We solve: How to use organisational data to support decisions?

Data of all kinds is of course already used for decision-making, and it comes in many different forms. Roughly speaking, data can be structured or unstructured. When data is structured, it is usually readily available, while unstructured data is not necessarily so. As the name suggests, structured data is clearly categorised according to the information, for example as an Excel spreadsheet. Unstructured data can be written text or even a person's own knowledge.

Whatever the form of the data, it is often the case that people do not know how to use all possible sources for the information they need to make decisions. One of the classifications used in this process could be as follows:

1. Defining objectives
2. Identifying sources of information
3. Collecting the data
4. Combining information
5. Editing and processing the data
6. Managing with data

The following is one such case of combining information from different sources to produce information for decision making.

Defining objectives

In many companies, the use of facilities is an important source of information worth money. Real estate is a major expense for organisations, and by optimising its utilisation, it is possible to improve the efficiency of use and even make savings. Satakunta University of Applied Sciences (SAMK) is no exception to this situation. Class sizes vary widely and so do the methods of delivery. As a rule, there are three types of delivery: face-to-face, distance learning and a combination of the two, i.e. hybrid. In addition to the delivery methods, the requirements of the facilities are determined by the needs of the subjects to be studied. In the case of SAMK, the use of facilities can be used to achieve a number of different objectives. For example, usage rates can be used to look at classroom occupancy rates in general, data to assist in work scheduling and the number of potential customers in the campus cafeteria.

Identification of data sources and data collection

Based on the definition of the objectives, the first step is to think about what data are available and how to make use of them. In the SAMK situation, the necessary data are available in three different systems. The work schedule data are in TimeEdit and the student numbers of the groups are in Peppi, which are needed for the situation we are aiming at. In addition to these, information on facilities is needed.

For the first two, the required source data is available through a REST API interface, which is usually available in existing information systems. The state data is in a separate Excel spreadsheet. After deciding on the collection method, one needs to decide where to collect the data. Usually for such reporting needs, separate data repositories are built with their own data structure for reporting purposes. This data warehouse can then be used, for example, to transfer the required data via these REST API interfaces. The data can of course be accessed directly from the data systems where it resides, but for larger reporting needs it is recommended to create a separate data warehouse.

In the case of SAMK, it was decided to use the Microsoft Azure Data Platform as the platform for the data warehouse. This platform contains, among other things, the tools required for building data warehouses, for data transfer in general and for data processing. There are many similar environments and quite often a simple database and the necessary tools to move and manipulate the data can be sufficient.

Combining, editing and processing data

Information is combined in a data structure according to the needs of the data structure. SAMK's needs require a work order to be known in the data structure, with the following information: date, time, implementation code, group code, facility code and implementation method. For groups: group code, implementation code and number of persons accepted for implementation. For premises, the following information: premises code, number of seats and purpose of the premises

Obtaining data from the data warehouse in a format that is readily available for reading and use requires a dedicated application. In the SAMK situation, Microsoft Power Bi was chosen as the tool. Other similar products include Tableau and Qlik Sense. These applications have built-in features that allow data to be refined or modified as required. In SAMK's case, the source data was of such quality that no major changes were necessary. In addition, of course, reports themselves can be built with various visual elements.

Data management

The information from these three different sources can then be used for data management or for decision-making in general. Of course, before going into production, it is important to ensure that the information is correct and that no errors have been made in the reports. Depending on the amount of data, this verification can be a big job, but it has to be done carefully so that no wrong decisions are made from incorrect data.

With the data what was done here with this solution can be found out:

• How much space is needed and whether this information can be used, for example, to change the way a space is used
• When making work orders, the report provides information on which space is suitable for the implementation and whether it is free on that day.
• The campus restaurant is informed about the potential number of diners in order to avoid food waste as much as possible

Once the goals are met, further development begins. This potential further development should already be kept in mind when starting to build a knowledge management system. If it has been well thought out from the outset, it is easier to start expanding and improving it.