Companies are more often aware of the importance to be one step ahead and even though predictions through Machine Learning algorithms have been around for a while, now these techniques are becoming very popular.
Many organizations try to assess their own ability to enhance the business goals through technology. CMM (Capability Maturity Model) schemes help to understand what the degree of formality and optimization of processes is and, particularly, what is the role of IT in the organization.
You can use your historic data for watching the state of your business, you can use your data for managing your business, for improving operating processes, you can consider your data as a differentiator key, or you can manage your data as your strategic asset.
The type of use of the information is closely related with the term “maturity”, from the lack of processes to continuous improvement. If you have data, you can learn from it and that is what Machine Learning (ML) is all about. This is a technical discipline that provides computers with the ability to learn from data and allow them to find hidden insights without being explicitly programmed where to look.
It can also be described as a method of turning data into a software that improves with experience. That’s why, Machine Learning is considered as a branch of Artificial Intelligence (AI) which aim is to develop techniques that allow computers to learn.
Machine Learning: The experience in the business
If you have a data-rich business, you can use Machine Learning for solving complex problems, recognize patterns, discover new knowledge and make intelligent decisions based on that data.
At Hexacta, for example, we use tracking information from our ALM tool to predict how many defects will be reported in the next iteration. Predictors such as the amount of lines modified in the source code, the amount of developers and testers, and the lifecycle phase of the project among others resulted quite accurate. With this information we are able to allocate the proper amount of hours for bug fixing in the planning meeting and also for determining how long the stabilization phase and go live will be.
In general, Machine Learning has been prioritized by the companies which drive the technology sector, like Google, Microsoft, Amazon, Apple, Facebook, and Twitter, among others. On the other hand, numerous companies have adopted the role of Data Stewardship, Data Scientist or similar, or have created new areas for data governance practices, including –of course– predictive analytics techniques.
All around the world, many industries are taken advantage of Machine Learning, for example in managing big volume of data, predicting business processes, minimizing costs, analyzing client’s behavior, estimating the scopes of the project, just to name a few.
There are several examples across industries that show what you can do with Machine Learning. Here are only a few of them:
- Financial services: Machine Learning is capable of analyzing massive volumes of information, providing customized financial advice, calculations and forecasts (regression algorithms). Fraud detection is one of the most common application. The technique consists basically to observe the behavior of historic transactions (i.e. credit card purchases) and then look for an outlier in that pattern (anomaly detection algorithms). In credit risk, it can learn from the loan applicant’s details (demographics and payment/credit history) to understand how those attributes affect defaulting behavior.
- Logistics & Transportation: there are many factors that can affect profitability: fuel cost, security measures, supply chain reliability, type of transportation (air vs land), domestic distribution networks, etc., as well as other external data like weather forecast which need to be taken into consideration in order to predict an optimized cost. The variety and complexity of these factors means that predictive analytics is the only option capable of harnessing massive amounts of data to produce real-world business solutions.
- Telecommunications: traditionally operators have employed machine learning (clustering algorithms) techniques to exploit user and traffic data assets to better understand customer behaviors, improve their experience and offer the best plans for every kind of user. It is also common to use historical data for finding patterns which can identify possible churners (classification algorithms).
- Government: state agencies can use Machine learning to increase operational efficiencies by analyzing datasets, finding patterns and anomalies, and making predictions about future events (clustering algorithms). Machine learning plus Internet of Things have experienced a boost in popularity in government (and industrial) solutions. It can forecast pollution levels several days in advance or it can predict the volume of waste inside dumpsters in order to optimize the trash service and the frequency of pick up (regression algorithms).
- Retail and entertainment: Machine Learning models used for product recommendations are built to predict which product a customer is most likely to buy. They take a customer profile (customer activities, recent purchases and personal information) and map this to the predicted likelihood of the customer responding to a given offering (recommender algorithms). Similarly to retail, in the entertainment industry, for instance, Netflix has revealed that 75% of the content watched on his services comes from its recommendation model.
- Healthcare: one of the most common application fields of ML is the knowledge discovery. It derives from medical datasets and it can be used to study patterns of healthcare delivery system, to provide more accurate insights and predictions related to symptoms and diagnoses, to customize health plans and so on.
To sum up
Currently, the reality is that we should be able to produce models that can analyze and comprehend bigger and more complex data in order to achieve high-value predictions which allow us to make better and smarter decisions in real time, save money and, above all, improve our productivity.
There is a saying that the best way to predict the future is to invent it. In the Machine Learning context, it is basically saying, instead of trying to predict what the future will be like, you can apply a ML model to gather fresh business insights and create your own future. Machine Learning is ready for your company, are you?
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