CFO Webinar 2021 August

-Introduction-

Organizations have been facing challenges on how to extract the appropriate data from the vast volumes available and reach out to the right businesses for decision-making. Data analysis in Finance is critical to the prediction of sales trends, business optimization, customer experience improvement, and business innovation. At the beginning of the session, a simple question, “Is your organization currently utilizing analytics in your Finance Department?” was asked to the participants. 87% answered that they use analytics in their day-to-day operations to an extensive or in a limited manner. Even the 13% remaining answered that they wish to implement initiatives. 

1. Analytics in Finance Overview: Analytics is the practice of discovery and communication of meaningful patterns in data to drive business strategy and performance.

Pankaj Arjunwadkar, Partner, Deloitte Tohmatsu Consulting LLC shared the following insights: Analytics has gained significant importance, which is explained in terms of the “4 Vs” – VOLUME, VELOCITY, VARIETY, and VERACITY. VOLUME is the term that describes how both internal and external data have been increasing, and the speed at which the data is generated is represented by VELOCITY of data. Multiple data sources generate data possessing distinctive features and characteristics is represented by VARIETY of data, and VERACITY of data depicts the uncertainty of data or the quality of data. These 4 Vs are the key elements of Analytics. The overview also covered the concept that Analytics is the practice of discovery and communication of meaningful patterns in data to drive business strategy and performance, which includes the following 3 steps:

1. Internal & external data sources
2. Analytic engine
3. Meaningful insights

Analysis needs to begin with combining data from various sources both inside and outside of the organization. Once it is collected, it is important to conduct data cleansing so that it can be used to generate insights. Using a mix of mathematics, statistics, and descriptive techniques to gain actionable insights from data is an extremely important step. Finally, visualization is used to bring meaningful insights to be delivered. Afterwards, 3 types of categories of Analytics were covered to explain how to elevate it into Finance – Descriptive, Diagnostic, and Predictive & Prescriptive Analysis. In Descriptive Analysis, the rich data is summarized into useful nuggets of information to lead to KPI, while Diagnostic goes beyond Descriptive to analyze root causes and guide toward action plans. Predictive & Prescriptive Analysis is statistical, mining, and learning techniques to analyze data which is needed to predict data that hasn’t been obtained yet. Steps from issues in organization leading to outcome were also walked through.

2. Illustrative Case Study: How a multinational beverage company leads to the solution.

In the case study, a multinational beverage company is struggling to forecast sales volume. They are regularly over and under producing products, which leads to missed revenue and high storage costs. They have a number of different systems that do not connect easily and have siloed data that are only superficially analyzed. The current forecasting model is very simplistic and based on a % of last month's sales. Headquarters have said they need to address the production misalignment to ensure they don’t lose market share. The question is, how they can become better able to forecast the demand for their product lines to ensure they optimize their resources?

The Advanced analytics lifecycle framework was explained to show how to lead to the desired outcome. The steps are: 

1) Identifying the organization issues;2) Preparing the internal and external data with cleansing; 3) Analyzing business trends, high performing products, successful marketing campaigns and sales activities using statistical metrics; and 4) Predicting demand, supply, targeted marketing campaigns, costs, downstream impacts with statistical modelling techniques and advanced machine learning. This enables revenue & market share expansion, resource optimization, customer loyalty improvement, real time insights, operation efficiencies and enterprise agility improvement. 

At the end of this section, a question was asked to participants: ‘What is the top issue you would like to address using predictive forecasting? Several answers such as, ‘Improving accuracy’, ‘Learning how to decrease errors’, ‘Commission forecasting and ‘Customer retention’ were seen. To meet these inputs, a demonstration followed. 

3. Demonstration: Predictive Analytics for Cashflow Forecasting

Oliver Will, Manager, Deloitte Tohmatsu Consulting LLC presented a demonstration referring to "ABC Company", which is a global snack manufacturing company located in Japan since 1970. They have been using scattered data from multiple source systems. This has brought continuous challenges to robust cash flow forecasting and timely reporting. The requirements are to make the data standardized with the options of other relevant functions, enabling the generation of reports with tailor-made dashboards and insights. 

The demonstration showed the collection of the data sets required for both Short- and Long-term Cash Flow Forecasting Models. Once it is prepared, all the data is combined for coding. The demonstration illustrated the process of data preparation - clean and aggregate monthly data (even for Long-term, monthly data is better to have for more accurate forecasting, e.g., 60 months of data are to be prepared for the 5-year Cash Flow model), calculate expected payments by multiplying probabilities with outstanding amounts, then calibrate a logistic regression model for the payment probability to use as an example. At the end of the demonstration, 60% of participants felt that this Predictive Cash Flow Model could be beneficial to their organization.

4. Closing: Successful Use Cases for Advanced Analytics in Finance

To close the webinar, Pankaj Arjunwadkar shared 12 practical examples of 4 categories forming the pillars of Analytics in Finance: Financial, Consolidation, Optimization, and Predictive Forecasting. It should be helpful for many of the organizations in sorting out large amounts of data, extracting the appropriate data item from the vast volume of available data, and reaching out to the right businesses for decision-making. Throughout the webinar, 'Predictive analytics' and 'Working capital analyses' were the most interesting topics for the participants. See more details on the presentation materials.