Have a Failing Big Data Project? Try a Dose of AI

Back in late 2017, Gartner analyst Nick Heudecker estimated that the failure rate of big data projects was 85%. Move the calendar forward two years and theres no solid evidence proving that the failure rate has improved in any meaningful way.But help may be on the way. A growing number of artificial intelligence experts are arriving at the conclusion that the technology has the potential to turn big data failures into resounding success stories. The trick lies in knowing how to use AI correctly. Chris Heineken, CEO and co-founder of AI consulting firm Atrium, is optimistic that AI and machine language (ML) will emerge as the keys to big data project success. Big data is all about making sense of massive amounts of structured/unstructured data and generally lays the foundation for predictive analytics,he explained.When used properly in a big data project's early stages, ML algorithms can also help identify the viability of answering key questions, such as how to improve lead conversion. "ML can also be deployed to identify whether the existing data architecture can support big data program objectives and, if not, help identify gaps in the data that need to be addressed." Heineken added.