Summary : When we talk about working on information, we usually distinguish the work that’s been progressively dedicated to machines (mass processing of data according to pre-determined plans) and what remains the field for humans, a sharper and more qualitative approach to scattered and unstructured data. This second point lead organizations to organize accordingly, distinguishing between those who search, prepare and use these data. A dichotomy that has many chances to be questioned in a near futur as machines are getting able not only to explore unstructured data but also to understand questions and give answers.
When we have a look a the main components of any information system, we can see two poles coexisting :
â€¢ the “mechanical” one. It’s made of applications that replaced humans over time because they’re more efficient and reliable for some tasks, providing a substantial advantage both in termes of speed and quality, what means in terms of costs. They allow the mechanization of repetitive mass processing that need more calculations and processing power than intelligence and ability to react in front of unpredictable things.
â€¢ the “intelligence and knowledge” one. It’s made of applications that don’t replace humans but are supposed to multiply their intrinsic abilities that a machine does not have. Its about communication and collaboration technologies.
If we focus on the second point, it’s obvious that no machine can understand and treat unstructured data with the needed fineness. Should the need be about searching, using and make a decision relying on a huge mass of unstructured information without the existence of an history demonstrating what “a good decision is”.
On this part, the superiority of human versus the machines is about decision making. As for what’s about information search, it’s rather a burden but a necessary burden because even if the machine is powerful enough it’s unable to process a qualitative and contextual search on information.
But how long will that last ?Years ago, Google revolutionized the world of search by relying indirectly on internaut’s knowledge and intelligence to improve the quality of searches. Later, engines like Exalead allowed to index data from any source and in any format. They projects like Wolfram Alpha offered not only to search information but also to provide answers. Recently, went a step further by being able not only to provide answers but also to understand questions expressed in human language and give the answer in the same way, combining “Content Analytics” and “Natural Language Processing”.
Let me also mention so observations a made on IBM Labs’ booths during Lotusphere : the analysis, filtering and understanding of a huge quantity of data to suggest users synthetized, contextualized and relevant contents is (fo good reasons) a major concern for Big Blue. This is made even more essential by the socialization of the informational context of both employees, organizations and customers that will lead to the explosion of the quantity of data to manage. This phenomenon being unavoidable, there are only two possible solutions ; ignore these data that are too numerous to be managed or try to make the most of it and identify its true substance. It’s obvious that only the second option makes sense.
So, what conclusions can we draw ?
â€¢ The matter is not only about finding information. It’s about understanding, contextualizing and filtering it.
â€¢ software intelligence will replace more and more human labor for such tasks. Machines will be more and more able not only to find the information where the answer is but to find the answer itself.
â€¢ the role of humans will move to complex decision making relying on the elementary answers provided by the machine. (Note that, as time will go by, the answers will be less and less elementary).
So everything would be perfect in a world where people would break themselves of burdensome tasks to focus on actions where their added value is the more important.
Not that sure. As the industrial revolution and the computing one did before, the revolution of intelligence and search will make many jobs obsolete, those that are today’s “simple executant” that search and prepare information for others, for decision makers. Some may remind of what happened to many personal assistants once anyone was able to type a document and send emails…
Of course, it will take a long long time to reach to this point but if nothing is done before, if no one pays attention to all the competences this revolution will make either mandatory or obsolete, the case of employees (old or young) prepared for a job that has reached its obsolescence point may be very serious.
In short, decision making will be more and more what people will need to be good at.