What Is Data Management and Why Is It Important? 

We live in a world where data is everywhere. It’s what makes it possible for you to use your phone and find out where the nearest gas station is or buy groceries online.

But as we rely more on this data, it’s important to remember that it’s not just numbers. When you’re making decisions based on information from the past and present, it can and will affect your future. 

That’s why data management technology is necessary: to ensure that the information used to make those decisions is accurate and secure.

What is Data Management? 

Data management is a process that involves collecting, organizing, storing, and retrieving data. It is an essential part of any information technology (IT) company. Data management plays a vital role in the success of any business, as it helps make better decisions by providing access to accurate information.

The History of Data Management

Data management technology blossomed in the 1960s and 1970s as IT professionals recognized the need to feed reliable data into computers and move the garbage out. 

In the early days of computers, IT professionals focused primarily on solving the garbage-in and garbage-out problems, recognizing that incorrect or inadequate data led them to erroneous conclusions.

There was a strong emphasis on data quality metrics and professional training by industry groups and associations in data management.

This decade also saw the introduction of mainframe-based hierarchical databases.

During the 1980s, that process was centered around the relational database, which emerged in the 1970s. Data warehouses were conceived during the late 1980s as early adopters began deploying them in the mid-1990s. 

The database deployments of the early 2000s were virtually monopolized by relational software. A range of NoSQL databases became available in this time frame. Although relational technology still has its largest share today; big data and NoSQL alternatives have given organizations a broader set of choices when managing their information.

Why is Data Management Important?

Data plays a significant role in today’s business environment as it helps organizations understand their customers better and improve processes. It also helps them make informed decisions about their operations and makes them more efficient. 

Data management allows businesses to compete effectively with other companies by providing timely information about their customers’ needs and preferences so they can make timely decisions about their products or services. 

There are many reasons why data management technology is essential in IT companies. 

Reliability

Reliability is a system’s ability to consistently produce accurate, complete, and timely data. Reliable data management systems are critical for all organizations because they can help to ensure that their operations run smoothly and efficiently while also assisting companies in protecting themselves from potential legal issues.

Reliability is essential because it gives businesses confidence in the information they’re using to make decisions and run their operations. 

When reliability is not present in a system, it can lead to problems such as inaccurate reporting or incomplete data sets—which can cause problems when trying to analyze trends or make predictions about future performance.

Security 

Data security is a critical part of data management. Data security is crucial because it protects the confidentiality, integrity, and availability of information that is stored on computers.

Data security protects data from unauthorized access, use, disclosure, and modification. 

It is essential to implement data security because it ensures that authorized personnel can access company data and that the data remains accessible to authorized individuals even if there are interruptions in the power supply or system failures. It also ensures unauthorized persons do not view confidential data as well as modify or destroy company data without authorization.

Visibility

Data management helps visibility by ensuring that all the data is available, accurate, and consistent. Data Management provides that all the data from data pipelines from various departments are stored in one place and are accessible to all users. It helps in better decision-making as it helps improve business processes by analyzing the data and taking appropriate actions.

Data management also helps improve productivity by allowing easier access to information for various users within an organization. Managed data increases accuracy by ensuring that all data is correct and consistent with other records.

Scalability

Data management is crucial for scalability. It ensures that your data is organized and accessible so that you can access it quickly and efficiently. Proper data management allows you to scale up your business as needed without worrying about losing track of your data.

With well-managed data, you can store it in multiple locations, which makes it easy to replicate if something happens to one of those locations. You can also ensure that anyone in your organization has access to the same information so no one is duplicating work or missing out on an opportunity because they don’t have the correct information available at their fingertips.

Who Uses Data Management? 

Data management is vital in many industries.

• The healthcare industry, for example, depends on data management to track patient records, streamline billing and payments, and ensure that all employees use the same information.
• Data management can help with inventory management, customer satisfaction, and marketing efforts in the retail industry. It also helps with employee training and development.
• Government agencies depend heavily on data management systems because they have access to sensitive information about citizens, from national security levels down to things like tax returns or health records.

It is critical to the running of any business. It is not only an essential part of daily operations, but it can also help optimize processes and cut costs.

Data management technology can help a company reduce costs by

• Automating processes where possible
• Using existing data more efficiently
• Streamlining existing infrastructure
• Creating better systems for data storage and retrieval

To be successful at managing data, however, companies need to have a clear idea of what types of information they’re collecting and why. This way, businesses can identify patterns in their data that can help them decide how best to manage it as we advance.

How Does Data Management Work? 

Data management is a process that involves several steps. 

First, the data architecture must be developed, which includes developing the data dictionary and creating metadata to describe the data. This step is necessary to ensure that all data is stored in an organized manner and meets current industry standards. The architecture should also consider any potential security or privacy regulations issues so that you can avoid any problems down the line.

After you’ve developed your architecture, you’ll want to generate the data itself. It can happen through various means, including manual entry or automated processes. Data can be generated in many ways: through customer surveys, customer interactions and transactions, or internal use.

Next comes integration, integration with other systems and applications so that you can get all of your data no matter where it comes from or where it is stored. It helps ensure that your organization has access to all relevant information, which is critical when making decisions about products or services.

When applying data governance, you use structured data management techniques like metadata, master data management (MDM), and business intelligence (BI). Data governance helps protect against misuse and abuse by ensuring that all employees have access only to the information they need for their jobs. It also helps ensure privacy by keeping private information away from prying eyes.

