What do you think about making Big Data the future of your professional career?
If that still sounds weird to you, it’s good to catch up. When we talk about the concept of Big Data, we refer to a true technological revolution, capable of qualifying the results of companies. This explains the great interest in the resources around the world, and in Brazil, this is no different. Around here, however, data science is still in its infancy, which represents an opportunity for those who jump ahead and improve their knowledge on the subject.
There are many people in need of qualified professionals who, with the use of tools, can analyze and interpret data to lead businesses to more assertive decisions.
What Is Big Data?
Big Data is a process of analyzing and interpreting a large volume of data stored remotely. Everything available online, in a non-confidential way, no matter how much information, is at your fingertips, and can be grouped according to interest. And that includes not only public databases, like YouTube for videos or Wikipedia, which functions as the largest encyclopedia on the internet.
Big Data can integrate any data collected about a subject or a company, such as purchase and sale records and even non-digital interaction channels (telemarketing and call centers).
Where there is a record made, technology catches up with it. Only inaccessible information is left out, such as your financial transactions and the private information of some organizations, for example. Everything that wanders the internet can be accessed, collected, and grouped.
The most incredible thing is that this is accomplished at great speed, with the use of specific Information Technology (IT) tools. It needs to be so, given the gigantic amount of information generated each day by various devices.
According to the concept of Big Data, therefore, it is possible to make the interpretation and analysis of this data for various uses. Recently, managers have used the “philosophy” of Big Data as a strategic support tool. What happens is that they have come to understand its importance to gain insights into market trends and consumer behavior, as well as improve the work process itself.
The indicators can help in making more assertive decisions and, mainly, more advanced than the competition. Needless to say, this is critical to ensuring the success of any business. Thus, all this information, available online as well as offline, can help the company grow. But that’s still not all to know about the importance of Big Data.
The History Of Big Data
Although its use has become more frequent in recent times, the term Big Data was born in the 1990s. And just look at where: NASA (National Aeronautics and Space Administration), the American space agency. At the time, DB was used to describe complex data sets that challenged traditional computational limits of capturing, processing, analyzing, and storing information.
In 2001, then-Vice President and Chief Research Officer of Enterprise Analytics Strategies, Doug Laney, articulated the definition of Big Data in three V’s:
Then, 12 years later, Express Scripts’ head of data, Inderpal Bhandar, argued that there were three other additional V’s:
As we shall see in the next topic, history would still reserve the proposition of a seventh V:
The model, then, was complete.
Due to its efficiency, organizations have begun to realize the power of using Big Data.
According to this report, published by the Forbes Magazine website in 2015, about 90% of mid-to-large companies were already investing in BD.
What Is The Relationship Between Big Data, Business Intelligence And Artificial Intelligence?
From what we have seen, it is clear that the concept of Big Data is also that of a data repository. That means we won’t find the answers we need ready, like a box of fortune cookies. To be applied to business objectives, the DB demands the work of professionals and tools capable of collecting and processing data in large volumes.
This is where Business Intelligence (BI) comes in, that is, how a company develops its market intelligence. This concept consists of the sum of skills, knowledge, techniques, and all the knowledge acquired about the strategic, tactical, and operational parts of data management. And Artificial Intelligence (AI), where does it come into that?
In this case, AI tools serve to extract insights from the data that comes from Big Data to make predictive analysis.
What Is Big Data For?
After seeing what Big Data is and its history, it is worth understanding what it is for. As you know, the amount of data generated worldwide is absurd, and the speed of this process is increasing exponentially. From 2021 to 2024, more information is expected to be created than in the previous 30 years combined, according to a survey by IDC.
Just to give you an idea, by 2020, about 40 trillion gigabytes have been generated, which gives an average of 2.2 million terabytes per day. This ocean of content hides information that can be valuable if it is properly collected, processed, and analyzed. This is precisely where Big Data and its technologies come in, to work with a large volume of data quickly, at an affordable cost, and in an effective way.
Thanks to these solutions, it is possible to make decisions and develop more informed and assertive insights.
