Pitfalls in Big Data Analytics and Ways to Avoid Them
The introduction of data science for decision-making has greatly altered the way businesses take their decisions. Experts believe that big data analytics tools are far more superior than humans in processing mountains of data to generate an insight and choose a decision alternative. However, a fundamental reality that humans themselves create such programs and tools […]
The introduction of data science for decision-making has greatly altered the way businesses take their decisions. Experts believe that big data analytics tools are far more superior than humans in processing mountains of data to generate an insight and choose a decision alternative. However, a fundamental reality that humans themselves create such programs and tools is almost always ignored. So, the ability of big data analytics tools to generate relevant insight is highly dependent on human inputs and interpretations. Here are five big data analytics pitfalls to avoid in order to generate a meaningful insight:
Causation Vs. Correlation
One of the greatest pitfalls in performing an analysis with big data analytics tools is that of decision maker attributing correlation with causation. Just because two data sets correlate with each other doesn’t mean one is causing the other. Usually, it happens because of some other hidden force. One of the most absurd examples of this statement is the correlation between increasing ice-cream sales and the subsequent increase in the rate of drowning. It is merely caused by an increase in temperature and heat, which urges more people to opt for swimming activities.
It is human nature to conform to things that are familiar and ignore others. Data scientists usually allocate more weight to evidence that confirms their hypothesis by ignoring those that could disconfirm their hypothesis. One of the best practice to avoid this pitfall is to get a “devil’s advocate” view to the problem by analyzing the opposing perspectives as well.
A majority of research processes determine the sample size, which closely reproduces the overall population. Too little of such samples or samples that do not characterize the overall population will not yield an accurate result. For instance, trying to carry out an A/B test on a website with only a few number of visitors or results that show little difference may not be statistically significant. Managers can use a statistical significance calculator tool from Kiss metrics for A/B testing of landing pages. A relevant t-test and z-test could also be carried out to check statistical significance of the results.
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Today, managers have access to a large stream of data, and decision-making on the basis of gut-feeling, the rule of thumb, and guesswork are largely eliminated with the advent of data analytics.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway,” said a leading data analytics expert from Quantzig.
For more than 14 years, we have assisted our clients across the globe with end-to-end data management and analytics services to leverage their data for prudent decision-making. Our firm has worked with 120+ clients, including 55+ Fortune 500 companies. At Quantzig, we firmly believe that the capabilities to harness maximum insights from the influx of continuous information around us is what will drive any organization’s competitive readiness and success. Our objective is to bring together the best combination of analysts and consultants to complement our clients with a shared need to discover and build those capabilities and drive continuous business excellence.