9 Data Science Myths Debunked

The benefits of data science are evident for all to see. Not only does it equip you with the tools and techniques to make better business decisions, the predictive power of analytics also allows you to determine future outcomes - something that can prove to be crucial to businesses. Despite all these advantages, data science is a touchy topic for many businesses.

It’s worth looking at some glaring stats that show why businesses are reluctant to adopt data science:

• Poor data across businesses and organizations - in both private and government costs the U.S economy close to $3 Trillion per year.
• Only 29% enterprises are able to properly leverage the power of Big Data and derive useful business value from it.

These stats show a general lack of awareness or knowledge when it comes to data science. It could be due to some preconceived notions, or simply lack of knowledge and its application that seems to be a huge hurdle to these companies.

In this article, we attempt to take down some of these notions and give a much clearer picture of what data science really is. Here are 5 of the most common myths or misconceptions in data science, and why are absolutely wrong:

Data Science is just a fad, it won’t last long

This is probably the most common misconception. Many tend to forget that although ‘data science’ is a recently coined term, this field of study is a cumulation of decades of research and innovation in statistical methodologies and tools. It has been in use since the 1960s or even before - just that the scale at which it was being used then was small. Back in the day, there were no ‘data scientists’, but just statisticians and economists who used the now unknown terms such as ‘data fishing’ or ‘data dredging’. Even the terms ‘data analysis’ and ‘data mining’ only went mainstream in the 1990s, but they were in use way before that period.

Data Science’s rise to fame has coincided with the exponential rise in the amount of data being generated every minute. The need to understand this information and make positive use of it led to an increase in the demand for data science. Now with Big Data and Internet of Things going wild, the rate of data generation and the subsequent need for its analysis will only increase. So if you think data science is a fad that will go away soon, think again.

Data Science and Business Intelligence are the same

Those who are unfamiliar with what data science and Business Intelligence actually entail often get confused, and think they’re one and the same. No, they’re not. Business Intelligence is an umbrella term for the tools and techniques that give answers to the operational and contextual aspects of your business or organization. Data science, on the other hand has more to do with collecting information in order to build patterns and insights.

Learning about your customers or your audience is Business Intelligence. Understanding why something happened, or whether it will happen again, is data science. If you want to gauge how changing a certain process will affect your business, data science - not Business Intelligence - is what will help you.

Data Science is only meant for large organizations with large resources

Many businesses and entrepreneurs are wrongly of the opinion that data science is - or works best - only for large organizations. It is a wrongly perceived notion that you need sophisticated infrastructure to process and get the most value out of your data. In reality, all you need is a bunch of smart people who know how to get the best value of the available data.

When it comes to taking a data-driven approach, there’s no need to invest a fortune in setting up an analytics infrastructure for an organization of any scale. There are many open source tools out there which can be easily leveraged to process large-scale data with efficiency and accuracy. All you need is a good understanding of the tools.

It is difficult to integrate data science systems with the organizational workflow

With the advancement of tech, one critical challenge that has now become very easy to overcome is to collaborate with different software systems at once. With the rise of general-purpose programming languages, it is now possible to build a variety of software systems using a single programming language.

Take Python for example. You can use it to analyze your data, perform machine learning or develop neural networks to work on more complex data models. All this while, you can link your web API designed in Python to communicate with these data science systems.

There are provisions being made now to also integrate codes written in different programming languages while ensuring smooth interoperability and no loss of latency. So if you’re wondering how to incorporate your analytics workflow in your organizational workflow, don’t worry too much.

Data Scientists will be replaced by Artificial Intelligence soon

Although there has been an increased adoption of automation in data science, the notion that the work of a data scientist will be taken over by an AI algorithm soon is rather interesting. Currently, there is an acute shortage of data scientists, as this McKinsey Global Report suggests. Could this change in the future? Will automation completely replace human efforts when it comes to data science? Surely machines are a lot better than humans at finding patterns; AI best the best go player, remember. This is what the common perception seems to be, but it is not true.

However sophisticated the algorithms become in automating data science tasks, we will always need a capable data scientist to oversee them and fine-tune their performance. Not just that, businesses will always need professionals with strong analytical and problem solving skills with relevant domain knowledge. They will always need someone to communicate the insights coming out of the analysis to non-technical stakeholders.

Machines don’t ask questions of data. Machines don’t convince people. Machines don’t understand the ‘why’. Machines don’t have intuition. At least, not yet.

Data scientists are here to stay, and their demand is not expected to go down anytime soon.

You need a Ph.D. in statistics to be a data scientist

No, you don’t. Data science involves crunching numbers to get interesting insights, and it often involves the use of statistics to better understand the results. When it comes to performing some advanced tasks such as machine learning and deep learning, sure, an advanced knowledge of statistics helps. But that does not imply that people who do not have a degree in maths or statistics cannot become expert data scientists.

Today, organizations are facing a severe shortage of data professionals capable of leveraging the data to get useful business insights. This has led to the rise of citizen data scientists - meaning professionals who are not experts in data science, but can use the data science tools and techniques to create efficient data models. These data scientists are no experts in statistics and maths, they just know the tool inside out, ask the right questions, and have the necessary knowledge of turning data into insights.

Having an expertise of the data science tools is enough

Many people wrongly think that learning a statistical tool such as SAS, or mastering Python and its associated data science libraries is enough to get the data scientist tag. While learning a tool or skill is always helpful (and also essential), by no means is it the only requisite to do effective data science.

One needs to go beyond the tools and also master skills such as non-intuitive thinking, problem-solving, and knowing the correct practical applications of a tool to tackle any given business problem. Not just that, it requires you to have excellent communication skills to present your insights and findings related to the most complex of analysis to other stakeholders, in a way they can easily understand and interpret.

So if you think that a SAS certification is enough to get you a high-paying data science job and keep it, think again.

You need to have access to a lot of data to get useful insights

Many small to medium-sized businesses don’t adopt a data science framework because they think it takes lots and lots of data to be able to use the analytics tools and techniques. Data when present in bulk, always helps, true, but don’t need hundreds of thousands of records to identify some pattern, or to extract relevant insights.

Per IBM, data science is defined by the 4 Vs of data, meaning Volume, Velocity, Veracity and Variety. If you are able to model your existing data into one of these formats, it automatically becomes useful and valuable. Volume is important to an extent, but it’s the other three parameters that add the required quality.

More data = more accuracy

Many businesses collect large hordes of information and use the modern tools and frameworks available at their disposal for analyzing this data. Unfortunately, this does not always guarantee accurate results. Neither does it guarantee useful actionable insights or more value.

Once the data is collected, the preliminary analysis on what needs to be done with the data is required. Then, we use the tools and frameworks at our disposal to extract the relevant insights and built an appropriate data model. These models need to be fine-tuned as per the processes for which they will be used. Then, eventually, we get the desired degree of accuracy from the model. Data in itself is quite useless. It’s how we work on it - more precisely, how effectively we work on it - that makes all the difference.

So there you have it! Data science is one of the most popular skills to have in your resume today, but it is important to first clear all the confusions and misconceptions that you may have about it. Lack of information or misinformation can do more harm than good, when it comes to leveraging the power of data science within a business - especially considering it could prove to be a differentiating factor for its success and failure.

Do you agree with our list? Do you think there are any other commonly observed myths around data science that we may have missed? Let us know.

Read more

30 common data science terms explained

Why is data science important?

15 Useful Python Libraries to make your Data Science tasks Easier