Learning Lab #2: Big Data and Predictive Analytics

People have always longed for the ability to predict the future. Today, many hope that big data and predictive analytics will help them do so. Although big data and predictive analytics can deliver major benefits, it is important to understand what they can realistically help organizations achieve.

Many businesses have accumulated massive sets of big data and invested in various types of data warehouses, with the goal of using predictive analytics to obtain a model of the future as well as a better understanding of the past. Lisa Parks, author of Cultures in Orbit: Satellites and the Televisual (Duke University Press, 2005), describes this as “diachronic omniscience,” essentially meaning the ability to gain knowledge and insight from analysis of comprehensive data patterns accumulated over time.

While one might think that the term “big data” suggests its value lies in getting as much data together in one place as possible, its true quality is based on the volume of data, its variety, and the velocity with which it was obtained. However, even a colossal amount of data is meaningless without tools to harmonize, unify, and/or sensitize the information. Thus, the power of big data comes from the insight, needs, and opportunities that can be gleaned from its analysis. Properly leveraging big data with predictive analytics provides relevant information and insights in real time, enabling a company to be proactive instead of reactive. Big data is not about predicting the future, it’s about understanding the past and then taking a leap of faith that the patterns will hold in the future. Stated simply, big data can help you mine for information to make better decisions or identify new opportunities for the future that others might miss.

Big data and predictive analytics can help tax professionals comb through massive amounts of transaction tax data for aggregation into returns, for audit defense, or for other analytics (like prepping data to run a reverse audit). Here are some questions for tax professionals to consider when thinking about the role of big data and predictive analytics:

• How could predictive analytics help to identify areas of audit exposure before auditors conduct a review, allowing time to plan corrective action or mitigate penalties?
• How might machine learning help sniff out risk areas for tax exposure?
• How soon are tax authorities likely to leverage big data technologies to aid in fraud detection?
• Regarding reverse audits, how might simulations help assess whether it is better to perform this work with internal resources or hiring a third party on a contingent basis?

Resources to facilitate learning:
https://hbr.org/2012/10/big-data-the-management-revolution/ar
http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation
https://www.sas.com/en_th/insights/big-data/what-is-big-data.html
https://whatsthebigdata.com/2016/01/09/iia-forrester-idc-and-gartner-on-the-future-of-big-data-analytics-and-cognitive-computing/