There’s a complex data environment in the retail world, particularly when it comes to anticipating demand, competition, and seasonality. Millions of data points are gathered by most ERP systems, and this data is often retained but not evaluated. It is with this data that we can use predictive analytics innovations such as recommended products, customer profiling, and forecasting of supply and demand trends.

Consumer Goods

Data in consumer goods and manufacturing is a big issue. Most ERP systems provide forecasting using moving averages or linear regressions which are rudimentary methods at best. This data can be interpreted by Machine Learning models to discover the true drivers of a business. There is a massive opportunity to utilize ML in countless areas, from shopping basket analysis, pricing, staffing, and all the way up the supply chain to ERP effectiveness.

Oil and Gas

Neal Analytics specializes in using Machine Learning and data science practices in conjunction with the SCADA data storage technologies typical of the Oil & Gas Industry. Application of industry-leading algorithms and data science visualization practices can lead to huge ROI. Our data science practices can help oil and gas companies realize maximal performance through improved data usage.


Manufacturers are challenged to see through the rapidly growing volumes of data in order to understand what’s really happening. Some manufacturing opportunities might be missed or never even realized. Neal Analytics can perform demand forecasting to optimize supply, to schedule maintenance for minimal operational impact, and to help your business proactively manage risks based on probabilities.


Neal Analytics is focusing on providing Machine Learning solutions to educational institutions. We developed research enablement and the Azure Advanced Analytics Kit to help schools use Machine Learning and IoT technologies. We have also created algorithms to predict and prevent student dropouts and to make personalized recommendations of content to students.


The healthcare and medical data environment is complex and has special considerations for HIPPA, security, and privacy. Neal Analytics can use predictive analysis to optimize healthcare processes, increase supplier and payment organization efficiency, and help patients. Readmission prevention, miscoding identification, constraint-based optimization for hospital operations, and personalized wellness incentives are all ways predictive analytics can drive superior results in the healthcare space.