Data management is the way companies store, collect and protect their data to ensure that it remains secure and usable. It also covers the techniques and tools that support these goals.
The data that powers most companies comes from diverse sources, is stored in numerous systems and places and is often presented in various formats. This means it can be a challenge for engineers and data analysts to find the right information for their work. This results in data silos that are not go to the website compatible in which data sets are inconsistent, as well as other issues with data quality that could limit the utility of BI and analytics applications and result in inaccurate conclusions.
A data management process can improve transparency security, reliability and reliability while enabling teams to better know their customers better and provide the right content at the right time. It is crucial to establish precise data goals for the company, and then develop best practices that develop with the business.
For example, a good process should support both unstructured and structured data, in addition to batch, real-time and sensor/IoT workloads–while offering out-of-the-box accelerators and business rules as well as self-service tools based on roles that allow you to analyze, prepare and clean data. It should also be scalable enough to adapt to the workflow of any department. It should also be able to allow integration of machine learning and accommodate different taxonomies. It should also be simple to use, and include integrated solutions for collaboration and governance councils.
