The term Big Data has been widely heard for many years. But not everyone has an exact idea of what this concept represents. The easiest way to explain it to an uninformed person is to explain with a practical example.
For instance, the analysis of big data allows you to display ads only to those consumers who are interested in a product or service. Take a look at the way it works in our article about DMP.
Another curious case has happened several years ago. The Target retail chain began to use big data and machine learning while interacting with customers. The algorithms analyzed how and under what conditions customer preferences have changed and made predictions. Based on these forecasts, customers received all kinds of special offers.
Once a schoolgirl’s father complained that his daughter got booklets with suggestions for pregnant women. Later it turned out that the girl was indeed pregnant, although neither she nor her father at the time of the complaint knew about it. The algorithm has caught changes in customer’s behavior that are specific for pregnant women.
So, what is Big Data?
Most often, the main definition of big data is the well-known “3V” (Volume, Velocity, and Variety), which was introduced by analyst Gartner Doug Laney in 2001.
The signs of Big Data
Relatively large amounts of data collected from various sources, such as payment transactions, user activity trackers, sensor data, etc. Together they congregate into a collection which is subsequently processed by technologies, such as Hadoop and Apache Spark.
Data comes in various formats, in structured and unstructured forms.
The data should be processed very quickly as speedy results are of the highest value. We need to process data very quickly, the results are often needed quickly, for online services for frequently processed data are needed in real time with the minimal delay.
In addition to the traditional definition of Big Data, modern research adds more Vs, such as:
A large amount of data and a variety of sources require quality and accuracy in data processing and analysis. There are questions raised concerning the reliability of the data itself and of the decisions based thereon.
The amount of distortion and “noise” in the data are taken into account.
Describes the expiration date and data durability.
Data flows can vary greatly in terms of peaks and drops, due to social media trends, daily, seasonal, and event peak data downloads and other factors.
Based on the selected characteristics and their semantic meaning, the following definition can be given:
“Big Data is information resources characterized by large volume, speed, heterogeneity and requiring specific technologies and analytical methods for their transformation into value”.
What are the benefits of Big Data for business?
In the fundamental document “Big Data: The Next Frontier for Innovation, Competition, and Productivity”, which in its time sharply raised global interest to Big Data, McKinsey identified 5 ways where big data creates value:
– Transparency – quick access to the necessary information can significantly reduce the time of operating activities and improve quality.
– Ability to experiment to identify needs, determine variability and increase productivity.
– Population segmentation to customize actions for a specific audience. Thanks to the wide capabilities analysis of Big Data, customer interaction will be even more personalized and targeted.
– Replacing or supporting a person’s decision making by algorithms.
– Creation of new business models, products and services.
Big Data and Machine Learning
What is the easiest way to understand how business learning models are applied to business? How are Big Data and Data Science connected?
Imagine a robot, which claims it can solve the most difficult tasks.
Solving such tasks immediately is unreal. The machine needs to write millions of rules and exceptions – so it does not work like that.
They do it otherwise – robots are trained on data, for example about your clients. They are learned to analyze, extract useful patterns and somehow use them. Here such disciplines as statistics, machine learning, and optimization will help us. And if you have already had Big Data, then the robot can potentially become even smarter – ahead of the competitors’ decisions not only in speed but also in the quality of solutions. Put simply, the more data we have, the more intelligent your robot can become.
Is there hype around Big Data?
The analytical company Gartner that studies trends and annually releases the Gartner Hype Cycle chart, which assesses the development and popularity of technologies, already in 2014 excluded “big data” from the “hype” list, explaining that these technologies had already become a standard for corporate IT.
According to experts, businesses looking ahead will be required to use Big Data, otherwise, they will be absorbed by more efficient market players. Increasing the competition level causes the need to improve efficiency.
Big Data – is it expensive?
The fact that Big Data is already a “yesterday hype” has many positive points. Currently, Big Data is considered not as something self-sufficient, but as only one of the tools for solving specific applied problems. Technologies of working with big data from rare and expensive became quite affordable. Open source code may solve the majority of tasks. In order to deal with big data, you should also pay attention to cloud providers, such as AWS, IBM, Azure, etc. with their platforms and services. Under this approach, POC or MVP engineering will take a few days and will require a relatively small budget.
As already mentioned, Big Data is a combination of technologies that are designed to perform three main operations:
- process large amounts of data compared to “standard” scenarios.
- be capable of processing the rapidly incoming data in very large volumes.
- should be able to work with well- and poorly structured data in on various aspects simultaneously.
The main reason why everyone wants to work with these technologies is the growing amount of information generated by mankind exponentially. As well as the fact that on such large samples the laws of averages begin to act that it is possible to reveal many previously hidden patterns. In its turn, this fact opens up a wide scope for their diverse analysis.