What this post is about
- Recognize the fundamental problem of data waste
- Recognize effective strategies
- Recognize the distinction between useful and superfluous data sets
- Approach implementation in an understandable way
- Create added value and minimize risks
Data is essential for modern businesses. Customer data, tracking evaluations, or cookies: for the development of successful strategies and sales-promoting marketing measures, the collection of action signals related to user profiles has become indispensable. Nevertheless, mindless collection harbors hidden risks that can have a negative impact on one's own company and society.
The more data owned, the more companies become the focus of hackers or data thieves. The business with customer data on the darknet is booming and unintentionally drags overly greedy organizations into questionable situations that also endanger the level of loyalty and fidelity of users and customers. In addition, the senseless hoarding of data waste affects the CO₂ footprint. Server farms cause high energy consumption, which drives CO₂ emissions and directly impacts your sustainability strategy. Fewer data centers as a collection point for data mean a better chance for the climate.
But how can data frugality now make its way into your business? We provide the right tips.
Setting up a smart data infrastructure is the inevitable basis. It should underline the business strategy and strengthen marketing projects. In consultation with the relevant teams, the goal can be formulated jointly, at the end of which there is always added value for all relevant departments. You can create the basis by asking the following questions:
- Which areas need data in the first place?
- What are the use cases for the collected data?
- How should the data economy strategy be monitored and maintained?
- What systems and processes are in place to do this?
When planning the data ecosystem, process plays an indispensable role in addition to analysis. A map of all programs, types of data collected, administrative pathways, or connections between internal processors bring an understanding of how data flows. Unnecessary detours or helpful shortcuts are better identified and give a clear picture of the processes. Answers to the following questions guide you through the structure phase:
- How does data flow in our system? Do they flow at all or are they collected each time?
- What does the data flow into?
- Who has what access and who uses what data?
- Who collects it?
On this basis, the organization of data collection can be clearly structured. Important: Streamlining processes minimizes risks!
Knowing at what point data minimization takes effect requires know-how. The best data makes little sense if it cannot be read. Regular training and seminars promote awareness of the topic and ensure that colleagues take full advantage of the value of data. Technical as well as analytical skills play a key role in this context.
The topic of data management and analysis is often seen as a side project in companies. However, the time required and the possible intensity of the support justify in any case the consideration of establishing a separate department just for this purpose. In the long run, this effort will be reflected in the quality and free all other teams from tasks.
The rule is to only collect data that is useful and furthers the business. If you base your data usage on transparent reasons, making it clear to users for what reasons data is collected, you will earn understanding and trust.
The creation of extensive data collections that exist just in case seems dubious and also harbors the high risk of data loss through leaks and hacks. For companies with customer contact, the responsibility to protect personal data must always be at the forefront.
Data makes developments visible and provides information about performance as well as areas for optimization. In this way, arguments for development projects can be better emphasized and decisions can be driven forward. Despite all the strengths and important impressions that user data contain, it is important to know with a clear eye what information is important and what information can be dispensed with in the course of data economy.