Big Data Analytics: A Comprehensive Guide to Characteristics, Types, & Real-World Trends


The term “big data” is self-explanatory: it’s a large collection of data. However, to be classified as “big,” data needs to meet specific criteria. Big data is huge in volume, gets even bigger over time, arrives with ever-higher velocity, and is so complex that no traditional tools can handle it.
Big data analytics is the (complex) process of analyzing these huge chunks of data to discover different information. The process is especially important for small companies that use the uncovered information to design marketing strategies, conduct market research, and follow the latest industry trends.
In this introduction to big data analytics, we’ll dig deep into big data and uncover ways to analyze it. We’ll also explore its (relatively short) history and evolution and present its advantages and drawbacks.
History and Evolution of Big Data
We’ll start this introduction to big data with a short history lesson. After all, we can’t fully answer the “what is big data?” question if we don’t know its origins.
Let’s turn on our time machine and go back to the 1960s. That’s when the first major change that marked the beginning of the big data era took place. The advanced development of data centers, databases, and innovative processing methods facilitated the rise of big data.
Relational databases (storing and offering access to interconnected data points) have become increasingly popular. While people had ways to store data much earlier, experts consider that this decade set the foundations for the development of big data.
The next major milestone was the emergence of the internet and the exponential growth of data. This incredible invention made handling and analyzing large chunks of information possible. As the internet developed, big data technologies and tools became more advanced.
This leads us to the final destination of short time travel: the development of big data analytics, i.e., processes that allow us to “digest” big data. Since we’re witnessing exceptional technological developments, the big data journey is yet to continue. We can only expect the industry to advance further and offer more options.
Big Data Technologies and Tools
What tools and technologies are used to decipher big data and offer value?
Data Storage and Management
Data storage and management tools are like virtual warehouses where you can pack up your big data safely and work with it as needed. These tools feature a powerful infrastructure that lets you access and fetch the desired information quickly and easily.
Data Processing and Analytics Framework
Processing and analyzing huge amounts of data are no walk in the park. But they can be, thanks to specific tools and technologies. These valuable allies can clean and transform large piles of information into data you can use to pursue your goals.
Machine Learning and Artificial Intelligence Platforms
Machine learning and artificial intelligence platforms “eat” big data and perform a wide array of functions based on the discoveries. These technologies can come in handy with testing hypotheses and making important decisions. Best of all, they require minimal human input; you can relax while AI works its magic.
Data Visualization Tools
Making sense of large amounts of data and presenting it to investors, stakeholders, and team members can feel like a nightmare. Fortunately, you can turn this nightmare into a dream come true with big data visualization tools. Thanks to the tools, creating stunning graphs, dashboards, charts, and tables and impressing your coworkers and superiors has never been easier.
Big Data Analytics Techniques and Methods
What techniques and methods are used in big data analytics? Let’s find the answer.
Descriptive Analytics
Descriptive analytics is like a magic wand that turns raw data into something people can read and understand. Whether you want to generate reports, present data on a company’s revenue, or analyze social media metrics, descriptive analytics is the way to go.
It’s mostly used for:
- Data summarization and aggregation
- Data visualization
Diagnostic Analytics
Have a problem and want to get detailed insight into it? Diagnostic analytics can help. It identifies the root of an issue, helping you figure out your next move.
Some methods used in diagnostic analytics are:
- Data mining
- Root cause analysis
Predictive Analytics
Predictive analytics is like a psychic that looks into the future to predict different trends.
Predictive analytics often uses:
- Regression analysis
- Time series analysis
Prescriptive Analytics
Prescriptive analytics is an almighty problem-solver. It usually joins forces with descriptive and predictive analytics to offer an ideal solution to a particular problem.
Some methods prescriptive analytics uses are:
- Optimization techniques
- Simulation and modeling
Applications of Big Data Analytics
Big data analytics has found its home in many industries. It’s like the not-so-secret ingredient that can make the most of any niche and lead to desired results.
Business and Finance
How do business and finance benefit from big data analytics? These industries can flourish through better decision-making, investment planning, fraud detection and prevention, and customer segmentation and targeting.
Healthcare
Healthcare is another industry that benefits from big data analytics. In healthcare, big data is used to create patient databases, personal treatment plans, and electronic health records. This data also serves as an excellent foundation for accurate statistics about treatments, diseases, patient backgrounds, risk factors, etc.
Government and Public Sector
Big data analytics has an important role in government and the public sector. Analyzing different data improves efficiency in terms of costs, innovation, crime prediction and prevention, and workforce. Multiple government parts often need to work together to get the best results.
