The importance of data management for digitization in companies

Data management guarantees success #

Data is worth its weight in gold: Whether customer, employee, product or project data - in the digital age, your company’s ability to manage and process data efficiently is crucial to your long-term success. Data management - often referred to as data management - is the key to getting to grips with the flood of information your company is confronted with on a daily basis. Consequently, a well thought-out data management system is a central prerequisite for digitization in companies: This is the only way to improve operational processes and gain a competitive edge.

But why is data management so important for process digitization? And what challenges and opportunities does it present? Find out in this article!

What is data management? #

Data management refers to the entirety of all processes, methods and technologies that companies use to systematically record, store, analyze and use their data. The main objectives are:

• Ensure the availability, consistency and quality of data
• gain valuable insights from the data
• make well-founded business decisions
• Increase productivity and efficiency

What does data management involve? #

Data management includes a variety of tasks such as identifying, collecting and storing data, data maintenance and analysis as well as certain precautions for data security. These sub-areas are particularly relevant:

Everything starts with data capture, collection or generation: while data capture means the selective input of data for electronic data processing, data collection takes place over a longer period of time. Data generation** refers to the generation and collection of large amounts of data using algorithms.

The task of data integration is to merge and harmonize data from different sources. In data modeling, you structure the data so that it can be processed more easily and quickly later on.

For data analysis, you evaluate the data using various analysis methods such as statistics, data mining, machine learning or business intelligence in order to identify patterns or trends in the data sets.

Data quality management aims to improve data quality and ensure that the data is correct, complete, up-to-date and unambiguous.

If you want to store data for the long term and make it accessible, you can arrange for data archiving on high-performance data carriers or in cloud storage. With metadata management, you also manage metadata such as the data type or creation date.

When it comes to data protection and data security, you should take various measures relating to encryption, access rights or backups to protect your data from unauthorized access or loss and to comply with legal requirements.

Data management as the basis for digitization in companies #

Technology companies such as SAP, Google and Meta recognized this decades ago: Data is invaluable. In our fast-moving world, it is the capital of many companies to recognize trends early on, make well-founded decisions and always be one step ahead of the competition. The availability, quality and use of data are therefore among the most important factors for digitization in companies. However, data management not only includes the introduction of new technologies, but also the optimization of processes and structures.

Without effective data management, companies cannot fully exploit the potential of process digitalization, as precise data processing is the basis for digital and automated business processes. This high significance of data in the value chain is now also prompting small and medium-sized companies to expand their data management in order to get more out of their data.

Process digitization: advantages thanks to data management #

Process digitalization is about digitalizing analogue processes or improving existing digital processes. A functioning data management system plays a central role in this transformation. It ensures that you have the necessary data in good quality, at the right time and in the right place. This enables you to automate processes and reduce human error.

Through effective data management, you can achieve higher data integrity and data quality, which in turn contributes to more informed decisions. You can also reduce costs by using data efficiently, avoiding redundant data and relying on storage technologies that are as affordable as they are secure. The optimization, digitalization and automation of processes are also benefits that result from good data management.

A concrete example of this is the use of data management in production. Modern production facilities use large amounts of sensor data to monitor and adjust production processes in real time. Without a solid data management system, such advanced production processes would be almost impossible to implement. But data management is also indispensable for digitization in human resources , marketing and accounting.

The opportunities of effective data management #

• Increased efficiency: Automated processes reduce the amount of manual work and minimize human error.
• Better decisions: Data-driven decisions tend to be more informed and lead to better business results.
• Cost savings: By optimizing the use of data and resources, you can reduce costs.
• Competitive advantage: Companies that use their data effectively can respond more quickly to trends, giving them an edge over the competition.

Data management challenges #

Although data management is essential for work and digitalization in companies, it poses the following challenges for many organizations:

1. Scalability: In the age of Big Data, a data management system needs to be scalable to keep up with growing data volumes. Only then can you store and use data from a variety of sources such as sensors, cameras or keyboards.
2. Data integration: Another problem is the complexity of this data. If you store your data in different systems and formats, you need to merge and harmonize it before you can use it effectively. Especially with large amounts of data from different sources, it can be time-consuming and difficult to create a uniform structure.
3. Data utilization: Collecting data alone does not provide any added value - because this data is not useful until you utilize it. It can take a lot of time and effort to prepare the data in the way you need it for analysis, be it by standardizing data formats, aggregating data sets or removing duplicates. If data management is too time-consuming, no analysis will take place and the potential value of this data will be lost.
4. Data quality: One of the biggest challenges in data management is ensuring data quality. If data is outdated, incomplete or incorrect, it can lead to incorrect analysis and decisions. Continuous data maintenance, in which you update and cleanse the data, is therefore essential.
5. Data security and data protection: Data protection and compliance regulations are multinational and change regularly. As the amount of data collected increases, so does the risk of data protection conflicts. On the one hand, you must ensure that personal data is protected against unauthorized access; on the other hand, you must be able to easily delete the stored data or adapt it to changed usage guidelines.

