In the current era of data-driven organizations, the impact of digital transformation initiatives grows each year. Companies seek to add new data sources from leading-edge initiatives, such as IoT, real-time applications, and mobile apps, and to bring existing processes and information into the 21st century by removing the paper and pen activities of the past. As these firms seek ways to differentiate their offerings for new revenues, target their consumers to improve customer experience, and lower their operational costs to improve margins, they need to adopt new techniques of managing and governing their new, digitally-transformed data assets.

New types of data-driven exploration and data science initiatives are quickly outpacing the capacity of traditional analytical environments, such as the enterprise data warehouse and the data mart. These traditional analytical environment architectures still have value to companies, but data-driven organizations are pushing the limits of those architectures. Companies search for more flexible and nimble methodologies and best practices upon which to base innovative, information-fueled initiatives.

Data-driven organizations must enable employees across the organizational structure to have access to the correct data required to make decisions. This facility is often an enterprise data catalog that contains a comprehensive and curated inventory of the metadata--technical, business, and operational--of the information within their data landscape. Yet, the growth and breadth of the data landscape require that modern data catalogs have the power of AI/ML to scale across multiple platforms and perform not just on samples of changing data, but complete discovery and inventory of the landscape on a regular basis.

With all of this information, AI/ML capabilities can catalog and categorize, identify, and match data in disparate locations and securely locate information for each member of the organization. Secure access to the data should be given to everyone--not just a chosen few.