Machine Learning: Value for business

Machine learning (ML) algorithms allow computers to define and apply rules which were not described explicitly by the developer.

There are quite a lot of articles devoted to machine learning algorithms. Here is an attempt to make a “helicopter view” description of how these algorithms are applied in different business areas. This list is not an exhaustive list of course.

The first point is that ML algorithms can assist people by helping them to find patterns or dependencies, which are not visible by a human.

Numeric forecasting seems to be the most well-known area here. For a long time computers were actively used for predicting the behavior of financial markets. Most models were developed before the 1980s when financial markets got access to sufficient computational power. Later these technologies spread to other industries. Since computing power is cheap now, it can be used by even small companies for all kinds of forecasting, such as traffic (people, cars, users), sales forecasting and more.

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Anomaly detection algorithms help people scan lots of data and identify which cases should be checked as anomalies. In finance, they can identify fraudulent transactions. In infrastructure monitoring, they make it possible to identify problems before they affect business. It is used in manufacturing quality control.

The main idea here is that you should not describe each type of anomaly. You give a big list of different known cases (a learning set) to the system and the system uses it for anomaly identifying.

Object clustering algorithms allow grouping a big amount of data using a wide range of meaningful criteria. A man can’t operate efficiently with more than few hundreds of objects with many parameters. The machine can do clustering more efficient, for example, for customers/leads qualification, product lists segmentation, customer support case classification, etc.

Recommendations /preferences/behavior prediction algorithms give us the opportunity to be more efficient interacting with customers or users by offering them exactly what they need, even if they have not thought about it before. Recommendation systems work really bad in most of the services now, but this sector will be improved rapidly very soon.

The second point is that machine learning algorithms can replace people. The system makes an analysis of people’s actions, build rules basing on this information (i.e. learn from people) and apply these rules acting instead of people.

First of all, this is about all types of standard decisions making. There are a lot of activities that require standard actions in standard situations. People make some “standard decisions” and escalate cases that are not standard. There are no reasons, why machines can’t do that: document processing, cold calls, bookkeeping, first-line customer support, etc.

And again, the main feature here is that ML does not require explicit rules definition. It “learns” from cases, which are already resolved by people during their work, and it makes the learning process cheaper. Such systems will save a lot of money for business owners, but many people will lose their job.

Another fruitful area is all kinds of data harvesting/web scraping. Google knows a lot. But when you need to get some aggregated structured information from the web, you still need to attract a human to do that (and there is a big chance that results will not be really good). Information aggregation, structuring, and cross-validation, based on your preferences and requirements, will be automated thanks to ML. Qualitative analysis of information will still be made by people.

Finally, all these approaches can be used in almost any industry. We should take it into account when predicting the future of some markets and of our society in general.