Types of Data Management Functions 

Data management systems 

Data management systems are ways of organizing and storing information so that it can be easily accessed by people who need it. These include databases, records management systems, and other software programs that help keep track of different types of information in an organized way.

Data Preparation

Data preparation is organizing and cleaning up your data before you feed it into a system. 

It can involve removing duplicate records, checking for inconsistencies in how different departments have recorded certain information, or simply ensuring that the data is formatted correctly for whatever system you use. 

Data warehouses 

Historical data is stored in a data warehouse and presented in an easy-to-use way so users can easily access and analyze it.

Data warehouses can be used for many different purposes, including improving customer service, identifying new trends in sales, and helping management make decisions about allocating resources.

Data integration (ETL) 

Data integration (ETL) is extracting and importing data from one system into another. It involves ensuring that the data is consistent across multiple systems, or it can connect different types of databases.

It is often used to cleanse and correct data inconsistencies before loading them into a target database.

Data governance

Data governance refers to the policies that determine how business intelligence teams decide what data will be stored, where, and how it will be used. 

Policies can include who has access to which datasets; how often those datasets will be refreshed, whether specific datasets should be kept private or shared across departments; when and how often backups need to occur; etc. 

It can also include defining what types of training will be required before someone can access specific datasets. 

Data modeling 

Data modeling is creating a model for your company’s internal systems, which will help you identify how best to structure your data for your projects. 

Data Architecture 

Data architecture is the overall design of an enterprise’s information systems. It includes all entities (such as application systems and databases) responsible for producing or consuming information within an organization. 

Data architecture also consists of the communication channels between these entities and “data governance” policies that ensure information quality throughout an organization’s entire lifecycle. 

Data Management Risks and Challenges 

Data management risks and challenges are common problems for many businesses.

Many factors can affect its quality and usability regarding the different types of data.

Compliance with regulations

The extent to which organizations comply is a significant challenge for data management. Organizations must adhere to standards like GDPR when collecting, storing, processing, and transferring or disclosing personal information. 

Failure to comply with these standards can lead to penalties from government bodies.

Processing and converting into usable data 

It can be a challenge because many different types of data are available, and each type needs to be processed differently to make it usable by a computer system or application.

Companies must first process the data into a functional form to use it effectively. It requires a lot of time and resources, which can be difficult for small businesses to afford. Effective systems for processing and converting data into usable form can help alleviate this problem by automating some or all of these processes.

Effective data storage 

The ever-increasing data volume is a significant challenge in data management. Organizations are generating more and more data every day, and it is becoming challenging to manage it all.

Data can be stored in various ways, such as on a hard drive or a cloud-based server. The main reason is that data can be stored in different forms. 

The choice of which form to use depends on what you try to achieve by storing that data. How you store your data will depend on how much risk you can take with it and how much protection you want from potential threats like hackers.

Security and protection against data breaches 

Security is another challenge organizations face when managing their data. Organizations must ensure their data is secure from external threats like hacking or malicious software attacks. 

A breach in security can lead to substantial financial losses for companies and damage their reputation within the market.

Data Management Best Practices  

There’s no denying that managing data is challenging. However, you can use several best practices to make your data management process more efficient and effective.

Precise alignment with business goals

First, ensuring that your data management practices align with your business goals is essential.

• Why do you want to collect this data in the first place? 
• What purpose does it serve? 
• Is there a better way to accomplish what you’re trying to do? 

If so, consider other options before launching an expensive or time-consuming project. Clear alignment with business goals will help you collect the correct information at the right time and place.

Balancing data access with security measures

Second, balancing access with security measures is essential for any organization looking to maintain control over its data. 

You don’t want people who shouldn’t have access getting their hands on it, but you also want everyone who does not need access to be locked out by too many restrictions! 

It can be tricky: finding the right balance between ensuring security and allowing easy access is vital. 

QA checks to ensure data quality

QA checks are essential for ensuring data quality. You want to ensure that what you’re collecting from customers or employees isn’t wrong or misleading, and doing so will also help keep any legal issues down the road at bay. 

It includes checking for consistency, completeness, accuracy, and validity. QA checks should be performed throughout collecting, storing, and using data as it moves through your organization.

Conclusion

Data management is the most important aspect of any organization. Without data management, businesses would be unable to keep track of their operations effectively, and they would have no way of knowing what was happening in their business.

Data management helps organizations gain a competitive advantage by allowing them to quickly and easily access data that can be used for decision-making. It also provides a way to ensure that the data is accurate and reliable. Interested in a Datorios crash course on how one company learned to manage its data? Check it out here

FAQs

What are the different types of data management systems? 

The different types of data management systems are

• Database management systems (DBMS).
• Data warehouses (DW).
• Data marts (DM).
• Data Lakes (DL).

Who is responsible for data management? 

Data management is an integral part of the business, and data management systems can be complicated to implement. It is why it’s essential to have an IT department that knows what they’re doing. 

However, suppose you don’t have much experience in this area or are trying to avoid spending too much money on IT services. In that case, it makes sense for your company to outsource its data management implementation process.

What is data management software? 

Data management software (DMS) is used to describe a collection of tools and resources that work together to manage data. DMS tools can collect incoming data, convert various kinds of data into a single storage container, or aggregate diverse data into a consistent resource such as a database. 

Try out an interactive demo of Datario’s ETL solution.