Big Data

Where are the Opportunities for Cognitive Computing Startups?

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Winning game show contestant, chef, travel agent, intelligent phone assistant, personal shopper, financial advisor and cancer fighter. These are just some of the roles that first-generation cognitive computing applications have played over the past few years. With so many ways to apply the power of cognitive computing to change how we work and live, the potential of this emerging market segment is limitless. Its potential could rival the one we saw at the onset of the commercial Internet in the mid-1990s. 

Broadly speaking, cognitive computing is defined as the simulation of human thought processes in a computerized model. These are next-generation computer systems that learn and interact naturally with people to extend what humans or machines could do on their own. Cognitive computing systems get better over time as they build knowledge and learn a domain. This primarily includes their language, terminology, processes and preferred methods of interacting. Unlike expert systems of the past that required rules to be hard coded by a human expert, cognitive computers can process natural language and unstructured data and learn by experience, much in the same way humans do. 

Continued investments in cognitive computing by market leaders such as IBM, Microsoft, Apple, Google and HP; the launch of several venture-backed cognitive computing startups this past fall and a slew of life changing applications being built on cognitive cloud platforms give further credence to Deloitte’s forecast that this market segment will reach $80 billion by 2018. 

Yet, as we all know, not everybody will be able to seize the opportunities in cognitive computing. Like every large emerging market, there will be a glut of entrants and over investment, which inevitably leads to a Darwinism of sorts. Given this, let’s take a closer look at three of the latest trends in cognitive computing and how they’ll shape the future of the industry. 

Trend #1: Vertical industry specializations 

Many cognitive upstarts are making a name for themselves by diving deep into industry-specific apps. Of course, this strategy isn’t exactly revolutionary. However, it’s creating a sales pipeline for these companies while also demonstrating the real-world benefits of cognitive computing. 

The industries that are most ripe for cognitive computing are ones where there is an abundance of multi-structured data, which can drive better business and consumer decisions. These include travel, retail, healthcare and financial services. 

In travel, cognitive apps eliminate the arduous process of surfing 20+ websites, travel aggregators and customer review sites when trying to research and plan your next trip. 

For retail, cognitive apps can act as a personal shopper answering frequently asked questions about products, tapping into the engagement on social networks and making recommendations based on the shopper’s previous purchases, interests, and needs. 

When it comes to financial services, cognitive computing addresses a variety of needs from personal advising to detecting potential fraud. 

In healthcare, cognitive computing will transform the industry from being a reactive, costly approach to addressing health issues to providing a more proactive, holistic approach to wellness. 

Healthcare is where a majority of cognitive startups are focused today. In fact, the abundance of healthcare-focused cognitive computing startups came up during a recent conversation with an industry analyst. 

In the near future, look to healthcare to lead the acceleration of cognitive computing. Subsequently, it will also be the first industry to drive that Darwinism effect. 

Trend #2: Deep personalization 

Technology, especially big data, has long held the promise of delivering more personalized customer experiences. Yet it often falls short of expectations. This is where cognitive computing can play a critical role in accelerating big data to drive personalized advice and insights for consumers and business leaders. 

The reality is that big data is inherently limited in its ability to deliver truly personalized experiences. There are three primary reasons for this. 

First is the sheer volume of data that needs to be analyzed in real time to understand meaning and insights while preserving data usage and privacy requirements. Second, there is a dearth of technology to contextualize big data and continually learn and improve from data and user interactions. Third, and arguably most important, is its inability to collect and source “dark data.”   

Dark data is data that is uncollected, neglected or underutilized. Examples include data that’s located in doctor’s notes in an electronic medical record, call center notes, geolocation apps, mobile devices, emails, images, social media threads, etc. By some estimates, dark data makes up 80 percent of data today and contains the hidden gems of information that unlock the keys to personalization and learning. Cognitive computing companies that can deliver truly personalized customer experiences will become the next-generation market leaders in this rapidly emerging space. 

Trend #3: Big push by the Big Four 

Hands down, IBM ushered in the era of cognitive computing. Now the Big Four consultancies are getting in on the action to accelerate the creation of cognitive ecosystems. We are already seeing companies such as Deloitte, PwC and Accenture step up their efforts in this area and allocate more resources to the advancement of cognitive computing. Many are already gearing up to train staff and have dedicated client innovation lab space to rapidly experiment with and develop cognitive systems.  

The road ahead 

While cognitive computing holds great promise and seems poised for success, there are three stars that need to align for it to really take off. 

First, vendors and early adopters need to take a top-down approach to consistently demonstrate business value and not solely focus on technology. An over reliance on technology-driven efforts that are focused on a single algorithm, or machine learning models are spawning a slew of “science projects” with no clear business impact or ROI model.  

Second, the industry needs to come to a consensus on its definition and standards. Without that, cognitive computing will never be fully embraced by the C-suite. Progressive players in the software, hardware and services space need to define methods, tools and standards much like we did with the advent of the Web. 

Third, while cognitive systems can deliver immediate benefits, it is important to remember that these self-learning systems get smarter and more immersive over time. Customers must therefore view cognitive computing as an iterative, strategic initiative where you “fail fast and fail forward.” 

Manoj Saxena is chairman of Cognitive Scale, a founding managing director of The Entrepreneurs’ Fund and chairman of SparkCognition. Previously, he was general manager, IBM Watson, where his team built the first cognitive systems. Prior to IBM, Saxena successfully founded and sold two venture-backed software companies within a five-year span. He currently serves on the boards of the Federal Reserve Bank of Dallas, Communities In Schools, and the Saxena Family Foundation. Follow him on Twitter.

 

 

Comments

By Brian Clark

Manoj,
Great article. My question is, what are you data offerings that apply to ATS hiring systems?

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