Data Science: A Double-Edged Sword!

We are living in a time where devices can talk, cars can self-drive, and software can predict several types of cancer. It is the time when people are talking about machines that can mimic human behavior. It is the time of data. Data that humans are producing in billions every hour. This huge amount of data has made predictions about human behavior so easy that every industry is looking to leverage it. And from the abundance of data & the advancement of technologies has led to the creation of a new stream called Data Science. 

Many have considered data science as the most hottest career of the 21st century. Some even boast about the high pay, respect & demand of data scientists across every industry.

But is it really the hottest of all careers? Is everything about data science as good as people are talking about it?

Questioning these facts, I am writing this article on data science that will give you a glimpse of both sides of data science.

First, let’s understand the concept of data science & then explore the positive aspect of data science!

What is Data Science?

Data Science is a field of science that allows experts to perform certain sets of tasks involving maths, statistics & computer science using some advanced tools to draw insights from a set of data. The knowledge of data scientists or data experts can vary as per the experience level.

At the core level, data scientists must know how to analyze large chunks of data, data mining, data cleaning & data visualization. Today, many consider data scientists as one of the best careers in the IT domain. No matter how lucrative it looks, since its first use, data science is evolving at a rapid pace that has provided huge possibilities across every sector.

Using data science in any field can help generate new insights that people can leverage to drive their goals. Be it finance, healthcare, education, retail, or any other field, it has proved a game-changer.

Also Read: 9 Data Visualization Software Applications for Enterprises

What does a Data Scientist do?

A majority of the data scientists have an advanced level of knowledge, training & experience of statistics, probability, mathematics & computer science. They can easily and efficiently do data mining, data cleaning, data visualization & info management for a huge pool of data.

Regardless of the sector, when given the goal & the purpose, data scientists can generate insights accordingly to benefit the organization. It can be the game-changer in funneling down the customer, customer segmentation, predicting customer behavior, detecting frauds & so much more. These experts are also well versed in cloud computing data warehousing & infrastructure design.

Also Read: Best Languages to Learn as a Data Scientist

What are the advantages of Data Science?

Data science can provide a wide range of advantages to the one who is seeking to benefit from it. Some of the crucial advantages include:

Product recommendation

Many consider it one of the most powerful aspects of data science. It can be efficiently used by the organizations & businesses for all sizes to recommend the right products to the customer. It helps in finding when & where a particular product can be sold easily. All these can be crucial in delivering the required product at the right time to the right customer. Product recommendation is also being used by many companies for creating new products to meet their customer needs.

Detecting & neutralizing frauds

Over time, data experts get the experience to use data for deriving insights that can predict & mitigate the risks of fraud. It can be done by using methodologies of networks, paths, statistics & big data for predicting fraud propensity models or algorithms that provide alerts to certain individuals to ensure timely response when any unusual pattern is recognized.

Customer experience

It is also considered one of the most notable benefits of data science. Data science can be used by sales or marketing teams for understanding the customers at a granular level. This understanding of customers allows experts to provide the best-personalized experiences ever possible. It can be the game-changer when it comes to retention of the customer or maintaining the positive customer satisfaction index.

Also Read: 9 Free Books for Learning Data Mining & Data Analysis

Why is Data Science important?

As of now, it will be cleared that data science is important for many industries. Before moving into the importance of data science, let’s look at some facts that show the significance of data science in real life.

#1. By the year 2025, the data science sector in just India will grow to 8 fold reaching $16 Billion.

#2. 60,000 searches are performed on Google every second i.e. 1.2 Trillion searches per year.

#3. In 2020, 1.7 MB of new info is created every second for each human being on the planet.

With these numbers, it will be clear how serious is data science in the current world. Now let’s move how data science can add value to any business.

Actions based on trends

A data expert or data scientists can use the data of an organization to explore & examine the trend defining new goals. It can help in recommending the best possible actions for improving the performance of the organization, enhancing customer engagement & ultimately, increasing the profit.

Adopting best practices

Data can be used to adopt the right practices focusing on issues that are more important. Data scientists can use the data to provide the right analytical information of all the products from the organization to its employees.

It can be beneficial for the employees in effectively using the products to drive positive action. Once each worker has a clear understanding of the product then they can efficiently address all the key challenges associated with the product respectively.

Filtering the right talent

Having the right & adequate team is very important for any organization. Data science can be used to drive insights from social media, job searches websites, corporate websites to find the best suitable candidate for a required job. It is more efficient & effective then the traditional method of reading resumes for each candidate.

After mining the vast amount of data present on the internet, recruiters can select the probable candidates & ask for their resumes. After selecting them, sophisticated games & data-driven aptitude tests can be introduced for a more speedy & accurate selection.

Making decisions based on data-driven evidence

Thanks to a large amount of data & the ability of the data scientists to derive insights from it, the need to take high risks decisions have minimized significantly. Data experts can now create an algorithm based on existing data that can allow managers to take the right actions based on previous trends. It can help in knowing the best possible outcomes for an organization.

Testing the decision

Taking data-driven decisions from the insights is not the entire story. Before taking any decision, it can also be tested to see the effect of the decision. Data scientists can check the key metrics that are required to measure & quantify all the changes for the success of any decision.

Empowering management for making a better decision within the organization

Not only for customers, but data science can also be used to make better & right decisions within the organization by the management. It ensures in enhancing the staff capabilities by tracking, measuring, recording performance & other information.

Target audience

Today, most of the companies are using some or at least one tool to collect their customer data. These tools can range from Google Analytics, Mailchimp, customer surveys & so on. It can be beneficial in identifying & refining the target audience.

