What is Data Management and Why It is Important

• 11 February 2022

Table of Content

• What is Data Management?
• Why is Data Management Important?
• Types of Data Management
• Data Management Best Practices & Techniques

What is Data Management?

Data management is a term used to describe the process of collecting and using data in an organized manner. In this way, data management is meant to reduce duplication and increase efficiency. When it comes to business, having a data management plan in place is one of the most important things that you can do. It will allow you to effectively collect, organize, analyze and use your company's data. And, as we all know, data is the lifeblood of any successful business operation.

Why is Data Management Important?

Data management is an essential first step towards implementing practical data analysis on a larger scale that provides valuable information that will benefit your customers and increase your profitability. Through effective data management, all employees can access reliable information for their needs. A few benefits of an efficient system for managing data include:

1. Visibility

Data management can improve the transparency of the business's data assets, making it easier for employees to find the appropriate data to conduct their research quickly. In addition, data visibility helps your business be more organized and efficient and allows employees to locate the information they require to perform their tasks more efficiently.

2. Reliability

Data management reduces the chance of errors by creating procedures and guidelines for the use of data and establishing confidence in the data that is utilized to make decisions throughout your company. With accurate, current data, companies can respond more effectively to changes in the market and customers' needs.

3. Security

Data management safeguards your company and employees from data loss or thefts and data breaches through encryption and authentication tools. Secure data management ensures that important company data can be retrieved and backed up in case the primary source becomes inaccessible. Furthermore, security is ever more crucial if the data you store contains personal information, which requires being managed with care to ensure compliance with the laws protecting consumers.

4. Scalability

Data management can help organizations efficiently scale usage and data times by implementing repeatable processes that ensure that metadata and data are current. If techniques are simple to repeat, your company will be able to avoid the expense of duplicate work, like employees doing the same research over and over or having to run costly queries over and over again.

Types of Data Management

Data management specialists typically focus on specific areas in the field. These specialities may be classified under one or more of the following areas:

1. Master Data Management:

Master data management (MDM) is the process of making sure that your organization is working with making business decisions based upon one version of the most current reliable data. Imbibing data from all of your data sources, presenting it as one continuous, reliable source, and replicating data across various systems requires appropriate tools.

2. Data Stewardship 

The data steward is not a person who develops policies on information management but implements and enforces them throughout the entire enterprise. The name itself suggests that the data steward watches over the enterprise's data collection and policy on movement, ensuring that practices and rules are followed.

3. Quality Management of Data 

If a data manager could be thought of as an electronic sheriff, a data quality supervisor could be regarded as an official in his court. Quality management is accountable for scouring the collected information to find the root cause of issues like duplicate records, incompatible versions and much more. Quality managers for data support the data management system that is defined.

4. Security of Data 

Managing data requires a high level of security. Although new practices such as DevSecOps include security considerations at all application development levels and data exchange. Security experts are still responsible for security management of encryption, preventing unauthorized access, preventing accidental deletion or movement, as well as other concerns that must be addressed front-of-mind.

5. The Concept of Data Governance: 

Data governance is the basis for determining an organization's information security. A framework for data governance is akin to a constitution that clarifies the policies that govern the ingestion, flow and security of institution data. Data governors manage their networks of stewards and quality management specialists, security teams and various other individuals and processes for managing data for an organizational policy for governance that supports an overall approach to managing data.

6. Big Data Management: 

Big data is used to describe the process of gathering analysis, analyzing, and utilizing enormous amounts of digital information to enhance operations. In general terms, this type of management of data focuses on taking in and the integrity and storage of the flood of data in raw form that other management teams utilize to enhance operations and security and provide business intelligence.

7. Data Warehouse 

Information is the foundation for modern-day business. The volume of data is a major challenge. What can you do with all these pieces of information? Data Warehouse Management is responsible for and manages the physical or cloud-based infrastructure used to gather the data in its raw form and analyze it thoroughly to provide business-related insights.

The specific requirements of a company involved in data management could require a combination of one (or all) of these strategies. The knowledge of management areas will provide administrators with the information they need to design solutions specifically tailored to their environment.

