What is data management and why should you care?

There is no exaggeration in saying that nowadays information is everything. Since in today’s digital economy the scale of data generation and consumption is massive, data management emerges as yet another item on the long list of “must know” business terms. What is data management and why is it important? Read on and find out how data management processes can accelerate your business transformation.

28 April 2022

Victor Marco

5 min

Blog / Business GrowthProject Management

Victor Marco Author

Victor Marco

Definition of data management

Data management can be defined as the process of collecting, organizing and storing data so that it can be used for making valid business decisions and lead to a company’s overall growth. Managing data involves adopting an effective data management strategy and finding cost-effective data management solutions that will help business users sift through the multiplicity of data sets to get relevant, reliable and up-to-date information. The ability to handle data that supports business analytics is one of the most crucial contributors to your company’s success.

Types of data management

In every data environment, the overall data management process is comprised of several interrelated elements that enable different functions and ensure the comprehensiveness of the process. Those key parts include:

  • developing a data architecture in order to create a strategy for managing organization’s data;
  • creating data models that document how data flows through systems, show how sets of information are related and organize them with the aim of meeting business objectives;
  • ensuring database deployments (or alternative data platforms) for data processing (i.e transforming raw data into structured data) and storing;
  • performing database administration functions in order to monitor the performance of database software;
  • consolidating information from various data sources and storing it in data warehouses or data lakes to facilitate trusted data analysis. A data warehouse is a repository for data from multiple sources that have already been structured and processed, while a data lake is a pool of unstructured data that is going to be used in machine learning and advanced analytics business applications);
  • ensuring data quality checks to identify errors and problems and remove them by means of data cleansing tasks;
  • establishing data governance procedures to maintain data security and its consistency throughout a company or organization;
  • using master data management tools and policies to improve the quality of data assets such as product or customer data, etc.

Who is responsible for data management?

Data management involves an impressive range of tasks, roles and duties that require certain skills and knowledge to perform them properly. There are many data management professionals whose expertise is crucial if the effectiveness of the data management process is to be maintained. Those professionals include for instance data architects, data modeling specialists, data quality analysts, data engineers, data integration developers, data stewards, database administrators and developers. They cooperate with data scientists and data analysts by helping design data pipelines and carrying out other tasks that are needed to prepare information for analysis.

The importance of data management

An effective data management strategy can successfully lead your company — of whatever size and type — through the treacherous meanders of the business world. Relevant data is an asset that may contribute significantly to various aspects of your business activity, from improving your marketing strategies and reshaping customer experience, to reducing costs and increasing your operational efficiency. While best practices are likely to fuel your company’s decision-making and support its development, lack of proper management may result in your business getting stuck among vast amounts of inconsistent or unreliable information. This, in turn, is likely to stifle the implementation of business intelligence guidelines.

Benefits of data management

Let’s now have a look at the list of benefits your company can get from adopting effective data management solutions:

Greater visibility of your company’s data — employees can easily find the right information they need to conduct analyses, which stimulates productivity and adds up to better organization.

Increased reliability of your data — ensuring high data quality and consistency helps better respond to your customer needs and adjust to today’s volatile economy.

Strong data security — adopting policies protecting your company from data losses or thefts and ensuring high data privacy increase your company’s trustworthiness among clients, especially if data contains any personally identifiable information (such sensitive data falls under certain protection laws, such as California Consumer Privacy Act or General Data Protection Regulation).

Scalable databases — the ability of your database to expand together with your business and still work efficiently by adjusting to changing demands is vital as it contributes to reducing costs of unnecessary duplication (e.g. running the same database queries or carrying out the same research many times).

Meet the challenge and achieve more

Constantly increasing volumes of big data, the necessity of engaging greater numbers of people in the process of accessing and analyzing data sets, moving data to the cloud, changing data compliance policies — these are only a handful of data management challenges that need to be addressed in planning a successful strategy for dealing with information.

In light of the above, you need to clearly define your business objectives, determine what data types you will need, concentrate on maintaining the high quality of information, assign different data management tasks to people with the best qualifications and put data security high on your list of priorities. This is how you can excel in managing digital data so that your company can handle it in a coordinated way and make sure the information is of value for data analytics teams.

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