×

CIOs have a central role to play in ushering cognitive capabilities into the enterprise. This responsibility challenges them to rethink much of what they know about business analytics, data management, security and privacy, and, more fundamentally, how to solve problems for business. In this article, the second of a two-part series, venture capitalist Manoj Saxena, who until recently led IBM’s Watson software division, and Paul Roma and Rajeev Ronanki, principals with Deloitte Innovation, Deloitte Consulting LLP, discuss the impact of cognitive computing on traditional IT disciplines, the role of the CIO in developing cognitive computing capabilities, wide-ranging security and privacy issues, and the value in failing fast. In the same way that cognitive is the next wave of computing, is cognitive analytics the next wave of business analytics? Roma: While true, that comparison alone overly simplifies cognitive. Cognitive flips problem-solving on its head. In the past, we relied mainly on personal experience to solve problems: Find an expert, and build a system or design a report that produces some metrics or KPIs that can be tracked. But by starting with experience, you're more likely to end up with the wrong answer. Now, rather than relying on experience—which is often subjective, limited, misguided, or even irrational—people are instead compiling the data that’s relevant to a problem, turning it loose on a cognitive app, and finding out what critical insights were hiding in the darkest recesses of that data. Saxena: This is at the heart of machine-augmented cognition. You’re not asking a question and looking for answers. Deep learning techniques are being applied against a relevant data set to reveal what you should know, or the likelihood of something being true—answers to the questions you never thought to ask. What is the CIO’s role in bringing cognitive analytics capabilities into the organization? Ronanki: The traditional role of the CIO has been that of an enabler of a business model. CIOs look at the business capabilities required by the business, and then build IT strategy and a fulfillment to enable those capabilities. Now, technology—including cognitive—increasingly shapes the business model. Rather than simply fulfilling capabilities outlined by the business, CIOs act as strategists and innovators, bringing technology to the table to influence and inform the business strategy; and as catalysts, influencing and shaping the enterprise and its culture to make use of the technologies. Saxena: That’s a much bigger responsibility. Meanwhile, we hear all the time from CIOs that technology has gotten too complicated and is moving too fast for them to get—and keep—a handle on it all. They also frequently lack the skills in-house to test various combinations of technology—mobile, social, cloud, big data, advanced analytics—in their industries and companies to unlock business value. Again, this is where greenhouses and garages can step in to help explore the possibilities, flatten the learning curve, and deliver some quick wins before scaling up. Roma: Traditional cycle times for developing and deploying systems are too long and no longer support the pace of change in business and technology. IT shops need to be more responsive and agile. Today, we can write a working application in the time it used to take to write an RFP. Are there prerequisites, particularly related to data and data management, for companies interested in applying cognitive analytics? Roma: There are no prerequisites to get started. There’s no upfront capital investment. No need to assemble a large team or set up a program office. No need to build an application. In fact, all those traditional steps could work against you because everything is changing so fast. Engage the ecosystem and start working on a targeted problem with the potential for quick results. It costs very little money or time to set up a listening post on social media using ready-made technology to gather customer sentiment on a particular product or service. Ronanki: There are two categories of data broadly: enterprise data and external data. Up until a few years ago, life was good for CIOs if they had established processes for collecting, storing, cleansing, and optimizing data for transactional purposes, and recording and reporting it for financial purposes. Meanwhile, however, unstructured data volume is growing roughly 60 percent faster than structured data, and it mostly sits outside the enterprise. CIOs have no way to control it. Again, that’s where the ecosystem comes in. CIOs will need to think well beyond their traditional approaches to data management, and start thinking more broadly about the discipline of data science. Their aim will be to provide the right information at the right time and right place when it’s needed, and with all the assurances around security, privacy, and trust. Data science won’t supersede or replace the other IT disciplines, but it will most assuredly affect everything else IT does—from running call centers to greasing the skids of supply chains. Saxena: It’s useful to think about the four V’s of data: volume, velocity, variety, and veracity. Eighty percent of the world’s data was created in the last two years (volume). It’s doubling every 18 months (velocity). It’s polystructured and not just single-structured (variety). And is it data you can trust (veracity)? The security and privacy concerns are potentially massive. Do people want light being shined on their very personal dark data? Or worse, have it fall into the wrong hands? Roma: Security is the biggest problem facing the entire cognitive computing industry. Saxena: The notion of opting in is going to become more and more important with consumers. If they know and value what they’re getting in return, they will opt in and give more and more information. But you must collect it with their consent. That being said, there is still the issue of every data source having distinct security policies and terms of use. That’s a fundamental problem that a lot of companies are now tackling. Cognitive Scale, for example, is building what it calls “multizone data sovereignty and security life cycle management” to reliably source, manage, and process data for use in cognitive apps. This includes monitoring who is using data and applying rules to ensure usage is within specified guidelines. A leading cancer hospital just bought a cognitive app and has five distinct data sources: its own EMRs housed in its data center, claims data from a third-party provider, data on nearby restaurants and hotels for the benefit of patients and families in town for treatment, and U.S. government data (census, Medicaid, and Medicare). Beyond all the usual security layers—at the device and network levels and so on—they also require security at the content level for each of these data sources. Roma: There’s actually a third component and that’s trust in the data. Trust means much more than having a big data set from a reliable source. When we talk about trust, we use words like completeness (are you getting the whole picture?), lineage (where does the data originate?), and much more. Another entire field is emerging within the ecosystem around trust. There’s still a lot to be worked out. I imagine failure rates will be pretty high for the foreseeable future. Saxena: Cognitive aside, we estimate that more than half of big data projects are failing. Part of the problem may be with the name itself. Too often, companies are focused on amassing a lot of data rather than the right data. It’s far better to start with a desired outcome and work back to consider the data that will help you achieve it. Many times, companies are surprised that it requires little data and dark data to drive great outcomes, not necessarily big data. Like anything, cognitive is a capability that CIOs will have to build and master. There will be a learning curve. If they fail fast but fail forward then they’re on the right track. They’ll be the strategists and the catalysts looking to make the most of an opportunity to deliver significant business capabilities faster than in the past.