• Generic ERP
  • 07.09.2022

Data Management. What is it?

Why every company needs a properly thought over data management solution…

Dr. Andreas Maier
Dr. Andreas Maier
07.09.2022
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“ There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days. “

Data management is organizing and maintaining data to make it easy to find and use. Good data management practices can help companies improve efficiency, make better decisions, and protect against data loss.

There are many different approaches to data management, but all involve creating a system for storing, organizing, and accessing data. Data can be stored in various formats, including spreadsheets, databases, and files. It is important to choose a storage format well-suited to the type of data being managed and the organization's needs.

Data management systems can track and manage all types of data, including customer, financial, inventory, and more. A good data management system will provide a way to access and update data efficiently and securely. It will also allow authorized users to share data with others as needed.

There are many different functions of data management, but all involve the organization and use of data. Some of the most common functions of data management include:

1. Collecting data

Data must be collected to be managed. Data Collection can involve gathering data from internal and external sources to the organization.

2. Processing data

The data must be processed to make it usable. Data Processing may involve cleaning or transforming the data so that it is in the correct format, sorting it, and adding/removing columns and rows as needed.

3. Validating data

The data must be validated to ensure its accuracy. Data Validation can involve checking for errors and ensuring that all the necessary information is included.

4. Storing data

The data must be stored in a safe and secure location to be accessed when needed. Data Storage may involve creating or using a database, spreadsheet, or file system.

5. Integrating data

The data must be integrated with other systems to be used effectively. Data Integration may involve connecting to a corporate database or importing/exporting data between different software applications.

6. Accessing data

The data must be accessible to authorized users to use as needed. Access Control may involve setting up user permissions and granting access rights as appropriate.

7. Protecting data

The organization must protect its data from unauthorized access, alteration, or destruction. Data Protection may involve using security measures such as passwords, encryption, and firewalls.

8. Securing data

The organization must also ensure that unauthorized individuals or organizations do not access its confidential information. Data Security may involve using secure transmission protocols such as SSL/TLS, HTTPS or Data Encryption.

Why is Data Management important?

Good data management is essential for any organization that relies on data for day-to-day operations. Businesses can minimize the risk of downtime or lost revenue by keeping track of what data is stored and where and taking steps to ensure its accuracy and integrity.

Data management also helps organizations make better decisions by providing a central repository for all the data they need to make informed decisions. By integrating data with other systems, businesses can create a single access point for all the information they need. Data Management can help reduce the time it takes to make decisions and improve the accuracy of those decisions based on data – and not guessing!

Additionally, good data management practices can help businesses protect against data loss. Businesses can minimize the risk of losing important information by storing data in a secure location and taking steps to protect it from unauthorized access.

How to turn Data into a high-value asset?

Businesses can turn data into high-value assets to make informed decisions and predictions. Big data is unprocessed data that is too large to be processed using traditional methods. Smart data is big data that has been processed, cleaned, or traversed to make it usable or human-readable.

Big data is a valuable resource for businesses because it contains insights and predictions that can help them improve their operations in unrelated formats. By processing and cleaning big data, businesses can access this information and use it to make better decisions. Big data is impossible to understand due to the sheer volume of data. A variety of data types also makes it difficult to analyze. However, businesses can use this information to improve their operations by processing and sorting the data into smart data.

Smart data can be derived from big data by analyzing the patterns in data. Sometimes Smart Data is augmented by combining various data sources. 
For example, businesses can learn more about customer behavior and preferences by analyzing data. This information can then improve marketing strategies and create better products or services.

Organized data is the key to making sound business decisions. Companies can use smart data to improve their operations by understanding how to manage data. 

Understanding different Data related topics

Database management systems (DBMS)

There are different database management systems, each with its advantages and disadvantages. The most common type of DBMS is the relational database management system (RDBMS), which stores data in related tables. RDBMSs are popular because they are easy to use and understand, and they can be used for a wide range of applications. Commonly querying – or retrieving data – can be structured through the so-called Structured Query Language (SQL)

Object-oriented database management systems (OODBMSs) 

store data as objects, which can be manipulated individually or as part of a group. This makes them well-suited for applications that require complex data handling.

In-memory databases 

store data in RAM instead of a storage medium (e.g. Hard drive, SSD), making them faster than traditional databases. They are often used for real-time applications like online banking or stock trading. Data management becomes more prone to failure if the underlying systems fail – redundant setups protecting data loss can be difficult to implement.

Columnar databases 

store data in columns instead of rows, making them more efficient when performing queries that scan through large amounts of data.

NoSQL Databases 

NoSQL is a database that stores unstructured information and is a great way to store large amounts of information without obvious relation to each other. 

Data warehouses and lakes

A data warehouse is a type of database that is used for storing and managing data. It is typically used for historical analysis, reporting, and decision support. A data lake is a type of system that is used for storing and managing data. It is typically used as a data pool for real-time analysis, including machine learning and artificial intelligence.

