FirstMark Capital’s Matt Turck has been publishing a report on the Big Data Landscape (now incorporating AI) for the last 6 years. The 2018 report warns of the great responsibility that comes with access to an increasing level of personal information, as data technologies continue to develop and evolve. At the same time, public awareness of some of the potential concerns over data access is also growing steadily. From an enterprise perspective, as we become more aware of and familiar with the AI tools out there to help us sort through the external data available, we’re seeing more enterprises become fully engaged in an overall digital transformation.
Here are some of our key takeaways from Turck’s report.
1.
One of the key themes has been corporate “digital transformation” – referring to the fact that many of the more traditional industries and companies are now fully engaged into their journey to become truly data-driven.
This speaks to last year’s study from MIT Sloan Management Review which highlights that only one in 20 companies has extensively incorporated AI in offerings or processes. As well, less than 39% of all companies have an AI strategy in place, and the largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one. Over the last year, more institutions have been making progress toward becoming AI and data-driven across the enterprise.
2.
Everything is rapidly getting digitized, and data technologies are becoming more adept than ever at processing and analyzing this massive data exhaust, increasingly in real time. From this can result both magic and abuse.
From the way we call a taxi to the way we find housing, to the way we shop, everything has changed thanks to the internet and thanks to the massive amount of data at our fingertips. However it’s never been more clear than it was this past year that ethical issues can arise when an organization has access to so much personal consumer data. Businesses more than ever are finding the need to be transparent about the way they’re using this data and to enable individuals to retain control where possible.
Alternatively, the amount of online breadcrumbs companies leave behind has enabled businesses to become much more competitive and savvy about their strategies, helping them to make more informed and predictive business decisions.
3.
The Big Data market (infrastructure, analytics) is cycling through the early majority of buyers and transitioning into the late majority of the traditional adoption curve.
As we’re seeing with rapid growth of the alternative data market in the finance industry, as businesses continue to invest in digital transformation and data infrastructure, the Big Data market will continue to grow and evolve.
4.
Large cloud providers increasingly compete with each other by offering a wide array of Big Data, data engineering and machine learning tools and as a result make it it arguably harder for startups to compete, at least for broad, horizontal opportunities.
Startups working in the AI and data analysis space continue to enter the scene, and their technology is attracting the attention of some of the leading AI-driven firms. But at the same time, tech giants are working to produce additional offerings in house, which can wipe out any directly competing startups in one fell swoop.
5.
We are just at the beginning of a wave of deployment and application of deep learning in the real world and still very much in early adopter territory for enterprise and vertical AI applications.
Outside Insight as a software category is still very much nascent. Enterprise adoption of AI applications is slowly growing, but the key takeaway here is that there is still a significant first mover advantage for any companies who adopt AI-driven external data analysis solutions.
6.
While AI advancement is very much a global phenomenon, with Canada, France, Germany, the U.K. and Israel being particularly active, China seems to be playing at a completely different level. According to CB Insights, China accounted for only 9% of global AI deal share but nearly 48% of global AI funding in 2017, up from 11% in 2016.
One thing is clear to anyone following the trail of online breadcrumbs: China is determined to emerge as a global leader in AI development.
Turck’s landscape runs the gamut of AI and big data companies, broken down by product type, tech, industry focus and more. From industry titans like Bloomberg, GE and Thomson Reuters, to usual suspects like IBM, CB Insights, Oracle and Microsoft, as well as a number of AI-driven startups the landscape continues to shape up.
Important to note as well are the big players continuing to drive AI development by investing in startups bringing new technology to the space, from Meltwater and Salesforce to Intel, Apple, Amazon, Alphabet and Facebook.
To see the landscape at full size, click here.
