Data can improve management decisions and accelerate processes. DataValueThinking identifies potentials and solution partners like bimanu offer pragmatic examples and references.

Do you also know this in your company: When people talk about customer data, this is the data from the xy CRM system. The financial data? We look at it in SAP. And then there is the large Excel of the controlling department … Then you look around in your production, there are also many data silos to get to the machine data. The challenge is to combine the commercial data with the technical information such as IoT data in a meaningful way and to break up the data silos.

Managers think in data pots or data silos. These data pots are in the heads of managers and employees. This also why we use the term data pots as data silos are known from IT landscapes and are referring more to technical infrastructures, in our case we refer to the business view of data. They can, but do not have to, correspond to the real IT systems and their data structures. In our DataValueThinking workshops we also deal with this topic and show potentials and improvements.

One exercise in the workshops is to collect the “data pots” and position them on a map of the data landscape. In a next step, ideas are collected for which decisions, reports or process improvements one could use these data pots and could make use of them. We have made the following observations about all workshops regarding the data pots.

Many problems are tried to be solved in the same world, the same data pot. Additional information and queries are added to the CRM system in a complex (and expensive) way, although the same information may be available in a different data pot. Often innovative and pragmatic improvements are made by simply connecting data pots and later than also via connecting the real IT systems behind the data pots.

The existing data landscapes of companies are characterized by two major areas. The existing ERP, CRM and controlling systems are a commercial, business management representation of the real world. In addition, there is the real world in production, warehousing and logistics, but also in buildings and plants. This real world is increasingly being addressed by sensors which is softening the boundaries between these two data worlds. This leads to conflicts of responsibility and an increase in interim solutions. These are often expensive and inefficient because the merging of the data worlds is not sufficiently considered in the minds of employees nor in the system architectures.

Many ideas are only implemented if the data is 100% accurate. So, I have to make expensive and time-consuming measurements, although I could derive this information also from existing statistical data. One example is the exact measurement of workplace usage by cameras or the observation of customer behaviour by correspondingly complex sensor technology. Apart from data protection issues of such solutions, simpler but less accurate solutions can ultimately achieve the same results through statistical simulations. They are, however, more cost-effective, and often also less critical in terms of data protection.

It is not always as obvious as in a workshop, where the head of the department, looking at the data pots, thought that this was the exact mapping of the organizational structure. But often data pots are also promoted by organizational structures and thinking in terms of responsibilities.

Of course, it is not enough to point out topics and observations in workshops. It is also important to give suggestions for possible solutions based on examples. Here we also use our technology partners, who in turn use the results and presentations of DataValueThinking for their own positioning. One example of this is our partner bimanu bimanu offers an integration and analysis platform for finding, realizing, and refining your data treasures.

In a short video, bimanu shows the role of data pools and what it means to connect them meaningfully However, bimanu is only one way of using data sensibly to increase the value of the company. In addition to the technical solutions and products, a prerequisite for this is to establish a data culture within the company. Use the methodological framework and creative approaches of DataValueThinking to unlock the hidden values of the data treasure in your company.

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