Data management is a method to the way businesses collect, store and secure their data, ensuring that it remains reliable and actionable. It also includes the tools and processes that assist in achieving these goals.
The data that drives most companies comes from diverse sources, is stored in a variety of locations and systems and is usually delivered in different formats. Therefore, it can be a challenge for engineers and data analysts to locate the correct data to carry out their tasks. This creates incompatible data silos as well as inconsistent data sets and other data quality issues that could limit the utility of BI and analytics applications and lead to incorrect conclusions.
Data management processes improve transparency, reliability, and security. It also helps teams comprehend their customers and provide the appropriate content at the right moment. It’s important to start with clear objectives for data management and then develop a set of best practices that can develop as the business grows.
A effective process, for example, should support both unstructured and structured data as well as sensors, real-time, batch and IoT workloads, while offering pre-defined business rules and accelerators. Additionally, it should offer role-based tools to help analyze and prepare data. It must also be scalable to adapt to the workflow of any department. It must also be flexible enough to allow machine learning integration and allow for different taxonomies. It should also be simple to use, and include integrated collaboration solutions and governance councils.