What is Big Data and how to bring it into your business’ day-to-day activities

In a world that is increasingly saturated with information and data, organizations that know how to take advantage of this potential and analyze it in a productive and useful manner, have a head start.

With such a huge flow of data, you have probably heard about Big Data. Whether at work or even on the news, this subject has never been as trendy as it has over the last few years.

Knowing how to analyze the large amounts of information available on today’s market provides your business with the possibility of getting to know your audience in depth and increasingly personalize your products and services.

Would you like to know how Big Data analytics works? Well then, keep reading this article on the subject to find out much more!

What is Big Data?

The term Big Data was coined in the early 1990s by NASA to describe huge clusters of complex data that challenge traditional computer limitations to capture, process, analyze and store it in an organized manner.

In the current Information Technology context, the term Big Data refers to all data (structured or unstructured) that is generated every second. Its analysis consists of a powerful tool that allows for greater potential when making decisions.

Providing traffic information in real-time, cross-checking information to find local occurrences of diseases, analyzing fuel expenses in the largest airports around the world …

There are several applications for Big Data when it’s analyzed correctly.

Every day in 2015, according to a BSA | The Software Alliance study, 2.5 quintillion bytes of information was generated. And Seagate, the data storage giant, projects that the volume of available information will exceed 175 zettabytes, quintupling the amount of existing data in 2018 (33 zettabytes).

According to other studies, it is projected that by 2024 servers will be responsible for the processing of data corresponding to a pile of books reaching the unimaginable distance of 4.5 light years. This hypothetical distance would be enough to go beyond the Milky Way!

Big Data analysis

With the above comparisons and predictions, we don’t have to tell you that it’s impossible for humans to catalog and interpret Big Data, right? And this is where Big Data analytics comes in.

From this analysis, all you need is a blog record, a call center call, a streaming service, a text posted on social media or from any other sources of information and data for a business to start extracting useful interpretations from this universe.

Thus, we can say that Big Data is actually a relative concept, since its own size depends on those using the data. For example, we can say that in 1663, John Graunt analyzed a large information base to study the Black Death epidemic in Europe.

The demographic census of the 19th century in the United States started to be mechanically processed by tabulation machines, consisting of another early use of Big Data.

Nowadays, in a scenario of constant digital transformation, the amount of data that can be used for analysis is much greater, as you can see. And therefore, your analysis will require the right tools. Keep reading and take a look at a few options. 

What defines Big Data?

A huge cluster of random information doesn’t mean that a large database is considered Big Data. To be referred to as such, it must have a number of characteristics that define it.

Thus, in order to consider that the data is sufficiently significant to be called Big Data, it is necessary to analyze 5 essential characteristics, known as the 5 Vs.

Volume

When we talk about Big Data, we’re invariably talking about large volumes of information. Therefore, it’s a huge flow of data generated every second. More than terabytes, Big Data works on a scale of zettabytes and brontobytes.

As a comparison, Facebook has a flow of 10 billion messages, 4.5 billion likes and 350 million photos shared every day.

Thus, Big Data analysis is focused precisely on dealing with this enormous volume of information, categorizing and allocating it by means of specific software.

Velocity

A group of data can only be considered Big Data if the data is created at high frequencies and speed. Therefore, large amounts of information aren’t enough, but it must also be increased in a continuous and fast flow.

Think about the process of viral social media messages, the verification of credit card transactions or even fundamental instances to calculate stock trading values, which vary from second to second.

In this sense, Big Data refers to a large flow of data created almost instantaneously, which changes how they must be analyzed.

Variety

In the past, most of the data generated by organizations used to be considered structured. This data could be tabulated and listed more easily.

Nowadays, over 80% of the data generated are considered as unstructured, consisting of photos, videos, audios, and messages.

Such variety in the composition of the data makes Big Data analysis necessary in order to manage this diverse universe of information, allowing the allocation side by side with other data generated in a traditional manner.

Veracity

In any type of data analysis, it’s crucial that the information under analysis be true. In times of fake news on the internet, treating false data might lead to erroneous results and equally disastrous decisions.

In Big Data, it’s impossible to control each piece of false information available on the internet, but we can compensate for incorrect data based on the statistical analysis of large volumes of information.

