Expectations in companies are high: data should increase sales success, minimize the risk of product innovations, bring clarity to strategic realignment, and much more. Data is capable of all of this - provided companies have professional data management and a clear data strategy. But this is exactly where many companies fail.
We explain which types of data management companies should be aware of, what economic benefits lie in intelligent data management and which solutions help to realize it.
Definition: What is Data Management?
Data management is, simply explained, a holistic concept for the collection, processing, storage and deletion of corporate data, combining procedural, regulatory and technological aspects.
Why Is Data Management Important?
Data used to be a side product, but today it is at the heart of digitization and transformation projects. With technological progress and exponential data growth, data has turned into a key factor for business success.
Those who succeed in managing data centrally, allowing it to flow across departments and synchronizing it in real time, strengthen their competitive position in competitive markets. Manufacturers then always have an overview of their inventories and can produce just-in-time. Suppliers communicate real-time goods movements and delivery dates to their business partners, enabling them to coordinate their processes and avoid bottlenecks.
If companies succeed in combining data from different sources and analyzing it skilfully, they also obtain valuable information - not only for process optimization, but also for product innovation and changing their business model.
When companies network their systems and data assets, data analytics can improve processes across all areas of the business - from logistics to HR and marketing all the way to sales. Here are just a few examples:
- The sales department is then aware of a customer's purchase history and requirements and is optimally prepared for sales talks.
- IoT systems in production report if wear and tear occurs on machines and automatically alert purchasing, which orders spare parts.
- Marketing knows the behavior of website users and can send personalized advertising to leads.
- Product management can perform market analyses automatically and adjust prices optimally to the competition on this basis.
- Accounting receives real-time notices when noticeable double payments have been made.
Intelligent data management also supports innovation within the company: Data on customers, their wishes and complaints, and their purchasing behavior can provide starting points for new product ideas, additions to the product range, or expansion of the business model.
Customers benefit in several ways from efficient data management. In support, they meet advisors who have all the background information on their concerns in front of them with just a few clicks, they find personalized offers on the website, and they can add and cancel many services themselves via online services.
Current Challenges in Data Management
The three most important strategic drivers in companies are revenue growth, cost reduction and risk minimization. In all three cases, the quality of data management is a decisive factor in success or failure.
Data can reveal revenue opportunities and promote sales, but its potential is only fully exploited in a few companies. There are many reasons for this. For example, employees in the specialist department lack access to data from which analyses could be generated, or the know-how to use existing tools. Often, data is not merged, so that insights are lost that only emerge from an overall view.
There is usually no lack of valuable data, but there is a lack of processes to efficiently harness data for business goals. Systems for cross-departmental analyses are not in place. This makes analyses time-consuming and expensive. In many cases, they are not performed at all. Yet data analyses could reduce costs in many areas of a company. One example is maintenance. Data is the basis for determining optimal maintenance intervals and thus reducing machine downtime and costs for repairs.
Data analyses bring transparency to corporate processes. They help to identify weaknesses and to make optimizations. Those who look at cost development, sales figures and churn rates can use their database to make more informed strategic decisions and minimize the risk of wrong decisions. With the right data tools, you can create valid forecasts for the future and proactively compensate for negative trends.
6 Facets of Data Management
Data management is a general term under which various sub-disciplines can be grouped. Which types of data management systems and which aspects of data management are particularly important for a company depends, among other things, on the size, goals and structure of the organization.
Data Warehouse Management
In the data warehouse, companies aggregate raw data from various source systems to prepare it for evaluation in BI and analytics applications.
Master Data Management, MDM
Master data such as customer addresses, supplier lists or product descriptions are needed in many departments. They can be stored in different systems in a leading way, for example in the ERP and CRM system. Master data programs establish tools and processes so that changes are reflected everywhere in one storage location and employees work with the latest database at all times.
Data Quality Management
Data Quality Management (DQM) covers organizational and technical aspects. Setting standards and rules for handling data is just as much a part of the discipline as the use of tools that cleanse data sets and standardize them according to specifications. In some cases, the services provided by the applications are also covered by data integration tools.
A data governance framework forms the core of professional data management. It defines responsibilities, access rights, processes and technologies for handling all data processed in the company. The framework is derived from the corporate strategy and shapes the operational business. It is also an important prerequisite for complying with legal requirements for data processing, for example the “DSGVO”.
Big Data Management
Rapidly growing, huge data sets pose particular challenges for data management. Especially when large volumes of data originate from various sources, traditional software systems are often no longer sufficient for their organization. Big Data Management deals with the optimal control and storage of these data sets.
Protecting data from unauthorized access and misuse is one of the basic requirements for data management systems. Data security concepts define processes and implementation solutions to comply with legally prescribed and individually defined security standards. Depending on how mission-critical data is to be classified, other physical and technological security measures (e.g., biometric authentication, firewalls, etc.) and authorization systems are introduced.
The Role of Data Management Software
Data management software is an effective tool for improving the added value from corporate data. However, only if employees are aware of the importance of data and use solutions correctly. Companies also need processes and rules (data governance) for handling data. Software alone is not enough to establish successful data management.
A data management consulting firm can help companies determine whether new software is really the biggest adjusting screw for improving data management or whether other measures promise more success.
At the beginning, there should always be a definition of goals: What is to be achieved with the optimization? What expectations are associated with new software? If companies really come to the conclusion after the consultation that it makes sense to implement new data management software, they ideally create a prioritized requirements catalog that helps them research the market for suitable solutions. A data management consulting firm can shorten this selection process. After all, the external experts have a precise knowledge of the market, can query key requirements in a targeted manner and identify suitable solution options.
Efficient data management cannot always be realized with a single application. Depending on the use case, different systems are used. The five best-known types of software include:
Master data management system
Software solutions for master data management bring together master data such as customer data (e.g., purchase history), product data (e.g., dimensions) or supplier data (e.g., addresses), in a central location and make it available for use in the various applications of the business units.
Enterprise Resource Planning systems manage all data generated in a company's core processes - from purchasing and production to logistics, finance and HR. This is usually also where transaction data is recorded.
The customer relationship management system primarily manages data about customers and their behavior. CRM systems support marketing and sales in successfully addressing customers and provide valuable insights for optimizing customer relationships.
Product information management systems store all product-related information on a central platform so that company departments such as marketing and logistics, but also external partners, can use uniform and up-to-date data for their applications at any time.
Digital asset management systems manage media data in various formats. Above all, the platforms are used for the management of images, videos and audios used throughout the company. Unlike PIM systems, they store not only product-related files, but also assets for corporate presentations, internal training and marketing purposes.
Frequently Asked Questions About Data Management
With holistic data management, companies maximize the added value from their data. If data is not managed in departmental silos but evaluated across departments, significantly more market-related information can be obtained from the data. Professional data management makes it easier to comply with legal and security regulations and to realize high quality and availability standards.
Companies should first define goals for their overarching data management strategy and then get an overview of existing business unit data initiatives. Then, top management can evaluate the initiatives, define relevant action areas and plan appropriate measures. As a rule, the introduction of new processes, technologies and roles is necessary. Whether the measures are working as planned should be checked regularly so that those responsible can correct their course if necessary.
There are diverse application scenarios for artificial intelligence in data management. For example, algorithms can automate processes and make reliable recommendations for action or products. However, intelligent, i.e. powerful, data management can also be implemented without artificial intelligence.
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