5 Misconceptions About Data Science

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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. Through this process, you answer questions such as what, when, how or who. Learning more about your customers and audience, for example, is one aspect of business intelligence. On the other hand, data science has more to do with predictive analytics. The goal is to collect enough information you can use to build discernible patterns and insights. For example, data science can help you understand why something happened, or when it will happen again. Additionally, data science can answer what will happen if you change various aspects of a process or business plan.

For this reason, data science is more about data mining and statistical or quantitative analysis. It’s also useful for predictive modeling, multivariate testing and process planning. To wrap this point up, don’t get the two concepts confused.

3. You Must Have Access to Lots of Data

Many small to mid-sized businesses believe it takes lots and lots of data to make use of these technologies. That’s not the case at all. Data in bulk is the goal, yes, but you don’t need millions of customers to extract insights.

IBM defines data science as being comprised of four essential “V’s” — volume, velocity, variety and veracity. If you can structure your data into one of these categories or concepts, it’s valuable. Veracity of current data, lots of variety and the sheer velocity of incoming data all make a difference, in addition to volume.

4. Qualifications Trump Talent and Experience

Scan any job board, and you’ll see many organizations and companies calling for data scientists who hold a Ph.D. in statistics, machine learning or even mathematics. We’re not here to argue the semantics of qualifications and certifications, but unless you’re hiring for a groundbreaking research team — like CERN — you don’t need anyone with a Ph.D., certainly not for building business insights.

Talent and experience are just as important in the world of data science as anything else. In fact, someone with years of experience might have a lot more to offer than someone fresh out of school with a prestigious degree.

We’re not telling you how to choose your future employees, nor are we telling you to overlook qualified candidates. Just don’t fall into the trap of thinking you need to build a team of data scientists with the highest certifications imaginable, especially in certain industries. Plus, by prioritizing talent and experience, you open yourself and your team up to a lot more opportunities.

5. Data Scientists Know How to Code

Yes, there are a lot of data scientists who also understand programming and know how to code and work with computer languages. That said, it does not mean they are experts on a variety of topics. They are essentially the renaissance men and women of the tech industry. Yes, there’s a lot they need to know, but they only focus on one or two of those skills at a time, the most pertinent of which is likely data science and analytics.

The infamous data science Venn diagram teases the idea of an individual who knows how to write code, hack incredibly complex systems, build an efficient machine learning platform from scratch and do the occasional data monitoring and translation on the side. In reality, not all your data scientists are going to be able to do Bayesian methods and code in Java.

Assess what your business needs and what your data calls for before you choose your data scientists based on a wide variety of skills and experience. All data science roles are different, as is everything else listed in that Venn diagram you love so much.

Data Science Is Not Magic

So many outsiders look at data science as magic, or at the least, some unique form of actual science. Mathematics, statistics, analytics tools — yes, these are all necessary, but data science is more of an art when you get right down to it. Extracting the kind of usable information modern businesses and organizations need takes both skill and talent, and let’s not forget about experience. Implementing machine learning and predictive tools can offset these somewhat, but that doesn’t change the underlying requirements. You still need actual data scientists to do the bulk of the work, who are knowledgeable and experienced with the kind of information specific to your business.

Remember these tips before getting involved, and be sure to do the necessary research. With the right people and knowledge on your side, you’ll be on your way in no time, rocketing to success.

About the Author

Contributed by: Kayla Matthews, a technology writer and blogger covering big data topics for websites like Productivity Bytes, CloudTweaks, SandHill and VMblog.



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