In today’s world, companies need more than ever fast insights to make the appropriate critical moves. Unfortunately, current BI tools through dashboards report ‘what’ happened and does not help to understand the ‘why’.
In this article, we cover how organizations may achieve ‘true’ insight (learning the ‘why’) fast by looking to future-facing technology: augmented analytics.
Augmented analytics describes the use of enabling technologies such as Machine Learning (ML) and Natural Language Processing (NLP) to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI solutions.
Through the integration with BI, Augmented Analytics takes traditional BI dashboards to the next level, improving the speed and quality of decision making.
The challenges facing FP&A today
In recent years there has been a constant increase in the pressure regarding both efficiency and value creation the FP&A must meet:
Pressure on efficiency
- Expectation of an increasing speed of response
- Exponential growth of data required to be processed, controlled and analyzed
- Increasing number and frequency of requests from business partners
At the same time, 81% of CFO’s expect staff to remain the same or decrease in 2021, according to the latest APQC study.
Pressure on value creation
- Expectation of data quality improvement (reliability, accuracy)
- Expectation of better data insight
- Expectation of better support in decision making
This pressure has been reinforced during the pandemic, with executives being even more demanding, as companies are experiencing more volatility, complexity and uncertainty than ever before.
Current BI solutions don’t address those challenges!
Financial analysis is still mostly done outside BI solutions
In today’s reality, in most cases, controllers download data from BI dashboards into Excel to determine root causes and uncover meaningful, actionable insights in their effort to piece the story together.
Financial analysis is performed completely manually
Current BI solutions still lack the capability to automate partially the process of producing ‘true’ insight. For instance, at the question “Why the sales increase in Q3 compared to Q2?” the next generation BI solution should be able to answer “sales increase in Q3 compared to Q2 by 15% due to:
- A customer churn reduction of 5% in the US, caused by a July 10% discount offer.
- An increase of customer numbers of 10% in Europe, as a result of your promotion campaign last September performed on social network.
How Augmented Analytics can help change the game?
Augmented Analytics leverages the power of Machine Learning (ML) and Natural Language Processing (NLP) to put data science capabilities into the hands of FP&A people.
Integrating Augmented Analytics into BI solutions would provide critical benefits to FP&A, such as:
- Saving time: Uncover the root cause of conditions like customer churn, sales revenue or growth margin drop, allows shortening the time spent by Financial controllers in analyzing information and producing the report.
- Speed: faster access to important insight, allows organization to make critical moves in a fraction of time they used to. In today’s world, it is a critical advantage for seizing opportunities and avoiding pitfalls
- Making a better-informed decision: artificial intelligence (AI) associated with BI solution can automatically analyze all data, not a portion of it (as today), providing insights that would have not been possible otherwise, improving the decision process.
- Reducing insight bias: makes it possible to confirm or refute intuition-based hunches from leadership on the fly.
- Democratizing data access: Using natural language query (made possible by AI), increase the range of employees in the organization who can intuitively find answers themselves and extract insights from data, thereby improving data literacy and freeing more time for FP&A staff.
How to make it happen?
Organization can make augmented analytics available to the FP&A department in two steps (extremely summarized here):
Step 1. The organization builds multiple ML models on its ML platform to diagnose conditions (Sales revenue, demand, profit margin, churn…) it wants insights from. The steps required to develop, deploy and maintain ML models are well documented and explained in multiple articles and webinars of FP&A Trends.
Step 2. The organization integrates those models, manually or automatically, into their existing BI solutions dashboard. In short, traditional key performance indicators (KPIs) presented in dashboards are completed with augmented analytics capabilities, from ML models build on the platform outside BI solution.
Although this approach can be implemented today, it does not offer the ease of use and speed of a one-stop-shop solution seamlessly integrating BI and Augmented analytics altogether. BI solutions are not there yet, but it may happen sooner than you think.
“Hope and pray” was never a strategy, and now “wait and see” is no longer an option in today’s environment either. It’s time for the FP&A function to reshape the way it operates.
AI combined with BI can help with it, by solving the impossible equation it faces today: Provide better insight in less time to decision-maker, with the same number of staff.
Today's FP&A challenges will not be solved by augmented analytics alone, but it can certainly contribute significantly.