In a sign of things to come, a few months ago startup RelateIQ announced that it has raised $40 million in venture financing, the largest amount to date for an emerging class of enterprise applications driven by big data. These applications combine the flexibility of SaaS with intelligence gleaned from big data that help users make quicker and better decisions in their jobs virtually every day.
It’s a big bet in a space that is just beginning to show its enormous potential. Up to now, big data has been defined by players like Palintir, a leader among companies focused on addressing shortcomings in the enterprise infrastructure. Specifically, these big data infrastructure companies have enabled complex organizations and businesses—from Homeland Security to Wall Street–to analyze and utilize their treasure troves of information. To date, the market for these innovators tops a hefty $16 billion, according to IDC research.
But here’s the thing: Much of the value derived from these technologies thus far is primarily accessible only through data scientists and engineers. They spend countless hours culling through and then analyzing information to reap the most meaningful insights they can. Business users, charged with making split-second decisions every day affecting everything from sales to marketing to finance, have been left waiting—and wanting.
Until now, RelateIQ is just the latest in a new breed of so-called Data-Driven Application upstarts poised to fill the gap. They are coupling big data with machine learning to fundamentally change the way business users make decisions. In the case of RelateIQ, which targets customer relations management (CRM), the company hopes to make it simple for users to track and manage all their business relationships by analyzing data from call logs, emails and calendars to recommend follow ups, optimal times to reach out, and make sure that no promising relationship is overlooked.
If everything goes as expected, virtually every category of enterprise applications will be transformed by the insights automatically derived from a multitude of data sources. In just the last 12 months, an estimated $150 million has been spent funding startups focusing on everything from security to ITSM to advertising to human resources.
Their missions follow much of the path already blazed by large, consumer-facing Internet companies, such as Facebook, Amazon, Netflix, and LinkedIn. They have leveraged the massive amounts of data they collect and process to provide friend recommendations, personalized content, suggested products, and to help target potential new employers and employees. These processes have led to smarter, faster and more strategic decision-making.
Business users increasingly want the same level of sophisticated, quick insights powered by smarter software. And they are likely to get them from the dozens of companies sprouting up across the country.
This new generation of big data enterprise startups is a natural evolution from what began in the late ‘80s with the emergence of client-server companies. Peoplesoft and Siebel provided packaged software to help companies manage their employees, finances and customers. Then, about a decade ago, SaaS companies like Salesforce and Workday leveraged the Internet to change the way software is consumed and delivered. As powerful as the move to SaaS has been, it is clear that businesses today expect more than just automated processes. They want help making quicker, better and smarter decisions, every day.
That is where the powerful convergence of big data and SaaS comes in. Though virtually every sector will be moving toward these innovative software tools within the next few years, here’s a breakdown of some of the industries most likely to feel—and embrace—the transformation first:
- Cybersecurity: Security companies have long focused on new technologies to defeat malware, but recently user behavior has come under scrutiny as well. A new generation of companies like Fortscale is tracking user access logs to find unusual patterns that may show either a compromised computer or an employee with malicious intent, which they call the Snowden problem.
- Human Resources: HR organizations have long embraced systematic and technological ways to measure employee performance and potential. Recently, analytics has become part of this. Gild is one company taking a software developer’s entire online presence (including open source code but also social media profiles) and comparing it with known successful engineers at a hiring company. These profiles let them determine if an engineer is not just a good fit professionally, but also culturally.
- Sales and Marketing: The art of converting marketing leads into sales leads and then bankable deals has always been a bit of black magic practiced by top sales people. Infer is a company working to bring data science to the problem by building an enormous model of a company’s past leads to close deal conversion, and tying those deals to all kinds of outside data they can track down. Then, they use that model to predict which incoming leads are most likely to convert, and thus, where to allocate salespeople’s time.
The multi-billion-dollar opportunities for this next generation of big data innovators are as immense as the problems they are trying to address. And if they get it right, they will undoubtedly touch not just every function within a company but every person as well.
John Walecka and Scott Raney are partners at Redpoint Ventures, a backer of RelateIQ and Infer. Chris Child is a senior associate.