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Big Data Landscape

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Big Data Landscape

Nice job aggregating many complex, independent stories. We should continue to thank Google for releasing so much research to the community, spawning this amazing disruption in data and analytics. For thirty years, RDBMS first enabled, then inhibited a data driven world. Its exciting to see that the past 10 years have spawned so many solutions to this next generation of data expansion challenges. I am personally very excited about the next steps in the distributed, yet synchronized data world, and what that it will bring. We are close to: “Your data”, “My data” and the virtual access to a map of “Our shared data”. Data physically located in thousands / millions of hubs of private ownership is a reality 50 years in the making, yet new virtual fabrics with acceptable governance of data sharing and CRUD, will allow safe, communal use soon. Analytics over this superset of virtual, communal data will yield discoveries that we cannot even imagine today. We learn a lot by looking at our private data, we have learned how to enlarge our data collection for better analytics. But… we will learn infinitely more when we can look over the entire worlds contributed data and correlate it to the context of our private data!
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Big Data Landscape

Another key thing to understand: Big Data success is not about implementing one piece of technology (like Hadoop or anything else), but instead requires putting together an assembly line of technologies, people and processes.  You need to capture data, store data, clean data, query data, analyze data, visualize data.   Some of this will be done by products, and some of it will be done by humans.  Everything needs to be integrated seamlessly. Ultimately, for all of this to work, the entire company, starting from senior management, needs to commit to building a data-driven culture, where Big Data is not “a” thing, but “the” thing.
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Big Data Landscape

In turn, AI is now helping Big Data deliver on its promise.  The increasing focus on AI/machine learning in analytics corresponds to the logical next step of the evolution of Big Data: now that I have all this data, what insights am I going to extract from it? Of course, that’s where data scientists come in – from the beginning their role has been to implement machine learning and otherwise come up with models to make sense of the data.  But increasingly, machine intelligence is assisting data scientists – just by crunching the data, emerging products can extract mathematical formulas (watch Stephen Purpura, founder of Context Relevant here) or automatically build and recommend the data science model that’s most likely to yield the best results (watch Jeremy Achin, CEO of DataRobot here).  A crop of new AI companies provide products that automate the identification of complex entities such as images (watch Richard Socher, CEO of MetaMind, here; Matthew Zeiler, CEO of Clarifai, here; and David Luan, CEO of Dextro here) or provide powerful predictive analytics (e.g., our portfolio company HyperScience, currently in stealth).
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Big Data Landscape

The early years of the Big Data phenomenon were propelled by a very symbiotic relationship among a core set of large Internet companies (in particular Google, Yahoo, Facebook, Twitter, LinkedIn, etc), which were both heavy users and creators of a core set of Big Data technologies.  Those companies were suddenly faced with unprecedented volume of data, had no legacy infrastructure and were able to recruit some of the best engineers around, so they essentially started building the technologies they needed.  The ethos of open source was rapidly accelerating and a lot of those new technologies were shared with the broader world.  Over time, some of those engineers left the large Internet companies and started their own Big Data startups.  Other “digital native” companies, including many of the budding unicorns, started facing similar needs as the large Internet companies, and had no legacy infrastructure either, so they became early adopters of those Big Data technologies.  Early successes led to more entrepreneurial activity and more VC funding, and the whole thing was launched.

Big Data Landscape

In many ways, we’re still in the early innings of the Big Data phenomenon.  While it’s taken a few years, building the infrastructure to store and process massive amounts of data was just the first phase.  AI/machine learning is now precipitating a trend towards the emergence of the application layer of Big Data.   The combination of Big Data and AI will drive incredible innovation across pretty much every industry.  From that perspective, the Big Data opportunity is probably even bigger than people thought.
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Big Data Landscape

The more forward-thinking large companies (call them the “early adopters” in a traditional technology adoption cycle) started early experimentation with  Big Data technologies sometime in 2011-2013, launching Hadoop pilots (often because it was the chic thing to do) or trying out point solutions. They hired all sorts of people whose job titles didn’t exist previously (such as “data scientist” or “chief data officer”). They went through various types of efforts, including dumping all their data in one central repository or “data lake”, sometimes hoping that magic would ensue  (it generally didn’t).  They gradually built internal competencies, experimented with different vendors, went from pilots to departmental deployments in production and are now debating (or, more rarely, implementing) enterprise-wide roll outs.  In many cases, they are at an important inflection point where, after several years building Big Data infrastructure, they don’t have (yet) much to show for it, at least from the perspective of the business user in their companies.  But a lot of the thankless work has been done, and the disproportionately impactful phase where applications are deployed on top of the core architecture is now starting.
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“We are entering the most exciting time for big data,” according to Matt Turck, in our latest interview. In 2010 only 2.5 percent of the Series A market was committed to big data. Today, the sector amounts to more than 7.5 percent of total venture investments. So where are we in the world of big data, and is the recent obsession with AI still fundamentally related to big data?
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In a tech startup industry that loves its shiny new objects, the term “Big Data” is in the unenviable position of sounding increasingly “3 years ago”.   While Hadoop was created in 2006, interest in the concept of “Big Data” reached fever pitch sometime between 2011 and 2014.  This was the period when, at least in the press and on industry panels, Big Data was the new “black”, “gold” or “oil”.  However, at least in my conversations with people in the industry, there’s an increasing sense of having reached some kind of plateau.  2015 was probably the year when the cool kids in the data world (to the extent there is such a thing) moved on to obsessing over AI and its many related concepts and flavors: machine intelligence, deep learning, etc.
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Ultimately, the continuing maturation of the ecosystem will see AI act as the catalyst toward the rise of the application layer of big data. However, we are still in the very early days of the big data landscape. With that in mind, and the continuing discovery of dataset applications, the big data opportunity is bigger than anticipated.
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The recent obsession and progression of AI would not have been possible without big data. Turck even suggests that “AI is the child of big data.” Despite the fact that the algorithms behind deep learning were created decades ago, it was not until they could be applied to mass datasets quickly and cheaply enough that their potential was reached. This led Turck to state, “AI allows big data to deliver on its promise.”
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