The intelligent application of data and machine intelligence is changing the global economy before our eyes.
Big data’s prologue was etched in high-performance silicon and transported over blazingly fast networks. Indeed, today’s entrepreneurs sit on the shoulders of giant breakthroughs in semiconductor, networking, and storage technology. Looking forward, rapidly decreasing prices and powerful new infrastructure make it is possible to ingest, store, and analyze data at phenomenal rates. These infrastructure improvements underlie similarly significant advances in the realm of machine learning, both in its operation at scale as well as the rebirth of deep learning.
We believe the iconic companies to define the emerging Data Revolution will take shape along the following lines.
- Machine Intelligence
- Rethinking the Enterprise
- Industry-focused Applications
The next decade will usher a broad transition from data as storage cost to data as strategic asset.
As such, enterprises will increasingly lean on machine learning to make business sense of their data. Luckily, academia and industry are both bursting at the seam with novel approaches to statistical learning: everything from new tensor methods and advances in NLP to a Cambrian explosion of techniques around deep learning. Whether these advances find their application in the rapid analysis of healthcare image data or serve as the backbone for intelligent bots in the enterprise, their arrival is a leading indicator of more to come.
Rethinking the Enterprise
Over the past two decades, enterprise software followed a well-worn pattern.
IT buyers translated corporate requirements into business logic with state maintained in a proprietary datastore. This workflow/datastore composite served business well in its time. These days, however, workflow has become table stakes in a broader transition to intelligent enterprise applications. From sales software that predicts customers from lead lists to HR applications that score staff in danger of leaving, every business function and role will demand software that automates through a combination of prediction and context. This categorical turnover from workflow to intelligence offers entrepreneurs a rare opening as enterprises rethink their entire software stack.
The machinery of global commerce turns on long-standing industries that touch every corner of the economy.
Categories like healthcare, transportation & logistics, and construction, to name just a few, are slowly waking up to the transformative potential of new data and machine-learning products. For example, drone image data run through neural networks can automate construction equipment, cheap chemical sensors can improve crop yields, and a location-aware mobile application can elastically price auto insurance by the mile.
Founding teams pairing deep domain expertise with technological excellence will be well equipped to properly define and address highly specific but looming challenges in the industries that in aggregate constitute the roughly $75 trillion dollars of global GDP.
Whether they tackle a specific product-market or focus instead on building the next generation of horizontal analytical tools, we look for entrepreneurs with a shared vision to redefine the way enterprises harness the power of data.