Last Updated: February 19, 2019
The following Q&A is an executive discussion on AI technology for predictive data analytics conducted with AI technology engineer, VP of Engineering, Jose Paul, of Prevedere. Today’s data teams are challenged with the demands of reactive forecasting. It is estimated that they spend roughly 80% of their time finding, cleansing, and organizing data before it can be utilized effectively. Ad-hoc requests also distract data analytics teams from strategic data application for planning. Technology data solution companies like Prevedere can expedite the entire process of collecting and cleaning external data.
A clear focus on data tasks that add long-term value requires organizations to prioritize data workflow and align AI technology
with their company’s overarching business goals. The next logical leap in the equation challenges businesses to ask how their work will be used to drive decisions.
This level of alignment determines and drives internal course correction and resource tools that data teams need to focus more on delivering strategic insights.
Organizations need to allow their data teams to go beyond traditional analysis techniques and find innovative ways to account for major turning points that can change consumer behavior or drive demand. With the strategic use of relevant, quality data analytics and business intelligence, companies are empowered to plan effectively.
Recently Prevedere’s VP of Engineering, Jose Paul, lead a discussion that delved deeper into solutions for these common issues today’s businesses face. Jose discusses how Prevedere Predictive Analytics Cloud creates a compelling solution to help businesses plan and forecast for the future. Predictive data analytics and machine learning can greatly speed up the time it takes for teams to uncover unknown relevant business intelligence insights and identify the unique forces that matter most for future performance.
Executive Discussion on AI Technology For Predictive Data Analytics
Here’s what innovator and VP of Engineering at Prevedere, Jose Paul has to say about predictive data analytics advancements through AI technology:
Q: What makes Prevedere’s Prevedere Predictive Data Analytics Cloud (EPAC) compelling for businesses?
Jose Paul: The Prevedere Predictive Analytics Cloud provides a new way for companies to incorporate external data into their planning and forecasting processes. Leveraging our unique combination of technologies and data, businesses can easily incorporate the insights and knowledge they glean from EPAC to augment their existing processes and people. EPAC allows companies to make smarter and more proactive business decisions.
Q: What does the Prevedere Predictive Analytics Cloud mean for Prevedere’s customers?
Jose Paul: EPAC means faster, more data-driven decisions that result in actionable information for companies, from reducing excess inventory to improving marketing ROI. EPAC allows companies to evaluate and take action on trends within their industry to affect sales, costs and profitability.
Q: Prevedere calls the Prevedere Predictive Analytics Cloud the “first augmented analytics solution.” What makes it the first?
Jose Paul: We are the only company to offer our proprietary AI technology to identify leading indicators from millions of unique data sets. Even if a customer could source the data on their own, without the Prevedere engine, customers would have to manually identify which drivers are the most meaningful.
Further, companies would constantly be trying to maintain their repository of external data and manually testing to ensure that changing economic conditions didn’t affect the leading indicators over time. With EPAC, the newest data is always available in the cloud, and our tools make it easy for users to explore and identify the most relevant information. The predictive models created through the EPAC are “live,” based on the latest available data at any given time.
Q: What’s unique about the Prevedere Predictive Analytics Cloud?
Jose Paul: Because EPAC is cloud-based, companies can access the tools and information without any implementation hurdles. Further, our global repository is comprised of almost 2.5 million unique public and private data series. It is actively updated, curated and cleansed for the utmost quality. Coupled with our proprietary AI technology leveraging our StrengthScore™ as a cornerstone of the Prevedere process, companies can quickly sift through the data to find the specific leading indicators impacting their business.
Q. How does the StrengthScore work?
Jose Paul: StrengthScore is Prevedere’s proprietary methodology that allows the AI Modeling Engine to filter and rank the best data series to use in its predictive model building. StrengthScore factors how closely correlated the dependent variable is with the independent variable being analyzed, the quality and history of the independent variable, and several other statistical calculations.
Finally, StrengthScore, like Google’s Page Rank, factors in how frequently the data series being analyzed has been used in the thousands of other predictive models built on the Prevedere Predictive Analytics Cloud. That last element is critical in validating the true real-world application of the data series in question, quickly identifying the best industry-specific data series to use in predictive modeling.
Q: Why is the Prevedere Predictive Analytics Cloud built on Microsoft Azure?
Jose Paul: The Microsoft Power BI cloud was a natural choice for us given the flexibility it offered us in terms of infrastructure and technology platforms. This allowed us to develop quick prototypes and scale our systems efficiently. We decided to build our systems in Azure because its services aligned well with our AI technology choices. Azure support has been very responsive to our questions as we made our decisions and built out the system. You can learn more about our predictive data analytics solutions with Microsoft on our website.
We also had a Q&A about how Microsoft uses Prevedere predictive data analytics to improve forecasting for manufacturers with Bill Moffett, their Global Industry Senior Product Marketing Manager, Manufacturing for Microsoft.
Q: How will the Prevedere Predictive Analytics Cloud save time for data analytics teams?
Jose Paul: EPAC is a critical resource for data teams, providing a competitive advantage over other companies that don’t leverage our tool. Time is a fixed commodity and data teams can either use their time to source and maintain external data or they can leverage Prevedere’s EPAC, freeing their smart people to focus on activities that drive value and revenue.
Prevedere has taken over the work of building and maintaining our global repository of almost 2.5 million unique public and private data series, so that our customers can focus on the core activities of the business.
Q: How will the Prevedere Predictive Analytics Cloud give customers a competitive advantage?
Jose Paul: EPAC gives customers a competitive advantage in multiple ways. By identifying leading external indicators, companies can adjust their strategy to increase revenue and decrease costs with increased confidence and visibility. Companies that take external factors into account and incorporate them into their forecasting/planning process will be consistently more successful than their counterparts.
Companies intelligently investing in tools that leverage machine learning to augment existing BI investments and processes take the guesswork out of forecasting and gain actionable intelligence that produces results. For a more in-depth look at how to avoid the top business intelligence mistakes, and accurately turn data into decisions, download our complimentary whitepaper “The Evolution of Business Intelligence.”