Although sometimes separate, Big Data and collective intelligence can offer far more than expected when used together. History is better, data is improved, forecasts are made easier, and interpretation is smoother; these two datasets bring a capital gain to any company project that cannot be ignored.
Big Data and collective intelligence for your projects
Whilst it has now become a key element of digital transformation, using Big Data is also turning out to be a useful tool for fostering collaborative working. The shorter, more efficient and more collaborative means of communication that digitalisation represents aside, Big Data symbolises an all-important resource for any team or company.
Thanks to this mass volume of data, work groups that have included people with the skills to read and interpret the data set are not only armed with physical resources but also knowledge, which has been, up until now, inaccessible. As a dataset, Big Data naturally compiles facts, events or publications that belong to the more or less distant past. Accessing this knowledge will, then, enable the team to rethink its models and way of working by taking inspiration from examples of what has worked well previously.
As Big Data is neither limited by time nor location, it has vast potential. When paired with artificial intelligence, processing thousands of data from different sources, apprehending the future through predictive analysis and detecting recurring schemas becomes a reality. Predictive analysis is improving day by day thanks to Machine Learning algorithms, which are perfected on a continual basis. Today, however, Big Data analysis does not yet meet all our requirements and still has a long way to go before being a true match for the human brain. Collective intelligence, then, has an important role to play as it possess human sensibilities and can interpret a situation according to a number of existing factors, draw up an action plan and also take final decisions.
Moreover, the bigger the data, the quicker and more efficient the reading has to be, hence the need to bring several people onto one single project. By collecting the data available from Big Data and each group member, the structure of any project will be reinforced and long-lasting.
Collective intelligence for reinforcing Big Data efficiency
Collective intelligence can be found in particular using one of the human race’s main, key assets: language. Language brings symbols which can be computer processed, and therefore go on to strengthen the efficiency of Big Data. When we know that the latest goldmine for companies is in the capturing, understanding and interpretation of human correspondence (through social media intelligence for instance), transforming language into symbols therefore encourages the development of Big Data. For example, Facebook has introduced a set of unique symbols (“reactions”) to translate data which is not structured on its network. From comment to click, the symbol and its meaning can subsequently be interpreted by machines.
To elaborate on this further, philosopher Pierre Levy has developed an algorithmic symbolic language called “Information Economy MetaLanguage”, which aims to collect, gather and translate expressions into interpretable symbols. Thanks to collective intelligence (his project is in open access mode), this algorithm makes it possible to connect data, to make it available to the general public. We can, then, think about working together with researchers, or about collaborative creations in the art world for example. This idea of manipulating the symbols algorithmically can be developed further in able to improve collective intelligence. It is, ultimately, collective intelligence that connects the ideas together.
With the support of people who can exploit the more or less structured data on offer through Big Data, collective intelligence is being strengthened by resources which are now accessible and can be exploited. Conversely, human intelligence means that today Big Data can be expanded with data which has been, up until now, uninterpretable. It can also, then, reinforce its power to act. The natural gap between structured and unstructured data (knowing against knowing how to be) is closing, as the different elements are working together and producing beneficial effects in both directions.