Machine Learning and Cognitive Capabilities Extend SaaS Solutions


The concept of “Machine Learning” describes a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.  Put differently, machine learning focuses on the development of computer programs that can teach themselves to change or run predictive models that learn from new and existing data in order to forecast future behaviors, outcomes and trends. Cognitive systems, meanwhile, a somewhat less advanced form of AI, apply pattern recognition and logical reasoning capabilities to process and categorize unstructured data.

Software as a Service (SaaS) solutions, whether organically “hatched” on the Internet or evolved from earlier generations of traditional data center solutions, are typically exposed to enormous amounts of data that reside in the Internet ecosystem, much of which is widely available for all manner of analytics.

As such, several categories of SaaS solutions are potential candidates to apply both machine learning and cognitive applications to leverage the “sea of data” available via the internet.

Customer Relationship Management (CRM) is a SaaS category that is aggressively working to extend its solution portfolio through machine learning.  As an example, (SFDC) has made no less than five acquisitions over the past two years to establish and fortify their machine learning and cognitive engines and capacity.  SFDC is leveraging machine learning to take advantage of social channels, mobile apps, online ads and comparison websites in order to extend the way companies engage with their customers.

The recent acquisition of LinkedIn by Microsoft (MS) took place in a very competitive arena where both MS and SFDC were vying to acquire the vast amounts of professional data contained in LinkedIn and capitalize on its use for “training data” inside their respective machine learning and cognitive engines.

How MS intends to capitalize on the data mining and automation of LinkedIn information assets are product development questions that are driving intense speculation.  However, Microsoft could use smart tools to leverage the LinkedIn big data in its core businesses by:

  • Expanding Microsoft’s addressable CRM market by $315B and increasing their competitive advantage against SFDC and Oracle via social selling
  • Evolving its product strategy by identifying the trends captured in LinkedIn and the professionals who have the skills to design and deliver new products and services
  • Providing insights that help Microsoft’s Bing advertising business by timing advertisements based on any number of patterns and conditions detected within LinkedIn big data
  • Leveraging machine learning automation to stitch together and unify the underlying metadata of Office 365 and Dynamics CRM with LinkedIn industry and professional data to publish precision work-plans on a weekly or daily basis

All of these areas are speculative, but well within the realm of possibility.  And while customers are unlikely to see robust commercial offerings around the MS/LinkedIn collaboration in the short term, they should expect a lot of pressure to enroll in unneeded online services based on “roadmaps” and/or other promises yet to come.

In this climate, enterprises must remain attentive to these developments and understand how to build a business case around the services arising from these innovations, as well as how to effectively source a solution that meets their needs.

About the author

Louis joined the team in early 2014 after nearly 20 years with Microsoft Corporation. Louis has compiled a track record of Enterprise client success underpinned by customer focus, strategic thinking, organizational agility, problem-solving acumen and impactful knowledge transfer which has established his reputation as a Microsoft licensing expert.