How big data is collected

Big Data: How big data is collected and what specialists are required for this. The transition from ordinary to large data arrays is a special facet of technology that needs specialists and skills to work on this interface.

Despite the fact that there is still a shortage of IT specialists working with Big Data, the market is developing rapidly: solutions for banking and telecommunications appear, the technical operation of equipment is envisaged, and the consumer receives personal recommendations on content.

Data Collection, Granularity, and Real-Life Matrix Examples

Data is a fixation of events in time that arise in any type of interaction: a person with a company, a machine with a company, a machine with a machine, or a machine with a person.

To take a real world example: a simple measurement of the temperature in a room over a period of time produces a series of data. Collecting, storing and processing data comes at a price. Therefore, each company determines for itself what kind of data it will need to solve its problems: from internal processes to interaction with external counterparties or monitoring of employee behavior.

Some data is not used within the organization, but is valuable to another company. There is a question of exchange of useful information. For example, if a retailer simply saves all transactions and is unable to conduct research due to an incomplete overview, there is always the option of partnering with another company that has the desired related data.

Of course, with such collaborations, data anonymity and depersonalization are observed, information sets are encrypted and scrambled. Thus, there is no mention of companies being able to transfer personal data of users.

Big Data solves business problems: from watching a TV show to the effectiveness of outdoor advertising

Every industry has a need and applicability for big data products. As a rule, such products allow you to get a more complete picture of the task, which was previously impossible. The traditional approach is to conduct panel studies, when the evaluation of the required indicators is based on a representative but rather limited sample.

For example, to find out what Spaniards watch on television, the company analyzes only 10,000 panel sheets and then extrapolates to the entire population of the city. If 1,000 out of 10,000 people watched the show, that means about 10% of everyone did.

Big data allows you to use significantly larger samples for analysis, since it analyzes, albeit anonymously, real data about user behavior. A good example of what this can do is the history of field analytics.

Traditionally, limited sets of measurements were used in this area: for example, at certain points in the city at certain times, the number of passing cars was counted. Analysts then extrapolated the data using a complex mathematical model for the entire month.

As a result of the study, the client received an average audience estimate per day for each billboard. The use of data obtained from navigation systems and mobile applications has made previous models significantly more accurate.

Analysts can assign audience profiles for each layout based on day of week/time. And the use of Wi-Fi sensors in the structures made it possible to study the behavior of the audience: to see their areas of residence and interests.

Based on big data, you can make correct predictions and train machine learning models. For example, making an assumption that the borrower, based on some characteristics, may not be trustworthy. Or, based on circumstantial evidence, assume that the unit in the factory will soon require maintenance.