What is Big Data and Why it Matters?

For some of us, data science is defined as a passion. For others, it represents the means through which they can attain a better understanding of their industry and act in accordance with the brand-new, more complex big picture.

No matter the category you’re a part of, there’s certainly some kind of curiosity and interest that led you to us. And we’re here to feed your spirit of inquiry and to hold your hand while walking through the paths of learning.

There’s a lot we can share with you, but first things first: what is data science and what’s it about? Mentioned for the first time in 1974, by Peter Naur, data science defines as a field that unifies mathematics, programming, expertise, scientific methods, algorithms, processes and systems in order to distil actionable insights for a broad spectrum of uses and industries.

Nowadays, we’re both talking about data and big data when it comes to business analytics. Simply put, more and more companies rely on numbers, to make informed business decisions. But what is big data, why is it important and how can you use it to improve your everyday work and results?

What is big data: an approachable definition

Before talking about big data, its uses, applications and benefits, let’s define data. In computer science, there are two categories of software: programs and data. Data is the information that programs use in order to centralize, translate and manipulate data into a binary digital form.

As technologies such as AI, social media, mobile devices and the Internet of Things have evolved, traditional tools are no longer qualified to deal with the volumes and complexity of data. Nowadays, the data and analytics landscape is getting larger and more complex each day.

According to Mattturck, in 2020 we could already count seven main categories: data infrastructure (Hadoop, Vertica), data analytics and machine intelligence (SAS, Google Analytics), data applications for enterprise (6Sense, Bluecore), data applications for industries (Oracle, Uber), data open sources (Talend, Microsoft Cognitive Toolkit), data sources and APIs (AWS Data Exchange, Bloomberg), as well as data resources (Facebook, Data Camp). This led to the emergence of big data analytics software, which can gather and process large amounts of complex data generated and submitted through numerous sources. 

For businesses and organizations, big data is a mix of structured, semi-structured and unstructured data with sizes varying from terabytes to zettabytes, that can be used to collect, review and analyze large amounts of data, in order to understand market trends, industry insights and reliable patterns that could change strategic direction and support better business decisions.

For instance, Philips uses big data analytics with the purpose of medical equipment preventive maintenance, a lot of important e-commerce and marketplaces such as Emag use big data to gain visibility and engagement in social media through suggested posts, and tourism and transportation companies use big data insights to promote themselves through highly used maps apps like Waze.

Characteristics of big data

Doug Laney defined big data in 2001 through 3 main characteristics, also known as the 3 Vs of big data: variety, volume and velocity. As data science evolves constantly, we can now talk about the 5 or 6 Vs of big data, which also include veracity, value and variability.

How big data works

As previously defined, big data includes structured, semi-structured and unstructured data originating from numerous sources and locations that flow from owners to users. In order to understand how big data works, you must consider its origins and path. In order to set a big data strategy and attain your data-driven decision process goal, you must first identify these sources, access, manage and store the information and analyze it.

You will need big data implementation first and foremost, so here’s the 3-step process to do this:

→ Integration – use data integration mechanisms and technologies to analyze and scale huge amounts of information.

→ Management – you’ll need storage solutions such as cloud storage, premises or both. Our recommendation is to go full cloud and benefit from the possibility of resources spinning up according to your needs.

→ Analytics – go in-depth with your sources and translate them through data visualization, to enable better, data-driven business decisions.

The history of big data analytics – fun facts and milestones

→ 1981. Big data history started with Herman Hollerith, an US Census Bureau employee who founded the Hollerith Tabulating Machine, in order to efficiently handle data that, otherwise, would’ve taken 8 years to be processed

→ 1927. Fritz Pfleumer started storing information on tape, replacing wire recording technology with magnetic technology. He patented this storage method in 1928

→ 1943. Another machine named Colossus was created by the British with the purpose of cracking Nazi codes during World War II. It could scan 5k characters a second

→ 1945. John Von Neumann published Electronic Discrete Variable Automatic Computer, the first documented paper about program storage, which became the foundation of computer science and architecture

→ 1952. President Truman secured the formal creation of the US National Security Agency, to decrypt and intercept messages during the Cold War. Computers could already operate autonomously and automatically collecting and processing tasks

→ 1965. The US government created the first data centre, for the storage of fingerprint sets and tax returns

→ 1969. ARPANET began, as UCLA’s host computer managed to send information to Stanford’s host computer. By 1973, ARPANET was using a transatlantic satellite

→ 1977. Personal computers became a market product

→ 1989. Tim Berners-Lee first mentioned the concept of the World Wide Web and, by 1990, he had already written the IT commands that represent the understructure of nowadays web: HTML, URL and HTTP

