Data Science experts on how business can benefit from big data

According to the company’s experience, many business owners are confused because of the big data boom. They want to introduce the technology into their business but do not know how. “We have to explain that this is not a magic elixir and it is necessary to wisely accumulate the information. The strategy of “collecting absolutely everything” does not bring valuable insights in most of the cases.”

– Each year the volume of data collected in the world is growing by about 30%, and since 2006, the amount of information has increased by 30 times.  The companies that benefited from big data had a good data strategy in the first place.

Social media contain 25% of all the Internet data. Such volumes of unstructured data can not be fully analyzed manually. In order to extract valuable insights from such data it is necessary to collect the data and automatically pre-preprocess it – conduct sentiment analysis, extract named entities (proper names), determine the subject of the statement, etc. On the basis of preprocessed data, a specialist can compose reports that help to make business decisions and implement changes in a service or a product.

Now a lot of attention is paid to image processing. People share a lot of personal information on social media. Instagram of an ordinary person entirely reflects their life preferences: where they go, what they eat, with whom they communicate. There is a lot of data of such kind, and it is not structured in any way. Extraction of such information can help improve many business processes, for example, targeted advertising.

The processes of collecting and analyzing big data are changing. Now we need to create models that capture complex patterns, but at the same time can scale to large audiences of a million or ten million active users. This requires a mathematical base and deep knowledge of software engineering. Badly written code simply does not work, since there is much more data available.

The demand for data scientists is very high at the moment, especially those who are developers with knowledge of machine learning. The market is looking for people who know how to deeply analyze data and have strong engineering skills.

However, it is impossible to create a universal solver. So far all existing applications are associated with weak artificial intelligence (AI). Algorithms in these applications are aimed at a specific task. The same with Siri, it consists of a chain of weak AIs that can, show an exchange rate or find something on the Internet. Self-driving cars also include many models that are trained to calculate the distance to the nearest obstacle.

But due to the fact that each model is only a weak AI, the work of data scientists is still valuable. Creation and implementation of truly innovative solutions require a lot of analytical, mathematical and technical skills. People need to explore data, design algorithms and write programs, providing them with data, and finally getting trained predictive models – weak AIs.

When you have tens or hundreds of gigabytes of data and it is scattered and unstructured, you can use deep learning – a method that helps you to extract valuable knowledge directly from raw data. It works the following way: the input layer consists of raw data, and at the output layer there is a target variable, the one we want to predict. Next, an End-To-End system is constructed. A human doesn’t participate in the process a lot, influencing only the structure of the neural network and the parameters of its learning. The system is computationally complex, but it allows to process the data that was hard or even impossible to work with before, especially, texts, videos, and images.