Accounting is a practice of helping business leaders make informed business decisions. As quoted in a Business Insider article, “The CFO of the future will need to be the connector between the business and data scientists.” Peter Sondergaard, Gartner: “Data is the new oil, and analytics is the combustion engine.”
For several years, the profession has discussed Big Data. Businesses often ask, “What is it?” “Can it really help me?” “Is data and its interpretation only for large corporations?”
In finance, we stay attuned with both financial and nonfinancial information. Data is everywhere. We truly live in an era of the Internet of Things. This data can be turned into a wealth of information. Consider your digital marketing efforts. What if you could turn clicks and shares into revenue? Could a study of store customer foot traffic help identify staffing (a cost) and/or product placement?
Data scientists study unstructured data (i.e., email, reports, text documents, images) and structured (CRM and ERP systems, inventory, transaction information). As CFOs and financial leaders, we need to understand how such expertise can improve our business.
The big data process is a system of collecting, organizing, and analyzing data. Data is turned into information. Objective evidence and information helps leaders make informed business decisions. “A picture is worth a thousand words.” When presenting data to key stakeholders, consider how you present the material. What “pictures” do you provide? Dashboards, charts, infographics?
Analytics falls into 4 categories.
Descriptive Analytics involves traditional, historical information. Examples: Employee evaluations can be used to predict turnover. Current product reviews can be used to predict future sales.
Diagnostic Analytics describes the reason for historical results. “Why did this happen?” Examples: Variance analysis comparing budget-to-actual results. Causal analysis which describes why certain results occurred. The use of analytic dashboards describing why.
Predictive Analytics analyzes historical data and trends in an attempt to determine what will happen. Examples: Preparing a cash flow projection report or what-if scenarios when forecasting.
Prescriptive Analytics focuses on finding the best course of action given available data. It represents the final step.
To position oneself as a business partner, financial leaders need to bring this lens to the table.
“From the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two days…and the pace is accelerating.”
Executive Chairman, Google