A firm called Next Pathway is offering marketers, sales leaders and other business a short cut to taking advantage of big data and artificial intelligence by alleviating the manual and onerous process of having developers create code to meet their requirements.
Headquartered in Toronto and with a recent sales office opened in New York, Next Pathway last week launched Fuse, which it said will save organizations 75 per cent of the time normally associated with data integration projects. Fuse lets organizations take terms they’ve already defined in a business glossary and search, change or consume it in other applications like AI-based systems.
Traditionally, marketing, sales or other areas of the business would have to compile terms relating to data in spreadsheets and then hand them over to developers to write code for what’s known as the “extract, transform and load” (ETL) process before it could be entered in a data warehouse. According to Chetan Mathur, Next Pathway’s CEO, Fuse will let business users access data when they need it and get to their desired results more quickly.
“Companies are spending millions of dollars in some cases on one-off big data projects,” he told B2B News Network. “Hey, it’s all the same data, guys.”
Think of an organization that wanted to get a “360-degree view” of its customer data, for example, such as what individual customers spent with different divisions of a bank. The name and address data might be spread across multiple sources, but bringing it together for use in analytics applications or AI has lead some firm to hire scores of developers to handle ETL chores, Mathur said.
Next Pathway has been in operation for more than 10 years and has been quietly building a portfolio of tools to collect, clean and prepare data for use in big data and, more recently, AI projects. Other products include Cornerstone, which helps organizations deal with the burden of bringing data into big data application environments such as enterprise “data lakes” (EDL) without coding. These back-end kinds of problems don’t tend to get as much attention as the more innovative-sounding AI and analytics tools, but Mathur said they represent some of the biggest barriers to realizing the vision behind big data and AI strategies.
“EDL 1.0 has failed miserably,” he said. “That’s not a criticism, because it was early days back then. The maturations of the products just weren’t there.”
Firms that rushed to buy analytics applications, meanwhile, discovered the information in their organization couldn’t be used, creating disappointments and in some cases hurting the credibility of CIOs and other executives.
“Many organizations didn’t standardize the way they ingested the data,” Mathur said, nor did they properly capture the metadata or use tagging effectively. “It was almost like storing all your files in one Windows folder.”
Fuse, which is already being used by at least one Canadian financial services institution, will be marketed as a way of achieving some of the aims enterprises have long desired. This includes identifying a firm’s most profitable or valuable customers, predicting what the next best offer to a customer should be and preventing fraud or money-laundering activities.
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