Scratch the surface of any mid-size or larger company, and you’ll find business intelligence. BI refers to any technique for turning raw data into fodder for making smart predictions. Although BI has been around for well over a hundred years, it’s only in the past three decades that BI really took off as part of a data-driven strategy for managing an organization.
BI needs lots of data, which can be anything from transaction logs to sales receipts to factory throughput to logistics. That data is usually structured, and so you need a powerful database to store it in — and the ability to run complex queries using that database. BI also needs the ability to merge and correlate data from disparate data sources, and multiple database. It needs analytics capabilities, which finds correlations in all that data. It needs reporting and report distribution to give decision-makers timely and appropriate summary data and analytics, and increasingly, recommendations.
Those are the baselines, the bare minimum characteristics to make BI work. Over the years, BI platforms have become more sophisticated, and can provide dashboards based on key performance indicators, as well as alerts when those KPIs are forecast to change; the ability to incorporate unstructured data (think social media or free-text comments); and interfaces to Enterprise Resource Planning (ERP) suites.
BI has also evolved from being the source of month-end management reports to offer instantaneous, real-time controls, which can be delivered right to a manager or executive’s smartphone. The better and faster the data, the better and faster the BI.
The latest twist on BI is machine learning, which is an application of artificial intelligence. ML extends BI to discover and learn from patterns in the data that a human might not see — and use those patterns to spot anomalies that people would often overlook. That means that ML-powered BI can make predictions and guide decisions based on, say, production schedules, invoicing, supply-chain status, customer feedback, macroeconomic conditions and the weather forecast for the next 30 days.
Use-cases for ML-powered business intelligence are everywhere. For example:
- In retail, BI can track individual brands and products to determine exactly which are profitable and which are not. That data can be correlated against customer demographics, locations of stores and online shoppers, to find patterns and make ML-based predictions about which products to sell, which to close out, how to adjust pricing, and how to market specific products to specific groups of customers.
- Autonomous vehicles require the ability to train the car’s software to operate flawlessly in all road conditions, weather types and according to varied driving laws. Machine learning and BI techniques are involved in that type of data analysis and training the vehicles.
- Big Pharma uses gene sequencing and 3D protein maps to model new drug candidates, understand the root causes of diseases, identify biomarkers associated with specific diseases and personalize treatments based on a patient’s genetics. BI and ML help researches analyze incredible amounts of data to find patterns, spot anomalies and make predictions.
Companies have invested lots of capital in business intelligence for the past 20 or even 30 years, and that will continue. However, the new investment area is machine learning. To the happy surprise of many business leaders, ML-powered BI tends to pay for itself fairly quickly due to early wins, and we are still scratching the surface.
The technical challenge is that ML requires different computing infrastructure than traditional BI databases and analytics software. ML techniques need significant numerical capabilities, in addition to being able to process tremendous amounts of data within a CPU or a GPU. That’s an area where WekaIO can help, through software that eliminates performance barriers in the clusters and servers used to train ML and Application Infrastructure Provider (AIP) applications.
For years, business intelligence has helped business managers and executives make data-driven decisions. Machine intelligence makes those decisions even better. See how we can help implement ML-enhanced BI in this paper, “Faster Deep Learning for AI and Analytics.”