Data management is the practice of collecting, organizing, protecting and storing an organization's data so that it can be analyzed to make business decisions. As organizations create and consume data at unprecedented speeds, data management solutions become essential to making sense of massive amounts of data. What is data management? Data management is the practice of collecting, organizing, and accessing data to support productivity, efficiency, and decision-making. Given the critical role that data plays in today's business, a robust data management strategy and a modern data management system are essential for all companies, regardless of size or industry.
Data management is the practice of managing data as a business asset and unlocking the value of data while ensuring that it is accurate and protected. A good data strategy helps deliver the right knowledge and actionable information at the right time and place for the right purpose and consumer of the right data (human or machine). Data management methods also enable data analysis. Data and data analysis are the enablers of digital business and the digital economy.
The core technology used to deploy and manage databases is a database management system (DBMS), which is software that acts as an interface between the databases it controls and the database administrators, end users, and the applications that access them. Big data in simpler terms means data in substantial quantities, the collection and analysis of large data sets is known as big data management and is done using big data tools in modern enterprises. In order for data management tools to understand and work with different types of unstructured data, for example, new preprocessing processes are used to identify and classify data elements for easier storage and retrieval. The corrective costs of improper data management can be significant and can amount to millions of dollars for a single occurrence.
Ultimately, the biggest benefit that a robust approach to data management can provide is better business performance. And IoT and industrial IoT run on a constant stream of machine and sensor data, flowing at a million miles per minute. Alternative data platforms to databases include file systems and cloud object storage services; they store data in less structured ways than conventional databases, offering more flexibility in the types of data that can be stored and how it is formatted. Advanced analytics (often leveraging machine learning) also relies on large amounts of high-quality data to produce relevant, actionable information that can be acted upon with confidence.
Familiarity with management areas provides data managers with the knowledge they need to create customized solutions for their environments. Data management systems are based on data management platforms and include a range of components and processes that work together to help you extract value from your data. As a result, organizations may need to hire new workers or retrain traditional DBAs to meet their big data management needs. Data management processes help organizations identify and resolve internal pain points to deliver a better customer experience.
This document describes estimated data usage, accessibility guidelines, archiving approaches, ownership, and more. Data warehousing must be able to change as quickly as technology demands, so any approach must be flexible and have a reasonable archiving approach to keep costs manageable. Beginning in the 1960s, industry groups and professional associations promoted best practices for data management, especially in terms of professional training and data quality metrics. If all of the above practices are diligently followed and the available data is accurate and reliable, this will eventually translate into greater efficiency and productivity of the organization's employees.