In other words, based on a comparison, it’s possible to reach a satisfactory level of veracity, thus allowing for a foundation that is closer to reality and that helps in future planning.

Value

Finally, the last V is what makes Big Data relevant. There’s no use in having an infinity of information generated every second at your fingertips if you cannot make them have value and be useful to your business.

Adding value and significance to large volumes of data is what makes it possible to use Big Data as an important tool for your business.

Therefore, always keep in mind that there isn’t only the cost involved in gathering and analyzing the information, but also the cost required to make it valuable.

What are the different types of Big Data?

When we talk about Big Data, we can categorize the existing data into two types, according to its structure: structured and unstructured.

Structured data

Structured data present a defined structure, including categories, such as definitions, location, sales, relevant customer information like profiles, contact information, etc.

Commonly found in traditional databases, it’s based on the need to store information. Therefore, it’s possible to easily locate where each type of information is, so that it’s easy to visualize patterns in the distribution of this data.

Practically every business uses structured data storage software, such as ERP, CRM, systems geared towards the financial sector, Human Resources systems, among many others.

Unstructured data

In turn, unstructured data present a high complexity. This is because you can’t find any type of structure in it, requiring preparation prior to the analysis.

Basically, most of the data generated today is unstructured, including in this universe, all data present on social media, news portals, etc.

Images, videos, comments, audios, and texts are information that is extremely complex to analyze, but at the same time, is completely relevant to understand a business’ audience, for example.

So, how can you monitor unstructured data?

Nowadays it is possible to analyze and monitor social media to extract relevant and valuable information regarding a certain keyword, for example.

In this sense, it’s easy to find out what people are saying about your business or even, conduct market research about your niche.

Given the complexity of this type of information, prior treatment of this data is required, since the analysis software cannot distinguish positive comments from those that are sarcastic and ironic, for example.

In addition, unstructured data require the creation of tags, which can categorize certain information according to the context being analyzed.

For the time being, this type of work needs to be done by people, which makes the treatment of data of this nature extremely arduous and complex when compared to data with a well-defined structure.

What are the Big Data categories?

Now that you’re familiar with the types of Big Data – structured and unstructured -, it’s time to understand how the data is categorized for analysis.

Here, we mix up the data regardless of their structure, which makes the types presented earlier, show up in each of these categories.

Social data

This category includes data originating from individuals and the information capable of translating behaviors. In other words, through social data, it’s possible to identify patterns and recognize profiles to apply corporate strategies in a targeted manner.

When we encounter the pattern of searches made by Google, for example, it’s possible to notice how people tend to be predictable. This makes finding behavioral patterns possible.

Enterprise data

Here we gather the data generated by organizations every second. This category includes information from financial, human resources, and operational sectors, and from several other business areas.

Although certain people neglect the generation and the existence of this information as valid data, the fact is that it might be essential for measuring the staff productivity cycles, and identify production bottlenecks.

Personal data (or the data of things)

As a relatively new concept around the world, this type of data refers to data generated by smart objects, such as refrigerators, TVs, cars, cellphones and others that are connected to the same network and “talk” to each other.

Better known as the Internet of Things (or IoT), this is a major trend for the next few years. And its application is easily seen in apps such as Waze, for example.

The information generated automatically by each smartphone with an active app in a city — the distance traveled and speed, for example —, is capable of providing a complex information network of the traffic conditions in real-time, which can be displayed on panels in the main points of urban centers.

Cross-referencing data

The cross-referencing of these three Big Data categories is what makes the information crucial so that the analysis can become useful for a business, for example. However, it is necessary to take care so as not to get lost in the infinity of data that exists in this universe.

How can you use Big Data in your business?

As you’ve probably noticed, more useful than Big Data itself is the analysis and use of the available information. And this work of cross-referencing and interpreting data is what we call Big Data Analytics.

In this sense, the application of Big Data analytics will depend greatly on your business’ reality. Obviously, relying on the assistance of technological tools to gather and filter data is key, especially when the use will be targeted.

Marketing

Marketing is clearly one of the areas with the most potential in using information from Big Data analytics. In a world where the need to provide personalized customer service and products keeps growing, knowing your audience is essential for your success.

