Predicting the future can create good business
After Big Data and Analytics, there is another area of computing in the spot of the corporate world: Machine Learning, focused on pattern recognition.
The amount of collected and stored data continues to grow at a blistering pace. Every day, more and more devices are monitoring equipments, environmental conditions and human activities, increasing the volume of data by almost 50% per year. From the business point of view, it is clear that it is not enough to accumulate a huge amount of raw data, but you need to know what to do with all that information. If you rely on forecasts from IT experts, it’s time for the “digital prophecies” to gain space in the business.
For quite some time, companies are using Big Data and Analytics capabilities to identify previously undetectable and complex relationships between large clusters of information. More recently, Machine Learning has started to combine these two features, creating intelligent algorithms capable to forecast or take decisions from data inputs, instead of following static instructions previously established.
A few examples are enough to get an idea of the impact of these technologies in business. The ability to recognize abnormal scenarios in financial transactions can facilitate the identification of fraud even before the transaction is completed, for example. Mapping the e-commerce user profile and predict behavior patterns enables the supply of goods and services from the interest and real willingness to purchase a potential customer at any given time.
Even more conservative areas are also making use of this technology. Insurance companies, for example, had begun to identify the people offering potential risk more precisely , facilitating the rick analysis for future customers. The financial investment industry is another one that will be influenced by the Machine Learning. Indeed, several initiatives are already forecasting the stock market behavior taking some economic variables em combining them into a full picture.
In the Industry sector, it is possible to predict the approximate time when a specific equipment will fail to operate efficiently or even crashing. A company can thus plan the replacement parts in advance, based on the performance of the equipment and not their average life expectancy.
Elections forecasting from research done by social networks pools are anticipating, surprisingly, election results. The health sector must also undergo major changes. It is possible, for example, to anticipate the likelihood of emergence of epidemics from analysis of risk factors. In brief, it is also possible to provide, through the analysis of synapses, the relative risks to the development of memory and dementia disorders, such as Alzheimer’s, in asymptomatic populations.
These technologies are much more interconnected than it may seem at first. You need to know how to combine the resources and apply them to every problem and objective, enabling preventive action rather than reactive. In this way, one can prevent the emergence of problems that may compromise the integrity of administrative processes of a company.
Published in CIO Magazine in Jul,6,2015