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Data management is the process of creating and enforcing policies, procedures and procedures for handling data throughout its entire lifecycle. It ensures that data is useful and accessible, assists in compliance with regulations and enables informed decision-making.
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 results in a plethora of data that needs to be consolidated and pushed to business intelligence (BI) and analytics systems as well as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and generative artificial intelligence (AI) tools to gain advanced insights.
Without a clearly defined data management plan, businesses can end up with uncompatible data silos and inconsistent data sets that hinder the ability to run business intelligence and analytics applications. Poor data management can also affect the trust of customers and employees.
To address these challenges to meet these challenges, it’s crucial that businesses come up with a data management plan (DMP) that includes the processes and people required to manage all types of data. A DMP can, for instance can help researchers decide the appropriate file name conventions they should use to organize data sets in order to store them long-term and make them easy to access. It can also contain data workflows which define the steps to take to cleanse, validate and integrating raw data sets as well as refined data sets in order to make them suitable for analyses.
A DMP can be utilized by companies that collect consumer data to ensure compliance with privacy laws at the state and international level, such as 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 to deal with security threats to data and audits.