Data is the new oil. You may have already heard this phrase that illustrates well the importance that big data has for any organization, be it companies or governments, and society.
Big data is a large volume of data that, analyzed and interpreted, can predict or answer something. Today, having data projects is a matter of survival.
Therefore, in this article, you will find everything about the concept, how it works, what its applications are and how to deal with so much data from different sources.
What is big data?
Firstly, big data is the term used to define a large volume of data. Furthermore, it means the collection, management and analysis of this data.
In English, big means big, and date means data. Daily, it is possible to generate 2 quintillion bytes across all sectors.
Thus, the estimate is that the global big data market will reach the value dand 234 billion dollars in 2026.
Therefore, the concept is the sum of structured and unstructured data from different sources – cloud, server network and algorithms – which can be used in different applications.
Data is all information obtained internally or externally from various sources, such as customer registration, social networks, external research, market analysis, etc.
What is big data for?
Big data will be used to collect, store and analyze data to provide a holistic view of what is happening in a given area.
This vision guides companies' decision-making in their strategic planning.
An example of this operation is the investigation about how the mood of Twitter users affects the Dow Jones index, one of the main indicators of the North American market.
When did big data emerge?
The term big data first appeared in 1997 in an article written by NASA researchers Michael Cox and David Ellsworth about big data.
In the article about Computational Fluid Dynamics, they used the expression in reference to the large volume of data that could not fit into a computer's memory, local disk, or remote disk.
Big data examples
Examples of big data are present in different applications in different areas of knowledge, from marketing and entertainment to transportation.
When you come across a announcement On Facebook, for example, there is big data, because what appears to us will be the result of the use of our data to personalize it.
Another example happens at Amazon, which is able to target its marketing based on users' browsing habits through predictive analytics.
For urban mobility, for example, we will use it in car rentals, bicycle rentals and electric bicycles.
How important is big data?
Big data is important for different sectors of business, governments and society, as data analysis helps in decision-making and identifying new opportunities.
Furthermore, the use allows detect and reduce fraud, increase profits, win over voters, reduce production costs, improve safety and be energy efficient.
For a company, big data can help find new business opportunities, study customer profiles and evaluate their satisfaction and develop and sell new products.
What are the 7 vs of big data?
The five Vs are: speed, volume, variety, variability, veracity, visualization and value. The last two characteristics were already present in traditional data analysis.
See below for more about each of these concepts:
Speed
The speed of data is the property, and will relate to how quickly it is generated, collected, analyzed and used.
Volume
Refers to the amount of data processed on the internet. On average, they will generate 2.5 quintillion bytes daily.
Variety
It is related to the different data structures to be stored. It concerns the types, format, nature and origin of data. Generating data via satellite is different from obtaining it online.
There are three types: structured, such as tables and spreadsheets; semi-structured, an example are XML files; unstructured, which are photos and videos on social networks, apps, for example.
Veracity
The wide variety of data must be true, useful and accurate so that it can be stored and processed at the right speed. This prevents bad data from accumulating in the system.
Variability
It concerns the change that occurs in a data or a set of data over time. If the data constantly changes, this compromises its homogeneity.
Preview
It is the way to present and view data. Tables and graphs, for example, are efficient resources for presenting large volumes of data.
Value
It is the result of the sum of the previous aspects. So, after storing, processing, evaluating and visualizing the data, the company needs to be sure that it is getting value from the data.
5 big data tools to know
Data that does not make sense until it is transformed into useful information and knowledge that can be used to make decisions or create business strategies.
To do this, you need the right programs and applications. Here are five big data tools you should know about.
Hadoop
100% is an open source platform for large-scale data processing. The Hadoop ecosystem is one of the most important tools for this data.
Tail CPD
It is a digital marketing solution to manage and unify customer data through Artificial Intelligence and Machine Learning.
Tableau
Data visualization software transforms data into visual information.
Power Bi
It is a Microsoft solution that brings together a set of software and applications used for organizing and interpreting large volumes of data.
Google Cloud
The Google platform is a provider of cloud computing resources with diverse functionalities, from storing and processing data to creating applications.
How does big data work?
Big data is the sum of data generated by digital technologies when we use apps, interact on social networks and search, for example, and from other sources.
However, not only the volume of data – text, images, photos and audio – that we will create daily, but also the way it is processed and stored.
This data is processed and analyzed by organizations in order to provide insights, problem solutions, new opportunities, and improvements in the quality of services and products.
Where is big data used?
We will use it in practically all sectors that impact everyday life. Today, almost everything we do uses big data, from industry to retail.
Detect fraud, optimize customer relationships, manage risks and have a marketing personalized are some bank actions in which big data is used.
In media and entertainment companies, such as video and audio streaming, the concept serves to collect, analyze and use user insights and offer content suggestions.
Meteorology is another area in which the concept is applied, whether for weather forecasting or for studying patterns from natural disasters to the impact of global warming.
How to interpret big data?
Interpreting big data requires a series of actions and measures that begin with data collection. First of all, you need to determine the source and quality of the data.
After finishing, the data will need to be visualized in a way that makes it understandable to everyone. Each type of data requires a type of visualization.
Another important point is having the right person to interpret data. A customer support specialist can understand consumer patterns on a website.
Based on these observations, this professional can think of solutions to better evaluate the company with its customers, for example.
What is the difference between big data and data science?
The difference between big data and data science is that the first concept concerns the set of data and the second is the science that studies this data.
Data science aims to derive value from the volume of data gathered in big data. The study involves the data itself, the process of capture, transformation, generation and analysis.
As it is an interdisciplinary area, data science involves a variety of disciplines such as statistics, computer science and mathematics.
Big data and analytical intelligence: understand the relationships
The relationship between big data and analytical intelligence is one of interdependence since analyzing a large volume of data requires analysis tools.
In other words, analytical intelligence transforms a quantity of raw data to discover and interpret patterns that it reveals.
These patterns can provide information, knowledge and insights for a company's decision-making.
What is big data analytics?
It is the process of extracting, organizing, processing and analyzing a large amount of raw data.
There are three categories of data for analysis: social data, enterprise data and personal data.
Social data is data about people, generally from social networks. Enterprise data or corporate data is information generated by company operations.
Personal data is any information relating to a person, such as age, nationality, color, religion, sex, education, etc.
Big data: how can it be applied in companies?
You can apply it to companies to bring a competitive advantage, such as selling more or reducing expenses, creating a product or service or in innovation projects.
In fact, being a company data-driven, that is, data-driven, is at the center of iFood's innovation projects. One of them is the use of drones to optimize delivery logistics.
Other applications of big data are audits - finding fraud, errors and waste, to meet regulatory body standards, in operational activities and research projects.
5 tips for putting big data into practice in companies
Before a big data project is put into practice, it must be technically viable, that is, with a prepared team, calculated risks and knowledge of the business value.
According to the book Big Data: A Managerial View, by Fernando Amaral, a successful project has satisfied users, increased sales, reduced fraud and costs.
See below tips for putting big data into practice.
- Have a project goal: There is no point in a project delivering value but exceeding the time stipulated for completion. Keeping track of the target within the set deadline is essential.
- Make a business-oriented project: Before choosing which technology will be used in a big data project, you must evaluate what you want to produce and what the objective of the project is.
- Involve the team and stakeholders: You must prioritize everyone who gets involved. Team and sponsors must see value in the project.
- Build a business case: This document must include the justification for the project, which business requirement it will meet and what the business value will be delivered.
Assess technical feasibility: Issues such as accessibility of the data source, implementation cost, volume of the data stream must be studied and well evaluated.
