Data management is a strategy to how businesses collect, store and secure their data, ensuring that it remains efficient and actionable. It also includes the tools and processes that assist in achieving these goals.
The information used to manage most businesses is gathered from many different sources, and stored in a variety of systems, and delivered in various formats. This means it isn’t easy for data analysts and engineers to locate the correct data to perform their job. This can lead to discordant data silos and Full Report incompatible data sets, and other data quality problems that limit the utility and accuracy of BI and Analytics applications.
A process for managing data will improve the visibility and security as well as reliability, enabling teams to better understand their customers and deliver the appropriate content at the right time. It’s crucial to set clear data goals for the company, and then establish best practices that develop with the business.
For instance, a successful process should support both unstructured and structured data, in addition to real-time, batch and sensor/IoT applications. All of this is while providing out-of-the- accelerators and business rules along with role-based self-service tools that help analyze, prepare and cleanse data. It should be scalable to meet the requirements of any department’s workflow. In addition, it must be flexible enough to accommodate different taxonomies as well as allow for the integration of machine learning. It should also be simple to use, with integrated solutions for collaboration and governance councils.