Everything that is available online, in a non-confidential manner, regardless of the amount of information, is within the reach of Big Data and can be grouped according to interest.
And this includes not just public databases, like YouTube is for videos or Wikipedia, which serves as the largest encyclopedia on the internet.
Big Data can integrate any data collected about a subject or a company, such as purchase and sales records and even non-digital interaction channels (telemarketing and call centers).
Where there is a record made, technology catches up . Only information that is truly inaccessible is left out, such as your financial transactions and private information from certain organizations, for example.
Everything that wanders around the internet can be accessed, collected and grouped. The most incredible thing is that this is done at great speed , using specific Information Technology (IT) tools.
It is necessary for it to be this way, given the gigantic amount of information generated every day by different devices. According to Big Data, therefore, it is possible to interpret and analyze this data for various uses.
These include defining a company’s marketing strategies , reducing costs, increasing productivity and giving the business a smarter direction. Recently, managers have been using the Big Data “philosophy” a lot as a strategic support tool.
What happens is that they began to understand its importance in gaining insights into market trends and consumer behavior, in addition to improving their own work process.
The indicators are capable of helping in making more assertive decisions and, above all, more advanced than the competition. It goes without saying how fundamental this is to ensuring the success of any business .
Therefore, all this information, available online and offline, can help the company grow. But that’s still not all there is to know about the importance of Big Data. In the next topics, we will delve deeper into this issue.
Understand the 7 V’s of Big Data
As we discussed earlier, Doug Laney defined Big Data based on three V’s . Then they became six, and later seven , according to the model that is used today. Let’s better understand what each one represents in this data management?
1. Volume
Big Data groups together a huge amount of data that is generated every second.
Just imagine all the emails, videos, photos and messages that circulate on social media every day.
Thus, the DB acts to deal with this volume of data efficiently, making it possible to group it using software.
2. Speed
It is the agility with which data is produced and manipulated .
Big Data will analyze data the instant it is created without needing to store it.
This happens with credit card transactions, messages going viral on social media, publications on websites and blogs, among others.
3. Variety
Data can be generated in various structured (numeric) or unstructured formats.
This last category includes audio, video, email, text and quote files, and financial transactions.
4. Value
There is no point in having access to a large amount of information if it cannot add value , right?
It can be said that the value of Big Data lies in the accurate analysis of the data and the information and insights provided to companies from its content.
5. Truthfulness
It would not even be necessary to remember how important it is for the information gathered to be true .
In times of fake news, however, it seems impossible to control the generation and dissemination of this type of content, which often ends up being used as if it were real.
What Big Data does is allow the analysis of large volumes of data, which compensates for possible misinformation .
If several sources point to a contrary understanding, this is a warning sign that the original message is false.
6. Volatility
This is one of the biggest challenges facing Big Data today.
Data flows are increasing in speed and variety, but they also have periodic peaks, which vary according to trends.
Some of them can be very difficult to manage , especially the unstructured ones.
It’s difficult, but not impossible.
7. Visualization
In the last of the V’s, the message is short and sweet: the data needs to be presented in an accessible and readable way .
Without this, after all, how can we understand them and take advantage of them?
How does Big Data work?
To better understand how Big Data works, it is easier to divide this processing into steps .
So, let’s go to them:
Data collection
Also called data acquisition or recording, it is the phase of gathering all that large volume and diversity of information .
While being collected, this information must already undergo some type of filtering or formatting , eliminating errors and incomplete data.
This type of care is essential to avoid harm in the following stages, as can happen in the analysis process if there is corrupted data.
Data integration
After this first moment, it is time to integrate this data.
As they are of different sources, formats and characteristics, they must receive specific treatments .
It is here, therefore, that validation, acceptance, security and data categories criteria must be defined, according to their sources.
Data analysis and modeling
This is one of the most important phases in Big Data, as it is where data begins to gain value and transform into information .
To do this, it is necessary to have qualified professionals and the support of artificial intelligence and machine learning technologies , which will make this work more agile and assertive.
Furthermore, it is also important to research new types of data visualization so that valuable discoveries can be made that favor a better interpretation of the information.
What are the different types of data?
So far, we have learned a lot about Big Data, its history, importance and main components, all represented by the initial letter V.
You may have noticed that the issue of data variety is striking .
They come from different sources, also changing according to the format, structured and unstructured.
Structured data is data available in a rigid or specific format .
This way, it is possible to predict what will be inserted in a certain field of a table, for example.
Unstructured data, as the name suggests, does not follow a rule and is presented as it appears.
This is the case for images, videos, text documents, emails and social media posts.
All this data comes from three locations.
- Social media data : typically captured in an unstructured form, but is becoming increasingly attractive for marketing and sales
- Transmitted data or streaming data : data that reaches IT systems from a network of connected devices
- Publicly available sources : these are data available on
public channels.
After identifying the data source, it is necessary to begin considering the decisions to be made by the company using this available information.