→ 1999. The Internet of Things (IoT) was attributed to the concept that defines it. In the following 14 years, it has evolved into what it is today and came to encompass the internet, wireless communications, micro-electromechanical and embedded systems

→ 2005. Roger Mougalas used the “big data” term to describe large sets of data that could not be processed through traditional tools developed so far. Later that year, Hadoop was built – a software framework that could gather and process structured and unstructured data from most digital sources

→ 2016. The Federal Big Data Research and Strategic Development Plan was released by the Obama administration, in order to explore possible applications that would be of service to society and the economy

→ 2022. It is expected that, by 2025, the world will come to count over 180 zettabytes of data. Today, we can already talk about improving human understanding through Data Visualization. There are lots of tools that offer big data visualization models, such as Tableau, Domo, Qlik, Reltio or Sisense

The value and application of big data

Big data provides an in-depth, integrated overview that could never be fulfilled through standard analytics processing. No matter the size of your business, big data can assist you to accomplish objectives such as:

Improved customer experience – big data provides you with deep insights about your leads and customers so that you can refine your approach regarding their encounter with your business

Problem fixing – you can use big data to pinpoint the process or funnel blockers and diagnose your issues, then find the appropriate solutions for them

Revenue multiplication – by improving customer experience, you’ll also increase your conversion rate. Big data can also support you in adjusting your offer in accordance with market demand, therefore, it’s an effective approach to increasing your revenue

Cost saving – as big data is scalable, it’s best known for its cost efficiency and diminished churn

Types of big data

From the perspective of their structure, there are three types of big data:

Structured data – usually stored in databases, structured data is defined through a stereotyped format, a well-defined structure that follows a data model and a constant order, which both humans and machines can easily retrieve

Semi-structured data – unlike structured data, semi-structured data doesn’t correspond to a model or a schema. Its structure, though, comes from organizational properties. It can be stored in relational databases

Unstructured data – does not conform to a model and is not organized in an established manner and can combine various information, from dates and numbers to facts. It can’t be stored in databases and traditional or standard analytics programs find it difficult to understand

From another perspective, that of categorizing big data according to its purpose, there are several other types of data, which can include lead data, demographics, usage data, purchase data and others.

And there’s also a more empiric approach, that classifies big data technologies as it follows:

Data storage – offers the infrastructure to fetch, store and manage big data easily, as well as to connect it to different purpose technologies or, in other words, different programs.

Data mining – is used to identify and extract patterns and trends from raw information and to translate it into useful, actionable insights.

Data analytics – data is decluttered, curated, organized and transformed into information that could improve the business decision-making process and maintain the competitive advantage of an organization.

Data visualization – transforms data into an easy-to-understand, impactful visual representation. Tableau is one of the most powerful tools for data visualization; with a friendly interface, simple functions such as drag-and-drop and a variety of available graph representation forms (pie and bar charts, box plots, Gantt charts), Tableau supports users to create and share visualizations and dashboards here and now.

Big data analytics – types and examples

Big data analytics are divided into four main categories:

→  Descriptive analytics – information that can be effortlessly read and explained, such as company sales and profits.

→  Diagnostics analytics – just like in healthcare, data diagnostics refers to mining a possible issue, recovering missing data and identifying the cause of the problem. Bugs in a payment page could be easily discovered through diagnostics analytics.

→  Predictive analytics – uses AI, machine learning and data mining to forecast trends, by comparing past data to present data. 

→  Prescriptive analytics – relies on Artificial Intelligence and machine learning, just like predictive analytics, but with a different purpose: risk management.

Big Data management technologies and tools

In the past few years, technology has evolved enormously, enabling software developers to create a wide range of friendly, intuitive and trustworthy tools to support data analytics:

Hadoop – a storage and processing solution that uses an open-source framework to manage structured and unstructured data.

Spark – a real-time data processor that uses an open-source cluster computing framework.

Data integration software – programs like Apache or Amazon EMR that facilitate data streamline across various platforms.

Stream analytics tools – instruments that filter, aggregate and interpret multiple format data coming from various sources.

Distributed storage – databases that can identify missing or corrupted data, by splitting across the servers.

Predictive analytics hardware and software – use machine learning and algorithms to forecast possible results, by processing large amounts of complex data.

Data mining tools – programs that grant searching through structured and unstructured large data sets.

NoSQL databases – non-relational data management systems used for gathering and interpreting raw and unstructured data.

Data warehouses – storage solutions for massive amounts of structured data gathered from various sources.

