Data Management: Definition and Importance

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Data, in simple words, is information, facts, and figures. Most of the data are available in digital form. Data plays an important role in decision-making. Today, organizations have a lot of information available. It has become an information overload. To address these challenges, best practices in data management are required. In this blog, we will discuss data management definitions, types, benefits, and importance.

Definition

 Data management is the process of collecting, organizing, and using data. Data management ensures that the organisation uses the data efficiently and securely. Effective data management helps the organization generate more value and revenue with existing data.

Types of Data Management

There are various types of data management strategies available. However, an organization can use any technique depending on its requirements. The following are most common types of data management strategies listed below,

1.      Data Integration

Data Integration is the process of combining data from different sources across organizations. This provides a unified view that can be used for data analytics and decision-making.

2.      Data Modeling

Data modeling is a visual representation of the data present in an organization. It helps data analysts and engineers to create a unified view of the data. The goal of data modeling is to explain different types of data present and stored in the system. Therefore this helps to group and organize the data.

3.      Data Storage

Data storage means recording and then saving files and documents digitally for future use. This storage system can be optical, electromagnetic, or different media. Therefore, data storage helps to keep the data safe and recover quickly in case of any unexpected crash or cyber-attack.

4.      Data Catalog

A data catalog is an organized pool of all the data available in the organization. It manages the data with the help of metadata. With data catalog analysts and other users can find the data they need. 

5.      Data Processing

Data processing is collecting the raw information and then transforming it into usable information for the organization.

6.      Data Governance

Data governance is the process of managing the availability, quality, and also security of data in an organization. This is based on internal policies and standards. Data governance ensures that data is not misused.

7.      Data Lifecycle Management (DLM)

Data lifecycle is managing the data throughout the lifecycle. Starting from data entry to data destruction. Data lifecycle management has the following five phases,

1.      Data Collection

2.      Data Storage and Maintenance

3.      Data Processing

4.      Data sharing and usage

5.      Data deletion and archiving

8.      Data Pipelines

The data pipeline is moving data from one place to another. Data movement has three elements. First a source, then step-by-step processing, and lastly the destination.

9.      Data Security

Data security is the process of protecting the digital data. This not only includes protection from corruption, and unauthorized access, but also data theft. Data security is important to protect customer/ user information from hacking.

10.   Data Architecture

Data architecture is a framework for how the data moves through the storage system. Starting from data collection to transformation, distribution, and lastly consumption.

Importance of Data Management

Data management has become more important than ever. Organizations have terabytes of data about the customers as well as products. To make sense of this data they need data management. Effective data management can help organizations turn the raw information into valuable insight. Therefore, they can get more value with data in less effort.

Visibility

Data management brings all the information available across businesses into one place. Thus it gives you control and visibility of your data in a big-picture view.

Reliability

Data accuracy is very important to make the right decision. Data management ensures information reliability.

Security

Data management brings all the information under control. Therefore it helps to secure it for data breach and unauthorized access.

Scalability

You can manage large amounts of data automatically with data management solutions. Therefore organizations don’t need to work on data manually. Hence this process saves a lot of time and ensures consistency across the enterprise.

Profitability

You can make profitable decisions with the help of insights you get from data management. With the availability of advanced analytics data can give you valuable information about the consumers. Which indeed makes it easier to reach to right conclusion.

Transparency

Transparency is really important for relationship building with the customers. In fact, sharing the data policies with customers helps them to build trust in your organization.

Consistency

Everyone can see only one centralized view of data with data management. Therefore it reduces the misunderstanding of information.

Compliance

Organizations are obliged to give customers control over their information. Data management helps to stay compliant with CCPA, GDPR, and other regulations. Hence you can avoid regulatory fines and also improve your customer relations.

Challenges of Data Management

With increasing data, challenges for data management are also increasing. Data management software should be able to address the following challenges,

Increased Data Volumes

With an increasing number of applications, data are also increasing in every department of the organization. Hence, the IT department prepares and maintains the database of all the cases. As more data piles up, it becomes difficult to keep track of data.

New Roles for Analytics

Organizations are making more decisions based on data. Hence more people have access to data for analysis. However, if the person is not capable enough to understand the data then it’s a challenge.

Compliance Requirements

Data management software should adhere to constantly changing compliance requirements. Besides this makes sure that people are using the right data.

Identify Your Business Goals

A clear goal will help to decide the process for data management like collecting, storing, cleaning, managing, and data analysis. Subsequently, it ensures that you keep and organize only the relevant data for decision-making. And therefore your database is not crowded and manageable.

Focus on the Quality of Data

Check the data at regular intervals, to make sure that it is updated so that it doesn’t negatively impact the analytics. Also, there should be a process to identify the correct and consistent format, and spelling errors across all the data available. Hence the organization needs to set up a quality check on data.

Give Access to the Right People

It is critical to set up different levels of permissions for different teams/persons. So that the right person can have access to data at the right time, otherwise, this will lead to a loss of money and time.

Prioritize Data Security

Data should be handled properly by each member to ensure security. The process should meet the compliance requirements for protection. However, organizations should also be prepared for any data breach from outside.

Henry Harvin Data Science Course:

 With the increase of data every day, Data Science is one of the most sought-after skills today. The Henry Harvin data science course will certainly help you acquire the right skill set with practical experience. This course is one of the top-ranked available courses. Besides this comes with hands-on experience in Python and data science.

The course comes with 1-year gold membership. You can also attend an unlimited batch for 1 year without any extra fees. Trainers at Henry Harvin are industry experts in their respective fields. In addition to this internship is also offered by Henry Harvin or its partner firms.

Conclusion: 

Data Science has revolutionized the way the world functions. Without a doubt, it has opened the world to unimaginable possibilities. From a study about cures for a disease, a company’s revenue strategy, building constructions, and then the targeted ads on social media. All is happening due to data. 

Data science is reshaping industries by converting data into valuable insight. As a result enables organizations to make informed decisions, and drive innovation. And therefore improve the productivity and quality of life.

Recommended Reads

• Top 5 Data Science Projects for Beginners in 2024
• How To Learn Data Science In 2024
• 10 Best Python for Data Science Books to Read in 2024
• What is Data Science and its Career Path?

FAQs

Q.1 What does a data scientist do?

Ans. A data scientist extracts, organizes, and then cleans the data available in the organization. They also use statistical analysis tools to analyze the data. Moreover, these insights help organizations to make the right decisions.

Q.2 Where do data scientists work?

Ans. A data scientist works in different industries for instance technology, healthcare, marketing, etc. In brief, any industry where data are used.

3. What are some data management software?

Ans. The following are some of the best data management software and tools available today,

• Astera
• MuleSoft
• Dell Boomi
• Talend
• Informatica
• Azure Data Factory
• Alooma
• Blendo
• SQL Server Integration Service
• Atacama

4. Difference between database and data management?

Ans. A database is data that is arranged orderly so that it can be accessed, updated, or manipulated easily. On the other hand database management is the system which is responsible for managing a database, for example, SQL, Oracle, etc.

5. Is data science a good career option?

Ans. With the daily increasing digital information, the demand for data science is also growing. Indeed, it’s a lucrative career option that offer’s handsome salary range.

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