The Basics of Intelligent Forecasting

Q&A with Rich Wagner

In this Q&A, Rich Wagner, Prevedere CEO, unpacks Intelligent Forecasting at its core. There are several misconceptions about implementing it into an already established business process. But, in reality, the process is simple. With knowledge of today’s technologies and forecasting processes, executives can better understand the value that intelligent forecasts bring to their organization.

 

Q: What is Intelligent Forecasting?

 

Intelligent Forecasting is a modernized way to forecast and plan that takes advantage of data and technology available today, not always used in the current forecasting process. This means that companies can leverage data outside their four walls as easily and readily as the data inside. They can also take advantage of advanced machine learning, AI, or even just a new statistical approach to make a more holistic and more accurate forecast.

 

Q: What are the key data assets needed for Intelligent Forecasting?

 

Data by itself is just noise, and it can be very messy. The first component of Intelligent Forecasting is structured data in a time-series format. This is critical when leveraging Intelligent Forecasting to forecast future periods, whether next week, next month, next quarter, or the next five years. To be able to predict those future periods, businesses need time-series data as their input. Internally, time-series data could be historical sales or revenue, but it could also be volume or quantity. 

 

External data also needs to be in a time-series format to build a forecast. It can be structured data, things that come out each week, each month, or each quarter, like housing starts, building permits, unemployment numbers, and so on. However, it could also be some unique alternative data sets such as consumer behavior or mobility data, as long as it is in a time-series format.

 

For Intelligent Forecasting, data is going to be anything that is in a time-series format that encompasses economic conditions, financial metrics, and financial behavior. This could pertain to the overall economy, consumer behavior, online customer activity, even climate data. New data sets are also available, such as pandemic-related impact and the government stringency index that businesses can add to their forecast in 2021.

 

The other important aspect of Intelligent Forecasting is that businesses find good leading indicators of where their business is heading, like where demand will be for their products and services. It is not just time-series data but also data that can identify factors in advance that change periods compared to actual sales or the volume of products and categories in different markets. If businesses look for that data, they can identify leading indicators and build some very accurate models for the future.

 

Q: How does AI improve these results? And how is it used?

 

AI can be somewhat of a scary topic to many people. It’s helpful to simplify it by saying artificial intelligence is the ability for computers to process lots of information faster than humans can. In this case, there are millions of data inputs that are possible. There are billions of calculations over time and statistical rigor that businesses need to apply AI to because it will speed up the time to value or time to insight.

 

AI can be anything from neural networks that classify data. Businesses take a training set where economists and data scientists have tagged this different data outside their four walls by what category of data it is, whether it is mobility data, consumer behavior, economic, or financial. Then, when new data flows in, neural networks have a remarkable ability to classify that data. Ultimately, companies can very quickly leverage all this new data in ways that would otherwise have to be done by hand.

 

The other piece to AI in Intelligent Forecasting is leveraging the power of AI to build and test many models to find the right combination of indicators. This can be a very daunting task for businesses to identify because there are so many possible combinations.

 

Here at Prevedere, we leverage what our economists do with an AI engine and call it augmented intelligence. It is not just artificial and something that’s going to think for you. This is augmented intelligence where we take how data scientists, how economists, how business users think and couple that with the power of algorithms that can process things very quickly. That is our AI.

 

Q: Is this a new way of forecasting a replacement for the existing internal process or an add-in?

 

Intelligent Forecasting services will not replace the internal knowledge that businesses have on their company, that sales teams have from the customers they talk to, or that operations teams have from understanding production and supply issues. 

 

Think of it like baking a cake when using just one ingredient, maybe two; it will not taste very good. But if we could combine all the right ingredients, which means both external influences and internal knowledge, to build a consensus, forecasts will be more accurate. 

 

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About Rich Wagner, Prevedere CEO

 

Rich’s career spans more than 20 years of technology innovation and leadership, during which he has provided extraordinary value to global enterprise companies. In 2012, Rich launched Prevedére to provide corporations worldwide the ability to significantly improve their financial forecasts, projections and reporting capabilities combining internal and related global external data. With Prevedere, companies gain a competitive advantage because they are able to generate profitable business insights with accurate forecasting that encompasses ever-changing external factors.

 

Prior to Prevedere, Rich was Director of IT Innovation and Strategy for Momentive, a global leader in Specialty Chemicals. While at Momentive, Rich noticed a significant void in the ways companies managed financial planning and analytics, which led to the birth of Prevedere’s innovative SaaS solution that ensures companies are looking at the right factors that drive business revenue and profit.

 

As a forward-thinking predictive analytics thought leader, Rich has contributed to publications such as ChiefExecutive, Supply and Demand Chain Executive, Wired, Manufacturing Business Technology, CMSWire, Website Magazine and FORBES.