A QUICK SUMMARY – FOR THE BUSY ONES
Myth 1: Big Data is too complex to handle
The truth: Big data tools and technologies are manageable with training and expertise, just like any other technology.
Myth 2: Big Data in the Cloud faces a potential risk
The truth: Cloud security measures constantly evolve to protect sensitive data, making it secure for big data storage.
Myth 3: Big Data is costly
The truth: Publicly managed cloud platforms can provide cost-effective solutions, eliminating the need for hardware and software purchases.
Myth 4: Big Data is for big enterprises only
The truth: Big data, including affordable options like Hadoop, is accessible and useful for both small and large organizations.
Myth 5: Big Data is here to replace humans
The truth: Big data enhances human decision-making and creativity but cannot fully substitute for human insight and reasoning.
Myth 6: Big Data is an all-in-one solution
The truth: Successful utilization of big data depends on skilled professionals and resources working together.
Myth 7: Big Data guarantees a higher ROI
The truth: Big data can contribute to ROI, but it requires mature decisions and cannot guarantee success.
Myth 8: Big Data will replace data warehouses
The truth: Big data platforms like Hadoop complement data warehouses, rather than replacing them, for specific tasks.
Myth 9: Big Data guarantees a better decision-making process
The truth: Big data provides insights, but the quality of decisions still depends on human judgment and external factors.
Myth 10: Big Data is an IT-related concept
The truth: Big data impacts various departments within a business, including sales, marketing, finance, operations, and human resources.
To discover the details, proceed to the main section of this article.
TABLE OF CONTENTS
Businesses deal with an abundance of data on a daily basis and the volume of data being created and stored is an ever-increasing phenomenon, which needs scrutiny and analysis. This is where the concept of big data analytics comes in.
Big data uses data points from a set of raw data and identifies relationships to gain new insights and make predictions about the future. The raw set of data is used to build models, run simulations and alter data points to see how each alteration impacts the resulting information/insight and prediction. This has been adopted by organizations to aid with smart and more informed decisions.
With its soaring popularity, a few major misconceptions have blurred the functionality of big data. A few of these myths will be debunked as part of this article. A word of advice: ‘less criticism, more openness’ is what the world needs more of!
Given the volume of big data generated on a real-time basis from multiple sources like audio, video, and images, it can get a little dirty! Big data has been introduced to make manual processes automatic, thereby reducing complications of handling all this data.
Big data analytics uses different technologies and simulation tools that may seem too big to handle at first, but so did cell phones and computers! All inventions required a rigorous learning process before it became easy to operate and use.
It’s the same case for big data tools. A few specialized tools are used to store, process, analyze and visualize data points, which require a few training sessions to get used to.
Have an expert help you with all this data, data technologies and how to go about getting used to them. The technical framework for big data is not as complex and elaborate as it is claimed or thought to be! Imagine what you could do with such data once you have mastered the art of big data. This should be motivation enough, right?
Hacking has challenged a number of data protection services and has stirred up a commotion among those who doubt cloud technology. Even so, there is a need to break through this misconception that the cloud is unsafe.
This is not entirely true because, with every major data hack, there is a constant upgrade on security measures and protocols to ensure the protection of sensitive data.
Cloud security services have become more stringent, and cloud vendors regularly perform software and hardware updates, ensuring data protection and restriction to third-party authorization against any and all threats. Audits have become commonplace to keep the cloud services protected from all sorts of security breaches.
This misconception has led to myth number 4. Big data solutions are manifold, so judging from a general viewpoint and calling big data a hefty investment is faulty. If you decide to use a publicly managed cloud platform for all your business data needs, the cost can be minimized beyond anticipation. Bear in mind that you are saving on other overheads (such as labor) while using cloud services to manage data.
If you’re looking for cost-effective solutions, there is nothing better than a publicly managed cloud that requires no hardware/software purchase or installation. In addition to this, there are no infrastructure constraints for data storage. If you are thinking whether or not it’s worth trying, go for a test drive for 4-6 weeks on the cloud and see for yourself!
