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Automation and advanced analytics reveals change in customer demand at caring facility, Granhøjen.
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Healthcare & Hospitals
- Professional Service
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Predictive Maintenance
- Process Control & Optimization
- Remote Asset Management
Services
- System Integration
- Data Science Services
- Software Design & Engineering Services
The Challenge
At the privately-owned residences in Holbæk, it is important that the staff, services, and buildings continually adapt to the number of residents and their changing needs. Over time, Granhøjen has expanded from 40 to 120 employees, and as a result getting a complete overview of the company’s key figures has become a heavy administrative task. “The figures we used to give the board were a month old and out of date by the time we presented them,” says Mads Olsen, Chief Operating Officer at Granhøjen, “I felt frustrated that we lacked quick and easy access to information and weren’t able to make management decisions based on up-to-date data. It used to take around 25-30 hours a quarter to collect data via Excel for each board report.” Granhøjen also lacked insight into their residents and the services they required. There was no process in place to understand why residents had been discharged. It was unclear if residents had left because they had gotten better, or whether there was something about Granhøjen’s services they were unhappy with. This knowledge was retained by staff who spoke directly with the municipalities in charge of the residential contracts, but none of this knowledge was captured and used systematically for management information or process improvement.
About The Customer
Granhøjen is a rehabilitation centre based in Denmark. It has been offering housing, employment and treatment to adults with mental illnesses and social problems for more than 30 years. The facility has created a community where residents have the opportunity to work, get active and learn new skills. Through this process, the residents can create an identity that is not linked to their social problems or illness. They have approximately 120 staff including therapists, psychologists, and interdisciplinary staff who determine the best possible treatment for each individual.
The Solution
Mads Olsen expected a data project, that would proactively improve the business, to take a couple of years to implement and cost thousands of dollars. However, as soon as he saw Viteco’s solution, SPEED, he knew they could get results quickly, and cost-effectively. SPEED is Viteco’s approach to delivering fast BI projects. It is a fixed price package that combines Exasol - the leading innovative in-memory analytic database technology, Yellowfin - a powerful suite of BI and analytics products, and Viteco’s Data Suite tools and SPEED methodology. Using this combination of tools and accelerators, Viteco are able to deliver a minimum viable product (MVP) within four to six weeks. The team at Granhøjen were excited to get a Proof of Concept (PoC) in motion. Viteco worked with them to extract data from their business applications, load the data to Exasol, and run a workshop to define the reporting requirements before building a set of reports in Yellowfin. With Yellowfin, Viteco have found that you can report at a much earlier stage and do the ETL programming afterwards. The turnaround time for Granhøjen’s management team to extract their first reports was exactly a month and a half. “The consultants at Viteco quickly understood our business and what data I needed to access,” says Mads Olsen. “The price-to-value of Viteco’s BI solution, SPEED, on trial was exceptional. We knew straightaway we wanted to continue with the program. The cost of Yellowfin licenses alone was covered with the purchase of the complete solution, and we’ve saved about 25-30 hours a quarter by automating data collection.”
Operational Impact
Quantitative Benefit
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