On a recent Wednesday in May Fidato Partners held the first of many breakfast seminars to discuss challenges that impact our industry and clients. The audience and active participants were the Finance leadership (CFO, FP&A Directors, Controllers etc) from almost a dozen companies, both large and small, from the Philadelphia area. From Fidato we had our founder John Rapchinski and several of our senior technology leaders that have broad experience in Finance and Technology projects across many industries.
This first Breakfast Seminar was to discuss the challenges of IT and Finance departments in regards to Business Intelligence and data integrity, scalability and functionality. What we have seen is that business groups within an organization have purchased business intelligence tools (such as Tableau, Qlik, Power BI, etc) with low initial costs and that are easy to use in order to view and slice-and-dice data. In theory, these implementations allow for quick turnaround times and put the technology into the hands of the users that want deeper insights.
Any of these BI toolsets can show a very elaborate and sophisticated story based on the underlying data, which makes these tools great commodities to have in your analytics suite. However, what we have found in many cases it is that the data is not always complete, the calculations and FP&A standards are not the same in all departments or across all regional offices, and that the data being used as the input into the toolset is not always timely and accurate.
Fundamentally these challenges break down into three major questions around the implementation of a BI tool and the underlying data:
- How did it work out overall?
- How did it scale across business units and regions?
- Could you always trust the data you saw represented in the BI tool?
With this setup as the premise of the conversation, we had a very successful event, not only because we were able to interactively showcase a powerful BI tool and some impressive dashboards, but because we had a great discussion with our FP&A attendees that represented many types of industries (professional services, medical devices, consumer products, healthcare, clinical research, etc). The discussions had the same theme for these organizations as the root cause of the problem: a lack of consistent language, understanding and models on what the underlying data and BI metrics actually mean. On top of the communication issues, companies were also challenged with being able to pull data from many different systems to ensure that all of the relevant information is available to make accurate and strategic business decisions. In the end, the discussions were not about IT and the analytics tools but rather about the business units being on the same page for what was meaningful to the organization and how can they get access to everything they need.
At the end of the discussions there were several lessons learned that everyone agreed on:
- A Business Intelligence solution first needs a planning and communication mechanism in place to ensure that all metrics mean the same to all departments (a set of well-defined standards). When a C-level executive asks how were sales in a specific region for a product, everyone should be answering the same way. Unfortunately this question would often be answered by one group replying back with Gross Sales, another group reporting Net Sales and a third group making up an entirely different calculation.
- IT must be a part of any solution to ensure that the solution has all of the different data sources needed for insights. However, all parties must understand that the building out of a business intelligence solution requires a partnership between the IT department and the business departments in order for the solution to be successful.
- Data will change, new insights will be needed and a business intelligence solution needs to be flexible enough in its architecture to allow for these business changes to be implemented.
- IT and Finance have different meanings for the same words and this is part of the disconnect between the business and IT that often causes the confusion of what was expected and what was delivered. For example – an IT person may say that I am going to allocate a percentage of sales across a newly created dimension within an organization to help with planning of this data. To a finance person, Allocation has a completely different meaning on how that data will be distributed across the same newly formed dimensions of data. We saw many examples of this kind of disconnect in planning and communication resulting in BI implementations not providing the expected business value.
We are looking forward to holding many more business intelligence and data discussions in the future to help dive further into these lessons learned, methods for successful implementations and discussing how to design flexible solutions to keep up with rapidly changing business needs.
By: Chuck Rivel and Amit Basu