What is Data Management? How it Works

Data Management process

Every modern business recognizes the importance of data and relies on it to make business decisions. Data management helps businesses to organize and access data from multiple sources. It becomes important for organizations to get the most out of their data scientists. To understand data management, you must be aware of the large concept of data science and why it is in huge demand these days.

As data science is one of the fastest-growing fields today, many youngsters aspire to build a career in this field. This is the reason behind the increasing popularity of data science courses in the market. You can also build a career in data science and make your future bright by enrolling in the right course. For now, let us understand what data management is and how it works for businesses:

What is Data Management?

Data management is the process of extracting, organizing, protecting, and storing data of an organization to use for business decisions. As companies are generating and consuming large amounts of data, having proper data management systems is important.

Data management software like MySQL, Oracle, etc., are designed to ensure that the data used for decision-making is reliable and up to date. From data preparation to governance and security, these management systems help with everything.

The processes included in data management are as follows:
  • Collecting, validating, and storing data
  • Integrating different data types (structured and unstructured data) from diverse sources
  • Ensuring high availability of data and disaster recovery
  • Governing data quality and data access
  • Ensuring data privacy and security

How Does Data Management Work?

Companies first need to understand the data management tools available to them before implementing a management policy. The goal of data management must be to serve data in such a way that makes it useful for effective business decisions. You must know the different stages of data management to understand its workings.

Stages of Data Management

Data Management consists of the following stages:

Data Processing

In data processing, the raw data is ingested from different data sources, such as mobile apps, IoT devices, APIs, etc. Then this data is loaded through data integration techniques, such as ELT (extract, load, and transform) or ETL (extract, transform, load). The data is then filtered and merged to fulfill the business requirements.

Data Storage

Different data sources are consolidated in the storage stage. Data warehouses are storage systems that incorporate data in a structured data format. Other data storage systems, like data lakes, store data in structured and unstructured formats.

Data Governance

Data Governance consists of processes that ensure effective usage of the data assets. The governance processes revolve around quality, access, security, and usability, etc. Data tracking tools like Azure Data Catalogue and Talend improve the data tracking and structure for organizations.

Data Security

Data security processes protect the data from unauthorized users, theft, and corruption.

Data Management Best Practices

To address the data management challenges properly, you need a comprehensive set of best practices to follow. Though the best data management practices may vary based on the type of industry and business, the following are some practices that work for every organization:

Identify Your Business Goals

In every business process, the first step should be to identify your business goals. Before you start with the data management process, you must be very clear about what you want to achieve with this data.

Well-determined goals help you ensure that you are only storing and organizing relevant data that is useful for decision-making. By doing this, you can save your data management system from being bulky and unmanageable.

Focus on Data Quality

You must ensure that your data management processes are in place to improve the data quality. The goal of data management systems is to provide organizations with reliable data, which is only possible if the system focuses on quality. You must perform regular checks for accuracy to ensure that the data is relevant and up to date.

Make Data Accessible to The Right People

Even the best quality data is of no use when it is not accessible to the right people. Organizations need to make sure that the right employees can access data whenever they need it.

It is not necessary to make data accessible to everyone, but different levels of permissions can be set up so that each employee can access data to perform their respective jobs.

Consider Data Security

Making data accessible inside an organization is okay, but it should not be accessible to outsiders. The entire data handling team must be aware of the importance of data security. You can ensure data security by having the right data management systems.

Also Read: Are Data structures and Algorithms important for Data Science?

Data Management in Data Science

As data science is all about extracting and analyzing data to make important business decisions, data management automatically comes under the umbrella of data science. Pepper data management makes it possible for data scientists to analyze data in the right manner.

If the quality and accessibility of data are not ensured under data management, it cannot be analyzed properly. Outdated data can result in a negative analysis of data. This is why data management becomes essential in every business to implement data science technologies effectively.

Build a Career in Data Science

Data Science is one of the fastest-growing fields today. Almost every company is working with big data and for this, they required skilled data science professionals. This has led to a high demand for data scientists in the job market, making it an exciting career option for young graduates. To build a career in data science, you need to have a background in mathematics, statistics, computer science, programming, or any other quantitative field.

After completing graduation in these subjects, you can enroll in a data science certification or PG program. Once you have the certificate and the required skill set, you can enter the corporate world as a data scientist and build a bright career in the field. Just make sure to perform thorough research on the data science courses available online and offline.

Data Science Courses at Edvancer

Edvancer offers data science courses in Hyderabad, Bangalore, Delhi, and Mumbai with online self-paced and live classroom modes. Edvancer offers the following courses in Data Science:

All four courses are among the top data science courses in India. These certifications provide you with complete coverage of data science topics. IBM’s professional certification and certified data science specialist courses are the best options for beginners. However, if you are already in the field of data science, you can further your career with the PG Program or the Executive Program in Data Science by Edvancer.

With these courses, you also develop your practical skills by working on real industry projects. Moreover, Edvancer lets you choose one of the two learning modes, i.e., self-paced learning and live classes, at your convenience. You can opt for any learning style that suits you and study data science at your comfort level.


1. What are the steps involved in data management?

The steps involved in data management are data processing, data storage, data governance, and data security.

2. What are some best practices for data management?

Some best practices for data management are listed below:

  • Determine your business goals
  • Consider the quality of the data
  • Prepare a data management team and make data accessible to them
  • Ensure data security.
3. What are some challenges associated with data management?

Some major challenges associated with data management are:

  • Large volumes of data to manage
  • Multiple data storage
  • Maintaining Data Quality
  • Going from unstructured to structured data
  • Data security
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