subscribe to new apps or cloud services without the need for any kind of procurement process. This can leave IT and governance teams in the dark about where data is being generated, stored and shared, adding to the complex governance challenges they face.

Discover, classify and correlate 

As the complexity of infrastructure increases, this resulting data sprawl is one of the biggest issues facing those responsible for governance. In many situations, legacy software tools are at the heart of failed governance projects and are identified as the weak link between objectives and delivery.

But in reality, there’s an uncomfortable truth that often explains poor decision-making around the choice and use of data governance tools: Any organization that has bought tools that don’t start with discovery and classification are fundamentally missing the point of what they are doing.

To explain, inside many organizations that claim to focus on data governance, the process is reliant on tools that produce a CSV of objects with no insight about where violations might exist. For example, they struggle to tell the difference between Personal Information (PI) and Personal Identifiable Information (PII). While most PI data doesn’t identify a specific person and isn’t as relevant to identifying governance violations, discovery tools still present that information to users, adding huge complexity to their processes and forcing them to revert to a manual process to filter what’s needed from what isn’t.

Instead, it’s critical that organizations are able to view, classify and correlate data wherever it is stored, and do so from a single platform - otherwise, they simply can’t add value to the governance process. In the ideal scenario, effective governance tools will enable organizations to correlate their governance processes across all data sources to show where PII is being held, for example. The outputs then become much more accurate, so in a scenario where there are 10 million findings, users know with precision which of them are PII. This represents the gold standard for where data governance needs to start. 

organizations also need to investigate their overall risk strategy for certain types of data on certain locations. To explain, it might be acceptable to hold certain HR spreadsheets on internal systems, but not in Salesforce or Dropbox. But, without the element of discovery and classification to match against the classification of the end data source, organizations aren’t matching their data governance risk to their corporate governance risk.

With the right tools in place - tools that blend discovery, classification and correlation from a single platform - organizations can drive much more value from their data governance projects. In doing so, they create a win-win scenario for themselves and their stakeholders where data governance is no longer a barrier to better performance, but offers a route to avoiding the frustration that comes with failed governance technology investment.

Michael Queenan, co-founder and CEO, Nephos


Michael is a co-founder of Nephos and is responsible for the overall strategy, direction and branding at Nephos.