With digitization, the importance of data is growing – this is nothing new. But what does that mean in concrete terms for companies? Companies that only use their data passively, for example, to retrospectively evaluate the success of the past quarter, are wasting the enormous potential of the information. Used as a strategic asset, data sets can actively generate profits – for example, by driving business growth, identifying new business opportunities, or proving compliance with legal regulations and thus avoiding fines. However, the data must be reliable and trustworthy.
However, a recent survey by ASG Technologies , According to 63% of the companies surveyed used terrible data, i.e., incorrect, outdated, or inaccurate data, for business decisions. More than half missed such a business opportunity. For 64%, this even resulted in additional costs. This illustrates a problem: When trying to get the maximum value from the data as quickly as possible, many companies ignore the fundamentals of stable data management. However, if you want to benefit from your data in the long term, you need a structured approach that includes the essential work and is designed to always get the most out of the data. A maturity model can support this.
Table of Contents
First Things First: The GroundWork
First of all: there is no such thing as “one” way to successful metadata management. Every company is subject to different requirements and is structured differently. Accordingly, the individual steps must be designed individually. But the process of monetizing the data always starts with the realization that data management needs to be optimized. The aim is usually to make better operational decisions and get the maximum economic benefit from the data. It is precisely in this “awareness phase,” The problem was identified that the maturity model begins.
Prerequisite: Recognize The Problem
Companies in the awareness phase are already using data to run their business. Accordingly, they also have data management structures. It is not uncommon for the information to be stored in silos and divided into specific business areas, products, or target groups. The problem: This structure prevents a company-wide view of the entire database. Redundancies and uncertainties in how certain data elements are used are the results. The trustworthiness of the information cannot be assessed. In this first phase, the companies recognized the need for action and are now faced with the challenge of democratizing the enormous amount of data.
Step By Step: The Maturity Model
Step 1: Basic Work – The Complete Data Inventory
In the beginning, companies have to record the complete data inventory. Since new data is regularly added, an automated process must be introduced that continuously updates this inventory. This is one of the most critical steps that is often skipped. That is precisely what causes problems afterward. Because to assess the trustworthiness of the data, the employees have to know which data is stored, where source it originates, and in which context it is located. Only then can you use it as a reliable basis for decision-making. Corresponding DI tools recognize stocks and automatically integrate information on the source, application, or associated business processes.
Step 2: The Data Management
Once the data inventory is complete and up-to-date, companies can focus on data management. Critical processes here are data governance, data exchange, and data rationalization. Data governance provides the framework and rules for data management. At the same time, it also controls compliance with defined process, quality and safety standards. In this way, data governance not only ensures an understandable, correct, and secure database but is also an essential factor in compliance with data protection guidelines. With data, sharing employees can work together across departments.
For this purpose, a self-service inventory is created to find all the relevant data for your task. Such self-service data access plays an important role, especially when it comes to promoting innovation. Data rationalization reduces costs and makes it easier to find data. For this purpose, the data stocks are categorized and linked to an essential business value. A business glossary summarizes the relevant business terms.
This assignment of business terms to data elements is the basis for automated data lineage, which tracks the origin and flow of the data through the company. This allows duplicate or unused data to be identified and removed. The advantage: If fewer data elements, storage, and administration costs decrease, and valuable data can be found more easily.
Step 3: Monetize Data.
After steps one and two, the companies already have the essential information structure to derive economic benefits from the data. To complete the process, five aspects are crucial to the last step:
- Identify data that will drive the business forward
- Identify databases to link them to business use
- Add metrics to see who is using data for what purpose and what value is derived from it
- Automatically capture and tag new data sources to identify monetization opportunities
- Determine the value of the data and forward it to external users
Conclusion
The increasing mass of data, legal requirements, and customers’ expectations are putting more and more pressure on companies. According to Accenture , extracting the data value has long since become a competitive necessity. Reliable data is precious for business decisions, product launches, and market positioning and can provide a decisive competitive advantage. The three steps of the maturity model help companies know their data precisely and know where they can profitably. This enables you to get the most out of the data in the long term.