Data Management Best Practices & Techniques

Data management is an essential business driver to ensure that data is collected, stored, validated, and secured consistently. Therefore, it is crucial to implement the appropriate methods to ensure that the customers can be confident that they have secure and available data. To ensure that the information you have is managed efficiently, here are the top seven methods for your company to consider.

1. Develop strong file naming and cataloguing conventions

Data must be locatable if you are going to use it. Without management capabilities, it can't be accessed. Provide users with an easy-to-search, standardized file name and file formats that will make it easy to search and discover data sets over time.

• For listing dates, a standard format is YYYY-MM-DD or YYYYMMDD.
• To list time, it's best to choose to use a Unix timestamp or an established 24-hour time zone, like HH:MM: SS. When your business is national or global, users can search for data by time zone and note where the data comes from.

2. Take note of metadata when evaluating data sets

In essence, metadata is descriptive details about the data that you use. It should contain information on the data's contents and its structure and permissions to make it searchable for use in the future. Suppose you do not have this specific data that can be searched and allows for discovery and reusability. In that case, you can't count on utilizing your data in the future.

Catalog items like:

• Data author
• What kind of information does this set contain?
• Descriptions of fields
• Where and when the data was first created
• What was the reason for this data to be developed, and what was the process?

It will assist you in establishing and comprehending the lineage of data as the data moves from its source to where it will end up. It is also useful when mapping relevant data and establishing data relations. Metadata that supports a reliable data lineage can be a crucial step toward establishing a strong data governance procedure.

3. Data Storage

If you're ever planning to gain access to the information, storage plans are a crucial component of the process. Find a strategy that works for your company for all data backups and preservation techniques. The solution suitable for large-scale businesses may not be suitable for smaller projects. Take a look at the requirements of your business.

There are a variety of storage options to take into consideration:

• Desktops/laptops
• Networked drives
• External hard drives
• Storage with optical technology
• Cloud Storage
• Flash drives (while they are a straightforward technique, keep in mind that they become less durable over time and are likely to be lost or damaged)

The 3-2-1 method

An easy, widely used storage method is the 3-2-1 technique. The 3-2-1 method offers the following strategies 3: Keep 3 copies of the data 2: using two different types of storage techniques, 1 and the other being stored offsite. It allows for intelligent access and ensures that there's always a backup accessible if one type or storage location goes missing or destroyed, but without becoming too complicated or redundant.

4. Documentation

In the context of best practices for managing data. We shouldn't forget to document. It's usually a good idea to prepare several levels of documentation that give a complete explanation of the reason for the data's existence and how it is used.

Documentation levels:

• Project-level
• File-level
• Software that is used (include what version the program is to ensure that users who are coming from the future use another version, they will be able to resolve the differences and issues that could arise)
• Context (it is vital to provide any kind of context to the project, such as why it was initiated, what hypothesis were being proven or disproved and so on.)

5. Engagement in data culture

An investment in the culture of data is to ensure that your management prioritizes experimentation with data and data analytics. It is crucial when strategy and leadership are required and when the budget or time are required to ensure that appropriate training is provided and taken advantage of. Furthermore, having executive sponsoring and lateral buy-in can help to improve collaboration on data across teams in your company.

6. Trust in the quality of data for security and privacy

Establishing a culture of data quality is the commitment to creating a secure environment and has strong privacy standards. Security is essential when providing safe records for your internal communication strategies or establishing relationships of trust with clients to ensure the security of their personal information and data. The management procedures you employ must ensure that you have the right processes in place to demonstrate that your network is safe. Your employees are aware of the crucial importance of protecting data privacy. Data security has been recognized as one of the most significant factors in determining a company's future or consumers' buying choices in the present digital world. One privacy breach with data is not enough. Make plans for it.

7. Make sure you invest in quality software for managing data

When putting these best practices in conjunction when evaluating these best practices, it is suggested that, if not mandatory, purchase high-quality software for managing data. The process of putting all the data you're creating into practical software will allow you to discover the information you require. You can then build the appropriate data sets and schedules that meet your business requirements. Data management software can work with both external and internal data assets and help you create your ideal governance strategy.