Product can connect to a third-party machine learning API

Description

Third-party Machine Learning (ML) API integration in Corporate Performance Management (CPM) software provides a path for harnessing advanced artificial intelligence capabilities. The requirement implies the ability of CPM to access and utilize ML algorithms from external sources. This functionality empowers the software to deliver more advanced forecasting, predictive modeling, and business analytics, thereby contributing to improved decision-making processes.

Example Use Case

Scenario: A mid-market e-commerce business utilizes CPM software to manage its financial performance. However, it is seeking to enrich its forecasting capability by leveraging Machine Learning for more accurate predictions of future sales and purchasing trends.

Solution: By enabling a connection to a third-party ML API, such as Google's Cloud Machine Learning Engine, the CPM software can enhance its predictive analytics. Using historical data, the ML API provides more accurate forecasts, enabling the company to optimize inventory management and prevent stock-outs or overstock situations.

Considerations

Everyone is talking about this, but explaining how it is different from predictive methods (which have been around for decades or even centuries) has been lacking. To be clear, Predictive Analytics and Machine Learning are very different things. Some vendors are mixing the two, claiming they have an AI tool when in fact it is the same predictive methodology that SPSS has had for 50 years.

In short, predictive methods require structured data to produce a forecast. For example, you have 5 years of sales data for a cleaning service in a specific territory broken down by commercial and residential. Predictive methods can easily be used to generate a forecast.

Machine Learning tools are useful for unstructured data. Imagine a data warehouse full of data points that have not necessarily been tied together by a human. The ML tool will seek patterns in the data and extrapolate potential connections between them. The non-linear nature of this makes it difficult to interpret, but its ability to look at a massive dataset and generate insights can be invaluable. Let's say you're a law firm with customer data, attorney booking data, payment patterns and settlement agreements. An ML may be able to look through that data, the fees you're charging, the settlements received (by scanning PDFs) and extrapolate earnings for a specific attorney over time.

Questions to Ask a Vendor

• Integration Capabilities: How easily can your system connect to third-party ML APIs? Does it require considerable technical expertise?
• Machine Learning Platforms: Which specific ML platforms can we connect to? Or have you baked this into the product?
• Machine Learning Scope: How much of our planning and actual data can the ML analyze at once?
• Machine Learning Outputs: Will the system output visualizations, data tables, specific types of reports from the ML tool? Does it populate a forecast version like we would expect from a predictive analytics tool?