Thus, analyzing Big Data will allow you to find behavioral patterns in your audience’s consumption, in addition to identifying the effects of marketing actions according to the dissemination channel, the time of the year, the type of approach, the product being promoted, among others.

One good example is McDonald’s, the largest fast-food chain on the planet. It has over 37 thousand restaurants spread around the world, generating consumption data at a mind-blowing frequency 24/7.

And the business obviously takes advantage of this source of data to analyze and match information and thus, understand its audience’s consumption patterns. Through it, menu items have been created based on these reactions on social media, for example.

Quality Control

Preventing production failures is one of the business’ main goals. And with Big Data, it’s possible to analyze the data generated in the productive chain about defects per unit, yield, fill rate, etc.

Thus, it’s possible to find bottlenecks in production and check which factors are responsible for the slowness of processes, thus increasing productivity and product quality.

Financial Sector

Without a doubt, the financial sector receives the most data in large organizations, because it relates to practically all other business areas.

And this interaction is usually extremely neglected by most of them, with vendors who don’t understand production costs or marketing professionals who aren’t familiar with product profit margins, for example.

One of the largest American banks, JP Morgan, tries to avoid this type of problem by applying Big Data analytics to make predictions about trends and thus, provide investors with the best times to make stock trades by means of dense and complex algorithms.

Internally, it’s possible to apply the same logic in businesses of any size, integrating financial analysis with other sectors by means of the comparison of specific indexes.

What are the main Big Data analytic tools currently available?

In a scenario of constant application of Big Data analytics, there will naturally be several tools for this type of work. This variety of software and platforms, essential for an efficient and productive data analysis, capable of supporting the decision-making process.

Below, we’ve listed the main Big Data analytics tools currently available on the market. Check them out!

1. Import.io

Import.io is a platform for the extraction of open-source data without the need to enter access codes. In practice, the entire web environment is viewed as an enormous database.

This tool is easy to use: the user enters an internet address and Import.io automatically extracts all the data considered relevant from the website, allowing you to export it in different formats.

2. Apache Hadoop

Data extraction is important, but not as important as the storage of this information. And when it comes to a large volume such as Big Data, being able to save space is essential.

Apache Hadoop is merely a powerful software program capable of manipulating the size of any file in an easy and fast manner.

3. Oracle Data Mining

The data “mining” and filtering process is one of the most massive steps of Big Data analytics. Therefore, in order to assist in this task, there’s nothing better than relying on Oracle Data Mining, a powerful tool that offers mining algorithms capable of providing insights and predictions about the information available.

It’s also possible to create predictive models and create the audience’s behavioral projections, in addition to outlining profiles and identifying opportunities, anomalies and the possibility of fraud.

4. Statwing

When talking about Big Data, we invariably need to address the subject of statistical analysis. And Statwing is one of the most useful tools for this type of task.

This software allows the importing of a spreadsheet to its platform, checking the data automatically. Thus, it’s possible to perform a detailed analysis of the available information, easily comparing, tabulating and creating charts.

5. Tableau

Being able to easily view information is essential for any analysis. To meet this need, Tableau is one of the best software options on the market.

With it, you can easily create maps, charts, and tables, in addition to various other graphical features, to clarify and facilitate the understanding of the projected information.

Tableau’s major difference is its speed when creating your charts and the possibility of updating in real-time. Thus, Big Data analytics becomes much more visible and easier to be interpreted.

6. Chartio

One option for the creation and generation of reports fully available via browser is Chartio. With it, you can combine different databases and finalize the work by exporting the reports directly via PDF file.

Chartio’s features vary according to the plan chosen. It offers paid and free options.

So, is Big Data really that important?

Nowadays, the Big Data analysis is one of the most important processes to understand the torrent of information available for any business in today’s market.

However, understanding what can be done with this type of technology and of course, pinpointing Big Data’s use for your business are essential.

The fact is that, with the progress of information technology, many possibilities have opened up, making businesses radically transform when using the right tools for the right purposes.

Big Data is merely one of the elements that make up the engine of digital transformations that take place nowadays, right before our eyes. Its impact is so vast in how organizations operate in the market that rarely is a decision made without basing it on Big Data analytics.

Did you enjoy our article? Would you like to learn a bit more about Big Data and data analysis? Well then, make sure you check out our post about Data Science and how to use it in your business!