5 Misconceptions About Data Science Posted on : Dec 29 - 2017

Despite the massive advantages and benefits big data, machine learning and predictive analytics have to offer, data science is still a touchy subject for businesses of all sizes. Not only are many reluctant to adopt the related systems and hardware, but when they do make the leap, they lag when it comes to properly using the information collected.

Poor data across businesses, organizations and the government contribute costs of up to $3.1 trillion a year to the U.S. economy. To make matters worse, 14.9 percent of marketers claim they do not know what big data is, let alone how to use it. Both these stats show a general lack of knowledge when it comes to big data and data science. Learning how to use the data, for instance, is just one component of the industry that also seems to be a huge hurdle.

You may be asking: What are the misconceptions floating around about data science? What do project administrators and business managers need to be aware of? Let’s take a closer look and find out.

1. Access to More Data Translates to Higher Accuracy

If you’re going to start collecting large stores of information, and use modern systems and tools to analyze said information, this is one misconception you need to eliminate right now. More data does not necessarily mean higher accuracy. It doesn’t mean more insights, nor does it mean you’re getting more value out of your data. Data all by itself is worth absolutely nothing.

You see, after collecting your data, you should sift it through a series of steps.

• Step one is understanding what data sets you need to analyze, and how best to accomplish the task.
• Step two is extracting usable information or actionable insights from said data.
• Step three is deploying those insights to perfect your processes.
• Step four is to continue fine-tuning everything and create a well-oiled digital data machine.

You’ll notice every one of those steps requires you to understand the data in question, and comprehend how it can be used. None of them have anything to do with the quantity of that data, because it doesn’t matter how much you have. What matters is how you can use it, and where it will apply in regards to your business practices.

2. Data Science and Business Intelligence Are the Same

Business intelligence and data science are often confused, especially by those unfamiliar with the industry. They are not synonymous, however. Business intelligence involves data, yes, but it’s more about the operational and contextual aspects of your organization.  View More