This one is the biggest misconception of all and one that is wrong on so many levels. Many believe that since big data is considered a costly investment, it is only affordable to large enterprises. No, this is not the case. Have you heard of Hadoop? It’s the most affordable choice there is for both big and small organizations.
It is also wrong to assume that small businesses are not sufficiently equipped to leverage big data. Small businesses have a more personalized approach to running and managing day-to-day operations, and by this logic, they are likely to be more efficient with the management of big data using open-source softwaresuch as Hadoop.
“Machine algorithms will replace humans forever!” No machine can be substituted for human insight and intelligence. Even with big data, data analysts are required to operate the analytical tools and machines. Not only this, but machine algorithms also cannot provide unique reasoning like humans do.
Also, big data cannot replace human creativity. Based on the various insights provided by big data, decisions are made by human brains and the creativity therein. This is not achievable through machine algorithms, as not even robots have a creative instinct similar to humans. Hence, this particular myth lacks an adequate basis.
It has been established previously that big data can get messy and converting this raw data into meaningful insights is essential. This may only be possible by having the right amount of resources at the right time. You are going to need people with skills, experience and the ability to handle data in such volumes. Only then can insights and data-driven decision be made.
Without this executive support, big data is nothing more than another great invention of the modern-day man. The success of big data depends on data scientists, analysts, support staff and business managers. All of these together with big data analytics make an all-in-one solution.
The precedence for this misconception is deep-rooted. People believe that technology can automatically fetch optimal results. This is not true because even with technology, you cannot control the external factors impacting a business and its various decisions. If external factors could be controlled, there would be no need for making data-driven predictions. It’s as simple as that!
Big data cannot guarantee a higher return on investment (ROI), but it can be manipulated to do so with the help of mature decisions.
Let’s reiterate, though: this is not a surefire outcome to be anticipated. It’s a more suitable approach to use big data analytics to build models and data sets that can work out statistically relevant results and then convert them into profitable decisions.
Following from the previous myth where humans will be replaced by machine algorithms, big data is also believed to replace data warehouses. Again, this is a false assumption that holds no substance.
Big data platforms such as Hadoop have been designed in order to complement the traditionally used database management systems. Sure, the creation of big data was inspired by traditional data warehouses and management systems, but more organized automated versions were to be brought together to support these DBMSs and warehouses.
Despite big data platforms, and efficiency/cost-effectiveness in data storage and analysis, there is no comparable data warehouse in terms of how it processes structured data and runs predictable workloads side by side.
If used adequately with sufficient support from executives and analysts, big data can help draft stronger informed decisions. Again, this is not a guaranteed outcome at all! Big data analytics are tools that help uncover relationships and patterns from a given set of data points, but the ultimate decision-making capability lies with those using and interpreting these patterns and relationships.
Even if a trend or correlation has been accurately identified and uncovered, external factors will eventually decide on a good or bad outcome. Predictions can be made, but they aren’t necessarily always right. The take away: big data is a support function that can enable predictions.
Big data does more than streamline your business’s IT infrastructure. It’s much bigger and grander. It impacts the business and all its departments including sales, marketing, finance, operations and human resources.
It may seem like an IT term, but it does more than that. Big data is to be approached as an asset that can be used to solve many of the problems faced by a business on a day-to-day basis. Big data has the capability to knock sense into the most alienated of business operations. It’s a company-wide phenomenon and must be approached and used as one too.
Next time you come across a myth regarding big data, make sure to use all this information and help people understand what big data can and cannot achieve. Let’s learn to appreciate human intelligence and hard work that has enabled advances in technology like big data.
Every year, Brainhub helps 750,000+ founders, leaders and software engineers make smart tech decisions. We earn that trust by openly sharing our insights based on practical software engineering experience.
Software development enthusiast with 8 years of professional experience in this industry.
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