Top 10 Data Science Myths Debunked
In today’s well-interconnected world, it has become easier to share your word than ever. The more people become aware of these things, the more they have to say and share opinions.
The same goes for Data Science and everything around. There is a lot of praise for how data science and information interpretation have changed the way businesses operate. But, there are also certain myths companies have regarding big data analytics consulting, which discourages them from employing data science. While one cannot stop hearsay and misinterpretations, there are certain data science myths that we have debunked to bring you more clarity about data science and how it actually helps you take the lead with data science.
Myth 1: Data collection doesn’t involve much effort in data science
Truth: As much as we are surrounded by data, in reality, it isn’t readily accessible to utilize. Even if you acquire the data with effort, it’s in an unformatted form which requires manual and automated formatting to give it structure and make it meaningful. Such processing is difficult, time-consuming, and often challenging to source, collect and process the data.
Myth 2: Data science is just about building data models
Truth: This isn’t true. Building data models is the smallest piece of the data science pipeline. Do you know that data scientists dedicate just about 20% to data modeling and the rest all go for data cleaning and transformation? That is so because data doesn’t get retrieved from a single source but from multiple channels. As a result, they are prone to errors and junk records. Hence, For building accurate data models and fetching meaningful information, the transformation and cleaning of data are crucial, more than building data models.
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Myth 3: The introduction of AI will completely replace the role of Data scientists
Truth: While some forms of AI might have given a good impression of being autonomous, it is implausible to them with human intelligence. AI may help you carry out tedious and repetitive tasks in data science, but it is unlikely that it can eliminate the need for humans. Even the most advanced AI systems require human guidance to perform the commands. Besides, AI systems cannot evaluate business problems on their own. They aren’t aware of what trends and predictions mean in a real-world scenario. As a whole, they can’t make decisions for the business. So the myth that we can remove the need for human decision-making by automating things is absolutely unrealistic. The truth is we need human decision-making power more than ever with the introduction of AI.
Myth 4: Data science is excessively hyped
Truth: Most people share this misconception that data science is too hyped, doesn’t have much essence and will fade with time. However, the fact is, it’s here to stay and rule the world. Over time, data science has attracted a place for itself like no other. It’s one of the crucial aspects to determine the success of a business or organization, irrespective of the domain they belong to. According to statistics, approximately 2.7 Quintillion bytes of data are generated every day. That’s massively huge. With so much data surrounding us, data science is becoming the need of the time to structure, analyze and draw patterns out of it and bring forth solutions for business and real-world problems.
Myth 5: Data Science is a fancy name for computer science
Truth: This isn’t true. While most people consider them one entity, they are actually two distinct fields of the technological world and certainly not the same. At the core, computer science deals with building programs for accomplishing specific tasks, whereas data science is all about serving meaningful insights from a large amount of data. The main drivers of computer science are programmers whose primary focus is on writing codes and building better programs and tools. In Data science, it’s data scientists. They use programming for processing and analyzing data. In a nutshell, computer science is a valuable tool to ease the process of data science.
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Myth 6: Data Science only belongs to techies
Truth: Just because data science is born from a technical background doesn’t mean it only interests and is destined for the geeks. It’s for everyone. Every field can utilize data science and get benefits. The workings of data science revolve around data. Any information can be treated as data. In health care, the data is related to patients. In finance, it could be income and assets. Every field has its own version of data that can be analyzed, understood, and interpreted through data science.
Myth 7: Large quantity of data ensures the highest level of accuracy
Truth: This is partially true and partially wrong. Large amounts of data do not always guarantee higher accuracy in data models. The performance largely depends on how well you clean your data and extract the hidden features. After a point, your data model will reach its limit regardless of how much you increase your data set. When the data provided in the data model isn’t well processed, its accuracy is compromised. For the data model’s accuracy, it’s crucial to focus on quality and quantity data rather than focusing on just the data.
Myth 8: It’s all about tools, tools, and tools
Truth: While learning a tool eases the process and saves considerable time, the truth is that it can’t do all the tasks on its own. The role of data scientists is much beyond learning the tools and languages to derive solutions. It’s analytical and business skills that matter, which one can expect from machines. To be able to know what’s and how’s of business, derive objectives, and communicate the results with stakeholders in a factual manner is only possible through a human professional.
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Myth 9: All data positions (Data Analytics, Engineers, and Scientists) are the same.
Truth: Within the data science industry, the roles often get misinterpreted. Data Analytics, Engineers, and Scientists have specific responsibilities to carry out. Data analytics are responsible for their analytical part, which involves data collection and interpreting valuable information from it. On the other hand, Data Scientists use the same data to make predictive models that predict future decisions and guide businesses on the right actions. Data engineering consulting is responsible for building and maintaining data systems to store large amounts of data.
Myth 10: Data science isn’t efficient in deriving monetary benefits.
Truth: In fact, data science actually enhances the financial value of businesses. When data science is applied to data collected through business marketing channels, it offers a clear picture of what’s working for you and what is not. Data science also helps make decisions related to customer acquisition, onboarding, retention, upsell, and other underlying areas, thus indirectly deriving monetary benefits for businesses.
Data science is a field that can effectively help you lay out business strategies and make remarkable changes in how you employ business practices. If being a business concerns you about how to beat the competition and strategically plan your products/ services, you should definitely enlist the help of engineering consulting services to get through.
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