As technology advances, big data analytics has found another major use in the government and public sector: smart cities and infrastructure. With precise and thorough analysis, it’s possible to bring innovation and progress and implement the latest features and digital solutions.
Sports and Entertainment
Sports and entertainment are all about analyzing the past to predict the future and improve performance. Whether it’s analyzing players to create winning strategies or attracting the audience and freshening up the content, big data analytics is like a valuable player everyone wants on their team.
Challenges and Ethical Considerations in Big Data Analytics
Big data analytics represent doors to new worlds of information. But opening these doors often comes with certain challenges and ethical considerations.
Data Privacy and Security
One of the major challenges (and the reason some people aren’t fans of big data analytics) is data privacy and security. The mere fact that personal information can be used in big data analytics can make individuals feel exploited. Since data breaches and identity thefts are, unfortunately, becoming more common, it’s no surprise some people feel this way.
Fortunately, laws like GDPR and CCPA give individuals more control over the information others can collect from them.
Data Quality and Accuracy
Big data analytics can sometimes be a dead end. If the material wasn’t handled correctly, or the data was incomplete to start with, the results themselves won’t be adequate.
Algorithmic Bias and Fairness
Big data analytics is based on algorithms, which are designed by humans. Hence, it’s not unusual to assume that these algorithms can be biased (or unfair) due to human prejudices.
Ethical Use of Big Data Analytics
The ethical use of big data analytics concerns the “right” and “wrong” in terms of data usage. Can big data’s potential be exploited to the fullest without affecting people’s right to privacy?
Future Trends and Opportunities in Big Data Analytics
Although it has proven useful in many industries, big data analytics is still relatively young and unexplored.
Integration of Big Data Analytics With Emerging Technologies
It seems that new technologies appear in the blink of an eye. Our reality today (in a technological sense) looks much different than just two or three years ago. Big data analytics is now intertwined with emerging technologies that give it extra power, accuracy, and quality.
Cloud computing, advanced databases, the Internet of Things (IoT), and blockchain are only some of the technologies that shape big data analytics and turn it into a powerful giant.
Advancements in Machine Learning and Artificial Intelligence
Machines may not replace us (at least not yet), but it’s impossible to deny their potential in many industries, including big data analytics. Machine learning and artificial intelligence allow for analyzing huge amounts of data in a short timeframe.
Machines can “learn” from their own experience and use this knowledge to make more accurate predictions. They can pinpoint unique patterns in piles of information and estimate what will happen next.
New Applications and Industries Adopting Big Data Analytics
One of the best characteristics of big data analytics is its versatility and flexibility. Accordingly, many industries use big data analytics to improve their processes and achieve goals using reliable information.
Every day, big data analytics finds “new homes” in different branches and niches. From entertainment and medicine to gambling and architecture, it’s impossible to ignore the importance of big data and the insights it can offer.
These days, we recognize the rise of big data analytics in education (personalized learning) and agriculture (environmental monitoring).
Workforce Development and Education in Big Data Analytics
Analyzing big data is impossible without the workforce capable of “translating” the results and adopting emerging technologies. As big data analytics continues to develop, it’s vital not to forget about the cog in the wheel that holds everything together: trained personnel. As technology evolves, specialists need to continue their education (through training and certification programs) to stay current and reap the many benefits of big data analytics.
Turn Data to Your Advantage
Whatever industry you’re in, you probably have goals you want to achieve. Naturally, you want to achieve them as soon as possible and enjoy the best results. Instead of spending hours and hours going through piles of information, you can use big data analytics as a shortcut. Different types of big data technologies can help you improve efficiency, analyze risks, create targeted promotions, attract an audience, and, ultimately, increase revenue.
While big data offers many benefits, it’s also important to be aware of the potential risks, including privacy concerns and data quality.
Since the industry is changing (faster than many anticipated), you should stay informed and engaged if you want to enjoy its advantages.
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Open Institute of Technology (OPIT) masterclasses bring students face-to-face with real-world business challenges. In OPIT’s July masterclass, OPIT Professor Francesco Derchi and Ph.D. candidate Robert Mario de Stefano explained the principles of regenerative businesses and how regeneration goes hand in hand with growth.
Regenerative Business Models
Professor Derchi began by explaining what exactly is meant by regenerative business models, clearly differentiating them from sustainable or circular models.
