#40 Spending money on value creation or technical implementations?

Often, there are two challenges in implementing data economy. The first is to understand the business need and determine its value. In this regard, we have several other blog posts. Another common challenge is cost. In principle, data capturing is seen as consisting of costs such as data transfer, integrations, data retention, the cost and expertise of cloud service platforms, or the resources required. Many of these are easy to calculate even though there are several cost factors. The key question, however, is whether the money is spent more on technical solutions or value creation. If technical costs can be significantly reduced, it will be easier to implement profitable implementations from data to business needs. Therefore, one needs to think about how these things can be made more effective.


The first is to take into account integration platforms, where a large number of different systems are already supported and it is possible to get rid of one of the biggest costs, different integrations. In Europe, it has been studied that one integration costs on average around 15 000 € and takes 20-30 working days from design to tested implementation. By using integration platforms, several tens of percent of this cost can be dropped, both in terms of technical costs and time savings. When data is made to flow more cheaply and faster, it is possible to focus one's own time and effort on how the data is utilized.


Gartner has noted that over the next few years, application development will increasingly shift to low-code / no-code solutions where data can be utilized in hours or even minutes instead of weeks or months of development projects. As a result, it is possible to make the collected data available to those who use it faster but also more widely, which means that the data begins to have a greater impact on the business and the value of the use of the data increases. If everyone else is starting to use these technologies in next few years, why don't you start to use them before them?


As a third point, I highlight the analytical and visualization tools used. Often, static reports only reveal what is understood to be defined when the report is made. This results in constantly having to hire more resources to make new reports and develop existing ones for business needs. Another option is to use dynamic or customizable reports, allowing the user to drill deeper into the figures or perhaps edit the appropriate reports for themselves. Unfortunately, many of the applications made for this purpose are often far too difficult for those who need reports. If you need three days of training in using the tool to get the numbers you need out of the data, you’ve chosen a far too difficult solution to use. In terms of data usage, it is easy to understand that the more data is used, the more new needs become and the more skilled hands are needed to develop reports. Think about this right in time, you don’t want to choose a solution for visualization and analytics that users don’t know how to use themselves to a certain extent.


That is, instead of spending a lot of time and money on rigid technical implementations, do-it-yourself, or the old fashioned implementation, there are many ready-made solutions and self-service low-code / no-code solutions in the world that not only save you money but also you increase the speed of implementation, which is a very key issue for business competitiveness.