Understand The 7 V’s Of Big Data
As we talked about earlier, Doug Laney defined Big Data based on the 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.
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 the networks daily. Thus, the DB acts in a way to deal with this volume of data efficiently, making it possible to group it through software.
It is the agility with which data is produced and manipulated. Big Data will analyze data the instant it is created without having to store it. This happens with credit card transactions, viralization of messages on social networks, and publications on websites, and blogs, among others.
Data can be generated in various structured (numeric) or unstructured formats. In this last category, audio, video, email, text and quote files, and financial transactions are included.
There’s no point in having access to a lot of information if it can’t add value, right? It can be said that the value of Big Data lies in the precise analysis of the data and the information and insights provided to companies from their content.
Nor would it be necessary to remember as to the importance of the information gathered being 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 enable the analysis of large volumes of data, which compensates for possible misinformation.
If multiple sources point to a contrary understanding, there is a warning of falsehood as to the original message.
This is one of the great challenges of Big Data today. Data streams 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.
In the last of the V’s, the message is short and thick: the data needs to be presented in an accessible and readable way. Without this, after all, how to 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:
Also called data acquisition or recording, it is the phase of gathering all that large volume and diversity of information. While it is collected, it is necessary that this information already goes through some kind of filtering or formatting, eliminating errors and incomplete data.
This type of care is essential so that there is no harm in the following steps, as can happen in the analysis process if there is corrupted data.
After that first moment, it’s time to integrate this data. As they are of different fonts, formats, and characteristics, they should receive specific treatments. It is here, therefore, that criteria for validation, acceptance, security, and categories of data should 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 turn into information.
For this, it is necessary to have trained professionals and the support of artificial intelligence and machine learning technologies, which will make this work more agile and assertive.
In addition, 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 diverse sources, also changing according to the format, structured and unstructured.
Structured data is data that is available in a hard or specific format. In this way, it is possible to predict what will be inserted in a given 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 with images, videos, text documents, emails, and social media posts.
All of this data comes from three locations.
- Social media data: It is usually captured in unstructured form, but it is becoming increasingly attractive for marketing and sales
- Transmitted data or streaming data: are those that reach IT systems from a network of connected devices
- Publicly available sources: These are the data available on public channels
After identifying the source of the data, you need to start considering the decisions to be made by the company with the use of this available information.
We’ll move on to that later.
First, however, let’s dig a little deeper into the differences between structured and unstructured data.
Structured And Unstructured Information In Big Data
When we talk about Big Data, it is very important to make a distinction between what is structured and unstructured information, because this difference directly impacts the entire process of data collection, integration, analysis, and interpretation.
Structured information can be categorized more easily because it has a stricter standard.
Using a more technical language, are those types of data that can be placed in rows and columns, such as forms, for example.
On a landing page, when the user goes to fill out a registration, he only needs to complete the fields, such as name, age, and email, among others, and eventually answer a yes or no question.
Thus, it is much easier to extract information from this data, since it is already structured.
It is different, for example, from a video, an image, an audio, an email, or any other type of text in which there is no standard structure.
All of these examples of Big Data are unstructured data.
It is estimated that about 80% of the content available on the Internet is unstructured data.
Examples Of Big Data Applications
The application of the process can bring many practical results to your company.
Check out some examples of Big Data and understand how this set of technologies can contribute to your high corporate performance:
One of the main benefits that data processing and its consequent transformation into relevant information can bring to a business is the development of insights.
From a massive analysis of social networks, customer databases, and other types of materials, the company can develop products that meet the needs of its target audience with greater assertiveness.
Big Data helps anticipate demands and produce trends before they even burst into the market.
This is perhaps the practical implication that Big Data and its technologies add most to companies.
Until recently, processes were much more bureaucratic and manual, which, in addition to delaying decision-making, also makes any action much more error-prone.
With more automated, fast, and effective data processing, it is possible to find solutions, analyze scenarios, deliver feedback to customers, develop products, design, and prepare for moments of crisis in a much more organized and proactive way.
It’s been a while since the customer experience became one of the main factors that lead a person to consume and continue consuming a certain brand.
