After years of ambiguous expectations about data governance, organizations now have a better handle on how these programs can help them manage the exponential growth of the data they generate. More importantly, there is an understanding about how the integration, organization, and alignment of that data can help meet or exceed business and technology goals.
Many organizations today implement data governance programs themselves. However, instituting such a program is riddled with pitfalls, and developing the processes and methods to define the handling of data and instantiating the corresponding change management can be challenging.
Some of the first issues become apparent when organizations try to answer what are assumed to be easy questions such as “Who owns this data?” “What is the definition of this data?” or “Where did this data originate?” The difficulty in answering these questions is often exacerbated when that data traverses multiple systems, departments, and external third parties.
And, when you add external regulations or compliance considerations to this mix, hysteria can ensue. Yet, data governance best practices are critical to ensure data quality, reduce risk, and allow organizations to leverage the full value of their enterprise data.
To help strengthen a data governance program, there are several approaches that will help organizations to improve data quality, achieve business goals, and, ultimately, avoid the costs associated with bad data. In the end, most find that an agile and iterative approach to deploying data governance—as well as leveraging tools and processes that can be owned by the data governance team with low reliance on IT resources—is the ideal way to proceed.
Understanding Your Data
If you were unable to answer one or more of the questions above, you’re probably experiencing some of the most common difficulties related to data governance and big data. Nearly 90% of the world’s data today was created during the past 2 years. In addition, approximately 90% of that data is unstructured.
The vast amount of big data from the web and the cloud presents new opportunities for discovery, development, and cultivation of business intelligence for decision support in any organization. This accumulated data is processed and disseminated in various forms. As this occurs, it is important that the information is not distorted, disclosed, arrogated, stolen, and not intruded upon within specified rules and guidelines.
Consequently, the importance of government regulation and policies on the use of such collected data and associated privacy rights continues to grow.
9 Key Challenges
1-Keep It Secure
Ensuring the security of sensitive and personally identifiable information (PII) is a top priority for an effective data governance program. Having a place to view the data end-to-end in both a technical data lineage and business process lineage is even more important. Many enterprises struggle to reduce data security risks due to unauthorized access or misuse of data, while others have difficulty managing the confidentiality, integrity, and availability of data. By understanding the nature of the data, where it’s stored and how it’s used, enterprises can implement the appropriate data governance guidelines for data use and specify the right standards and policies around data ownership.
2-All Roads Lead to Data Quality
To keep data usable and reliable, users must trust their data. As data flows through the enterprise, the data must be accurate and timely and must contain the correct definitions and meaning. If you can’t pair the right definitions with accurate data, the data may be meaningless and insufficient. To derive business insights and analytics, enterprises must have accurate, standardized data across all systems and processes to make solid business decisions.
3-The New World of Data Privacy
Data protection regulations are changing, and abiding with these new laws requires a strategic process. One new regulation, in particular, that takes effect in May 2018 applies to any organization doing business in the EU—the General Data Protection Regulation (GDPR). This new law alone is driving many organizations to institute data governance since it’s imperative for them to have the necessary policies in place to protect sensitive information, as well as ensure third-party data security. To enable data privacy, enterprises must go beyond third-party pre-and post-risk assessments and implement a data governance framework to provide visibility into these policies and how sensitive data and third-party data can be used.
4-Policy Makes Perfect
No matter where you are in the implementation process of a data governance framework, having visibility into your data is a priority. Many organizations maintain legacy systems as new systems are implemented. Unfortunately, many times they falsely assume their new platform will work seamlessly with their old system. Others experience siloed systems that cannot cross-communicate across the enterprise. For others, they just don’t have the right business rules in place. In each instance, organizations severely lack any visibility into their data. Whether it is examining current workflows, developing data definitions, or the identification and documentation of appropriate business rules, creating the right business processes is critical to the success of a data governance program.
5-Establishing Best Practices and Value
Data governance adds focus and structure to data to help meet strategic and operational goals. In addition, it provides insights that help uncover missed opportunities and risks. Take, for example, a change to a data source. In the old world, the change would undergo some level of scrutiny to determine the impact, but it wouldn’t follow a process that would be considered a repeatable best practice. However, with data governance, a data change would be immediately visible through an impact analysis that shows where the data is used and how widespread the impact will be if the data source is changed.
6-Guided Analytic Activities
This permits the company to set up prepared business applications featuring dashboards, charts, and calculations that will all be updated based on user explorations via clicks and selections. This allows for greater ease at managing your data and defining consistent metrics across the organization. Through consistent shared metrics, everyone will know what KPIs you are covering. The end user normally has no ability to create his or her own data visualizations or bring in one of his or her data sources without the assistance of a developer.
7-Provide Timely Information
Being able to provide up-to-date information will make things easier when trying to determine business decisions based on the analytics in the data. In order to heighten the business, the company needs proof that their reasoning is backed up by their data. If new concepts or insights are raised, there’s no speedy way to test them if they cannot pull the data. Finally, things can get out of hand fast, forcing teams to fix a crisis with insufficient data analysis to make a business decision that can’t wait. This is where the guided analytics comes in handy.
8-Reduce Compliance Risk and Exposure
Exposing personally identifiable data and protected health information can be costly, both money- and reputation-wise. Everything is subject to the same exposure and regulatory scrutiny as source systems. Data governance can tie into an existing program, integrate with policies, align security and access policies, and monitor the use of these policies.
9-Information Lifecycle Management (ILM)
This approach to data and storage retention recognizes that the value of information changes over time and that it must be managed accordingly. Data has its highest value when it’s first created and used frequently. Understanding the information lifecycle helps to deploy the appropriate storage infrastructure according to the changing value of information. A path management application either as a component of ILM software or working in conjunction makes it possible to retrieve any data stored by keeping track of where everything is in the storage cycle.
Data Governance is an Art, Not a Science
Data governance is not a standardized solution, but many pieces can be automated, such as extracting metadata, to accelerate both deployment and ongoing operations. With enterprises facing increased global competition, rising client expectations, tightening profit margins, and increased regulatory demands, a cloud-based data governance solution can help organizations synthesize and visualize information about data in a manner which is easy to understand.
The right platform can also automate and aggregate data quality metrics to measure and analyze the accuracy, consistency, and reliability of data at rest and in motion. This enables businesses to not only report on data lineage and governance metrics, but to improve the information continuously.
The end result provides a holistic view of data from both a business and technical perspective, while also ensuring the appropriate data access controls are in place, making organizations better equipped to govern their data and take control of their business processes, thus reducing costs.