Data management is a method to how businesses collect, store and secure their data, ensuring that it remains useful and actionable. It also encompasses processes and technology that support these goals.
The data that drives most firms comes from various sources, is stored in various locations and systems and is often presented in a variety of formats. As a result, it is often difficult for engineers and data analysts to locate the right data to carry out their tasks. This results in data silos that are not compatible, data sets that are inconsistent and other issues with data quality that can limit the Data management effectiveness of BI and analytics applications and lead to faulty findings.
A process for managing data improves visibility, reliability, as well as security. It can also help teams better understand their customers and deliver the appropriate content at the right time. It is essential to establish clear goals for data management for the company and then devise best practices that expand with the company.
A good process, like, should support both structured and unstructured data in addition to real-time, batch, and sensor/IoT tasks, and offer pre-defined business rules and accelerators. Additionally, it should offer tools that can be used to analyze and prepare data. It should be flexible enough to accommodate the workflow of any department. It should also be able to allow machine learning integration and support different taxonomies. Furthermore it should be able to be accessed with built-in collaborative solutions and governance councils to ensure coherence.
