Predictive Analytics Redefine Data Utility
Ensuring a healthy future depends on understanding the past: That’s the fundamental premise of predictive analytics.
Data scientists collect and analyze raw data to find patterns and connections. They study whether past activities worked in unison and help decision-makers gather information to improve future operations. Predictive analytics could change how the world does business — and how you do business, too.
As early as 1958, when FICO founders first used predictive modeling to assess credit risks, industry analysts recognized the benefits of using past information to formulate future strategies. Today, the internet generates massive data quantities for that work.
For example, Microsoft analysts aggregated anonymous web search data for auto sales forecasters and parsed it out according to store locations. The result was a future sales forecast that supported streamlined inventory controls for each site. The forecast relied on each dealership’s past performance and relevant online consumer activity to predict future inventory demand.
As witnessed in the 2008-2009 financial crisis, predictive analytics can both, help and hurt those effected when the data does not follow the “norm.”
Royal Dutch Shell analysts use data they gather at each drilling site from probing into the earth. They use their tools to register whether the wave patterns tectonic plates make are distorted, indicating that they’re passing through oil or gas. They transfer this data to private sector analysts who compare the data to that from thousands of other sites and project which sites would be best for drilling.
Projecting where the most efficient drilling locations are has helped increase the bottom line for companies such as BP, Chevron, and ExxonMobil. Similarly, predictive analytic technologies can help major and midsize Oil and Gas increase their efficiency and allow them to remain competitive in a challenging time for the industry.
Analytics can reveal where business leaders lose money as much as they offer insights into where decision-makers may gain more revenue. Tracking economic metrics over time can give those in leadership roles the necessary data to make decisions based on facts. The auto dealerships mentioned above saw significant savings because they didn’t assume unnecessary overstocked, underperforming locations’ inventory costs.
This same case study also reveals how predictive analytics can generate localized supply and demand metrics to inform executive decision-making. The data Microsoft gathered included not just site-specific information but also consumer data, including online purchasing, browsing, and price-searching activities. The analytics team told leadership members what consumers wanted in their cars, and management directed operations to tailor purchasing and allocation decisions to reflect the research findings.
Often, predictive analytics reveal opportunities otherwise hidden deep in the “business as usual” model. Perhaps the biggest boon predictive analytics offers is its capacity to anticipate machine part failures before complete shutdowns are necessary. This technology is valuable for those in the oil and gas industry, including those companies handling oil field service and maintenance. Knowing when these failures are likely to occur reduces both safety threats and the bigger emergency expenses.