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Building A Bridge Between Artificial Intelligence And Business Intelligence To Maximize Business Outcomes (Part Two)

  • 1 mth ago

In part one of this series, I explained the broader challenge businesses face in using years of data on past performance to inform AI-level predictions and analytics. That challenge coincides with a shortage of data scientists and the need for better approaches to reaching, engaging, acquiring and retaining customers.

Much of the business intelligence (BI) domain is stuck in the 2000s, not yet embracing the advancements and capabilities of data science. But today, companies want to progress from business logic analysis to AI-driven analytics that can identify patterns that are hard to see in a dashboard or spreadsheet.

To start leveraging their BI data and make the leap to AI predictions, companies should consider these steps toward maximizing their data and their team’s potential.

1. Start with the question in mind. Focus on which business needles you want to move and have a concrete understanding of how you’ll use the model’s predictions to make that happen. If a customer hasn’t purchased something recently, it’s important to incentivize them to do so, but finding the right combination of incentive and channel can be tricky. This is a problem that AI can help solve. For example, data analysts can employ a predictive model-based scoring system to automate the identification of customers who may respond to a bigger discount and be retained longer. That capability means you can predict and shape future customer outcomes with a deeper understanding of what makes each customer come back.

2. Don’t stress about “perfect” data. A new data project can require weeks of validation and data preprocessing. If you have business analysts on your team who employ tools like Looker and Tableau, you probably have plenty of data for them to analyze. You don’t need to make sure every data point is accounted for. You can use the data you already have to create predictive analytics. How? Use your BI-ready data, which means the data is already in a state in which you can drive classic analytics, and select a predictive analytics solution that automates time-consuming data preparation to create an AI-ready dataset. It could save you months of data preprocessing—before any feature engineering can take place and a single model is created.

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