With Big Data, it’s much easier to give the attention and value that your target audience expects.
After all, you can have access to various customer data and thus maintain a closer interaction with them, offering personalized content to their demands.
Few failures are unpredictable, which happen even when all the necessary care is taken.
For the overwhelming majority of problems, it is possible to perform predictive maintenance and prevent future inconveniences.
With Big Data, you can prevent defects related to structured data, such as mechanical breakdowns that have to do with the year of manufacture or the model of the machinery in question, for example.
In addition, when processing unstructured data, it is possible to find problems in software update codes and sensor reports, for example.
All this before these failures happen and delay production.
The cost is also much lower than that of reparative maintenance.
Fraud And Compliance
With so much data generation, you need to be careful to keep this information (especially customer-related) safe and out of reach of cybercriminals.
With Big Data technologies, it is possible to find some patterns in data that indicate some kind of fraud.
From there, it becomes possible to develop preventive actions and send regulatory reports in a much more agile way.
The concept of machine learning presupposes the ability of software to learn without human interference.
The difference, in this case, is the ability that machines have to process huge volumes of data with great speed, as few humans can do.
Big Data, therefore, is where the information used for machine learning comes from.
Machine learning only makes sense because we’re handling data in very large volumes.
Thus, there is only learning when machines have unrestricted access to data of interest that is available remotely.
Promotion Of Innovation
Porter’s 5 Forces speak to the threat posed by new entrants to the market.
This is a story that repeats itself: a large hegemonic company in its segment, very comfortable with the success of its products, ends up dethroned by a disruptive business, idealized in a garage or by university students.
Before the era of Big Data, this would be an unbelievable narrative, but we see that it is not only possible but has been repeated relatively often.
This is because, today, success is more than ever based on the ability to innovate.
The DB is fundamental to this since the data is what will provide the necessary insights to guide the strategies to innovate products, services, and brands.
Why Is Big Data Important?
It is wrong to think that the importance of Big Data is related only to the volume of data available.
What should be taken into consideration is what you do with them.
Thus, we can say that Big Data is important to help companies analyze their data and use it in identifying new opportunities.
This varies according to your business model, the activity carried out and the goals and objectives outlined for the organization.
But there are common benefits within reach of every type of company.
Among them are: saving time, reducing costs, optimizing offers, providing new products, higher profits, happier customers, and more efficient decisions.
It is still possible to combine Big Data Analytics, which helps us understand what this volume of information can tell us.
Together, they can meet the needs of companies in various markets.
Here are some examples of Big Data Analytics:
- Marketing: analyzing the profile of the consumer, their behavior, lifestyle, and preferences, which facilitates the creation of new products and a more targeted communication
- Financial: preventing fraud and making predictions of economic and market fluctuations, which makes the investment safer
- Relationship and sales: offering more relevant products, with customer loyalty and decreased churn rates.
What Are The Main Challenges Of Big Data?
Although Big Data is a reality, we are talking about several technologies that are relatively new and constantly evolving.
Therefore, one of the main challenges of the moment is to train professionals to deal with the collection, integration, analysis, and interpretation of data and to stay up-to-date on the main trends in the sector.
In addition, the volume of data gets bigger every day.
It is necessary to find new alternatives to store this information and curate it because it is no use having the content and not knowing how to extract what is relevant in it.
Finally, it is important to have more and more policies that regulate the access, use, and privacy of data.
The General Data Protection Law (LGPD) is a great start, but there is still a lot to evolve.
How to apply Big Data successfully?
Any company has a lot to gain by including Big Data and its technologies in its corporate routine, but it is necessary to take some precautions for this application to be successful.
In order to avoid mistakes, we have put together a step-by-step guide to take advantage of all the benefits that Big Data has to offer:
1. Define the objectives for data analysis
Companies that use DB to guide their decisions do so strategically, which entails setting objectives.
After all, Big Data is not an “oracle,” or an all-powerful entity that has ready-made answers to everything.
In order for it to produce answers, it must first have an established purpose.
In the context of business, this means setting goals that are preferably SMART.
