What are the biggest challenges businesses face when it comes to data governance?
One of the biggest challenges facing enterprises today is balancing the breadth and depth of secure data. On the one hand, organizations want to generate and share as much data as possible with their clients. However, this ambition comes with significant risks.
- Cost management: Managing and storing large amounts of data can be financially taxing, especially given the variable pricing models of many cloud service providers. These costs can negatively impact a company’s bottom line.
- Performance issues: Large amounts of data stored on a suboptimal management platform can slow data retrieval and negatively impact the user experience.
- Security risks: More data often means more sensitive information. The risk increases significantly if a company’s infrastructure is compromised or data is accidentally shared with unauthorized users.
- Enhanced due diligence: Enhanced due diligence regarding data sources will become even more important as we move forward. Companies need a deep understanding of the origins of their external data to ensure that their data is accurate and properly sourced. In particular, the methods will evolve as more companies use AI to create or enhance their datasets. The sudden reaction is to use AI to validate incoming data, but this creates a recursive paradox.
Ultimately, the challenge is to make informed decisions about how much data to manage, what to prioritize, how long to keep it, when and how to publish it, and to ensure its accuracy and security. It is about ensuring sexuality. Finding the right answers to these questions is often more complicated than you think.
How do you think data governance will evolve in the next 10 years?
I believe there will be significant changes in data governance over the next 10 years.
- Consume data from source systems: Legacy systems struggled to expose data via APIs, but rapid advances in artificial intelligence, especially generative AI, make it possible to transform and consume data directly from source systems. becomes easier. This shift reduces the number of locations where data is stored, thereby minimizing exposure. However, it will also be necessary to strengthen the security of traditional “back-of-house” systems.
- Evolving regulations: As the frequency of data breaches increases, regulations must adapt quickly. Tools that enhance the use of data can also be misused by malicious parties, making it important for regulators to balance the need for safety with fostering innovation. This could be a great opportunity to introduce a set of global regulations within certain industries to streamline efforts and reduce redundancies.
- Consumer Rights: Consumers will continue to have greater rights when it comes to data control. As data, metadata, and the relationships between them become blurred, the concept of the “right to be forgotten” can become even more complex, leading to continued challenges in defining the boundaries of customer data.
- The need for a data dictionary: As data sets and access expand, a comprehensive “data dictionary” becomes essential. This could take the form of extensive documentation or innovative visualization of data lineage and architecture, perhaps leveraging the same AI technology.
- New roles emerge: Beyond IT, we expect to see a variety of new data governance roles emerge. As companies begin to treat data as a product, new types of business analysts, data scientists, and product owners will emerge. This trend will change the way organizations market themselves, with a focus on data provision followed by additional services.
- Increased costs and opportunities: The costs of data governance can be even higher, but smart companies can find innovative ways to monetize their data to offset these costs and improve their competitiveness. Sho.
