Article originally published on bobsguide.
Twenty years have passed since the rise of the data-warehouse, BI (Business Intelligence), and EPM (Enterprise Performance Management). Companies invested in technologies to capture data and gain insight into their business to maximise cash flow and profitability with mixed results.
In spite of significant investments by vendors, internal IT organisations, and businesses, answers to key questions remain elusive. How much better can my operations become? What is ‘better’ anyway and how do I measure it? What is the most impactful path to accomplish my goals? How do I stay responsive to changes in a dynamic environment to reach my company’s optimal balance for sustainable growth?
In other words, how I do reach my efficient business frontier?
We can tell a direction, but how far can we go?
Back then, serious technical and data barriers stood in the way. Data access limitations and processing power prevented our ability to quickly understand the impacts of operational drivers. BI investments required the existence of a strong business case and ROI, which was often a hurdle for finance organsations.
Harnessing the intricacies of business end-to-end and analyzing how each process influenced company performance requires robust statistical algorithms—knowledge not readily available or easily accessed. Finally, not only inner dynamics between finance and operations had to be identified, but also their strength and real potential impact. Strong what-if scenario analysis to aid prioritisation efforts requires advanced simulation and prediction capabilities that few possess.
BI provided a window on our business, big data opened up thousands
The rise of “Big Data” lowered this barrier. Cloud infrastructure and increasingly cheaper hardware empowered everyone to capture massive amounts of information. Advanced analytics could finally run with little to no limitation. However, the initial hype led to excesses and often little measurable results, leaving finance in no better position than before. Key learnings came from initial failures and forward-thinking organisations are continuing to forge ahead. Many organisations realise these technologies can help tackle challenges such as simulation and prediction that were previously out of reach. Optimal responses to business problems can be generated to accelerate, support, and improve decision-making processes.
Decision-making is the core of management. Outside of descriptive reports delivered by classic BI, plans/forecast created by operational teams and personal hunches, leaders don’t have much else to support their moves. What if they could be oriented to the operational areas that are actually the main cause of an issue? What if they could visualise correlations and simulate the effect of any change of operational drivers on financial metrics? What if they could assess the financial impact and the operational likelihood to succeed on any initiative they take?
What seemed like science fiction a couple years ago is actually at the core of the big data revolution. These approaches not only leverage proven statistical approaches (daily weather forecast, NBA stats, and election polls are a daily representation of that), they can also be applied to the massive datasets we now store and compute. The cherry on top: progress in User Interface (thank you Apple) and visualisation made these power tools accessible to all of us simply by masking all the complexity within packaged solutions.
The evolution in our ability to manage organisations is similar to the evolution of map applications on smartphones. Recall early releases of online maps: weren’t we thrilled to know where we were at any time? This is similar to the big step forward brought by BI: getting a picture of a situation up to the day made a big difference in the way financial teams worked. Rapidly, maps became proficient in creating shorter or faster routes between two points. Similarly, with EPM, we could set plans with simple alternatives.
Today, map applications are able to weave accurate predictions that not only readjust to the context but also expose new optimal routes as we drive and warn us of obstacles we may encounter. ETA’s are constantly recalculated depending on traffic evolution or the options we take. We can simulate travel time depending on future times, route preferences and quickly measure the impact of a late departure or the choice of taking the bridge or not.
Predictive Analytics deliver the same value to business. They empower managers to identify the best course of actions focusing on the best drivers from the millions of factors that could potentially influence your desired outcome. It analyses and correlates end-to-end processes to goals and renders them in easy to grasp format in seconds. Speed of navigation and instant calculations enable finance leaders to focus on what matters when they are in the driver’s seat: vision, strategy, management and a constant objective to take the organization to its furthest limits—to reach the efficient frontier.
When was the last time you looked through your procurement process end-to-end to see the net effect of how long the process takes to execute, and what influences timely completion of the process? Can you easily correlate and understand the implications of problems that occur in your order to cash process and how much working capital is impacted by those problems? What might you learn if you could look through the whole cash to cash cycle instead of one off disparate steps? What new solutions for business challenges will appear by visualizing processes in new ways? It’s the age old adage of being able to see the forest for the trees.
One of the greatest value (if not paradox) of the “big data” revolution is that it puts us back in a high adding value role, allowing us to see our world in new ways.
About the author
Gauthier Vasseur is the VP marketing at Trufa and a Stanford data instructor. Formerly director business intelligence at Google and group controller at Remy Cointreau, Gauthier has developed an expertise and passion for finance and operations analytics which he researches, teaches and markets.
Amber Christian is the founder of Ace LLC. She has worked on SAP solutions for over 13 years. She focuses on working capital solutions, implementing accounts receivables, accounts payable, treasury and cash management solutions in North America, South America, Europe, and Asia. She is a frequent blogger and conference presenter on a variety of SAP finance and treasury topics.