From there, the next step is to know where the data will be extracted from, considering its relevance to achieving goals and based on consistent KPIs.
2. Use metrics to help with this definition
Before thinking about “how” the analysis will be done, focus on “what” will be analyzed.
If we are dealing with a customer-facing solution of a product for the financial market, it may be coherent to analyze the history of interest rates over the last few decades.
Or, if the solution to be developed is for a company’s commercial team, perhaps the analyses should be based on indicators such as the aforementioned Churn Rate or Lifetime Value (LTV).
The most important thing is that the defined objectives have metrics that can tell whether or not they are being met, considering the data that is being processed.
3. Prepare The Data Well
As we have seen, Big Data is not an entity from which answers to problems are obtained instantly.
One of the most arduous jobs to do is filtering and formatting the data before processing.
When it comes to comics, we need to keep an important lesson: it’s not just any data that fits.
Not by chance, there are tools developed specifically to assist in this part of the application of Big Data, such as Big Query, Oracle Database, and MySQL, among others.
4. Dive Into The Analysis
Once the data is subjected to a first treatment and filtering, it can finally begin to be analyzed with the appropriate tools.
In this case, it is worth noting that the analysis of Big Data can be done considering four types of models:
- Predictive: whose purpose is to point to possible behaviors and events, given certain conditions
- Prescriptive: in which one seeks to know in advance the consequences of decisions and what to do from a defined scenario
- Descriptive: model used to understand a certain type of event or conjuncture of factors
- Diagnosis: which aims to point to the causes of a phenomenon, behavior, or event
5. Ensure Better Data Visualization
The results of BD analyses can be very complex to understand, especially for laypeople.
Therefore, the analyst needs to know how to present the results of his analyses so that the stakeholders can understand them, no matter how educated they are.
For this, different types of charts can be used, such as pie charts, bars or histograms, and organization charts, when necessary.
6. Find Key Insights
The final part of Big Data analytics is where the “magic” happens and the manager/analyst extracts the business insights that will make a difference.
A good example of this was the famous case of UPS, which extracted a very simple insight after extensive data analysis with Watson software.
In this case, the goal was to figure out how to save fuel on deliveries and, after a thorough work, what turned out was that always turning right was the best option.
What Are The Main Trends In Big Data?
Big Data technologies are constantly evolving, and it is already possible to point out some trends that are being put into practice and have everything to continue on the rise.
Big Data In The Cloud
Proof of this is that 37.5% of these companies already prefer to use hybrid cloud solutions, that is, that can be accessed from different environments.
The use of Big Data is part of the Digital Transformation movement, in which everything becomes connected to the web or, at least, subject to remote monitoring with the Internet of Things.
In this context, a Forrester report reveals that the last few years have been decisive in this regard, due to the increase in connectivity.
Since data is the new oil, nothing is more appropriate to the moment that they are traded as a true commodity.
Therefore, companies specializing in Data-as-a-Service (DaaS) solutions are emerging in the market, whose focus is to meet the demand of other companies for data in their business processes.
Blockchain In Data Analysis
Also called the “trust protocol”, the blockchain has already been used as a virtual environment for collecting, storing, and processing data in large volumes.
The reason for this is simple: in it, the security of operations is much greater, since it works autonomously, from encrypted codes that cannot be tampered with by external agents.
The data marketplace is one of the most promising markets and is expected to grow at a rate of 23.4% between 2022 and 2030.
All these trends lead us to believe that the use of Big Data is a revolutionary moment for humanity, as was the discovery of oil and electricity.
What else is coming?
Big Data Good Practices
Like all “raw materials”, Big Data needs to be used according to good practices to generate the expected results.
In this sense, Oracle points to some guidelines for optimizing data in the analysis of all models:
- Alignment of the DB with specific business objectives
- Standard-setting and corporate governance as a means to raise capacity building
- Adoption of a center of excellence for knowledge transfer
- Alignment of structured and unstructured data, crossing information from insights already extracted with raw data
- Use of the cloud as an operating model.