Many companies pursue sustainable business models in which they offset their negative impact by investing elsewhere. For example, businesses that are big carbon consumers will support nature regeneration projects. Circular business models are similar but are more focused on their own product chain, aiming to minimize waste by keeping products in use as long as possible through recycling. Both models essentially aim to have a “net-zero” negative impact on the environment.
Regenerative models are different because they actively aim to have a “net-positive” impact on the environment, not just offsetting their own use but actively regenerating the planet.
Massive Transformative Purpose
While regenerative business models are often associated with philanthropic endeavors, Professor Derchi explained that they do not have to be, and that investment in regeneration can be a driver of growth.
He discussed the importance of corporate purpose in the modern business space. Having a strong and clearly stated corporate purpose is considered essential to drive business decision-making, encourage employee buy-in, and promote customer loyalty.
But today, simple corporate missions, such as “make good shoes,” don’t go far enough. People are looking for a Massive Transformational Purpose (MTP) that can take the business to the next level.
Take, for example, Ben & Jerry’s. The business’s initial corporate purpose may have been to make great ice cream and serve it up in a way that people will enjoy. But the business really began to grow when they embraced an MTP. As they announced in their mission statement, “We believe that ice cream can change the world.” Their business activities also have the aim of advancing human rights and dignity, supporting social and economic justice, and protecting and restoring the Earth’s natural systems. While these aims are philanthropic, they have also helped the business grow.
RePlanet
Professor Derchi next talked about RePlanet, a business he recently worked to develop their MTP. Founded in 2015, RePlanet designs and implements customized renewable energy solutions for businesses and projects. The company already operates in the renewable energy field and ranked as the 21st fastest-growing business in Italy in 2023. So while they were already enjoying great success, Derchi worked with them to see if actively embracing a regenerative business model could unlock additional growth.
Working together, RePlanet moved towards an MTP of building a greener future based on today’s choices, ensuring a cleaner world for generations. Meeting this goal started with the energy products that RePlanet sells, such as energy systems that recover heat from dairy farms. But as the business’s MTP, it goes beyond that. RePlanet doesn’t just engage suppliers; it chooses partners that share its specific values. It also influences the projects they choose to work on – they prioritize high-impact social projects, such as recently installing photovoltaic energy systems at a local hospital in Nigeria – and how RePlanet treats its talent, acknowledging that people are the true energy of the company.
Regenerative Business Strategies
Based on work with RePlanet and other businesses, Derchi has identified six archetypal regenerative business strategies for businesses that want to have both a regenerative impact and drive growth:
- Regenerative Leadership – Laying the foundation for regeneration in a broader sense throughout the company
- Nature Regeneration – Strategies to improve the health of the natural world
- Social Regeneration – Regenerating human ecosystems through things such as fair-trade practices
- Responsible Sourcing – Empowering and strengthening suppliers and their communities
- Health & Well-being – Creating products and services that have a positive effect on customers
- Employee Focus – Improve work conditions, lives, and well-being of employees.
Case Studies
Building on the concept of regenerative business models, Roberto Mario de Stefano shared other case studies of businesses that are having a positive impact and enjoying growth thanks to regenerative business models and strategies.
Biorfarm
Biorfarm is a digital platform that supports small-scale agriculture by creating a direct link between small farmers and consumers. Cutting out the middleman in modern supply chains means that farmers earn about 50% more for their produce. They set consumers up as “digital farmers” who actively support and learn about farming activities to promote more conscious food consumption.
Their vision is to create a food economy in which those who produce food and those who consume it are connected. This moves consumers from passive cash cows for large corporations that prioritize profits over the well-being of farmers to actively supporting natural production and a more sustainable system.
Rifo Lab
Rifo Lab is a circular clothing brand with the vision of addressing the problem of overproduction in the clothing industry. Established in Prato, Italy, a traditional textile-producing area, the company produces clothes made from textile waste and biodegradable materials. There are no physical stores, and all orders must be placed online; everything is made to order, reducing excess production.
With an eye on social regeneration, all production takes place within 30 kilometers of their offices, allowing the business to support ethical and local production. They also work with companies that actively integrate migrants into the local community, sharing their local artisan crafts with future generations.
Ogyre
Ogyre is a digital platform that allows you to pay fishermen to fish for waste. When fishermen are out conducting their livelihood, they also collect a significant amount of waste from the ocean, especially plastic waste. Ogyre arranges for fishermen to get paid for collecting that waste, which in turn supports the local fishing communities, and then transforms the waste collected into new sustainable products.
