In this article we will explain what big data analytics is and why it is so important today.
What is big data analytics?
When we talk about big data analytics, we are referring precisely to that, the analysis of huge amounts of accumulated data to extract behavioral patterns.
They are characterized by the high speed at which they are generated, the enormous volume of data they analyze, the immense variety of typologies they encompass, and the degree of veracity they possess.
Types of big data analytics
There are 4 types of big data analytics that allow data optimization in different ways.
Descriptive analysis
Descriptive analysis explains with the help of gambling data vietnam phone number data and through graphs and reports what happened in the past , but not why, or what will happen in the future.
Thanks to this type of analysis, an e-commerce site can understand how many people have subscribed to its newsletter, but it does not know the reason why they did so. For this reason, companies should not be satisfied with this analysis, as it does not gather enough information to understand the user.
Diagnostic analysis
This analysis is about interpreting the data and answering why it happened. Patterns are identified and segmented based on them. In other words, they are shaped to understand them.
In the example above, we can tell if the person who subscribed did so because they are thinking of dedicating themselves to it professionally or if they simply want to receive discounts, among many other things.
Predictive analysis
Most useful for businesses. Analyzes data to predict what might happen.
This predictive analysis can provide many benefits if done correctly. The problem is that doing this is not simple, as it is based solely on forecasts and depends heavily on the quality of the data obtained and filtered in the diagnostic and predictive analysis, so it requires careful treatment and continuous optimization.
This allows, for example, to carry out marketing campaigns based on what the company believes the user is interested in at that time, based on the data it has previously collected.
Prescriptive Analytics
An evolution of the previous one, based on automation processes or A/B testing. This system, in addition to analyzing the data and predicting it, advises how to proceed based on it, recommending, for example, which road you should travel on if you don't want to get caught in traffic.
Prescriptive analytics uses advanced technologies, such as machine learning and algorithms, making it very difficult for most companies to implement.
Big Data Analytics: What it is and what it is used for
What is big data analytics for?
In this subsection we will highlight the use cases of big data analytics and how to apply them in companies depending on the sector in which we find ourselves.
Channel
Every project involves some kind of channel decision, whether online or offline.
When we conduct market research to determine where to open a new physical store, we can analyze data on sales potential based on location.
Banks have been doing this with their branches to close them as well, as payment automation continues to grow.
Risks
Returning to the banking sector, risk analysis is something that is widely used in big data analytics by banking entities. The most typical examples are:
Portfolio analysis: assessing solvency risk.
Recovery: the recovery processes.
Fraud
A decade ago, many people were still reluctant to enter their account number online for fear of fraud.
However, technological developments and the growth of big data analytics through cybersecurity have significantly reduced the chances of credit cards being stolen, thereby increasing consumer confidence when purchasing online.
Text Mining
Text mining is the process of analyzing collections of texts without needing to know the precise words or terms that the authors have used to express those concepts.
It is used to check incidents with clients and to perform sentiment analysis (widely used in marketing), among other things.
Text mining involves the following steps:
Identify the text.
Extract that text and structured data.
Build category models.
Analyze structured data.
Social Network Analytics
This refers to how we can identify customers that are related to each other , within the customer database that a company has.
It is based on establishing relationships based on relevant information you have about your clients. It works best when your clients are companies because you can obtain much more data.
Digital
It is obvious that one of the applications of big data analytics is the digital sector, as it mainly allows three things:
Real-time personalization for each user, to maximize the chances of success when launching offers. In fact, the advertising that appears on the Internet is based or should be based on the preferences of each person.
Real time bidding: the clearest example is Google Ads and its ad bidding.
Migration projects to the digital channel: the process of moving from the offline to the online world can be very tedious, both for service providers and demanders. Thanks to big data analytics tools, the process is automatic.
Big Data Analytics: What it is and what it is used for
Customer Intelligence
Collecting and interpreting consumer data to find the best communication or sales strategy is what we know as Customer Intelligence.
Thanks to this, we know the value of the customer for our company, calculating, for example, their life cycle or “Life Time Value”, a metric that we already explained in our article on customer-centric experience .
Sandbox
It is a data platform that allows you to stack all the data in order to build products or develop algorithms that are then applied to business situations and you can do it independently.