How you can use data management for digitization in companies #

If you invest in a robust data management system, you can overcome these data management challenges. However, successful data management requires careful planning and a clear strategy. Here are some best practices to consider when it comes to data management:

• Create awareness of the importance of data and encourage employees to collect, analyze and share data related to their work.
• Accordingly, you should provide training to ensure that your employees have the necessary skills and knowledge to handle data carefully and use it effectively.
• Last but not least, you should invest in tools and technologies that allow you to collect, store and analyze data effectively.

Proven data management systems #

In principle, big data is exactly what it sounds like - a huge amount of data. You need to structure and process this data in order to make sense of it. As a rule, you store data in databases, which are structured differently depending on their purpose. It can be challenging to provide a powerful infrastructure, especially when it comes to processing large amounts of data.

Data management systems are basic IT systems with which you capture, store and analyze data in your company. Various data management systems exist for different requirements, such as relational databases, NoSQL databases or data warehouses. Relational databases are the most widespread and are characterized by a table-based structure. NoSQL databases, on the other hand, are particularly suitable for processing large volumes of data.

Data management systems at a glance #

There is a wide range of tools and software that you can use for your data management. Here is an overview of some proven data management systems:

• Relational database management systems (RDBMS): Relational databases structure data using relationships that can be represented in tables. This makes it possible to store, retrieve and process data efficiently. The most commonly used database language is SQL, which is also the name of well-known relational database management systems such as MySQL, Microsoft SQL Server and PostgreSQL.
• NoSQL database management systems: Compared to RDBMS, NoSQL databases offer a flexible data structure and horizontal scaling that is better suited for large data volumes, high performance requirements and frequent changes. Examples of NoSQL database management systems are MongoDB, Apache Cassandra and CouchDB.
• Data Warehouses: A data warehouse is a database specializing in analytics that consolidates large amounts of data from various sources into a consistent data set. Data managers can then process and analyze this data in order to gain comprehensive insights into a company’s business processes, for example. Data warehouse systems worth mentioning are Oracle Exadata, IBM Netezza, Amazon Redshift and Snowflake.
• Data lakes: A data lake is a repository in which all of an organization’s raw data is stored. It can contain both structured data from relational databases and unstructured data (e.g. emails, PDF documents and image files). In contrast to the data warehouse, the data in the data lake is only processed for analysis or visualization when required. Data lake systems such as Amazon S3, Microsoft Azure Data Lake or Google Cloud Storage enable fast and flexible processing of large volumes of data.
• MDM tools: Master data management tools help you to manage and maintain your company’s master data. The most important master data includes information about customers, suppliers, products and personnel. With MDM tools such as SAP Master Data Governance or IBM InfoSphere MDM, you can eliminate inconsistencies and ensure that your core business data is unique, complete, error-free and up-to-date.

What is the best data management system? #

Choosing the right data management software depends on the specific requirements you have for data access, storage, security, integration and analysis. You can decide whether you want to store and analyze your data on your own servers (On-Premises ), in a cloud or in a hybrid mix.

One trend that can be observed is the increasing use of cloud-based data management solutions. A cloud-based data management platform allows you to store data securely and process it efficiently without having to invest in expensive infrastructure.

Relational no-code database in the cloud or on-premises #

In the table-based no-code database SeaTable you can efficiently store, retrieve and analyze your data without having to have any SQL knowledge or other prior technical experience. Not only can you easily structure and link data, you can also store files, images and documents.

Unlike other relational databases, SeaTable’s graphical user interface allows you to carry out analyses directly. Numerous statistics are available for further analysis. If this is not enough, you can visualize your data in galleries, calendars, organization charts, etc. with the help of plugins .

With the no-code app builder from SeaTable, you can build your own apps without any programming knowledge and thus control access to the data in your database precisely. For the app design, there are ready-made page types with visual elements that you can simply drag and drop together.

SeaTable gives you the freedom to choose between the convenience of a cloud solution and the data sovereignty you enjoy with a self-hosted system. Are your ears pricked right now? Then register for free and try out the SeaTable Cloud for an unlimited time.

Drive digitalization forward in your company #

In an increasingly digitalized world, data management is not only a technological challenge, but also a strategic success factor for companies. A suitable data management system forms the basis for your process digitization and enables you to fully exploit the potential of digitization in your company. If you have a complete overview of your data, you can make the right decisions and stay one step ahead of the competition in the future. Investing in a sophisticated data management system is therefore a must for any company that wants to remain competitive and take advantage of the opportunities offered by digitalization.

FAQ #

TAGS: Data Management & Visualisation IT Processes Low Code / No Code