Big Data Tools
As you already know, the interest of companies in Big Data has been accentuated in recent years.
At the time, 1,144 managers surveyed in 95 countries reported that 53% of organizations were already using Big Data with a focus on greater understanding and qualification of the customer experience.
Five years later, interest had increased, as might be expected, but there were still limitations to the use of BD.
The central obstacle involved technology and human capital.
Even today, although a portion of companies already have specialists in the area, capable of organizing this information, another part still faces difficulties in dealing with the analysis and understanding of them.
As Big Data projects have structured and unstructured data, coming from the most different places, it is necessary to perform a careful analysis so that they are used in the best possible way.
This needs to occur at the pace of the 7 V’s, which is not always within the reach of all organizations.
The good news is that there are Big Data tools available on the market capable of better managing the stored data.
Want to know what’s most important to know about these tools?
The concern here is about the characteristics.
We can call them prerequisites so that they can effectively help you in this mission of facing Big Data and using it to your advantage.
When choosing the tool with which you will work, the ideal is to know:
- What’s your interface like?
- How will users filter the data?
- What is offered in terms of updates?
- What are the security restrictions?
- How are reports presented?
Which Companies Use Big Data?
Big Data isn’t just limited to large companies.
Its importance is such that businesses of all sizes, from the most varied segments, can make use of its contributions.
To give you a better idea, look at the items below.
We use as examples some sectors of the economy and possible benefits generated from a Big Data strategy.
- Banks: understand and increase customer satisfaction; minimize risk and fraud
- Government: dealing with congestion; preventing crime and managing public services
- Manufacturing: increase quality and production, minimize waste
- Education: ensuring that learning is taking place correctly; identifying students with difficulties and implementing better assessment systems
- Health: know how patient care is going and improve care
- Retail: Increase the number of repeat trades, figure out how best to approach customers, and know the right way to handle transactions.
How To Work With Big Data?
Big Data indicates opportunities not only for companies but also for executives.
If you are looking for a career as a manager or a professional relocation, it is worth staying tuned as to the space that the market offers and what it requires to become an analyst in the area.
To work in the position, one needs to have technical knowledge in programming and also understand business, of course.
In addition, the professional needs to have a notion of mathematics and statistics applied to data.
The data scientist or analyst is responsible for fulfilling the requests of an organization’s planning area.
Still in this text, we will bring course information about Big Data.
Before, here’s a summary of the competencies and skills you want to become a Big Data analyst:
- Analytical skills to gain insights from the variety of data obtained
- Creativity in producing new methods to gather, interpret, and analyze a data strategy
- Notions of mathematics and statistical skills
- Mastery of computing, since programmers constantly need to create algorithms in order to transform data into insights
- Business competence and knowing the business objectives in place and the processes that drive the company’s growth and profit.
As an undergraduate course specifically aimed at training data scientists is still rare around the world, these professionals usually come from areas that are based on mathematics, such as Computer Science, Engineering, and Statistics.
If it’s what you want for your career, there’s already a tip to start with.
Let’s delve even deeper into the job market in this area.
What are the job titles in big data?
A course on Big Data opens many doors.
In addition to recruiting specialists in areas such as marketing, engineering, mathematics, and others related to business management, this market presents a wide range of functions.
Some of the career possibilities are:
- Data Engineer
- Data Scientist / Machine Learning
- Data Architect
- Data Visualization Developer
- Specialist in Business Analytics.
We have seen in this article that Big Data is a large set of stored data, capable of providing insights into market trends and the profile of consumers, as well as optimizing the work process.
At the moment, a few issues attract as much attention from managers around the world as this one.
With a model based on the 7 V’s (volume, variety, speed, value, veracity, volatility, and visualization), BD promises to help companies make much more assertive decisions, qualify their actions, reduce expenses, and increase productivity.
At the same time that it generates opportunities for organizations to qualify their results, it also opens doors for good professionals.
Human capital is one of the aspects still limited to the resource, which can be good for you, who seek to train and advance in your career.
Whether from a new degree, specialization, MBA, or master’s degree, investing in studies is essential to go further.