Big Data: storage and processing

Data warehousing is only suitable for structured data, as it usually relies on relational databases. The most common solution for big data storage is represented by data lakes. These have the ability to support different types of data and are usually based on clusters, cloud storage systems or databases.

What you need to know about big data storage is that you, like many other business caregivers, can opt for a big data environment that merges multiple systems in a distributed architecture. That means you can integrate a data lake with a data warehouse and a database, according to your needs.

Big data best practices

Big data can act as an extension of business intelligence, allowing businesses to deepen research and discover the core of their pain points and strengths. Here are some best practices suitable both for small, medium and large businesses that want to integrate big data into their decision-making processes:

Align big data with well-defined business objectives

Invest in upskilling for your team and have a standardized approach on managing data

Assign resources in order to mature your company-wide data architecture and adopt structured and systematic means to manage capital and costs, especially if the company is part of a group

Plan ahead, give direction and transfer responsibility to the teams assigned to gather, manage, analyze and interpret data

Why is Big Data important: the advantages and benefits of using Big Data analytics and solutions for your business

Big data can help you make the most of resource management and to improve operational processes. Optimizing product development, finding new growth opportunities both business-wise as well as revenue-wise and improving the decision-making process by relying on pure mathematics and facts are other advantages of the use of big data.

Using big data, you gain access to identify errors and their causes in real-time and to upgrade your overall business outcome, by simply extracting valuable insights and translating them into actionable plans. 

Another benefit of big data is that it helps organizations to forecast and better manage risks. It also enables businesses to revamp their product or service offerings, according to market needs and trends.

Cost efficiency is also one of the perks of integrating big data analytics in your business strategy, besides the obvious advantage of having the chance to enhance customer satisfaction by providing a better overall customer experience.

Use cases and industries that successfully implement Big Data solutions

In real life, big data analytics software like Vertica is being used in countless industries to support informed business decision-making. Put yourself in the shoes of the user/ consumer and observe it as entertainment platforms suggest a custom video or audio content, based on your preferences and previous content consumption. Or have a look at how it shapes education while supporting the development of new curriculums, based on today’s academic needs and tendencies. You can see how it’s being used under your eyes, in the healthcare field, to gather information for complete medical records and prevent diseases.

You can have a look at how governments collect data through satellites, traffic cameras, sensors, mail and telecommunication systems, for a better understanding and management of the public sector. You can intuitively predict it in marketing, as you’re being assaulted by targeted, high ROI ads. But you can also notice how banking institutions monitor illegal movements, such as money laundering, with the help of big data.

Financial services are also using it, whether we’re talking about fraud detection, risk management or blockchain technologies. And you can also be conscious that agriculture is also benefiting from it, by accurately predicting the harvests and also by automatizing farming industry processes. Simply said, all industries are using big data.

Big Data FAQ

If you already scrolled through this article and haven’t found yet the information you needed, maybe these Q&A sessions will be useful for you:

What is the definition of big data?

Big data refers to various, voluminous amounts of data generated with velocity. In other words, complex data coming from various sources at a quick pace, couldn’t be handled by standard analytics tools.

What are the 3 Vs of big data?

The three main Vs of big data are volume (data quantity gathered from myriad sources), variety (structured, semi-structured and unstructured data types) and velocity (the speed at which big data is generated). Newer approaches talk about the 6 Vs of big data and include veracity (how much can you trust data), value (the value for business of collected data) and variability (the ways in which big data can be used and formatted).

What are the types of big data analytics?

Big data analytics can be descriptive, diagnostic, predictive and prescriptive.

What is big data used for?

Big data technologies store, organize and analyze multiple format data originating from different sources, in order to identify patterns and design smart solutions.

Who uses big data?

Big data has multiple uses and applications in countless industries, from marketing and entertainment to healthcare, finance, banking and even agriculture.

What is an example of big data?

Social media platforms like Facebook or Instagram generate over 500 terabytes of data, daily, from photo and video content, instant chat messages and user-generated content (comments, reactions).

What are the sources of big data?

Big data mainly comes from machines, in-demand software, social media platforms and transactional sources such as e-commerce.

To conclude, big data is everywhere and big data analytics is suitable for every business size – especially since they’re scalable – no matter the industry. If you have more questions or would like to get in touch with one of our consultants, for a more informed decision, do not hesitate to contact us.

As the only Gold Vertica Partner in Romania, btProvider is an active part of the community that supports the use of big data in the professional landscape. We’re adding value not only by being a reseller for such next-generation technology but also through training, deployments on-premise, cloud or hybrid implementations, outsourcing services and non-commercial activities, covering all stages of a data project.