The crystal ball effect: Advanced analytics hits the enterprise white space
Analytics now sit squarely at the center of corporate strategy. Traditionally playing only a supporting role in executive decision-making, analytics are now used as the basis for strategy optimization by leading companies.
One major driver is a change in the business landscape, which now must support a service-based outcome economy. It is no longer enough to just meet customer desires, now companies must anticipate them—and many serve those desires through platform-based ecosystems.
Via just such an ecosystem, Amazon uses analytics to recommend books based on a buyer’s previous likes, past purchases, shopping cart contents and what similar customers have bought. Amazon calls this “item-to-item collaborative filtering,” an algorithm that tailors browsing for returning customers.
"Over the next few years, leading organizations will link analytic initiatives firmly to financial objectives, increase investments in advanced analytics, evolve comprehensive analytics centers of excellence, and incorporate a wider range of exogenous data.”
“By 2018, decision optimization will no longer be a niche discipline; it will become a best practice in leading organizations . . . “
– Gartner Predicts 2016: Advanced Analytics Are at the Beating Heart of Algorithmic Business
Analyzing annual sales by region, segmentation of customers by revenue generated
Social media campaign success analysis, churn analysis on lost customers
Sales forecasting, fraud detection
Price optimization, Marketing mix optimization
Number of hits on Glassdoor when searching for “data scientist.” The job title is listed as the number-one job in the United States, with a base salary of nearly $117,000.
(Source: "Data Science Education Evolves to Meet Surging Demand", Datanami, September 26, 2016)
Combining human and machine intelligence for better predictions
San Francisco-based Stitch Fix styles clients by anticipating what would fit well with their current wardrobe and style, based on Pinterest profiles and other social data. Self-learning algorithms produce recommendations for stylists who use their personal experience and knowledge of the customer to curate those recommendations down to just five items. As customers purchase, answer questions and/or communicate with their stylist, each “fix” (wardrobe items sent regularly for potential purchase) becomes increasingly accurate.
Utility predicts energy usage with analytics
The Salt River Project (SRP), one of the largest public power utilities in the United States, provides electricity and water to more than two million people in Central Arizona.
After installing smart meters, the utility was deluged with data—more than 100 million rows per day. Neudesic implemented a predictive analytics solution at the utility to accurately predict future energy demand. Having the right demand forecast allows SRP to produce the right amount of power as well as balance the power grid to minimize outages and overloads.
This solution along with the predictive analytics roadmap, allows SRP executives to strategically and incrementally invest at each stage of the solution development process.
“Neudesic’s Advanced Predictive Visualization and Cloud Bursting Solution gives us a strong foundation for using predictive analysis to increase the accuracy of our demand forecasting. Their implementation of Azure ML accelerates the iterative process, allowing us to arrive at our most accurate model sooner. We also now have the tools and roadmap to deploy an effective internal data science team.”
– Jason Wilhite, Manager, Business Intelligence Services, SRP