The trouble with tools
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.
