Data Management Advantages And Disadvantages

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Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is a crucial piece of deploying the IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other end users.

The data management process includes a combination of different functions that collectively aim to make sure that the data in corporate systems is accurate, available and accessible. Most of the required work is done by IT and data management teams, but business users typically also participate in some parts of the process to ensure that the data meets their needs and to get them on board with policies governing its use

This comprehensive guide to data management further explains what it is and provides insight on the individual disciplines it includes, best practices for managing data, challenges that organizations face and the business benefits of a successful data management strategy. You’ll also find an overview of data management tools and techniques. Click through the hyperlinks on the page to read about data management trends and get expert advice on managing corporate data.

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Types of Data Management tools

So you’d like to put the power of DM to the test? These are the most popular and useful Data Management tools for companies that are interested in making the leap to digitization:

Product Information Management (PIM)

This is the ultimate tool for manufacturers and retailers seeking a centralized control platform for all their product content.

In an automated and synchronized way, a PIM or product Information Management solution will take care of the managing, correcting and sending of product information across all sales channels, catalog design programs, and/or agents such as retailers and commercial outlets.

Master Data Management (MDM)

These are tools that manage the central and master data of a company, at the levels of business, employees, customers, accounts, operations, regulations and so on

MDM programs include data cleansing, centralization, key mapping, transaction control, multi-domain support, information distribution and global synchronization in different locations. Some examples are SAP NetWeaver, Microsoft MQL, Oracle or IBM InfoSphere.

Data Modeling

This is the process that adapts your data to the format needed in order for it to be stored in the company’s database. The types of data management tools in this category allow you to generate conceptual models and establish the rules for consistency and quality that your data must meet.

Data Warehouse (DW)

These are programs dedicated to generating storage locations for your data, therefore they are usually linked to hardware already available within your company and do not include management processes that are specialized according to data type. They are tools for storage, but by themselves they provide neither a structure nor smart analysis of information.

Other processes that may include their own tools are data quality analysis, metadata, data architecture, security and storage.

Another aspect is that data management tools can organize your data according to different criteria, such as a relational model, hierarchical (in tree format) or as a network

Such protocol will influence the type of DM you need; for example, in product data management, relational databases are useful for linking mutually dependent information fields such as model, size, and color.

Importance of data management

Data increasingly is seen as a corporate asset that can be used to make more-informed business decisions, improve marketing campaigns, optimize business operations and reduce costs, all with the goal of increasing revenue and profits. But a lack of proper data management can saddle organizations with incompatible data silos, inconsistent data sets and data quality problems that limit their ability to run business intelligence (BI) and analytics applications — or, worse, lead to faulty findings.

Data management has also grown in importance as businesses are subjected to an increasing number of regulatory compliance requirements, including data privacy and protection laws such as GDPR and the California Consumer Privacy Act. In addition, companies are capturing ever-larger volumes of data and a wider variety of data types, both hallmarks of the Big data systems many have deployed. Without good data management, such environments can become unwieldy and hard to navigate.

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