Data Management: A Comprehensive Guide to Maximize Benefits

Data Management is an essential practice for organizations looking to maximize benefits from their intangible assets. Learn how to create an efficient and comprehensive approach to Data Management with this guide.

Data Management: A Comprehensive Guide to Maximize Benefits

Data management is the practice of collecting, conserving, and using data in a secure, efficient, and cost-effective way. The goal of data management is to help people, organizations and connected things to optimize the use of data within the limits of policies and regulation, so that they can make decisions and take measures that maximize benefits to the organization. A robust data management strategy is becoming more important than ever, as organizations increasingly rely on intangible assets to create value. It is essential to consider the metadata of datasets carefully and ensure data quality with confidence in security and privacy.

To improve data management, all areas of the company must understand how certain data sets are going to be used. This document helps ensure that data management practices are addressed to adopt an efficient and comprehensive approach to data management. Most of today's challenges in data management stem from the faster pace of business and the increasing proliferation of data. When deciding on a platform, your data management team should have a good understanding of the type of data you have, how you want to host it, and what your ultimate data management objectives are. You can't have a big data model without a data management strategy.

Trying to do so would be like saying that your cluttered desk is a perfectly organized chaos in which you can find anything; over time, you will surely lose something important. You may have content management software (CMS) that organizes customer data in a search system; however, it may be more cost-effective and efficient to adopt customer relationship management (CRM) software. Data management systems are based on data management platforms and can include databases, data lakes and warehouses, big data management systems, data analysis, and more. All of these elements must be included in a total data management model; if one element is missing, some aspect of data management becomes complicated, if not completely damaged. In addition to being a way to eliminate duplicates and standardize formats, data management also lays the foundation for data analysis. Without proper management, you can end up with duplicate records, incorrect information, wasted time and storage space, and a host of other problems typical of a poor organization.

At its most fundamental level, data management works to ensure that an organization's entire data set is accurate and consistent, easily accessible, and properly protected. Data management is a total lifecycle information system that follows data from the moment it is created until it is no longer useful. With the right information from sources such as video cameras, social networks, audio recordings and Internet of Things (IoT) devices, big data management systems can be implemented. With that information, a data management team can make the best possible decision for the needs of their organization.