Data management is the process of creating and enforcing rules, processes and procedures for handling data throughout its lifecycle. It ensures that data is useful and accessible, facilitates compliance with regulations and permits informed decisions.
The importance of effective data management has grown significantly as organizations automate their business processes, leverage software-as-a-service (SaaS) applications and deploy data warehouses, among other initiatives. The result is a flood of data that must be consolidated and delivered to business intelligence (BI) and analytics systems and enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and Artificial Intelligence generative (AI) tools for advanced insights.
Without a clear data management plan, businesses can end up with incompatible data silos and inconsistency of data sets which hinder the ability to run business intelligence and analytics applications. Data management issues can affect the trust of customers and employees.
To meet these challenges, it’s essential that companies develop a data management plan (DMP) that includes the processes and people required to manage all types of data. For example an DMP can help researchers identify the naming conventions that they should use to structure data sets to ensure long-term storage and access. It can also contain a data workflow that defines the steps involved in cleansing, verifying and integrating raw and refined data sets in order to ensure they are suitable for analysis.
A DMP can be used by companies that collect customer data to ensure compliance with privacy laws https://taeglichedata.de/how-to-set-up-a-relevant-and-useful-deal-room/ at the global and state level, for example, the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also help guide the development of procedures and policies for addressing data security threats and audits.