In vogue for several years, analysis of Big Data enables companies to process huge volumes of unstructured data and derive strategic business intelligence. The classic analysis of Big Data is partly based on Machine Learning, but it should be noted that the statistical modelling and manual processing steps are essential to its success.
Recent advancements in Machine Learning, especially with AlphaGo from Google and Watson from IBM, are setting cases and are gradually taking their place in the corporate world.
Machine learning refers to the methods and technologies allowing machines to “learn” without using dozens of complex statistical models and rules. Take the example of AlphaGo, to beat the World Go champion, the algorithm was not based on rules indicating how to react to each situation during the game; it learned to play by processing a large dataset of games played by human players. Therefore, it is simply impossible for programmers and an adversary to predict its decisions.
The application of Machine Learning and cognitive technologies to enterprise data is still a recent phenomenon, which could lead to concrete results in a few years if investment and the necessary research are carried out.
Cyber criminals seek above all to remain anonymous. Complex persistent threats and difficult to detect thus represent a weapon of choice in terms of profitability.
With conventional network security systems, it can be extremely difficult to identify vulnerabilities and compromised devices. However, the use of these attack vectors results in abnormal activities (unusual connections, use abnormally high servers, IO abnormalities and other minor phenomena among the billions of operations performed). Users and automated systems based on the rules tend to generate false positives and negatives, but with Machine Learning algorithms, machine can identify abnormal activities within sets of very large data.
Businesses today know more than ever about their customers, or at least have an unprecedented amount of consumer data. With Machine Learning algorithms, it is now possible to process the data to extract useful information on consumer sentiment, which translates into a competitive advantage for companies.
Smart stock solutions operate Machine Learning to maximize the return on investment. To ensure success, companies in all sectors must indeed know when to buy or sell, and especially at what price. With Machine Learning algorithms, they can define appropriate rates based on a multitude of different factors.
As explained by an article in McKinsey Quarterly:
“In Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques and, in some cases, experienced 10 percent increases in sales of new products, 20 percent savings in capital expenditures, 20 percent increases in cash collections, and 20 percent declines in churn. The banks have achieved these gains by devising new recommendation engines for clients in retailing and in small and medium-sized companies. They have also built microtargeted models that more accurately forecast who will cancel service or default on their loans, and how best to intervene.”
Machine Learning helps companies target or create products based on the expectations and needs of different markets and is likely to emerge as the new essential business tool of tomorrow. Leaders in the field will use these approaches effectively to improve their knowledge of the market and take a step ahead of their competitors.