Data Science Today

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Today, big data technologies are already in full use in business. First of all, new trends have affected the consumer sector. Previously, marketers had to conduct selective research: interview people selected according to certain rules and draw global conclusions based on their answers.

These methods have many limitations, which means that the forecasts are not the most accurate. Today, researchers have at their disposal a huge amount of information that describes in detail consumer behavior, the needs and aspirations of almost everyone as well as data science outsource: 

  • credit card transactions; 
  • geolocation; 
  • queries in search engines; 
  • photos posted on social networks; 
  • words spoken next to a smartphone.

In our time, you still need to try very hard so as not to leave digital traces so you can hire big data experts. Retail chains were able to analyze the purchases of hundreds of thousands of specific customers and find out how the demand for certain goods changes during the day, week, month and how it is related to changes in hundreds of other factors.

The analysis of all this information, ideally, should make it possible to make the offer of goods and services as accurate and personalized as possible: at the right time, offer a person the right product for him.

Banks are particularly advanced in the analysis of big data. This approach makes it possible to automatically detect fraudulent transactions, assess the creditworthiness of customers, and better manage risks.

The so-called data mining is widely used in the financial sphere – methods that allow discovering new, previously unknown, hidden patterns in information. Previously, the experience and intuition of employees played a key role here.

What is intuition if not the result of a background analysis of it flowing into the brain from the external environment? However, today data science consultants and the human brain in this work are increasingly being replaced by artificial intelligence.

ЗV means big data

Big data is characterized by parameters that are abbreviated as 3V – according to the first letters of the English words volume, velocity and variety. The values of these parameters are high, and, what is important, they only get higher over time.

With volume, everything is quite obvious: the volume of big data is large and it is constantly growing. Just imagine: a single sensor that records one or another parameter (for example, temperature) once a second produces more than 31.5 million values per year.

In a modern refinery, there can be tens of thousands of such sensors. The social network Facebook now stores 250 billion images uploaded by users, while the number of individual publications – posts – is several orders of magnitude larger.

The speed at which new data arrives is also increasing all the time, as the number of information sources connected to the network and generating data increases. Faster data updates, in turn, affect our assessment of their relevance. What until recently was perceived as fresh information now seems hopelessly outdated. Find more info in DataScience UA.

Over time, two more Vs were added to the three Vs: veracity and value (some call others V). With reliability, everything is not so simple: with the growth of volumes and the speed of receipt of new data, their quality and accuracy are increasingly difficult to control.

On the other hand, new ways of checking them are emerging, including due to the variety of sources and types of data. So, for example, the navigator in your smartphone, due to the loss of the satellite signal, can lead you to the wrong place. But data from the cellular network, accelerometer and maps help to adjust the bottom line.

 

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