All trends have some myths and misconceptions. Data Science is no exception and still confuses a lot of people.
Data Science is a competitive weapon. Most business leaders have heard that Data Science can improve operational efficiency and customer relationships, but not how it should be implemented, or what the benefits might be.
Whether you are a recent graduate, an experienced professional or a leader, it is important to understand how data science works.
First, what is Data Science?
Data Science is a diverse field with professionals from several backgrounds, working in all kinds of domains. There isn’t only one definition, it is a general term that includes some of the most today’s hot topics, such as machine learning, analysis, modelling, and data visualisation.
We can say that Data Science is a process. It all starts with a hypothesis and then data is collected hoping to generate valuable insights. After, it will be used to test the hypothesis and build models. Finally, the results are analysed and presented to decision-makers as reports or dashboards. These models tend to approximate events or behaviours in the real world and so be used to make important decisions.
Companies can choose the way to implement Data Science in their businesses because there’s no “right” way to do it. It depends on many factors, such as expertise, tools, and data available to the organisation. That’s why is important to start with the definition and alignment of business goals.
Now, It’s Data Science Myth-Busting Time!
1. Data Science only fits best for big organisations with big resources
Big organisations usually have more resources and the financial resources needed to build a formal Data Science practice. But, that does not mean they will be successful.
Organisations of all sizes can succeed in their Data Science activities if implemented correctly by a competent team. It only requires a group of people who know how to extract valuable information from the available data. Data Science doesn't require a complex infrastructure to process and get the most value out of data.
Yes, some of the most sophisticated Data Science products are extremely costly to buy and difficult to use, but, it is not necessary to invest millions of dollars in software. There are multiple open-source tools that can help in the process of large scale data accurately and efficiently that are not difficult to set up and use, such as R and Apache Spark.
2. Data Science is just a buzzword
Data Science isn’t just a buzzword and has become the most essential aspect of any organisation and in determining their success.
We are generating 2.5 quintillion bytes per day in 2020, that's 90% of all the data ever created. With so much data being generated and from such diverse sources, Data Science will help to structure data, analyse data, draw hidden patterns for business and build solutions to solve crucial problems.
What distinguishes Data Science are the ability to access large amounts of data that can be stored cheaply, powerful computing power and quick access to predefined models. Organisations can learn more about themselves, their markets and their customers because the data they need is rich, easily duplicated, easy to share, and relatively easy to process. Those capabilities combined with today’s powerful programming environments, give developers control to manipulate, clean, process, analyse and visualise data.
3. Data Science it’s complicated
Many people think that combining the process of data science with organisational workflow is very challenging and even problematic.
Yes, Data Science can be a very complex task, but it doesn’t have to be. In fact, it’s better to start simply and then expand your capabilities. Although there is no single “right” path to adopt data science, the wrong path is overcomplicating the problem when a simpler solution is more effective and cost-efficient.
4. Complex models are better than simple models
Most people mistakenly think that Data Science is about building models, and Data Scientist’s work all day overbuilding those models. In fact, Data Science is more than just building models, building a model is a single layer of a data science project, the entire project has multiple stages.
Another common mistake is to think that complex models must produce better results than simple models in all situations. However, unnecessary complexity can lead to diminishing returns. It is better to spend less time adjusting the model and spend more time understanding and cleaning up the data. If the problem is relatively simple, complex models may be less efficient than simpler models, may be costly in terms of processing power, and may lead to difficult or unexplainable black-box methods. Simpler models are easier to understand and explain. For example, a relatively simple logistic regression model can be used to predict which of your prospects will likely buy your product.
5. It’s hard to find Data Scientists
People often confuse that being a Data Scientist involves writing lines of code and algorithms. They believe that to be an expert in the field of Data Science, it's mandatory to be an excellent programmer or to have a background in computer science or programming.
If we pay attention to the daily tasks of data scientists, we will realize that there is not a lot of coding involved. In fact, most methods or algorithms are readily available and only need to be adjusted. However, for this purpose, a logical mind is required. Although when we have to perform advanced tasks such as machine learning and deep learning, advanced statistical knowledge is required. But this does not mean that people without a degree in mathematics or statistics cannot become expert data scientists.
While it’s true that Data Science requires an understanding of statistics, businesses can take advantage of Data Science without having a statistician on staff. And most developers have a basic understanding of statistics because they took at least one course in college.
Data science is one of the hottest topics, and one of the most popular skills on resumes, right now. But to take full advantage of the potential of data science, it is equally important to conduct the necessary research and clear up all confusion and misunderstandings before actually participating. Lack of information or misinformation leads people to make assumptions that in most cases proving to be wrong.