
Challenge
As you might expect, BI and data analytics follow similar processes of data collection, data analysis, and information delivery. The data collection stage, in particular, is crucial, as the best results are achieved by ensuring that the information collected is complete and error-free.
Both terms also imply reporting. This means that the data is organized and presented in a way that can be visualized. While raw numbers are important, it is when data becomes visual that it really begins to demonstrate its value, facilitating insight and action.
Business intelligence and data analytics can also identify areas where companies are failing or at least not performing optimally. In other words, they use the data they collect to show where the pain points are, giving organizations a better view of where they may be failing.
While both BI and data analytics involve using data to uncover insights that will benefit the business, there is a major difference to be stated. Simply put, business intelligence deals with the present, while data analytics is more focused on the future.
How do you leverage these analytics in a tight budgetary environment and with ever-changing quality standards?

Solution
With Microsoft Power BI https://powerbi.microsoft.com/ MLS has enabled its client, a public Spitex, to provide data to each municipality that finances its activities, the state of profitability of its services, broken down by type and including the impact of travel.
The latter are optimized by an automated planning system that considers various criteria, including distance, in the allocation of resources.
Based on Python tools, https://www.python.org/ MLS produced predictive models for a Spitex that wanted to focus its prevention on the theme of fall risks.
The analysis of the data determined that the correlation between standard alarms and falls was very low compared to what could have been assumed.
Therefore, the analysis was reversed based on the actual cases and correlations of clinical and social data of the clients to reveal a series of indicators that were relevant in 80% of the cases.
Thus, business intelligence combined with data analytics allowed on the one hand to report convincingly to funders and on the other hand to implement concrete measures on the risk of falls thanks to a predictive model.

Impact
The analysis of the data determined that the correlation between standard alarms and falls was very low compared to what could have been assumed.
Therefore, the analysis was reversed based on the actual cases and correlations of clinical and social data of the clients to reveal a series of indicators that were relevant in 80% of the cases.
Thus, business intelligence combined with data analytics allowed on the one hand to report convincingly to funders and on the other hand to implement concrete measures on the risk of falls thanks to a predictive model.
Business analytics can be widely distributed across the enterprise because access to the tools is relatively simple as long as the data is of good quality. As for data analytics, the implementation of predictive models is more complex and requires the intervention of our services.
The real added value of an information systems provider lies in its business knowledge and the ability to work in partnership with customers to model present and future activity in order to optimize it.