Data management is an approach to the way companies gather, store and protect their data to ensure that it remains effective and reliable. It also encompasses the processes and technology that support these goals.
The data utilized to run a lot of businesses is gathered from multiple sources, compiled in various systems, and delivered in different formats. This means it isn’t easy for engineers and data analysts to find the appropriate data for their work. This can lead to discordant data silos and incompatible data sets, as well as other data quality issues that could limit the use and accuracy of BI and Analytics applications.
A process for managing data can improve visibility and security, as well as enabling teams to better understand their customers and deliver the right content at the right time. It’s essential to establish precise data goals for the company and then develop the best practices to grow with the company.
For instance, a great process should support both unstructured and structured information in addition to real-time, batch and sensor/IoT tasks. In addition, it should provide out of the box business rules and accelerators plus self-service tools that are based on roles to help analyze, prepare and clean data. It must also be scalable and be able to adapt to the workflow of any department. It must also be flexible enough to allow machine learning integration and to accommodate various taxonomies. It should also be simple to use, and include integrated solutions for collaboration and governance councils.