"Data wrangling" was an interesting phrase to hear in the machine learning (ML) presentations at Microsoft Ignite. Interesting because data wrangling is from business intelligence (BI), not from artificial intelligence (AI). Microsoft understands ML incorporates concepts from both disciplines. Further discussions point to another key point: Microsoft understands that business-to-business (B2B) is just as fertile for ML as business-to-consumer (B2C).
ML applications with the most press are voice, augmented reality and autonomous vehicles. What's caught the collective consciousness about those machine learning areas has been their B2C orientation. When people think voice response, most people don't think of customer support, but about Apple Siri, Amazon.com Alexa, Google Now and Microsoft Cortana. There is nothing wrong with that, B2C is a large opportunity and billions of dollars in opportunity await the market share war. However, B2C is not the only battle.
Business requires ever more complex infrastructure to manage the flood of information, combining physical and cyber landscapes to create a full picture, whether it be about supply chains or customer support. B2B solutions can be enhanced with ML technology, with just as much as impact and revenue potential as in B2C.
Microsoft tied itself to the IBM PC in the 1980s. Because of that, Microsoft was able to take over the business desktop. Business needs technology, and business technology tends to both be more complex and more invisible than consumer technology. In the business application arena, Microsoft is up against Oracle, SAP , Salesforce.com CRM and other firms not known for B2C products.
That's why Microsoft Ignite displayed corporate efforts to include machine learning fully across the customer service experience. At the core is the inclusion of ML into Microsoft Dynamics 365. Intelligent assistants aren't only a way to help consumers find answers. Customer support personnel are customers of help desk software. ML can work in real-time to provide additional information and even contact additional personnel for faster problem resolution.
The business intelligence market has been working with advanced algorithms for years, investigating data, discovering relationships and providing prescriptive and predictive analytics based on probabilistic models that aren't from the AI arena. That they provide similar benefit to models coming out of AI is more support for a broader definition of ML. Microsoft isn't hesitating to use what's right from both parents of the emerging ML solution set.
"Data wrangling" being borrowed from BI is important, because data is messy. Before being fed to systems, it often needs to be better organized, cleansed and even understood. It's the transformation that is core to business information systems. There's no need to re-invent the wheel, and Microsoft is leveraging BI knowledge for use in the ML space.
Machine Learning is at an inflection point not only because of technological advances. It has reached that point because so many areas of our business and personal lives can be enhanced through ML tools.
While the focus is often on B2C, we should not forget that B2B is important. Machine learning will be leveraged by most B2B systems and will continue learn from and borrow techniques from the BI market.