Organizational leaders who make decisions based on pockets of business data don’t get a 360-degree view of operating conditions. This lack can lead them to take actions that aren’t completely informed, resulting in lowered accuracy in projected business or operational outcomes.
Keep Regulatory Headaches at Bay
Limited views can also lead to regulatory headaches as corporate finance departments are under increased pressure to improve and accelerate closing and reporting processes.
As a PwC report on the topic explains, regulations such as Sarbanes-Oxley require companies to deliver reliable and transparent information” . . . and today’s public markets often reward companies for providing transparency that goes beyond regulatory requirements.”
The use of a predictive analytics platform can synthesize data that’s collected from across the enterprise, as well as market data and other external information. This expanded information helps company leaders act on a complete range of information, allowing them to forecast business and operational outcomes with greater precision.
For example, behavioral, transactional, and perceptual data from consumers may indicate that US consumers are growing more confident about the direction of the economy as well as their own financial status.
Historically, when consumer confidence reaches a certain level, it often indicates that consumers are more willing to buy big-ticket items such as cars, appliances, and television sets.
More Accurately Project Retail Sales
Business leaders for manufacturers and retailers use these insights along with current sales trends and customer data to better project where sales are expected to trend over the next three to six months. CFOs and other business leaders must balance operational costs with sales.
Decision-makers can use predictive analytics across a wide range of data sets for a more holistic view of how transportation, cost of goods sold, labor, inventory, and other operating costs are trending.
These insights, when blended with external data such as anticipated changes in energy costs, can be used to help leaders better forecast operating costs.
Additionally, these analyses can identify critical changes that may warrant actions. One example is an expected change in a state’s minimum wage and how that change would impact labor costs in industries such as retail and fast food.