Why Predictive Analytics?

Predictive analytics is the practice of leveraging statistical techniques, such as modeling, machine learning, and data mining to analyze historical data and make predictions about future events. In the past, this practice was entrusted solely to data scientists and the academic elite. But today, analyzing large amounts of historical data to create accurate projections of future trends is no longer such an exclusive operation.

With DFT’s Predictive Analysis offerings, any business owner can extract trends, predict outcomes and prepare for what’s to come. DFT helps companies leverage predictive techniques to identify untapped opportunities and expose hidden risks. Insight, rather than hindsight, produces better business decisions.

Common applications of predictive analytics include:

• Forecasting
• Demand/Sales forecasting
• Performance forecasting
• Decision support
• Diagnosis of problems
• Evaluation of risk
• Raise alert to potential issues
• Product development targeting
• Marketing/Product Acceptance

In the videos below, I provide some insights into Predictive Analytics and how the practice can improve your organization’s ability to turn disparate, ever-expanding data into meaningful, trustworthy information.

You Don’t Have to Be a Data Scientist to Use Predictive Analytics

Using Predictive Models to Leverage Your Marketing Spend

Building Practical Predictive Analytics Solutions with SAP HANA

And be sure to register for DFT’s Predictive Analytics Webinar Series, where I’ll provide more detailed information on what SAP Analytics tools are available, building analytics strategies, and how to design and implement predictive models.

The next webinar, “Building an Integrated Advanced Analytics Strategy,” is Thursday, September 17, 2015 at 2:00 pm EST and is a great “starting place” for putting together a Predictive Analytics strategy for your organization.

Click here to register!

 About Hillary Bliss
Hillary Bliss is a Senior ETL Consultant at Decision First Technologies, and specializes in data warehouse design, ETL development, statistical analysis, and predictive modeling. She works with clients and vendors to integrate business analysis and predictive modeling solutions into the organizational data warehouse and business intelligence environments based on their specific operational and strategic business needs. She has a master’s degree in statistics and an MBA from Georgia Tech.