Data Management: what is it, and why is it necessary?

Data Management encompasses all data management processes, tools and techniques. The aim is to ensure the consistency, quality and security of data sets so that they can be used. Find out everything you need to know: definition, techniques and tools, required skills, training…

For companies in all sectors, data is now collected as a valuable resource. They can be used to make better decisions, to improve marketing campaigns, to reduce costs or to optimize processes.

However, in order to be put to good use, the data must be organized correctly. Otherwise, an organization may be faced with inconsistent data sets, data quality issues, or so-called data silos.

In addition, with the rise of Big Data, companies need to muddle through ever stricter data processing requirements. In Europe, the DPMR imposes many constraints to ensure data protection. In order to respond to these various issues, “Data Management” is now essential.

What is Data Management?

The term Data Management refers to the entire process of ingest, store, organize and maintain data created or collected by a company. This concept encompasses a wide combination of functions to make data accurate, available and accessible.

According to the DAMA International consortiumdata management is ” the development and implementation of architectures, rules, practices and procedures to manage an organization’s information lifecycle needs in an effective manner “.

It is therefore a multidisciplinary fieldThe aim is to keep the data organised in a practical and usable way. The aim is that the data should be accurate, consistent, accessible and secure.

What is the purpose of Data Management?

Data Management allows eliminate duplicate data and to standardize their format. This is because the data come from different sources and can be of different types. Nor are they collected in the same way by each system.

That’s what creates data siloswith separate information between the different departments of the organization. Data Management puts an end to these silos.

In addition, Data Management is also used to pose the foundations required for data analysis. Without data management, analysis is unreliable or simply impossible. It is imperative to ensure the quality of the data.

A data management strategy well executed can bring many advantages to the company over its competitors. It leads to improved operational efficiency and better decision making.

By managing their data properly, organizations can also become more agile, detect market trends and take advantage of new opportunities faster. In addition, data management helps prevent leaks, privacy or compliance issues that can be very costly and damaging to a company’s reputation.

The different tasks of Data Management

Data Management encompasses many disciplines. Data Governance is the planning of the different aspects of data management. It aims in particular to ensure the availability, usability, consistency, integrity and security of data.

L’data architecture is about the overall data structure of an organization and how it fits into the overall enterprise architecture. Data modeling is the design, testing and maintenance of analytical systems.

The data warehousing is also part of the scope of Data Management, as is their security. Data must also be integrated and interoperable, which means transforming it into a structured form.

The Data Warehousing and Business Intelligence, aimed at analysing data to assist decision-making, is also part of Data Management. Metadata must also be managed.

Finally, it is imperative to ensure data quality through different monitoring and treatment practices. All these different elements are interdependent and must be included in a comprehensive data management model.

Data Management tools and techniques

There are a wide variety of technologies, tools and techniques that can be used for Data Management. First of all, there are database management systems (DBMS) for the storage and organization of data. A distinction is made between relational databases and “NoSQL” databases.

For Big Data management, environments built around open source technologies such as the Hadoop distributed processing framework are generally used. Other tools such as the Spark processing engine or the streaming processing platforms Kafka, Flink and Storm complete the picture. Cloud object storage services such as Amazon S3 are also used.

Data Management tools also include Data Warehouses and Data Lakes. Such repository platforms can be used for data management and analysis. Queries can be made to query the data, or analysis can be performed using Machine Learning models.

For data integration, the most commonly used technique is ETL: extraction, transformation and loading. This method consists of extracting data from its sources, converting it into a usable format and loading it into a data warehouse or other system.

Data Governance is based on different techniques. These include monitoring data sets for compliance. To ensure data quality, data is checked for errors. The Data Cleansing allows to correct possible errors and to delete corrupted or erroneous data.

I mean, come on, data modeling is to create conceptual, logical and physical models to visually document data sets and map them for processing and analysis needs. Examples include diagrams and schematics.

There are fully dedicated solutions to Data Management, bringing together numerous functionalities to support all the different aspects. Examples include SAS Data Management, Adobe Data Management Platform, Salesforce Audience Studio, IBM Data Management or Oracle BlueKai.

Data Management: required skills and training

Data Management involves many tasks. To perform them, it is necessary to have strong technical skills.

Several roles can contribute to Data Management. This is the case of the Data Architect, Data Modeler, Database Administrator, Data Engineers or Data Quality Analysts. The Data Scientists and Data Analysts can also take over certain management tasks.

A data management professional must have skills in computer science, database programming, Business Intelligence, Cloud Computing, and Machine Learning. Ideally, he or she should also have personal skills that foster collaboration, such as communication and innovation.

Data Management today is business essential in order to exploit the data and seize the opportunities offered by the Big Data. In this context, training in data management can be extremely useful in order to acquire all the required skills and learn how to use the tools.

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