We are approaching winter, the days are shorter but IT’s always there, running everywhere in your mind, including at night between two dreams: Master Data Management (MDM)! For some time now, your company is working to be data-driven and your evolution in this field has
Digital Transformation can mean a lot of different things to different people. But for everyone it means dealing with more data than ever before. Industry 4.0, Cloud, Big Data, Robotics, Artificial Intelligence, Machine Learning, blockchain, IoT (internet of things) and similar technological advancements are changing
Data is here to stay. In fact, your current data has already outlived or will most likely outlive its current systems and processes. What you need to be mindful of is that every time data goes through a data integration process, there are chances of
Managing reference data poorly can have a profound impact on your operations as well as business intelligence and analytical outcomes. Here are my 5 best practices for managing reference data: 1. Formalize reference data management (RDM) Most often than not, reference data is not maintained
A data sources inventory is meant to record basic information about the different systems across and/or outside your ecosystem from which you are capturing, producing, acquiring or procuring data. The data sources tend to be well known to their end users and administrators, but not
Managing and securing your organization’s master data is crucial for supporting any organizational goals which rely on data – so all of them!!! It’s also important for today’s business to work with their master data in an accurate, efficient, and well understood way. As such,