20 Myths About Data Visualization: Busted

The world of business is getting warmed up to the art and science of data visualization, and like any new technological advancement, a lot of myths and rumours are floating about data visualization too. Today, we will have a look at 20 such myths and debunk them, one by one.

 

  1. Data visualization powered by Artificial Intelligence will destroy expert jobs

The introduction of new technology historically has disrupted many jobs and industries, and there are fears that artificial intelligence will eliminate the need for experts to perform data visualization & analytics operations. Though the truth is that Artificial intelligence solutions are much better than people at solving certain kinds of problems but is not capable of dealing with truly novel situations, which is where humans excel.

 

  1. Data visualization infrastructure requires a big investment

These days it seems as if every technology endeavor must pass through a filter of financial soundness. “How much will it cost?” is one of the first questions IT and business managers get when they propose launching a project or deploying a new tool. Some assume that data visualization is by nature a costly undertaking and therefore limited to organizations with big budgets or lots of internal resources. But not all data visualization efforts require a major investment, as nowadays there are so many open sources as well as other tools available in the marketplace that can help you start to show the value of data visualization.

 

  1. Only data scientists can carry out data visualization

Data scientists are among the most in-demand of all technology professionals these days. Perhaps organizations can get by with fewer of these professionals if they redirect what they’re working on. But with the help of new and convenient data visualization tools, any person can create meaningful charts, reports, and dashboards in a few minutes.

 

  1. Data visualization is dependent upon data analysis algorithm

It actually turns out that with enough data, sometimes the algorithm doesn’t matter. Experts say that simple statistical models, coupled with extremely large amounts of data, achieve better results in data visualization than an “intellectually superior” model containing lots of features and summarizations.

 

  1. Data visualization is a long and elaborate process

These days getting things done quickly — whether it’s rushing a product or service to market or responding to a customer inquiry in near real time — is a big competitive consideration for companies. Data visualization and analytics sound like something that takes a long time to perform, running counter to the goal of achieving speed and agility. Experts suggest that though the myth still exists that these types of projects take too long and are quite complex, but in fact, at the end of the day it’s all about talent. With the right mix of skills and the application of agile methodologies, data visualization enables to answer big questions in days or weeks, not months.

 

  1. Data visualization based upon machine algorithms will replace human analysts

Though good data analysts have domain experience and well-developed analytical skills, they can’t perform all of the analysis tasks all by themselves without the support of analytical tools and machine algorithms. Human insight and judgment are invaluable and complex, and can’t (yet) be fully incorporated in machine algorithms. Further, apart from interpreting data, a data analyst can provide in-depth, unmatched explanation and can even recommend corrective actions on the basis of data visualizations, which the machine algorithms can’t do as yet.

 

  1. You don’t need data visualization to perform analytics

For many, the concepts of data visualization and analytics don’t go hand in hand. The thinking is that organizations don’t need to gather enormous volumes of data before performing analytics in order to generate business insights, improve decision making, etc. The benefits of data visualization analytics have not been well established yet, and companies with the resources can gain significant competitive advantage by leveraging their data stores as part of analytics efforts. Hence the idea that data visualization is not necessary for analytics is not true.

 

  1. The organization is too late to adopt data visualization

Too many companies assume that as everyone is talking about data visualization, everyone is taking advantage of data visualization. According to a recent survey, only 13% of large companies had actually deployed any data visualization solutions. Many more are planning investments in the area, so nothing is late as yet. Start small, prove the concept of an easy early win and start to build your data visualization team.

 

  1. Data visualization is a mysterious and hidden art

The discipline of data visualization has received lots of attention in recent years and sometimes generates confusion as to exactly what it is. Basically, it involves visual representation of data making use of algorithms to find patterns in data.

Experts suggest that data visualization seems mysterious because these algorithms are capable of analyzing more variables and larger data sets than the human mind can comprehend, though it is the natural evolution of statistical inference techniques that have been well understood for decades.

 

  1. Data visualization can benefit only huge business organizations

Not only big companies but SMEs have also begun adopting data visualization solutions globally. Outsourcing data visualization requirements are becoming very popular, and the rise of the ‘portfolio career’ means that there are increasingly more data visualization professionals out there to work with on a more ad-hoc basis. Many well-known data visualization companies are working with an increasing amount of fast-growing start-ups and SMEs, which is a testament to the growth in this area.

