Since the emergence of Big Data, there have been a number of industries that have witnessed major changes as they started using a large amount of information for solving their existing problems. The amount of data being created around the world has also increased at a very fast rate, with IDC projecting the global datasphere to reach 175 zettabytes by 2025, which shows the enormous scale of information that businesses have to manage.
Big Data has helped businesses to understand their customers, improve their operations and make better decisions, but there are still a number of myths that create confusion about its actual use. In this article, we shall cover the top 5 Big Data myths and truths in detail and understand what businesses should actually know before using Big Data for their business, so let’s begin with this!
1. Big Data Means Only a Large Amount of Data
When we talk about Big Data, the first thing that comes to our mind is a huge amount of data, but Big Data is not only about the quantity of information. NIST describes Big Data through characteristics such as volume, velocity, variety and variability, while other widely used frameworks also consider factors such as veracity and value.
For instance, what good are hundreds of thousands of customer records if they are old, duplicated, or inaccurate? Big Data may have structured data in the form of transaction records, or unstructured data such as text, images, and videos, which can present extra challenges when it comes to managing and analysing the data.
The speed at which information is generated is also important. IBM explains that data velocity refers to the rate at which data is generated, transmitted, processed and made available for use, which becomes particularly important when the value of information decreases quickly.
Truth: The real purpose of Big Data is not simply to collect a huge amount of information. It is to manage large, complex and continuously changing sources of data and convert them into information that can support a useful business decision.
2. More Data Always Means Better Decisions
People think the more data a business has, then the better it can make decisions, but this is not necessarily true because the information must be relevant and accurate. If the information is incorrect, incomplete or duplicated, then having more of it can make the decision-making process even more difficult.
Data quality has a direct business cost. Gartner says that poor data quality costs organizations at least $12.9 million per year on average, while inconsistent information across different sources is one of the major data-quality problems faced by organizations.
For example, suppose a retail company has information about millions of customers but many of its customer records are duplicated or contain old purchasing information. The company may have a large amount of data, but it can still make an incorrect decision because the information being used is not reliable.
This is even more evident when companies use Artificial Intelligence and machine learning to analyze their data. NIST describes veracity as the accuracy of data and points out that data quality can affect the accuracy of analytical results, while IBM also identifies veracity as one of the important characteristics of Big Data.
Truth: More data does not always mean better decisions. The business needs accurate, relevant and reliable data before it can expect useful results.
3. Big Data Is Only for Large Companies
Another myth is that Big Data is for large organizations with huge budgets, technological infrastructures and thousands of staff. Big Data tools can certainly be implemented by large organizations, but businesses of different sizes can use data analysis according to their needs and the issues they want to address.
NIST itself explains that the definition of a Big Data problem depends on the requirements of the application and the ability of existing systems to store, process and analyse the information. This means there is no single amount of data that automatically makes every business a Big Data organization.
For example, a small retail business can study its sales information to understand which products are selling more, while a manufacturing company can use information from machines to identify possible equipment problems. In the same way, a financial business can analyze transactions to identify unusual activities and take suitable action.
The important thing is that the business should first understand its requirement instead of simply purchasing the most expensive technology available in the market. There is no benefit in having complicated Big Data technology if the company does not know how to use it for solving its actual business problem.
Truth: Big Data is not limited to large companies. The technology, infrastructure and amount of data should depend on the business requirement and the problem that needs to be solved.
4. Big Data Always Needs Real-Time Analytics
When we talk about Big Data, many people believe that all information needs to be collected and analyzed in real time. Real-time analytics is very useful in areas such as fraud detection, stock trading, network monitoring and certain customer interactions where taking action quickly can make a major difference.
However, every business decision does not require information within a few seconds. For example, a company that wants to understand its yearly sales performance may not need every transaction to be analyzed immediately because daily, weekly or monthly information may be enough for making the required decision.