The main difference between data warehouses and data lakes is how they store data. Data warehouses store data in tables, while data lakes store data as files. This makes it easier to process and analyze data in a data lake than in a data warehouse.

Master data management (MDM)

Master data management (MDM) is creating and maintaining a single version of the truth for all data within a business. This includes the governance, consolidation, and quality of the data. MDM is important because it helps businesses improve efficiency, make better decisions, and protect against data loss.

One of the main challenges of MDM is ensuring that the data is accurate and up-to-date. This can be done by implementing a quality assurance process that regularly audits data. Another challenge is ensuring that all data is consolidated into a single repository. This can be done using a master data management tool or developing custom scripts to extract the data from different systems.

Governance is another important component of MDM. This includes setting rules and policies for how the data should be used and ensuring that everyone in the organization follows them. By implementing strong governance policies, businesses can reduce the risk of data corruption and misuse.

Data integration

Data integration is the process of integrating new data into an existing system. This can be done by ingesting the data into a data warehouse or data lake or provisioning it to a reporting system.

Integrating new data into an existing system can be challenging, especially if the data comes from different sources. There are several ways to integrate new data, each with its advantages and disadvantages.

The most common way to integrate new data is to import it into a database.

Another way to integrate new data is to use a data processing pipeline. Data processing pipelines involve breaking the data up into smaller pieces and processing them sequentially. This process can be used to clean up data or to transform it into a format that is easier to work with.

Finally, businesses can also integrate new data by gathering information from third-party sources. This can be done by using APIs to access the information or web scraping.

Data governance, security, and compliance

Maintaining data governance, security, and compliance can be challenging for businesses. This is because it can be difficult to ensure that the data is accurate and up-to-date and that it is being used following the organization's policies.

There are many things businesses can do to improve data governance, security, and compliance. One of the most important is to implement strong governance policies. These should include rules for how the data should be used and who is allowed to access it.

Another important step is to ensure that the data is properly secured. This can be done by encrypting the data or restricting access to certain users or groups. Finally, businesses should ensure they comply with all relevant regulations governing the storage and use of data.

Business intelligence and Analytics

Business intelligence (BI) and analytics are important tools for understanding and acting on the data in a business. BI tools can help businesses track performance, identify trends, and make better decisions.

One of the advantages of BI and analytics is that they can help businesses make sense of large amounts of data. This can be done by transforming the data into visualizations that are easy to understand or by using machine learning algorithms to find patterns in the data.

Another advantage of BI and analytics is that they can help businesses improve efficiency. This can be done by identifying redundancies in the data or recommending ways to optimize business processes.

The main challenge with BI and analytics is that they require a lot of data to be effective. This can be a challenge for businesses that don't have access to a lot of data or don't have the resources to process it.

How to create an enterprise data strategy?

  1. Identify the specific data type needs of your company
  2. Assign responsibilities for these types of data
  3. Establish governing procedures for collecting and handling the data

To create an effective enterprise data strategy, you must first identify the specific types of data your company will need and use. This includes both structured and unstructured data, as well as internal and external data to your organization. Once you have a clear picture of the data you need, you can assign responsibility for each type of data and establish procedures to govern the acquisition, collection, and handling of that data.

Data management is essential for any business because it helps improve efficiency, make better decisions, and protect against data loss. By organizing and maintaining your data to make it easy to find and use, you can streamline your business processes and get more value from your data. Additionally, good data management practices can help ensure the security and integrity of your data, which is essential for protecting your business against cyber threats.

Final thoughts about Data Management

As businesses grow and become more complex, the need for good data management practices becomes more important. Data management is organizing and maintaining data to make it easy to find and use. Businesses can improve efficiency, make better decisions, and protect against data loss by establishing procedures for collecting and handling data.

Data management is essential for any business because it helps improve efficiency, make better decisions, and protect against data loss. By organizing and maintaining your data to make it easy to find and use, you can streamline your business processes and get more value from your data. Additionally, good data management practices can help ensure the security and integrity of your data, which is essential for protecting your business against cyber threats.

If you're looking for a comprehensive data management solution, SIX ERP has all the tools and storage options you need to get more insights into your company and use it as a central management tool. With our powerful data analysis capabilities, you can quickly and easily get a clear picture of your company's performance and make better decisions for the future.

Contact us today to learn more about our data management solutions and how they can help improve your business or book a personal demo.

About the author
Dr. Andreas Maier

Andreas is a result-oriented CEO who brings nearly 30 years of experience gained in the high-tech industry. His experience ranges up to leading positions in Fortune 100 companies such as rentalcars.com (PCLN) or Intrasoft International, a leading EU based R&D software vendor. He holds a Ph.D. in Neural Networks from the University of Cologne, Germany. 

In the past Andreas has successfully founded and co-founded several startups among others XXL Cloud Inc., eShopLeasing Ltd, and WDS Consulting SA. His expertise is strongly focused on modern headless Commerce and the optimization of processes in IT ecosystems.

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