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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Master the AI Era: Key Skills for Success
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 24, 2025 6 min read

The world is rapidly changing. New technologies such as artificial intelligence (AI) are transforming our lives and work, redefining the definition of “essential office skills.”

So what essential skills do today’s workers need to thrive in a business world undergoing a major digital transformation? It’s a question that Alan Lerner, director at Toptal and lecturer at the Open Institute of Technology (OPIT), addressed in his recent online masterclass.

In a broad overview of the new office landscape, Lerner shares the essential skills leaders need to manage – including artificial intelligence – to keep abreast of trends.

Here are eight essential capabilities business leaders in the AI era need, according to Lerner, which he also detailed in OPIT’s recent Master’s in Digital Business and Innovation webinar.

An Adapting Professional Environment

Lerner started his discussion by quoting naturalist Charles Darwin.

“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change.”

The quote serves to highlight the level of change that we are currently seeing in the professional world, said Lerner.

According to the World Economic Forum’s The Future of Jobs Report 2025, over the next five years 22% of the labor market will be affected by structural change – including job creation and destruction – and much of that change will be enabled by new technologies such as AI and robotics. They expect the displacement of 92 million existing jobs and the creation of 170 million new jobs by 2030.

While there will be significant growth in frontline jobs – such as delivery drivers, construction workers, and care workers – the fastest-growing jobs will be tech-related roles, including big data specialists, FinTech engineers, and AI and machine learning specialists, while the greatest decline will be in clerical and secretarial roles. The report also predicts that most workers can anticipate that 39% of their existing skill set will be transformed or outdated in five years.

Lerner also highlighted key findings in the Accenture Life Trends 2025 Report, which explores behaviors and attitudes related to business, technology, and social shifts. The report noted five key trends:

  • Cost of Hesitation – People are becoming more wary of the information they receive online.
  • The Parent Trap – Parents and governments are increasingly concerned with helping the younger generation shape a safe relationship with digital technology.
  • Impatience Economy – People are looking for quick solutions over traditional methods to achieve their health and financial goals.
  • The Dignity of Work – Employees desire to feel inspired, to be entrusted with agency, and to achieve a work-life balance.
  • Social Rewilding – People seek to disconnect and focus on satisfying activities and meaningful interactions.

These are consumer and employee demands representing opportunities for change in the modern business landscape.

Key Capabilities for the AI Era

Businesses are using a variety of strategies to adapt, though not always strategically. According to McClean & Company’s HR Trends Report 2025, 42% of respondents said they are currently implementing AI solutions, but only 7% have a documented AI implementation strategy.

This approach reflects the newness of the technology, with many still unsure of the best way to leverage AI, but also feeling the pressure to adopt and adapt, experiment, and fail forward.

So, what skills do leaders need to lead in an environment with both transformation and uncertainty? Lerner highlighted eight essential capabilities, independent of technology.

Capability 1: Manage Complexity

Leaders need to be able to solve problems and make decisions under fast-changing conditions. This requires:

  • Being able to look at and understand organizations as complex social-technical systems
  • Keeping a continuous eye on change and adopting an “outside-in” vision of their organization
  • Moving fast and fixing things faster
  • Embracing digital literacy and technological capabilities

Capability 2: Leverage Networks

Leaders need to develop networks systematically to achieve organizational goals because it is no longer possible to work within silos. Leaders should:

  • Use networks to gain insights into complex problems
  • Create networks to enhance influence
  • Treat networks as mutually rewarding relationships
  • Develop a robust profile that can be adapted for different networks

Capability 3: Think and Act “Global”

Leaders should benchmark using global best practices but adapt them to local challenges and the needs of their organization. This requires:

  • Identifying what great companies are achieving and seeking data to understand underlying patterns
  • Developing perspectives to craft global strategies that incorporate regional and local tactics
  • Learning how to navigate culturally complex and nuanced business solutions

Capability 4: Inspire Engagement

Leaders must foster a culture that creates meaningful connections between employees and organizational values. This means:

  • Understanding individual values and needs
  • Shaping projects and assignments to meet different values and needs
  • Fostering an inclusive work environment with plenty of psychological safety
  • Developing meaningful conversations and both providing and receiving feedback
  • Sharing advice and asking for help when needed

Capability 5: Communicate Strategically

Leaders should develop crisp, clear messaging adaptable to various audiences and focus on active listening. Achieving this involves:

  • Creating their communication style and finding their unique voice
  • Developing storytelling skills
  • Utilizing a data-centric and fact-based approach to communication
  • Continual practice and asking for feedback

Capability 6: Foster Innovation

Leaders should collaborate with experts to build a reliable innovation process and a creative environment where new ideas thrive. Essential steps include:

  • Developing or enhancing structures that best support innovation
  • Documenting and refreshing innovation systems, processes, and practices
  • Encouraging people to discover new ways of working
  • Aiming to think outside the box and develop a growth mindset
  • Trying to be as “tech-savvy” as possible

Capability 7: Cultivate Learning Agility

Leaders should always seek out and learn new things and not be afraid to ask questions. This involves:

  • Adopting a lifelong learning mindset
  • Seeking opportunities to discover new approaches and skills
  • Enhancing problem-solving skills
  • Reviewing both successful and unsuccessful case studies

Capability 8: Develop Personal Adaptability

Leaders should be focused on being effective when facing uncertainty and adapting to change with vigor. Therefore, leaders should:

  • Be flexible about their approach to facing challenging situations
  • Build resilience by effectively managing stress, time, and energy
  • Recognize when past approaches do not work in current situations
  • Learn from and capitalize on mistakes

Curiosity and Adaptability

With the eight key capabilities in mind, Lerner suggests that curiosity and adaptability are the key skills that everyone needs to thrive in the current environment.

