Read Part 1 here > Data analytics is a complex process that demands time and effort from data scientists. From cleaning and prepping data to performing data analysis, data scientists go through an extensive procedure to uncover hidden patterns, identify trends, and find correlations in data to make informed business decisions. The task of integrating, cleaning, and organizing data assets often take up the bulk of the data scientist’s time.

To be able to deliver the latest and greatest services to customers and clients today, telcos must employ different vendors, subcontractors, and technology partners to fulfill market needs. While this allows organizations to cover all the bases, it also means disparate data sources, different technologies and schemas, and distinct internal workflows and processes—all of which can result in a disjointed customer experience, all the way from sales to service.

Data is an essential asset for any business, with comprehensive efforts made to generate, source, and prepare it for analytical use. But just as important as collection and cleaning is ensuring its accessibility for users across the organization. This highlights the need for an organized data inventory—a directory that makes it possible to easily sort, search, and find the data assets required. In other words, you need a data catalog, a core component of master and meta data management.