Since spreadsheets were invented in the 1980s, they have been the tool of choice for anyone doing planning and analysis. Financial analysts use them for the annual budget, the long-term strategy plan, allocations, profitability modeling etc. Sales and operations planners use them for capacity planning, sales planning, scheduling and much, much more.
Spreadsheets were more than just a productivity tool to perform the same tasks faster: they brought opportunities to do things BETTER! Better models and more detailed analyses led (potentially) to better business decisions! And a bigger workload for analysts.
Let’s fast forward to our 21st century present where we are used to having easy access to data in our personal lives, and naturally expect the same to be true in our business lives. According to McKinsey, Deloitte and other analyst firms, expectations of the Finance function are evolving towards providing real-time, data-enabled decision support. Spreadsheets can no longer keep up, but this trend rightfully places Finance at the strategic center, using its historical ownership of whole-organization financial data, performance metrics and analysis as the springboard.
Speed and scale are the forces driving this evolution. 10 years ago, investing in a planning software platform to supplement your spreadsheet models would put you in front of the curve. Today, this is only the starting point! You need to reevaluate your data capture, data flows, and business workflows in the context of your strategic goals. If you are not capturing data up-front at the frequency and detail you need, you will never get the return you hope for from your big analytics investment. If your planning models include complex mathematics and clever algorithms that no one understands and buys into, you will not be able to manage performance effectively. If you do not have data governance processes in place to preserve the integrity of the data as it moves through your organization, no one will trust - or use - your dashboards and reports. And if you are hoping to leverage the power of predictive analytics and other Artificial Intelligence (AI) based technologies in the future to improve demand forecasting, or optimize your pricing decisions, keep in mind that data is The Fuel for AI, and the more the better. Your return on investment will only be as good as the quality/volume/availability/detail of the data you have.
The bottom line is that if you implement a new technology (like planning software) without taking the organizational context into account, you will probably be disappointed with the outcome.
The good news is that proven frameworks and methodologies already exist in both business and technology that can guide you.
- Broadly speaking the term Digital Transformation is about using technology to transform your organization and its processes. While much of the media coverage focuses on transforming customer experiences and business models, there is a third family of applications centered on transforming operational processes, which is where Finance and Performance Management fall.
- Digital Transformation gives us a language to explain why some companies deploy technology with much more success than others. The secret lies in understanding that technology is only one part of a system that also includes people, and how they work with each other. This article “Why So Many High-Profile Digital Transformations Fail” contains specific examples that resonate even within the more constrained world of the Finance function. If AI is part of your wish list, read about how “Building the AI Powered Organization” is more about breaking down siloes and changing how people collaborate than about technology!
- Design Thinking and Agile Software development methodologies are not the same, but they are user (customer) centric and share two important characteristics: iterative development, and feedback loops (to use the Agile terms). Design Thinking calls it Prototyping and Testing.
Essentially, it’s common sense: no matter how good your idea or how many experts you have consulted, there will always be unknowns. Therefore, the most sensible approach is to build in opportunities for learning into your process. And the way to do that, is to take small steps, reevaluate progress at every step, and be ready to adjust your goals based on what you learn. This piece called “Digital Doesn’t Have to Be Disruptive” provides some practical examples.
- Data engineering, and its related disciplines of data modeling, data governance, data science and business intelligence are well established fields backed by a ton of academic literature, and practical guides. Look for experts to partner with who understand how to apply these technical skills effectively in service of clearly defined business goals. When combined with an Agile approach and a Digital Transformation mindset, the chances of success increase significantly.
At the end of the day, taking things in small steps, getting/responding to feedback, and having engaged business users have always helped to make planning and analysis software implementations successful. Here’s what has changed:
- There is now a language “digital transformation”, born of urgency that explains it.
- Neglecting the people and process aspects, are a recipe for failure.
- The urgency is real: companies that accept the reality that you have to build your analytics capabilities on firm data foundations (even if this means slowing down and investing in the near term) will outperform the ones who think that they will be able to keep up by throwing more people at more spreadsheets.
Want to learn more? Join us on Thursday, January 9th for Digital Transformation in Finance and Performance Management Webinar.