Overview
Today, organizations are flush with data, and the role of Data Governance is front and center, playing a vital role in supporting the efficacy of large amounts of data and feeding the future of Artificial Intelligence. As Data Governance continues to trend as a highly valuable component of business growth, it has also opened some uncertainty for organizations. Additional concerns around data disposal, vendor selection, policies, and more have hindered, even sometimes sabotaged, data governance initiatives. So, it is no surprise that organizations have a vested interest in building smarter Data Governance strategies, void of missteps and costly errors, one that can sustain for years to come.
In this blog, we will examine some key misconceptions about Data Governance and identify best practices for setting up a successful Data Governance strategy for your organization.
The Evolution of Data Governance
In the past, Data Governance was considered a desolate and solitary job, one that often was not prioritized as organizations focused on mining data, and lots of it. In this journey of collecting and maintaining huge volumes of data, organizations realized they were multiplying data sets, creating data silos, and introducing data breaches which diminished the consistency and value of their data, often damaging their company’s reputation and resulting in operational inefficiencies. With the immense responsibility of all this data, it soon became clear that organizations must move from descriptive to predictive analytics to find smarter ways to analyze, access, utilize, and manage data across the organization. This evolution blazed a trail for Data Governance. Now more than ever, organizations are focused on gathering and delivering accurate and actionable data that drives key insights for business growth through strong Data Governance practices.
A Data Governance State of Mind
Contrary to popular belief, Data Governance is not an IT problem or responsibility, it is a cultural change, one that everyone in the organization should embrace. Due to its expansive reach across the entire operation, Data Governance relies on multiple business units working together to develop strong processes and procedures that help facilitate good data. To accomplish this, organizations often need to tackle four key misconceptions within their organization.
It’s Not Worth The Effort
Most data leads have a perception that data governance is an overhead, it is a tedious and time-consuming effort before any Return on Investment (ROI) is seen. While these are some of the challenges facing new data governance programs, they can easily be addressed with a well-crafted maturity model, road map, and a comprehensive communication and training plan backed by a mature data governance team.
Just Set It And Forget It
Many organizations are under the misconception that Data Governance is a short-term project or initiative that has a defined start and end date. While some components of Data Governance are assigned and completed during the implementation phase, the number of tasks will keep changing and cannot be subject to a defined end date. Data Governance should be seen as a capability, hence like any other technical capability in an organization, it needs a dynamic and skilled team structure and an innovative way to engage and develop that team so that they are always measuring and improving the data governance practice. With the right state of mind, an organization can embrace a strong data governance culture. As with most organizational capability initiatives, it is imperative to have a clearly defined Data Governance Program, Vision, and Mission to ensure there is organizational alignment, an appealing and motivating message for stakeholders to get behind, and a “lens” from which the program can be steered.
The following depicts the four crucial Data Governance capability-building blocks.
People
- Data Governance is all about policies and processes - and it is people who define them and ensure that others adhere to those
- Data Governance is a culture that needs to be accepted for it to be effective by everyone in the organization
- Technology needs to be supported by people playing an important role in a successful Data Governance capability
- People act as a backbone for Accountability which is a crucial Data Governance principle
Process
- Process establishes a methodical approach that can be used from Strategy to Operations
- Process defines enterprise-wide standards in terms of data utilization, data validation, data cleansing and reconciliation
- Process defines a strong foundation for the framework
- Integrity which is one of the Data Governance principle goes hand in hand with the process
Organization
- Organization owns data and is also an owner of the Data Governance program which constitutes the important roles and position for governing the data
- Organization invests in the Data Governance program and eventually benefits from it
- People, process, and technology are subsets of an organization
- Data Governance framework is customized for every organization as per the business need
Technology
- Technology is the building block for the Data Governance framework and makes it usable
- Technology plays a major role in providing the features which are aligned with the Data Governance needs of the business
- Technology makes it all happen
All Data Is Good Data
One of the most overlooked components of Data Governance is Data Disposal. We often think of Data Governance as just maintaining the data, but data disposal is also very important. Organizations often hesitate and refrain from disposing of data that they have been collecting for ages due to the fear of losing prized information and the potential impact it will cause if they dispose of the wrong data.
Conversely, holding on to data for too long could also lead to potential risks due to data breaches and other unauthorized exposure of protected data when data is not governed well. While most Data Governance programs include policies and procedures for gathering and retaining data, data destruction is often forgotten or seldom leveraged efficiently. It is extremely important to have a governance policy on data destruction that specifically highlights what, when, and how the data needs to be erased.
Key considerations for destroying data as part of your Data Governance strategy include:
• Ensure that data deletion does not affect compliance or business recovery initiatives.
• Establish a data retention period or time frame to limit exposure of protected data.
• Develop relevant access rights for data deletion to prevent misuse of data.
Case Study> Learn about the devastating outcomes incurred by Latitude Financial, victim to one of New Zealand’s largest data breaches due to holding data beyond a reasonable retention period.
One Size Fits All
Data Governance is about building a process and a framework to make the data more trustworthy, and it often begins with the implementation of a robust tool or platform. However, there is a lot to consider when selecting the right platform to support your unique data governance requirements.
One major misconception is that most leading platforms encompass every aspect of Data Governance including Master Data Management, Data Quality, Data Security, and Data Management Life Cycles. Additionally, there is a myth that tools do it all when they are just one of the foundational steps to building your data governance program.
That is why when it comes to vendor selection and identifying a data governance tool that best fit your overall data governance strategy, it is best to engage with an expert in Data Governance who specializes in understanding your organization’s needs and who can assist in choosing a best-in-class solution.
In Conclusion
Whether you are starting a Data Governance journey or looking to fine-tune your current Data Governance strategy, it is important to fully understand the concept of Data Governance and all that it encompasses. Building a strong Data Governance program requires buy-in from the entire organization and should be looked at as an extension of operational functions, which is consistently helping to drive revenue growth. By leveraging the right business practices and tools and establishing frameworks that includes data policies, data catalog, business glossary and data lineage, it is possible to have standards and processes in place that result in trusted, compliant, and secure data.
Kajal Sonar - Director, Data Management
Kajal has over 19 years of experience in enabling client transformation initiatives and has been primarily responsible for designing, architecting, and managing the delivery of Data Management projects across various domains.