There is an important difference between how quickly data is produced and how quickly a business needs to make a decision. IDC projected that nearly 30% of the global datasphere would be real-time by 2025, which also means that a substantial amount of data does not necessarily need to be consumed in real time.
The business should therefore understand how quickly it actually needs the information before investing in real-time Big Data systems. If a decision can be taken after a certain period, then there may not be much benefit in spending additional money only for getting information a few seconds earlier.
Truth: Real-time analytics is important when the business needs to take quick action, but it is not necessary for every Big Data project.
5. Big Data Analytics Automatically Creates Business Value
This is one of the biggest myths related to Big Data because many businesses believe that collecting information and using analytics will automatically improve their business performance. Big Data analytics can provide useful insights, but these insights have to be understood by the people in the organization and then used for taking the right business decisions.
Research from McKinsey shows why this human and organizational part is important. Companies where employees consistently used data as a basis for decision-making were nearly twice as likely to report reaching their data and analytics objectives and nearly 1.5 times more likely to report revenue growth of at least 10% over the previous three years.
For example, a retail company may use Big Data analytics and find that the demand for one particular product is increasing. This information can be very useful, but the actual benefit will come only when the company uses this information to improve its inventory, purchasing, pricing or supply chain activities.
The same thing can happen in the manufacturing industry where Big Data can help identify signs of equipment failure. The system may identify the possibility of a problem, but the employees still have to take suitable action to inspect and maintain the equipment.
Thus, there is a difference between collecting data, finding an insight and creating a business result. Big Data analytics can support the decision, but the organization still has to act on that information to get the actual benefit.
Truth: Big Data analytics can provide valuable insights, but technology alone does not create business value. People, processes and decisions are required to convert an insight into an actual result.
What is the Right Way to Use Big Data?
After understanding these Big Data myths, it becomes clear that the main objective of a business should not be to collect as much data as possible. The business should first understand what problem it wants to solve and then identify the type of information, technology and analytics that can help in solving that problem.
This becomes even more important as businesses start using Artificial Intelligence with their data. IBM’s Global AI Adoption Index found that 42% of surveyed enterprise-scale organizations had actively deployed AI, while another 40% were exploring or experimenting with it; data complexity was identified as a barrier by 25% of respondents.
The Indian market provides another useful example. IBM reported that 59% of surveyed enterprise-scale organizations in India had actively deployed AI, while another 27% were exploring its use, showing how closely data and AI are becoming connected in business operations.
Data quality therefore plays an important role in this regard because businesses need reliable information before they can depend on the results of their analytics. Along with technology, businesses also need proper data governance, skilled employees and clear processes so that the information can be converted into useful business decisions.
A business should therefore ask some important questions before starting a Big Data project, such as:
- What problem are we trying to solve?
- What type of data do we actually need?
- Is the data accurate and reliable?
- Where is the data coming from?
- How quickly do we need the information?
- Which technology is suitable for our requirement?
- Who will analyze the information?
- How will we use the insights?
- How will we measure the result?
These questions can help an organization avoid unnecessary investment and focus on the information that can actually provide value. They also help the business understand that Big Data is a business decision as much as it is a technology decision.
Conclusion
Thus, Big Data has become an important part of modern businesses as organizations are generating and collecting information from customers, applications, machines, websites, transactions and many other sources. NIST and IBM both show that Big Data involves more than volume, with characteristics such as velocity, variety and data reliability also affecting how information is managed and used.
However, having a large amount of information does not automatically make a business data-driven. Gartner’s estimate that poor data quality costs organizations at least $12.9 million a year on average shows why businesses have to pay attention to the quality of information instead of simply increasing the amount of data they collect.
The connection between data and business action is equally important. McKinsey’s research shows that organizations where employees consistently use data in decision-making are nearly twice as likely to report reaching their data and analytics objectives, which demonstrates that the value of Big Data depends on what organizations do with the information they collect.
The biggest truth about Big Data is the value does not come from having more data, but from knowing which data matters, whether it can be trusted and how it can be used for solving a real business problem.
