Data management

Data management, which employs techniques like master data management, data virtualization, data catalogs, and self-service data preparation and wrangling, offers uniform accessibility, distribution, governance, and security of data to suit an organization’s requirements.

Companies have access to more data than ever before in the modern digital economy. Important business decisions can be built on the intelligence provided by this data. Companies must make an investment in data management systems that enhance visibility, dependability, security, and scalability to guarantee that employees have the correct data for decision-making.

What is data management?

Data management is the process of gathering, arranging, safeguarding, and archiving an organization’s data so that it may be used for analysis and business decision-making. Data management solutions are crucial for making sense of the enormous amounts of data that enterprises are producing and consuming at previously unheard-of rates. Leading data management tools now guarantee that decisions are always based on accurate, current data. The software assists with data preparation, cataloging, search, governance, and other tasks so that individuals may easily locate the data they require for analysis.

Types of Data Management

In the data environment of an organization, data management plays a variety of roles that simplify and speed up time-consuming tasks. Among these data management strategies are the following:

Data governance establishes guidelines, procedures, and rules to protect the integrity and security of data.
Data architecture offers a structured method for organizing and producing data flow.
Data security protects data from corruption and unwanted access.
Data modeling records the way that data moves through a program or company.
ETLs (Extract, Transform, Load) are created to import data from one system into the data warehouse of the company after being transformed.
Data warehouses are locations to combine several data sources, deal with the numerous different types of data that enterprises retain, and offer a clear path for data analysis.
Data catalogs assist in managing metadata to build a whole picture of the data, giving an overview of changes, locations, and quality while also making the data simple to find.
Data preparation is used to rectify errors and combine data sets as well as clean and transform raw data into the proper shape and format for analysis.
Data pipelines allow for the automated transfer of data between systems.

Why data management is important

Security

Data management uses methods for authentication and encryption to safeguard your company’s data from breaches, theft, and loss. Strong data security guarantees that crucial business data is backed up and retrievable in the event that the originating source is unavailable. If your data includes any personally identifiable information that must be properly handled to adhere to consumer protection rules, security becomes even more crucial.

Scalability

Utilizing repeatable procedures to keep data and metadata current, data management enables enterprises to increase data and usage occasions effectively. When procedures are simple to repeat, your business can minimize the needless expenses of duplication, such as workers completing the same research repeatedly or running expensive queries repeatedly.

Visibility

The visibility of your organization’s data assets can be improved through data management, making it simpler for individuals to reliably and quickly locate the appropriate data for their research. Your business may become more organized and productive by ensuring that employees can easily access the data they require to conduct their jobs.

Reliability

What Are the Key Use Cases for Data Management?

Virtual Data Layer

You may access, integrate, and provision all the data your organization need using a virtual data layer. Delivering and protecting data across siloed repositories are problems that can be resolved by deploying a virtual data layer.

Data-as-a-Service (DaaS)

Your company has the flexibility to meet the data service needs of both internal and external customers thanks to data-as-a-service (DaaS).

Multi-Domain MDM

You can manage, model, and regulate your master data domains throughout your entire organization using multi-domain master data management. Your operations can be streamlined, and the quality of your analytics and reporting can be improved, by having consistent and reliable master data.

Logical Data Warehouse

The design can change to meet the changing data and analytics needs of your company. A logical data warehouse may accommodate changing requirements without resulting in data silos, in contrast to fit-for-purpose data management techniques.

Reference Data Management

A subset of master data called reference data is used to categorize financial hierarchies, cost centers, and postal codes. You can manage classifications and hierarchies across your systems and business lines with reference data management.

Anything 360

A comprehensive 360-degree picture of all your customer data is essential if you want to understand your customers better, improve your supply chain, or hasten the development of new products. For your organization to succeed digitally, you must have a 360-degree picture of every entity that interacts with your master, reference, streaming, and transactional data.

Data management keeps developing to meet problems

In the modern digital economy, data management is essential, thus it’s critical that systems develop further to match your organization’s data requirements. It is challenging to grow capabilities without compromising governance or security using traditional data management methods. To ensure that reliable data can be found, modern data management software must handle a number of issues.

Challenge 1: Growing data amounts

Each department in your company has access to different kinds of data and has particular requirements to make the most of it. Following data preparation for each use case, traditional models call on IT to manage databases or files. An organization can easily lose track of the data it has, where it is, and how to use it as more data amasses.

Challenge 2: New analytics roles

More of your employees are expected to access and evaluate data as your firm increasingly relies on data-driven decision-making. Understanding naming conventions, complicated data structures, and databases might be difficult for someone whose skill set does not include analytics. The potential value of the data is lessened or lost if conversion of the data requires too much time or effort.

Challenge 3: requirements for conformity

It is difficult to guarantee that people are using the proper data because compliance rules are always changing. In order to comply with privacy laws, an organization’s staff must be able to rapidly discern which data they should or shouldn’t be using, including how and what personally identifiable information (PII) is ingested, tracked, and monitored.

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What is Data Management?