Data only becomes information when you place it in the right context. Conversely, information is only information if it contains the right data.
When you are actively involved in a process, your brain is likely to convert data into information. The context is already present in your mind, causing data to almost naturally fall into place.
If you are further removed from a process, you need more context. The larger the organization, the greater the distance and the more complex the information needs often become.
In this situation, data needs to be provided with more context to be unequivocally converted into information. It is like a form of communication. You explain more details to someone who knows little about a subject than to someone who is an expert.
Reports and dashboards are also communication. They tell a story in the form of information. Just as you consider who the recipient is when writing a reminder, you do the same with reports. Otherwise, you won’t achieve your goal.
With a client who uses our software MA!N in more than 60 countries, we also had the challenge of converting data into information. This client has dozens of MA!N databases, and MA!N reads data from as many other systems.
The client wanted to reduce risks by having more global insight into trends and customers. They wanted to move away from an approach where employees had to analyze data country by country. The basics for this were already present within MA!N.
MA!N reads data from more than 60 countries. This data is spread over multiple databases, and each country logs into its own environment. This gives a local credit manager direct insight into everything happening in a country, such as the age of invoices, collection actions, disputes, productivity, and more.
In addition to collecting raw data, MA!N adds extra context, such as predicting the outstanding balance at the end of the month. This provides the organization with valuable information they use for risk management and cash flow management.
It doesn’t stop there. A globally operating organization wants information that goes beyond an individual country. A customer might be active in multiple countries, or there might be a need for insight at a regional level.
In addition to country-specific environments, there are also regional databases and a global overview. In the latter, information from all countries is collected and combined into information that tells something about the global operation.
This consolidation is not only for key figures but also for customers. Suppose you work for a customer like Sony in 50 countries. MA!N consolidates all these individual Sony customers into one Global Customer account. This allows the organization to quickly get an overview at a global level of, for example, the cost of credit and credit risk for international customers, without having to collect information per country first.
In this example, MA!N not only collects and presents data but also combines data from different sources, consolidates elements (customers) into one, and performs predictive calculations (outstanding balance at the end of the month). It creates context, turning data into information.
To convert data into information, you need to know the information need. This is important for determining the context, but also for how you want to handle the data. You can perform real-time calculations each time, but if it concerns historical data, it is faster, cheaper, and less error-prone to store that data periodically.
For instance, you periodically store data when you want to compare periods over time. These are static data that you only need to store once for recurring use.
The information the client from the example uses ranges from DSO to percentage automation. In the latter case, the client looks at the tasks that MA!N performs without the intervention of an employee, both for the organization as a whole and for individual countries and customers.
The result for the client is that less time is spent on creating reports and analyses. MA!N automatically generates reports and sends them to the right people. Where employees were previously mainly occupied with collecting data, the focus is now on analyzing and benchmarking data. Plus, costs are lower, there is more control over risks, fewer write-offs, and the DSO is lower.
By converting data into information, you can manage better, and that yields profit.
You might also be interested in our article on data exchange between MA!N and other systems.