 

  1. Data visualization technologies are difficult to understand

With the ever-increasing number of technologies available today, selecting the right combination of data visualization tools to deploy and integrate to get the desired results from the analytics team is no difficult thing. The real difficult part, however, is putting together the data visualization organizational structure and operating model to put all of what is required from a people, process, or technology perspective together.

 

  1. Data visualization works with only huge data size.

The bigger the data size, the bigger the volume of potential oversights. If you do not have a precise understanding of which datasets you need to analyze, you won’t know where to start, and it may actually be better to examine the issue on a smaller scale. You don’t need a data lake to understand why sales of umbrellas are spiking in the rain, but no data lake will ever help you to understand this unless you tell it that it is raining and what the consequences are.

 

  1. The data visualization analyst needs to be a super-human

Data is already too big, and it’s getting bigger by the day thanks to high volume, high velocity, and high variety or granularity. A team of data visualization analysts won’t be able to handle all of the data in a few years from now. We need continual development of tools for users to do their own first level of analysis. Expert analysts will only be needed for deeper analysis and for providing a bigger picture. With the emergence of data visualization and analytics tools, the pre-eminence of the data specialist is already on the wane. A good balance needs to be maintained between human analysts and analytical tools.

 

  1. Data visualization should be a separate department

In some organizations, data visualization operates as a department on its own, and in others, it is deeply embedded into a cross-functional team, experts suggest. However, with the explosion of data across all areas of business and the speed at which change is occurring, the department model does not work. As organizations become more customer-centric, data visualization and analytics specialists should be at the core of a business unit, not operating as a department that you call for support.

 

  1. Data visualization needs perfectly clean data

Let’s face it, a lot of enterprise data is in a bit of a state. Companies haven’t needed to put it to work at this scale previously, but there is a whole array of tools to help clean it up. Master data management and data governance software is capturing and cleaning the required information so that only the useful stuff stays in the frame. With data visualization and analytics tools becoming ever more user-friendly, more people than ever before are able to gain insights from the data visualization process.

 

  1. Data visualization removes human bias

The way automated systems perform is not supposed to be biased. But technology is built by humans, so eliminating all bias is nearly impossible. Some think data visualization and analytics remove human bias. But this is not true at all, suggest experts. Algorithms for data visualization and analytics are tuned using ‘training data’ and will reproduce whatever characteristics that training data has.

 

  1. Data visualization can make business forecasts.

Data visualization can indeed provide you with a load of numbers to interpret about the future. However, these numbers will be based on the past and their interpretation will very much depend on the questions that were originally asked and the selection of data that was used. No, it doesn’t make business forecasts.

 

  1. Data visualization algorithms are fail safe

People inherent trust statistical models and algorithms to a high degree, and as organizations build their data visualization and analytics programs they increasingly rely on sophisticated models to support decision making. Because people don’t understand the models, algorithms, and other advanced data visualization practices, they place their trust in them. Users don’t feel like they have the knowledge to challenge the models, so instead, they must trust the smart people who built them.

Experts suggest there is still a lot of ground to cover before we can blatantly trust machine learning and the results, and until then we need to challenge the people who build algorithms and models to explain how answers are reached.

 

  1. Data visualization and analytics is just for PhDs

It’s great to have lots of well-educated people on the data visualization and analytics team, but it’s not a requirement for success. Companies tend to think that without PhDs on board they won’t be able to perform best-in-class analytics, though modern data visualization and analytics demand a blend of skills — those who are savvy in emerging technologies and tools. Building ‘pods’ with different skills including data visualization architects, data engineers, data scientists, experts in data visualization, and more, is what makes the difference.

 

  1. Data visualization is always good data

Data visualization is very useful and has started to provide a competitive advantage to several businesses. But is all of the data in ‘data visualization’ good? The answer is no. Data visualization contains many errors corresponding to erroneous data, as well as missing data. Such data is likely to mislead and create errors and may need an intelligent model to sift through the data before data visualization and analytics. It is important to include only the data sets for data visualization and analytics that appear to be more relevant.

 

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