Markets have been more volatile than ever. It creates a need for decision makers to utilize technologies such as artificial intelligence and machine learning (AI/ML) to better understand the external factors that impact their business. By identifying these factors, organizations can better plan for changing market environments and seize market opportunities. However, manual modeling is a time-consuming process and results in a limited number of models and tests. Also, updating those models is slow and laborious. With the addition of market volatility, it creates multiple challenges for CFOs, managers and financial planning specialists. With limited exposure to external drivers of demand and delivery, the process becomes very costly. Developing accurate forecasts requires integrating exogenous data with the internal performance data, but it’s challenging to find quality external data and then get that raw data clean enough to input into any model. My colleague, Robert Kugel, recently shared his perspective on using external data for forecasting, budgeting and planning to enhance predictive capabilities.
I assert that through 2025, 9 in ten analytics processes will be enhanced by AI/ML to streamline operations and increase the value that can be derived from data. Prevedere offers such a capability with its predictive
Prevedere is a predictive analytics software company that offers technology based on econometric modeling. Its predictive planning platform can be integrated into existing processes, enabling organizations to understand and quantify the impact of external factors as part of their strategic planning and forecasting. Prevedere can analyze global economic data series from thousands of public and private sources to identify which indicators are most suitable for an organization’s requirements. Its econometrics-based ML engine creates, simulates, scores and refines predictive models and then generates economic baseline forecasts. Organizations can use it to create what-if forecast scenarios, for planning. The platform offers the capability to automate the process of monitoring forecast models' predictive health and provide early warnings with alerts when predictions change. The Prevedere Advanced Predictive Planning (APP) platform is its patented technology for identifying relevant external factors, creating forward-looking predictive models, enabling what-if analysis of potential future conditions, and monitoring for future market shifts. Prevedere offers model-ready datasets from various external sources which are then used by its Global Intelligence Cloud that can augment internal data with external market signals for predictive planning. Prevedere Discover is its search and correlation engine that enables organizations to identify the most predictive leading indicators specific to their business and/or markets. It uses Prevedere Indicators global data repository and compares years of trends, patterns and lead/lag behaviors of various drivers to determine which external forces actually impact a client’s line of business.
Prevedere APP combines external data and predictive AI modeling to deliver a detailed and accurate plan. It provides CFOs, FP&A, supply-chain and category-management leaders the insights they need to make data-backed decisions. Prevedere caters to various industries including consumer goods, retail, manufacturing, private equity, automotive and travel and hospitality. CEOs, CFOs, FP&A, S&OP and operational planners can create forecast models and outlooks to mitigate risk and maximize opportunity for their roles and functions. Prevedere also offers a web-based approach that can be integrated into existing S&OP platforms such as Hyperion or SAP BPC. It also provides a plugin for Microsoft Excel and Office 365 to download any dataset or forecast model directly into spreadsheets for further analysis. Prevedere announced a new Leading Economic Indicators Dashboard that provides a monthly, in-depth, economist-led data report on the latest cyclical drivers. It enables organizations to track individual business data, industry trends, economic changes, and other external factors for business analysis and forecasting.
Prevedere Predict is its predictive modeling engine that takes the leading external indicators identified by Prevedere Discover and builds a multitude of predictive models. By utilizing cause-and-effect econometric forecasting methods, driven by AI/ML algorithms, variations of predictive models can be created based on permutations of the external indicators. The outcome for an organization is a series of forecasts that enable market-validated forecasting and planning. Organizations are now integrating economic scenario planning into their processes. Scenario planning is about identifying a specific set of uncertainties and developing different “realities” of what might happen in the future. Prevedere Scenario creates forecasts from economic what-if simulations and Prevedere Predict models. It then applies planning guardrails by adding optimistic and pessimistic predictions to baseline forecasts. Prevedere Protect automatically monitors the performance of predictive models created by Prevedere Predict and Prevedere Scenario. It triggers alerts based on future market shifts and anomalies to enable planning pivots and reinforce strategic confidence.
Prevedere is going strong in the planning and forecasting space. It should also consider expanding into other market segments by modifying its technology to cater to various predictive analytics use-cases. In today’s fast-paced environment, informed insights can enable organizations to gain a competitive edge in the market and mitigate risks. Prevedere can enable organizations to use AutoML to predict various scenarios and navigate market volatility. Organizations in the process of digital transformation and looking to streamline operations and run predictive analytics with AI on big data should consider Prevedere. It offers various integrations for enterprise software and technologies including Microsoft, Tableau, Workiva, Salesforce and Snowflake.
Regards,
David Menninger