Data Science

Busting Data Science Myths

Busting Data Science Myths

The process of changing into data science is complicated, rather frightening! And it is not because you have to learn maths, statistics, and coding! You have to do that, but you also are required to simplify the myths you’ve heard from people around you and discover your way through them!

Presently, companies worldwide are implementing data science to their daily activities to bring value to their customers. Recently, data science is being used as a competitive tool.

Fortunately, concerning data science, businesses can select how they would like to apply data science in their organizations because there is no ‘accurate’ way to do it.

The application of data science depends on the tools, data, and expertise available to the organization. The most efficient application of data science is one that begins with and aligns with the business objectives.

Data Science Myths 

1. Data Science and Business Intelligence are the same

Those who are not acquainted with what data science and Business Intelligence imply usually get confused, and think they’re the same. But, they’re not.

Business Intelligence is a collective term for the tools and techniques that provide answers to the operational and contextual areas of your business or organization.

However, data science has a lot to do with gathering information to develop patterns and insights.

Learning about your customers or your targeted audience is Business Intelligence. Having an understanding of why something happened, or whether it will happen again, is data science.

If you want to measure how changing a specific process will affect your business, data science and not Business Intelligence is what will assist you.

2. Data Science is only meant for large organizations with substantial resources

Several businesses and entrepreneurs are incorrect of the opinion that data science is, or works effectively only for large organizations. It is an incorrectly perceived idea that you need sophisticated infrastructure to process and get the most value out of your data.

In reality, all you require is a bunch of smart people who understand how to get the best value of the available data.

When it comes to taking a data-driven approach, you don’t have to invest a fortune in building an analytics infrastructure for an organization of any scale.

Several open-source tools can be comfortably leveraged to process large-scale data with effectiveness and accuracy. Everything you need is a concrete understanding of the tools.

3. Data Scientists will be replaced by Artificial Intelligence soon

Although there has been an improved adoption of automation in data science, the idea that the work of a data scientist will be relieved by an AI algorithm soon is somehow compelling.

Presently, there is an acute insufficiency of data scientists, as suggested in the McKinsey Global Report.

Can this change in the future? Will automation replace human efforts when it comes to data science? Undoubtedly, machines are better than humans at discovering patterns.

Nevertheless, sophisticated algorithms come in automating data science tasks, and we will always require a capable data scientist to supervise them and adjust their performance.

Not only that, but businesses will also frequently need experts with strong analytical and problem-solving skills with significant domain knowledge.

They will always need the help of a person to communicate the insights coming out of the analysis to non-technical stakeholders.

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

Data scientists are here to stay, and their demand is not expected to reduce soon.

4. It would help if you had a lot of data to get a useful Insight

Various small to medium-sized businesses don’t implement a data science framework because they think it takes several data to be able to utilize the analytics tools and techniques.

When data is being presented in bulk, always helps, true, but don’t require hundreds of thousands of records to identify some pattern, or to extract significant insights.

Per IBM, data science is described by the 4 Vs of data, i.e. Volume, Velocity, Veracity, and Variety. If you can model your existing data into one of these formats, it automatically becomes beneficial and valuable.

Volume is essential to an extent, but it’s the remaining three parameters that adds the needed quality.

5. More Data Means More Accuracy

Lots of businesses gather massive hordes of information and utilize the modern tools and frameworks available at their disposal for analyzing this data. Unluckily, this does not always assure correct results. Neither does it guarantee beneficial actionable insights or more value.

After the data is collected, the preliminary analysis of what has to be done with the data is required.

Then, we use the tools and frameworks at our disposal to extract the significant insights and develop an appropriate data model. These models need to be adjusted as per the processes for which they will be utilized.

Then, finally, we get the desired degree of accuracy from the model.

Data in itself is entirely useless. It’s how we work on it, more accurately, how efficiently we work on it, that makes all the difference.

Data science is one of the most common skills that you need to have in your resume today. Still, it is essential to initially eliminate all the confusion and misconceptions that you may likely have about it.

Insufficient information or misinformation can do more harm than good when it comes to influencing the power of data science within a business.

Particularly, considering it could turn out to be a differentiating factor for its accomplishments and losses.


When addressing most things in life, people are generally great at determining what is acceptable by using their common sense and experience to measure whether something they’ve heard or read is accurate or not. This approach works effectively until it doesn’t. 

Most people’s intuition is not correct, e.g., more data must be better. The company trying to sell a multi-million-dollar big data platform for massive organizations will always say that you have to have large budgets, large teams, and large platforms for big data.

The company selling machine learning and AI consulting services will try to sell you on “more” because that’s how they earn their money and stay in business.  

Data science doesn’t inevitably have to be a costly and complex undertaking that needs an astonishing staff of Ph.Ds. Presently, the needs of businesses are increasing at a fast speed.

One way of guiding organizations accurately and helping them in overcoming their challenges is to supplement your business and computer science knowledge with a basic knowledge of statistics to start developing business models.

This helps in widening your knowledge to drive your company down on successful data science pathway.