The Value of Data Analytics

by Kevin C. Bach, CPA, CVA

Your business is awash in data that, if displayed and mined properly, can help you grow and profit. Make that data work for you. Through data analytics, you can better see trends and patterns to help your business improve performance and identify risks and red flags. In addition, building analytical models can allow you to forecast where your company is going, and help you create a plan to optimize and direct that growth.

So, what is data analytics? Data analytics is defined as the science and art of discovering and analyzing patterns, identifying anomalies and extracting other useful information in data. It also encompasses identifying a question or problem and utilizing the data, both financial and non-financial, to determine an answer or solution. Practically speaking, data analytics is turning your run of the mill data collection into a plan for the future of your company.

There are four main areas of data analytics: • Descriptive Analytics – What happened? • Diagnostic Analytics – Why did it happen? • Predictive Analytics – What will happen? • Prescriptive Analytics – What should be done?

Descriptive Analytics – What Happened?

Descriptive analytics is the utilization of analytics to describe what happened. Generally, descriptive analytics entails the gathering of historical data/transactions

and using this data to answer questions about the past and provide context to historical activity. • What were the Company’s sales last month, last quarter, last year? • Who were the Company’s largest and most profitable customer last month, last quarter, last year? • Who were the Company’s largest vendors last month, last quarter, last year?

One of the first steps in building an analytics platform is determining what questions the company’s management has. Descriptive analytics are usually the entry into analytics and ground level on development of data aggregation, future analytic projects and business intelligence. These initial questions provide the foundation for setting goals and determining key performance indicators (KPIs) by which the Company wants to evaluate its operations. Utilizing a dashboard or a series of graphs and charts, a Company can visually see the answers to What Happened?

Diagnostic Analytics – Why Did it Happen?

Diagnostic analytics is the utilization of analytics to describe why it happened. Diagnostic analytics take the development of descriptive analytics a step further and is generating a root cause analysis for: • What caused the Company’s sales last month, last quarter, last year to increase or decrease? • Why were there changes in the Company’s largest customer? • Why were there changes in customer profitability?

Diagnostic analytics generally look to: • Identify outliers and anomalies –

Upon review of descriptive analytics, a company must look for events that transpired without a reason or large transactions that are skewing the analytics. An example would be a customer creating a large purchase as they got additional debt or grant funding. • Drill-down into the data – The team will drill-down into the data and the analytics to look for patterns and trends that could explain why an event happened. Establish causal relationship – The team will look for relationships that are directly caused by the occurrence of other events. An example would be the increase in website traffic could be correlated to additional ad traffic or a change in the Company’s SEO methodology. Identifying causal relationships can be done with multiple methods but can include time series and regression analysis and probability theories.

Predictive Analytics – What Will Happen?

Predictive analytics is the utilization of analytics to forecast what will happen in a business. Predictive analytics is the usage of everything gathered in descriptive and diagnostic analytics along with the continued use of modeling, machine learning and risk assessments to make predictions about future events/trends by scoring data and forecasting. A company’s use of predictive analytics is a major improvement from looking at historical transactions/events and now looking to analytics for forecasting the future.

Predictive analytics generally use models to analyze relationships, typically between the performance of an event and that event’s attributes. The goal is to determine and assess the likelihood that a similar event in a different sample of data will result in the same or specific performance. With the increase in big data and data storage, the size of a company’s sample data is growing, which allows for more precision on the likelihood of future events. In addition to prescriptive models, predictive analytics also employ descriptive and decisions models. Descriptive models quantify relationships in a series of data and then employing those relationships across future events. Decision models are also utilized where the relationships across all items of a decisions are utilized. Thus, the results of predictive models, plus a decision made and the forecasted results are merged to predict the results of the decision made.

Prescriptive Analytics – What Should be Done?

Predictive analytics is the utilization of analytics to forecast what should happen. Prescriptive analytics is generally the final stage of a data analytics platform. Using prescriptive analytics, a business can begin to plan for predicted outcomes, then simulate and optimize the best way to address those outcomes, generally with the use of machine learning and artificial intelligence.

While prescriptive analytics is generally the final stage of analytics, it is continuously changing for a business as the prescriptive analytics takes in new data and processes it. Utilizing new data and revised predictions, a business can now have improved prediction accuracy and potentially improved indicators for business decisions.

The applications of data analytics are as boundless as your imagination. The beauty of data analytics is the ability to gather data across a variety of platforms, whether it is a point-of-sale system, accounting package, SEO and website traffic, or even inventory movement on a production floor to other external sources such as weather statistics or vehicular traffic activity. The ability to combine these data points and look at historical events and patterns is a start; but using these events and patterns to model the future is the vision.

The question your company needs to answer is what business question you want answered. Once you arrive at a question, the key is to ascertain what data is available and if the data is not currently available, what changes can be made to collect the right data. Quality data is critical as it will be relied upon to answer questions of the past and build models into the future. Evaluate the data a company is currently collecting and think of additional data which can be collected which will help answer the question.

The cost benefit is clear. Taking your data analysis to its next level can lead to improved prediction models, customer relations, corporate compliance and better communication with stakeholders on past or future events. In short, can you afford NOT to dive into data analytics?

Kevin C. Bach, CPA, CVA, is a partner at Henry+Horne. He specializes in “big data” and has built the firm’s data analytic niche. He is experienced in setting up interactive dashboards and visualization tools across a variety of platforms to improve internal operations and review operations for effectiveness. He will be speaking on this topic at the ASCPA Governmental Accounting Virtual Conference — Feb 4-5, 2021. You can reach him at kevinb@hhcpa.com.