In this post, we will discuss how the information processed in Situm that we have presented in our new APIs can be integrated into Business Intelligence tools. This allows you to create reports that combine geopositioned data with the organization’s information to respond in a simple way to operational issues of great importance when managing large spaces and workforces.
Our new APIs for Reporting
The Situm Platform extracts real-time information from more than 300 million geo-positioned events every day. We are no strangers to how data analysis processes can bring competitive advantages for companies. Therefore, we have a clear goal: we want to provide our customers with relevant, useful, and accurate information for their decision-making processes.
To that effect, Situm Dashboard provides reports that provide answers to key questions such as:
- How many visitors have I had?
- What have been the most visited places in my buildings?
- How many people have passed through a certain point of the building?
- Where have my machinery and workers been?
- Where has a certain worker moved through?
Not only that, but Situm Platform also provides a reporting API, which allows integrating all this information into third party systems with minimal latencies.
Some of these reports can be integrated as-is, although it is common to want to adapt or modify them to fit specific use cases and analytics. In this sense, Situm APIs can also be integrated with BI tools, such as Power BI, which allow you to quickly create reports combining internal information with that generated in Situm.
Situm & Microsoft Power BI
Power BI is an interactive data visualization and Business Intelligence software developed by Microsoft. Integrating REST APIs with Power BI is simple and has great potential, as it allows you to combine data from different sources to perform a joint analysis. This allows you to combine data generated by Situm with your own information: for example, cross the stay times of users in specific areas of the building with the work management system to easily audit the service.
We have helped multiple organizations to develop their data intelligence dashboards based on the Power BI template available on the Situm platform. This contains several predefined reports on the time spent in the spaces defined by the manager through the geofences tool and, in turn, is a good starting point to develop new Power BI reports in which geolocation information is analysed and crossed with data from the organization.
As we will see below, this resource is extremely easy to use, just follow the steps below:
1. Open the template with Power BI. First, you will see a form where you must fill in some data fields. This data will be used later to make the Situm API calls. Here you will be asked for two dates (formatted YYYYY-MM-dd) that will delimit the time range of the data to be downloaded. Due to the large volume of data we handle and the API limits that currently exist, we recommend that you limit the first load to a maximum period of one month. These values can be changed later to explore longer periods.
2. Configure authentication. Next, you will need to select the “Basic” authentication type and enter your Situm Dashboard username and password. Once authenticated, the data will be downloaded.
Once the download process is completed, you will be able to see the following reports for the evaluation of the time spent in the geofences you have created:
Visits and stay times on geofences. This report shows information about the stay times on geofences and the number of times each one has been visited. It has several filters (date range, buildings, geofences…) so that you can narrow down the data and focus on the ones you are really interested in analysing.
Users stay times on geofences. Analysis of the time spent in geofences focused on the users of your organization. You can see which geofences a user has visited, the cumulative time spent in each of them and a detail of the geofence sessions that have occurred in your organization. Like the previous one, this report also incorporates several data filters so that you can narrow down what you are really interested in visualizing.
3. Build your own panel. If you already have experience with Power BI, you can start by exploring the basic data model we have implemented and create your own dashboards and filters.
You can also extend the model we provide by adding new data sources through the SITUM API. You can see all available endpoints and their query parameters here.
4. Save the file. The file we provide is a template. Once loaded, you can save it as a book to avoid being asked again for the configuration parameters when you open it again.
At this point, you could also import your own data from any of the data sources supported by Power BI to make a combined analysis of your data with the one provided by SITUM.
Check out the next video to watch the whole process:
How to extend Situm’s data model to improve analytics with PowerBi
As our data model is for general purposes, it may not completely fit your needs. For example, you may want to perform grouping and geofence categorization or incorporate other data sources internal to your organization into the analysis. Here’s how to do it.
Let’s say there are different categories of workers in a shopping mall:
- Assistance services.
Furthermore, let us assume that the areas are classified according to a strategic identifier:
- Areas of direct service to tenants: the fronts of the different leased premises.
- Transit areas: areas where the worker does not perform actual work (hallways, elevators, common rest areas…).
- Private service areas: areas where the worker is performing tasks related to the direct management of the property…
In this case, each area can also be associated with one of the different groups of employees within the centre (cleaning, maintenance, etc.) discussed above. For example, if the WCs are associated with cleaning, this means that the users included in this group perform their work there. We would have then a specific filter by user group to perform analysis by groups. With this, we would like to create a report that provides the following information:
- The percentage of the total time the worker has spent in the different spaces in relation to the activity they have performed. This information will allow us to identify how much time they spend in transit or in areas not directly related to their work to see how to correct this (schedule changes, improve work planning…)
- The average length of stay by activity and employee. This would allow us to know if the permanence is on time, if the area is visited many times, or if it is a prolonged stay.
- Which are the geofences where the users spend the most time. This will make it possible to identify which are the more visited areas and to check if there is a balance in the time spent in the different areas.
- The time that the different services provide to each of the tenants to justify the agreed times or specific claims.
- In short, to easily check whether the service is being provided in an adequate and optimal way and, if not, to easily identify the most relevant points for improvement.
1. Import users and groups’ information
First, we will import the information of the users and the groups they belong to. In this case, we will get it from an Excel workbook (you can see how to import data from Excel here).
2. Integrate information about geofences
Secondly, we will incorporate the job type and group information into the geofences that have been created in the Situm Platform. Fortunately, Power BI is extremely efficient at parsing CSV formatted data, so we can use the “code” or “information” field of the geofence editing form to enter this information. In this case, we will use the code field:
As you can see, the first field of the CSV refers to the strategic zone identifier, the second to the worker’s category and the third is a geofence identifier. It is very important to note that all geofences must respect the same field order. Once the information has been entered, with Power BI we will create new columns from the “code” field with the option to split column by delimiter. The result will be as follows.
If we go to the data model screen, we can see how Power BI has already inferred the relationship between the group and the users.
3. Build the Power BI panel
Finally, all we have left is to create the panel with the information we have incorporated.
We can help you!
As you have seen, the analysis of geopositioned information with Power BI has a huge potential and allows to solve problems such as:
- Routes and schedule optimization.
- Cost allocation to activities.
- Congestion analysis in certain areas.
- Ratio computing and productivity indicators.
If you are interested in creating Power BI analytics or implementing specific metrics with the information generated in Situm, our support team will be happy to provide you with the template and assist you in the process of building your dashboard.