Data management is the process of developing and enforcing policies, procedures and procedures for handling data throughout its lifecycle. It makes sure that data is available and useful, facilitates the compliance of regulators and makes informed decisions and ultimately creates a competitive advantage for businesses.
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. This leads to a plethora of data that must be consolidated, and then sent to business analytics (BI) systems as well as enterprise resource management (ERP) platforms, and the Internet of Things (IoT) sensors,, machine learning, as well as generative artificial intelligence (AI) tools, for advanced insights.
Without a clearly defined data management strategy, companies can end up with data silos that are incompatible and inconsistent, which hinder the ability to run business intelligence and analytics applications. Poor data management can also reduce the trust of customers and employees.
To meet these challenges, it’s essential that companies come up with a data management plan (DMP) that includes the necessary people and processes to manage all kinds of data. A DMP, for example can assist researchers in determining the naming conventions for files that they should use to organize data sets to keep them for a long time and make them easy to access. It may also include data workflows which define the steps to take to cleanse, validate and integrating raw data sets as well as refined data sets to make them suitable for analyses.
A DMP can be used by companies that collect consumer look at more info data to ensure compliance with privacy laws at the state and global scale, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also be used to guide the creation and implementation of procedures and policies that address security concerns for data.