By Rich Wagner, President & CEO of Prevedere
Big data has been a hot topic for years, but few companies know how to harness the unprecedented amount of available information for bottom-line benefits.
Consider this: According to an IBM report on big data, nearly 15 petabytes of data are created each day - that's eight times more than all of the information in all of the libraries in the United States. Facebook alone processes 10 terabytes of data daily. For perspective, the entire Cornell Law Library is only one terabyte of information.
With this influx of information, it's no wonder executives are overwhelmed, with publications running headlines like "BIG Data Equals BIG Headache For Executives" (Forbes) and surveys showing that companies still aren't applying data adequately. A report by the Economist Intelligence Unit, for example, cites that 35 percent of executives lack the understanding of how to apply big data, and 62 percent of chief information officers (CIOs) report that big data buzz has resulted in unrealistic expectations from executives. However, harnessing big data - especially the external factors that impact business performance - is critical to maintaining a competitive advantage in today's rapidly changing business landscape.
The trick is being able to take the extraordinary amounts of data and boil it down to the information that truly affects business performance and operating decisions. Understanding how these key micro- and macro-economic factors can improve financial performance and decision making is critical for companies seeking to come out on top in today's highly volatile market, and the good news is that new technologies are making it easy to pinpoint the factors that correlate directly to a business - without the need for statistical degrees or a sophisticated understanding of economics.
Armed with critical information on how external drivers like foreign markets, commodity prices, manufacturing activity, consumer behavior, online traffic and weather data impact performance, businesses are poised to improve decisions in several key areas, including sales and demand forecasting, identifying marketing opportunities and threats and ultimately enhancing financial performance.
Sales and demand forecasting
Accurate sales and demand forecasts are imperative to making smart decisions companywide, yet most quarterly forecasts miss the mark by 13 percent, according to research from KPMG International. That means decisions on supply purchases, product offerings and availability, pricing, promotions and more are based on erroneous assumptions. Typically, such forecasts have been created using internal insights into sales and demand from previous quarters, but correlating those internal insights with the external factors that truly drive demand is crucial to improving these forecast measures. For example, Tiffany & Co., which obtains half of its sales outside the U.S., attributes nearly seven percent of its May-July losses this year to changing currency rates. Additionally, the global demand for luxury diamonds declined significantly during the month of August thanks to a slowdown in China, the world's second largest economy. By integrating such information into sales and demand forecasts, companies are in a better position to plan for and react to such changes in the global economy.
RaceTrac Petroleum, for example, was able to predict its foot traffic with 99 percent accuracy by examining how external factors impacted its sales, finding that weather was a particular determinant to traffic patterns and sales of key items. By incorporating its sales and vendor data with external drivers of performance, RaceTrac now stocks its shelves based on a more complete picture of business performance and has improved its forecast accuracy and financial performance as a result.
Marketing opportunities and threats
Incorporating big data in the form of external insights into sales and demand forecasting also drives smarter marketing decisions. Knowing how demand changes in light of changing weather, changing consumer confidence levels and changing disposable income levels allows companies to price more competitively - running promotions when interest wanes and keeping prices steady during strong markets. Likewise, such analysis is vital for determining when to introduce new products. For example, a car manufacturer that knows it can expect declining sales three months after housing costs start to fall may want to hold off on introducing a new model until housing prices start to rebound. If at the same time it knows that used car sales surge in similar conditions, it may want to encourage dealers to stock up on used inventory to meet increased demand.
A clearer picture of companywide performance
Internal performance data plays an important role in helping executives better understand how their company performed year over year (YOY). Yet without incorporating external drivers into this analysis, financial teams will only understand a portion of the factors that impact bottom-line revenue. Today, there are millions of data sets from organizations and governments available. Companies must make sense of this big data and integrate it into their internal forecasting process to create a reliable set of economic indicators that reveal the ebb and flow of customer demands. With this data, they can better plan for "what-if" scenarios, predict performance and communicate potential changes.
A seamless forecasting process
Businesses large and small are constantly faced with the challenge of how to better predict company performance. With the vast amount of exogenous data available, executives now have access to the information they need to make smarter decisions regarding sales and demand forecasts and budgets. Making sense of that data and correlating it to business performance is the critical challenge now faced by corporate executives, but smart technologies are making this information more accessible than ever before.
