Machine Learning Adoption Offers Significant Opportunity Ahead

Abhey Lamba and Parthiv Varadarajan
Abhey Lamba and Parthiv Varadarajan
August 7, 2017



We hosted ten software/internet/semiconductor companies that address the burgeoning opportunity in machine learning and big data applications during our Silicon Valley Seminar. While learning about the significant TAM opportunity given potential use cases, we gained insight into the business models under consideration as vendors tackle the opportunity. Our key takeaways are below.

Key Points

Data and the ability to mine it remain top priorities. In addition to data accessibility, the ability to gain insights from data via machine learning techniques remains an area of investment among vendors and customers. SaaS architectures lend themselves to better data access, given the consolidated architecture in the cloud vs. on-prem implementations that could be more segregated. Sharing of insights across customers in the installed base could result in data privacy concerns, given the competitive nature of these insights.

Vertical application is a potential n-t route to ML adoption. Given the seemingly infinite use cases of ML, early adoption could benefit from vertical-focused solutions that address very specific pain points as potential customers test the waters with early investments. Various demonstrations by Linc Global clearly articulated the value proposition by such solutions. Over time, we think ML can offer TAM expansion opportunities for various vendors. For instance, Splunk could enter into customer retention and revenue optimization markets for certain verticals due to its strength in data centers.

Adoption hurdles likely, as with any emerging technology. Data privacy, as mentioned, is a key detractor of ML adoption that is likely to persist, though, the conversation could evolve to the ultimate, significant value proposition of such capabilities. This issue is especially top-of-mind in end-consumer facing applications. Further, we also find the massive innovation in the open-source ML ecosystem, while very encouraging, ultimately somewhat confusing for customers as they look to make early investments. The sheer competition for analytical frameworks could make monetization for vendors challenging.

Coverage takeaways. We note: 1) CRM and NOW are favorably positioned, given the breadth of use cases/verticals addressed, and concentrated investments in ML (e.g. HR bot for ServiceNow); 2) we like Splunk's presence in the infrastructure/analytics stack with very tangible benefits for customers; 3) TDC could remain under pressure as cloud-native vendors such as Snowflake address data warehousing needs, particularly for mid-market customers; 4) challenges for open-source business models could persist amid intense competition.

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