Towards cognitive computing

In short. By increasing the amount of interactions and shared information, social computing made businesses meet their own limits : the incapability to deal with all the data at their disposal at the appropriate speed and scale. That’s an opportunity for a new era, the Cognitive Computing one where smart systems will allow to make informed decisions in an always moving context.

First we had computing, then social computing. There are many chances the next wave will  be about cognitive computing.

The facts are here : the result of social computing is an always increasing mass of data generated by people both inside and outside organisations. Sometimes in purpose, willing to share an information, an opinion, or involuntarily when the data is generated and captured at the occasion of basic actions of our day to day lives. These data make it possible for businesses, for decision makers to improve the perception they have of their environment, to develop a kind of situational intelligence, to identify the right people and information at the right time to solve problems and make decisions.


Decision makers lack situational intelligence

By generating and using these data, customers and employees (who are different sides of a single person) transform organizations. Or, rather, create a need for transformations. The ones by delivering data that have to be harnessed in the decision making process, the others by asking for processes and tools that will help them to make the most of these data.

The transformations in question impact organizations far beyond the usual scope of data management, beyond IT departments. As a matter of fact, those who’ll experience the biggest changes will be human resources, marketing and financial departments. There’s no use for IT to give them the means to deal with data if they are not ready to. All the more since mass data is usually dirty data that needs business context to be cleaned and made actionable. The understanding and use of these data will lead to better or even reinvented business processes, adaptive and manageable in real time, not as planned activities but as human matter.

In concrete terms the question is to guess how a marketing director will be able to find, in an ocean of data, the few drops that will help him making the right decision, how a CHRO will identify the existing competences and those who’ll be needed tomorrow.

Driving business by looking in a distorting rear-view mirror

Considering that’s only a technology matter would be a big mistake. If we rely on commonly stated numbers, more than 33% of decisions makes do not trust the information they have to make decisions. There’s nothing surprising here : decision makers most of times fail into one of these cases

– information come from the processing of data that represent pas situations while we all know our environment is more likely to change than being repeated. That leads to two consequences. First one is decisions makers driving by looking in a rear-view mirror (at beast). Second is that the model used to turn data into information is not accurate anymore, what means that what the mirror shows is not what’s ahead.

– the time needed to update the model makes it de facto obsolete. The patterns that help turning data into information should be understood and updated in real time. If not, businesses will always draw the wrong conclusions no matter they have the right data.

– to much data leads to too many indicators that are impossible to follow and flood decisions makers. The purpose is not to process all the available data but identify those that make sense, correlate them and make sense of them.

Analytics : cornerstones of the social information system

The way we’ll shape organizations, drive day to day activities and prioritize actions and investments will depend on our ability to understand and make sense of data. Not less. One of the biggest challenge will be to move from the understanding of a collective relationship (customer segments, employee categories) to a more individualized one (customers and employees as people with their own stakes and patterns).

That highlights a new dimension, common to all the matters we use to discuss. What does collaboration has in common with talent management, marketing and customer relationship ? The ability to understand these data and make sense of them. And if it’s easy to understand how all these discipline interact together, they also share a common denominator : analytics that make data captured in a field are reusable and actionable un another. The whole with the purpose in making decisions rely not anymore on the analysis of the past but a deep and real time understanding of the patterns that drive the evolution of a complex environment, based on fact and data instead of trend generalization.

Cognitive computing : human and learning Big Data

A new era is starting, where small and big decisions will me made, at any level, by people having a comprehensive view of their environment and of the way it moves. New ways of doing things that will be supported by new platforms and a new approach to computing : Cognitive Computing.

Of course there’s nothing new here. The matter has been popular for months under the name of Big Data. In fact, even if we should not pay more attantion than deserved to wording, I see a big difference between Cognitive Computing and Big Data. If Big Data is about mass data processing, it’s only one side of Cognitive Computing which is also about data analysis and tagging, pattern discovery and the ability the system has to learn from his own experience. Cognitive Computing also have an human side. This evolution won’t be neutral  in terms of competences to develop, operating models to improve and business processes in which the place of people will also change. If  there’s not doubt that in a close future machines will replace humans in discovery and rough data processing activities, people will still be key for interpretation…provided the information they’re given is of quality, well targeted and they have the required knowledge and training. Cognitive Computing is what will help to move from data do information, a system which call is to become a learning one.

If analytics become the new business “lingua franca” and cognitive computing the discipline they serve, we should already start wondering about the consequences of such changes, know its potential and limits to make the most of it and not be outflanked by a new Frankenstein. If employees and even students are still struggling to master the basics of social computing, this essential literacy businesses lack, we have lots of reasons to see the next step as a major concern.

Make decisions at the speed and scale of business

Social computing and the participative approaches it supports shows the incredible potential of a system allowing information sharing and networked interactions at a wide scale. It also shows us to what extent our organisations and us are limited in front of such a phenomenon that can easily go beyond us. For once, Cognitive Computing is not a new buzzword that aims at making us forget that the previous one failed, it’s the price of its success. We have to build organizations to face this flow od data and turn them into informed and tangible actions at a scale we’re not able to operate at. Doing that at the speed of scale required by our business environment needs appropriate systems and organizations.

If computing was a matter of data, social computing a matter of people, cognitive computing is more a convergence than a next step : the need of using people and data together. Let me bet that in less than two years, participation and adoption won’t be a concern anymore. The only thing that will matter what the sense to make of data and interactions.

Head of People and Business Delivery @Emakina / Former consulting director / Crossroads of people, business and technology / Speaker / Compulsive traveler
Head of People and Business Delivery @Emakina / Former consulting director / Crossroads of people, business and technology / Speaker / Compulsive traveler

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