Moving Towards a Regenerative Future
The masterclass concluded with a Q&A session, where it explained that working in regenerative businesses requires the same skills as any other business. But it also requires you to embrace a mindset where value comes from giving and that growth is about working together for a better future, and not just competition.

Riccardo Ocleppo’s vision for the Open Institute of Technology (OPIT) started when he realized that his own university-level training had not properly prepared him for the modern workplace. Technological innovation is moving quickly and changing the nature of work, while university curricula evolve slowly, in part due to systems in place designed to preserve the quality of courses.
Ocleppo was determined to create a higher learning institution that filled the gap between the two realities – delivering high-quality education while preparing professionals to work in dynamic environments that keep pace with technology. Thus, OPIT opened enrolments in 2023 with a curriculum that created a unique bridge between the present and the future.
This is the story of one student, Ania Jaca, whose time at OPIT gave her the skills to connect her knowledge of product design to full system deployment.
Meet Ania
Ania is an example of an active professional who was able to identify what was missing in her own skills that would be needed if she wanted to advance her career in the direction she desired.
Ania is a highly skilled professional who was working on product and industrial design at Deloitte. She has an MA in product design, speaks five languages, studied in China, and is an avid boxer. She had the intelligence and the temperament to succeed in her career, but felt that she lacked the skills to advance and move from determining how products look to how systems really work, scale, and evolve.
Ania taught herself skills such as Python, artificial intelligence (AI), and cloud infrastructure, but soon realized that she needed a more structured education to go deeper. Thus, the search for her next steps began, and her introduction to OPIT.
OPIT appealed to Ania because it offered a fully EU-accredited MSc that she could pursue at her own pace, thanks to remote delivery and flexible hours. But more than that, it filled exactly the knowledge gap she was looking to build upon, teaching her technical foundations, but always with a focus on applications in the real world. Part of the appeal was the faculty, which includes professionals who are leaders in their field and who deal with current professional challenges on a daily basis, which they can bring into the classroom.
Ania enrolled in OPIT’s MSc in Applied Data Science & AI.
MSc in Applied Data Science and AI
This is OPIT’s first master’s program, which also launched in 2023, and is now one of four on offer. The course is designed for graduates like Ania who want a career at the intersection of management and technology. It is attractive to professionals who are already working in this area but lack the technical training to step into certain roles. OPIT requires no computer science prerequisites, so it accepted Ania with her MA in product design.
It is an intensive program that starts with foundational application courses in business, data science, machine learning, artificial intelligence, and problem-solving. The program then moves towards applying data science and AI methodologies and tools to real-life business problems.
The course combines theoretical study with a capstone project that lets students apply what they learn in the real world, either at their existing company or through internship programs. Many of the projects developed by students go on to become fundamental to the businesses they work with.
Ania’s Path Forward
Ania is working on her capstone project with Neperia Group, an Italian-based IT systems development company that works mostly with financial, insurance, and industrial companies. They specialize in developing analysis tools for existing software to enhance insight, streamline management, minimize the impact of corrective and evolutionary interventions, and boost performance.
Ania is specifically working on tools for assessing vulnerabilities in codebases as an advanced cybersecurity tool.
Ania credits her studies at OPIT for helping her build solid foundations in data science, machine learning, and cloud workflows, giving her a thorough understanding of digital products from end to end. She feels this has prepared her for roles at the intersection between infrastructure, security, and deployment, which is exactly where she wants to be. OPIT is excited to see where Ania’s career takes her in the coming years.
Preparing for the Future of Work
Overall, studying at OPIT has helped Ania and others like her prepare for the future of work. According to the Visual Capitalist, the fastest-growing jobs between 2025 and 2030 will be in big data (up by 110%), Fintech engineers (up by 95%), AI and machine learning specialists (up by 85%), software application developers (up by 60%), and security management specialists (up by 55%).
However, while these industries are growing, entry-level opportunities are declining in areas such as software development and IT. This is because AI now performs many of the tasks associated with those roles. Instead, companies are looking for experienced professionals to take on roles that involve more strategic oversight and innovative problem-solving. But how do recent graduates leapfrog past experienced professionals when there is a lack of entry-level positions to make the transition?
This is another challenge that OPIT addresses in its course design. Students don’t just learn the theory, OPIT actively encourages them to focus on applications, allowing them to build experience while studying. The capstone project consolidates this, enabling students to demonstrate to future employers their expertise at deploying technology to solve problems.
OPIT also has a dynamic Career Services department that specifically works with students to prepare them for the types of roles they want. This focus on not only learning but building a career is one of the elements that makes OPIT stand out in preparing graduates for the workplace.
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