When a company’s most important asset is arguably its data, data governance is a key starting point for setting the parameters for how that data is used. The how, where, why and who of data management is essential to improving the quality of data and making it usable, secure and above all reliable for making smart business decisions. Organizations are therefore faced with an important decision: adopt effective data governance to ensure growth, innovation and compliance, or risk a competitive advantage and the penalties for non-compliance with regulations, says Michael Queenan, co – Founder and CEO of Nephos Technologies.
Data governance inertia
Although business leaders realize the value of data governance, many are not moving forward. This could be due to a variety of reasons: a lack of budget or an agreed plan for effective data governance or a lack of skills to deliver their existing data governance tools. Harnessing the transformational potential of data has increased the responsibility to protect and appropriately use the information of all customers, partners or employees in the ecosystem, increasing the importance of data governance for compliance purposes .
According to a recent survey by Nephos Technologies of 900 CIO/CISO/DPO caliber decision makers, a quarter of UK companies fail to leverage their data governance programs and almost a fifth (18%) admit they don’t. just start. Nearly half (46%) say they don’t have the skills or expertise to properly use the tools they’ve invested in.
Research reveals that organizations understand the business benefits of good data governance. However, it also revealed the challenges leaders face in successfully implementing their data governance programs. These include a lack of expertise (38%), difficulty justifying the business case (38%), as well as a lack of effective tools (37%); executive support (33%); and cost (32%).
It is also common for companies to invest in data governance tools without being able to generate the right value. In fact, any unoptimized strategic gap can disrupt the governance process, causing both short-term and long-term problems. Some organizations don’t know what good data governance means and where to start.
The meaning of data governance
The Data Governance Institute’s definition reads: “Data governance is a system of decision rights and responsibilities for information-related processes, executed according to agreed-upon models that describe who can take what actions with what information, and when, under what circumstances, using what methods.It is the process of managing the availability, usability, integrity and security of data in business systems.
It’s a fairly straightforward definition, so perhaps a lack of understanding of its true value is preventing organizations from developing a clear and robust data governance plan.
Understanding Data Governance ROI
C-level executives are typically drawn to the business risk element of data governance, focusing on GDPR, data compliance issues, and their business risk assessment. But there is a tendency to overlook the actual levels of GDPR compliance, perhaps even unaware of what has been lost in the event of a data breach. Therefore, good data governance is not addressed.
The concept of return on investment does not easily apply to data governance, as its impact is usually only evident when something goes wrong, such as a cybersecurity breach. Without being able to define a value, it can be set aside in favor of more important priorities.
Yet the Nephos survey confirmed respondents’ appreciation of the benefits of a data governance program. Top executive goals included improving data quality (39%); compliance with regulatory requirements (37%); to increase efficiency and costs (36%); and better quality of data analysis and information (35%). Others included minimizing financial risk, improving data privacy/security, and providing timely access to relevant data.
The importance of data classification
Following data governance best practices will keep organizations on the path to success. A key first step should be to define what the data classification looks like for each unique situation. Customer data is stored in disparate locations and identifying and classifying this data correctly is essential for good governance, whether it is public, private, confidential or restricted. Following this, it is a good idea to apply a gap analysis to verify violations, perhaps limited data from public sources. Without it, any attempt at data governance is likely to fail.
Businesses cannot afford to neglect effective data discovery and classification. If teams responsible for providing data governance assume that there are tools that can access data sources to instantly analyze and identify governance violations, they will be ineffective. It is fundamental to define what you are looking for and what you want the data to accomplish, otherwise you will not be able to unlock the desired business value.
The Emerging Role of Data Governance as a Service (DGaaS)
Data governance is a complex process with many moving parts, ranging from data quality, master data management and the challenges presented by encryption, to choosing the right technology tools and enforcing policies.
By adopting DGaaS to drive business improvement or transformation and strong regulatory compliance, organizations are turning to the “as a service” model to fill gaps in capabilities, experience, and technologies governance issues that doom many projects to failure. This approach is designed to eliminate investment risk and provide the strategy and proven technologies needed to ensure the success of data governance projects.
Organizations need clear signage for an orderly approach to DGaaS because not everything can be achieved at once. Good data governance requires vision rather than mere tactics and it won’t happen overnight. It should be a long-term journey supported by a solid framework, rather than a standalone data project.
In a wide range of use cases, DGaaS can be applied to every major component required to ensure good data governance. It starts with a data discovery and classification phase, using software tools to analyze all the data, wherever it resides. With this detailed information, organizations can easily identify their data assets, where data is mismanaged and be more in touch with their risk levels.
The next phase of the data governance process focuses on creating and documenting processes efficiently, removing the operational and expertise burden and leaving them free to focus on creating value from data. Organizations now have the ability to apply these findings from the toolsets to achieve tangible business results.
This allows teams to take a more streamlined approach to planning, designing, and implementing a data governance strategy that is closely aligned with their core goals. Technology acts as an enabler for successful data governance, navigating the time-consuming minefield of implementation hurdles.
Focus on the transformational potential of data
The most important realization for leaders is that they can make the most of what they already have their data to transform their businesses. While data governance can play a valuable role in this, many organizations still struggle to get the most value from their data governance programs.
Aligning data governance initiatives with business objectives and ensuring access to the right human skills and experience, along with proven integrated tools, will effectively support the process. By ensuring data assets are formally managed, C-suite and business function leaders will have a clear view of the business and trusted data to enable transformational business decisions.
The author is Michael Queenan, co-founder and CEO of Nephos Technologies.