He also advocates for lifelong learning and teaches several key courses at OPIT which can lead to a Bachelor’s Degree in Digital Business.

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Lessons From History: How Fraud Tactics From the 18th Century Still Impact Us Today
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2025 6 min read

Many people treat cyber threats and digital fraud as a new phenomenon that only appeared with the development of the internet. But fraud – intentional deceit to manipulate a victim – has always existed; it is just the tools that have changed.

In a recent online course for the Open Institute of Technology (OPIT), AI & Cybersecurity Strategist Tom Vazdar, chair of OPIT’s Master’s Degree in Enterprise Cybersecurity, demonstrated the striking parallels between some of the famous fraud cases of the 18th century and modern cyber fraud.

Why does the history of fraud matter?

Primarily because the psychology and fraud tactics have remained consistent over the centuries. While cybersecurity is a tool that can combat modern digital fraud threats, no defense strategy will be successful without addressing the underlying psychology and tactics.

These historical fraud cases Vazdar addresses offer valuable lessons for current and future cybersecurity approaches.

The South Sea Bubble (1720)

The South Sea Bubble was one of the first stock market crashes in history. While it may not have had the same far-reaching consequences as the Black Thursday crash of 1929 or the 2008 crash, it shows how fraud can lead to stock market bubbles and advantages for insider traders.

The South Sea Company was a British company that emerged to monopolize trade with the Spanish colonies in South America. The company promised investors significant returns but provided no evidence of its activities. This saw the stock prices grow from £100 to £1,000 in a matter of months, then crash when the company’s weakness was revealed.

Many people lost a significant amount of money, including Sir Isaac Newton, prompting the statement, “I can calculate the movement of the stars, but not the madness of men.

Investors often have no way to verify a company’s claim, making stock markets a fertile ground for manipulation and fraud since their inception. When one party has more information than another, it creates the opportunity for fraud. This can be seen today in Ponzi schemes, tech stock bubbles driven by manipulative media coverage, and initial cryptocurrency offerings.

The Diamond Necklace Affair (1784-1785)

The Diamond Necklace Affair is an infamous incident of fraud linked to the French Revolution. An early example of identity theft, it also demonstrates that the harm caused by such a crime can go far beyond financial.

A French aristocrat named Jeanne de la Mont convinced Cardinal Louis-René-Édouard, Prince de Rohan into thinking that he was buying a valuable diamond necklace on behalf of Queen Marie Antoinette. De la Mont forged letters from the queen and even had someone impersonate her for a meeting, all while convincing the cardinal of the need for secrecy. The cardinal overlooked several questionable issues because he believed he would gain political benefit from the transaction.

When the scheme finally exposed, it damaged Marie Antoinette’s reputation, despite her lack of involvement in the deception. The story reinforced the public perception of her as a frivolous aristocrat living off the labor of the people. This contributed to the overall resentment of the aristocracy that erupted in the French Revolution and likely played a role in Marie Antoinette’s death. Had she not been seen as frivolous, she might have been allowed to live after her husband’s death.

Today, impersonation scams work in similar ways. For example, a fraudster might forge communication from a CEO to convince employees to release funds or take some other action. The risk of this is only increasing with improved technology such as deepfakes.

Spanish Prisoner Scam (Late 1700s)

The Spanish Prisoner Scam will probably sound very familiar to anyone who received a “Nigerian prince” email in the early 2000s.

Victims received letters from a “wealthy Spanish prisoner” who needed their help to access his fortune. If they sent money to facilitate his escape and travel, he would reward them with greater riches when he regained his fortune. This was only one of many similar scams in the 1700s, often involving follow-up requests for additional payments before the scammer disappeared.

While the “Nigerian prince” scam received enough publicity that it became almost unbelievable that people could fall for it, if done well, these can be psychologically sophisticated scams. The stories play on people’s emotions, get them invested in the person, and enamor them with the idea of being someone helpful and important. A compelling narrative can diminish someone’s critical thinking and cause them to ignore red flags.

Today, these scams are more likely to take the form of inheritance fraud or a lottery scam, where, again, a person has to pay an advance fee to unlock a much bigger reward, playing on the common desire for easy money.

Evolution of Fraud

These examples make it clear that fraud is nothing new and that effective tactics have thrived over the centuries. Technology simply opens up new opportunities for fraud.

While 18th-century scammers had to rely on face-to-face contact and fraudulent letters, in the 19th century they could leverage the telegraph for “urgent” communication and newspaper ads to reach broader audiences. In the 20th century, there were telephones and television ads. Today, there are email, social media, and deepfakes, with new technologies emerging daily.

Rather than quack doctors offering miracle cures, we see online health scams selling diet pills and antiaging products. Rather than impersonating real people, we see fake social media accounts and catfishing. Fraudulent sites convince people to enter their bank details rather than asking them to send money. The anonymity of the digital world protects perpetrators.

But despite the technology changing, the underlying psychology that makes scams successful remains the same:

  • Greed and the desire for easy money
  • Fear of missing out and the belief that a response is urgent
  • Social pressure to “keep up with the Joneses” and the “Bandwagon Effect”
  • Trust in authority without verification

Therefore, the best protection against scams remains the same: critical thinking and skepticism, not technology.

Responding to Fraud

In conclusion, Vazdar shared a series of steps that people should take to protect themselves against fraud:

  • Think before you click.
  • Beware of secrecy and urgency.
  • Verify identities.
  • If it seems too good to be true, be skeptical.
  • Use available security tools.

Those security tools have changed over time and will continue to change, but the underlying steps for identifying and preventing fraud remain the same.

For more insights from Vazdar and other experts in the field, consider enrolling in highly specialized and comprehensive programs like OPIT’s Enterprise Security Master’s program.

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