The goal is to provide a tool for small and medium sized enterprises (SME) that lack the resources and knowledge to implement a sophisticated business intelligence system or carry out data analytics. Yet, the solvency of such companies is now especially endangered. Without proper business insight into their financial situation, customers, goods, and suppliers, they may run into troubles of providing ongoing service. Therefore, I want to make use of my experience gained in several management reporting engagements, during construction of various data analytic tools, and many years of business consulting in order to support everyone with a ready-to-use tool in a popular technology setting.
What it does
The tool covers the whole process of transferring data into meaningful business insights and even provides recommendation out of its findings! Thus, it provides three different components: data transformation (ETL), data visualization/discovery and data analytics. During data transformation, the tool starts by extracting company data of different structure, transforms the data for further analysis, and loads them into a single data model. Customized dashboards provide self-service analysis based on the data within the data model. Leading practice dashboards about certain business aspects (e.g. Working Capital, Sales, Customer, Accounts Receivable) provide guided analytics and main drivers. However, another novelty is the data analytics component that analyzes the data according to all aspects within the business dimensions and the time dimension. An algorithm determines major business changes, evaluates them according to proven analytics methodology, compares them against each other, and derives actionable recommendations for the business owner. Eventually, the dashboards, financial insights, and recommendations can be shared with others in a automatically generated report.
How I built it
As SMEs require such technology support immediately, lack financial resources, and time to implement new applications, I use a platform that many are familiar with and often is already available. Therefore, I compiled it in the first draft in an excel-based environment. This gives us the advantage of low implementation cost, very low running costs, and easy sharing. Moreover, I constructed it in a modular way, so that during time more components, dashboards, and data can be added.
Challenges I ran into
During the development, I touched many aspects at once: Coming up with an appropriate solution, simulating appropriate business data, building a prototype with many different coding languages (Visual Basic, M-Language, DAX-Language) and tools within excel (Power Query, Power Pivot) that I am not all an expert with. Moreover, I had to decide for the right balance between the content of the prototype and preparing the concept for the pitch.
Accomplishments that I'm proud of
I am proud of the solution itself but also of how far sophisticated my prototype already is. Considering that all has been done from the scratch in 48 hours, I made quick progress. Moreover, I am proud of simulating approx. 220.000 realistic sales for a period of 2,5 years and included the COVID-19 influence in the figures. Thus, I was able to construct a working dashboard that allows different kinds of analyses.
What I learned
Being part of this hackathon and joining many webinars, I was impressed of how much people can achieve and realize when they come together on their own motivation to help others. For myself, I learned that many things are possible within 48 hours. Moreover, I realized that I cannot expect to have a fully working product in this short time frame but may focus on feasible aspects and still get an amazing prototype.
What's next for Performance Tracking and Recommendation Tool
As the current prototype is just a starting point, the next step would be, to implement the data analytics part and leading guided analytics dashboards to cover further financial analysis. The concept is clear and the algorithm is ready to get realized. Within a few weeks including testing, we could distribute the first tools to provide quick support to SMEs that are desperately in need. Even afterwards, we can add additional functionalities and scale it up to further use cases.