Data management: definition, benefits and best practices

What is data management?

If data is the modern day gold, data management mines it, washes it, preserves it, organizes it and maintains its value.

Data flows confront all companies today. They must control these. Strucutred management throughout the entire company makes this possible.

This is explained below.

Data management: definition

Data management is the targeted handling of data in a company. It includes creating, maintaining, securing, analyzing, processing, organizing and archiving data. in order to use it efficiently and securely.

The aim is to maximize the value of data. This requires proven strategies, methods and technologies. These ensure that you always have clean and up-to-date data that is relevant to actions and decisions. Effective data management is the overarching backbone that connects all elements of the information and data lifecycle.

Data management goes hand in hand with process management, which relies heavily on good data quality. When data is well enough organized, patterns can be identified. Process mining (software-based mapping of processes) is then possible.

The importance of data management

Having reliable, helpful, valid and secure data is crucial. It helps modern companies remain competitive.

Effective data management helps companies:

• make well-informed decisions.
• operate efficiently.
• meet compliance requirements.
• minimize risks.
• promote innovation.

Overall, data management makes it easier for users to obtain valuable information and use new technologies. For example, business intelligence (BI) can be used to analyze business data. It transforms data into actionable insights. Employees can use these to make better decisions.

Data management and data governance

These areas are closely intertwined. Data governance establishes the policies and procedures for how data may be collected, processed and maintained.

Data management takes over when it is time to actually handle data. It is the discipline that makes sure all of the established policies are followed. Data management actively controls data:

• Processing
• Backup
• Storage

Data lakes and data warehouses

Perhaps you’ve heard of „big data“. This is the term used to describe very large amount of data that can be difficult to manage.

When it comes to storing big data, the terms data lake and data warehouse come to mind. While they sound similar, they are very different in practice.

A data lake is a large pool of raw data. Typically, a data lake is easy to access. However, it offers unstructured data so it can be more difficult to benefit from these. Data scientists often use data lakes to solve complex problems.

The term data warehouse describes a repository of structured and filtered data that serves a specific purpose. It is big and more complicated in structure, so there are often rules and guidelines about how they can be accessed. A data warehouse is often used in the business sector, such as for running reports.

The advantages of adequate data management

There is no denying that targeted data management means profits in plenty of areas.

Here is an overview of the most important benefits of data management.

Advantage #1 – Fewer data silos. Data management tools and frameworks, such as data lakes, can reduce widespread data silos. They eliminate dependencies, uncover potential integrations and open data sets to a broader community.

Advantage #2 – Higher quality and integrity. Good control and governance ensure that data management goals can be achieved. These enable more informed decisions, among other things.

Advantage #3 – Lower costs. Using data efficiently and avoiding redundancy saves a lot of money. This allows companies to convert their data into added value much more directly.

Advantage #4 – Better security and compliance. By complying with regulations and industry-specific guidelines, companies avoid costly or even reputation-damaging missteps. Examples of such regulations are the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA),

Advantage #5 – Process optimization and process automation. Closely related to process management, successful data management helps to optimize and automate business processes. On the one hand, this increases operational efficiency. On the other, process automation reduces effort and costs.

Advantage #6 – Combine different data. In everyday business life, we encounter a wide range of data that comes from different sources. Thanks to data warehousing, these different types of data can now be merged, centrally queried and analyzed.

Advantage #7 – Scalability. If cloud platforms are used for data management, the computing power can be adapted on demand. The right processes can increase flexibili

Best practices for data management

Organizations must establish robust and effective data management practices that maximize the value and data security.

Here are some best practices:

1. Define goals, policies and strategy

Companies should begin by identifying their data management strategy. Precise data management goals should be established.

For example, some organizations focus on compliance. Others may be more concerned with performance or process automation.

Additionally, guidelines that define how data should be collected, stored, used or deleted need to be created in detail. Based on these, an effective strategy for data management can be developed.

2. Focus on quality

Data must reliable to be useful. The various data sources should, therefore, always be verified or checked. Regular checks are a good way to correct errors and avoid inaccuracies.

Another quality factor that is often underestimated is that data should be consistently formatted and structured. A data management platform can assist in these areas.

3. Introduce a classification system and metadata

Data needs to be organized in a way that makes sense. It must also enable employees to work effectively and purposefully. A data storage system is, therefore, required to provide clarity and make data easier to use.

It also makes sense to store metadata ( information about the data itself) in a data catalog. Capturing details, such as data type or creation date, make it easier for data analysts to find and use information.

4. Use automation

Workflow automation is easier when data is well managed. But, some data handling tasks, like migration and backups, can also be automated. Explore oppoprtunities to reduce dependence on humans for these recurring mundane tasks.

5. Create awareness

Many important processes and targets fail because employees are not aware of them. They also lack understanding of what target-oriented data management actually is. It makes sense to familiarize employees with best practices in data management. This creates awareness about how good data quality and security support the company.

6. Regular audits and reviews

The importance of diligence, structure and reviews cannot be overemphasized. Regular audits should be implemented. For example, checks may be done to determine if the data complies with relevant guidelines and standards. These also help companies continuously adapt strategies and processes to new requirements and technologies.

Data management
in ITSM

Data management plays a very important role in IT service management (ITSM). First, it ensures that customers are provided with seamless and reliable IT services.

Second, it can support IT Asset Management (ITAM). ITAM deals with software, hardware, and related data that are important to a company. All of this information is data that defines asset:

• Provision
• Maintenance
• Update
• Decommissioning
• Documentation

Example: Use of a CMDB

A configuration management database (CMDB) is extremely useful for the tasks described above. It stores data on assets, such as IP addresses, computers or operating systems. In addition, information about relationships between specifications, components or locations can be recorded in such a database.

IT can operate more securely and with less risks by using a CMDB. Consequently, configuration management – for the administration and control of IT resources – is important. It makes optimum use of data across different systems.

To the ITSM solution from OTRS

Data management with AI

Data management is an area that can benefit greatly from artificial intelligence (AI). This is particularly beneficial for big data management.

The use of AI provides possibilities that go beyond human analysis capabilities. It can also handle a high level of complexity and variety of data. When paired with structured data, this offers companies that have complex, sophisticated data reliability and real insights.

Here are two examples:

1. In the ITSM area, AI-supported summaries can reduce complexi texts to the main points. Truly relevant information and data is available more efficiently.
2. AI can make it possible – sometimes in real time – to make relevant decisions based on reliable data.

Artificial intelligence can be a great support for big data processes and extensive analyses. In fact, there are numerous AI benefits that companies can take advantage of in data management. These include, for example, fewer errors, more accuracy, and better scalability.

Conclusion: Data management – an important task

Alongside information management, data management is a central corporate task. It is about how structured, reliable and secure data is stored. It also focuses on the best possible ways to use data.

It should be emphasized that dedicated data management brings companies numerous advantages – quality, accessibility, security and data integrity. It also offers more efficient processes, cost savings and a better overview of the business. AI is being used more and more in this area, so keep up with trends in this area.

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