For instance, taking an insight into devices from Google analytics. It can be the game changer to optimize the website as per the audience. Or the gender ratio or the age of the customer. All these can be important to create & enhance the product that suits them best.

However, in most cases, just one metric like demographics or anything else can’t be that useful. It is mainly combined with other metrics to generate insights relevant to an organization. These patterns then can be used to learn & understand the customer or the audience. These can also be used for identifying any particular group of customers to tailor any product to enhance profitability.

So these were all the positive sides of Data Science. But what about the other side? Is it really that beneficial? Is data science as good as people think about it? Let’s find out!

Is Data Science worth the Hype?

What is worth learning & what is not is entirely up to you. I have found out that often people who are looking to have a career in data science are confused about the subject. In a majority of the cases, it is mostly because of all the lucrative payrolls & the demand of the data scientists that forces people to have a career in it.

Considering all the hype, people tend to ignore the dark secrets or the disadvantages of data science. People can find various data science disadvantages as per the industry. In this section, I will try to list some of the most common disadvantages of data science.

What are the disadvantages of Data Science?

You might be the only data scientist

Thanks to the need of the data scientists & the high salaries, it is very likely that you might end up being the only data scientists in your company. It can be more seen in companies having less than 100 employees. Though it is not bad but many times it can become hectic.

Regularly, people from various departments like customer support, sales & marketing, or engineering can come to you for particular metrics & data-driven insights to measure their progress & efficiency.

Being the only data scientist can overburden you because of all the requests from numerous teams at the same time. In such scenarios, it is very likely that you will end up saying ‘NO’ to some requests.

Furthermore, there is also a possibility that you will end up doing the same tasks over and over again. Sometimes data science does become a little repetitive & time-consuming. Actually, most of your data analysis will require a huge amount of time in just understanding & organizing the data. On a regular basis, a typical data scientist complains about getting less time for doing the more fun parts of data science i.e. machine learning, complex statistics & experiment with the data.

No idea about the data science

It is another big problem that is faced by data scientists on a regular basis. There is a high chance that most of the time people will have no idea about data science, how it works & what it can do?

There are people who think that data science is all about artificial intelligence & machine learning. Then there are people who think that data science is just about graphs, statistics, probability & plots. Some even believe that data science can help them to drive insights that can make them huge profits. Actually, data science covers it all (except for the huge profit part!).

Because of these misconceptions, often data scientists are bombarded with irrelevant questions in the interviews. There was also a case where a data scientist was more asked about AB testing, SQL analytics, coding a game in Python, Gradient Boosting Trees & so on.

It can be daunting at first because it is really difficult to know every aspect of data science. It is really a broad field of study. Instead, you can ask for any specific details regarding the organizations that you will be working on. It can help you in figuring out the actual demand for the organization.

Lack of leadership

The leadership in data science cannot be as per the expectations. It is very likely that many people who are in charge of decision-making from data science are neither versed with data science theory or techniques nor they are properly educated. These can be the people who have heavily relied on non-data-driven techniques or plug-&-play features that can be used as per the requirement.

There are only a few companies that actually have a team consisting of either a head data scientist or data science manager or any other senior position. Because of this, you may end up reporting to someone who is just in charge of any particular product or department. This can also lead up to creating an issue since they may not fully understand your insights or findings.

Even if you are not reporting to someone, then also you will end up being on your own since it is very likely that there won’t be any knowledgeable person to discuss the issues, plans, or anything else. This situation can become a mess for you especially if you are new to this field.

Data cannot be always of value

In data science, it may be possible that people won’t find the impact of your findings in several instances. Many times, data scientists are employed to support roles.

Decisions of several organizations can also be influenced by past experiences rather than the analysis of data scientists. Moreover, as a data scientist, you may not be as valuable to your organizations as you may think. Because the business of your organization can go ahead even without you just like previously before your appointment.

Rather as a data scientist, you have to find a situation where you can be incredibly valuable for the job. And it is somehow tough for many data scientists.

Another major challenge is to quantify the impact of your analysis. For instance, consider that you have helped a product manager to answer a particular query with the insights from data. But how will you measure whether this was really impact-full or not?

For these situations, I will rather suggest you document each of your findings, insights & then calculate the monetary benefits of your work based on certain factors. These can be capital, investments, employee salaries, & so on. It will be useful during appraisal or promotion based upon your work. You can even seek help from others to work on these methods.

Quality issues with data

Often, data analysis has serious quality problems that no one will teach you. If you are learning data science from books or online platforms like Kaggle competitions or Udemy tutorials, then always remember that the problem taught is way different then the problems that actually exist in the industry.

While learning with the above resources, you are often introduced to ‘clean’ data that is well-documented & structured as per the requirements for suitable data science techniques. In reality, this is never the case.

Actually, some data scientists even compare the data to the garbage bag that is ripped and has all the content leaking all over the floor for which your senior has asked you to clean & organize.

Moreover, the data will be poorly documented, tough to find & organize. It is very much possible that you can also be introduced to the unstructured data in complicated formats like JSON. You can end up with the data having punctuation, minimal content, or even emojis. And if you don’t have the quality date, you won’t get the quality results to meet all the stakeholder’s expectations.

Whenever a data scientist says that cleaning data takes most of the time then believe them with closed eyes. It really takes the time & it is the most difficult part of the job. There was also a survey conducted in 2016 where 3 out of every 5 data scientists accepted that the most time they spent in data science work is in cleaning & organizing the data.

And if you are spending most of your time cleaning or organizing your data then don’t forget you will be left with very little or no time for data analysis, plotting & implementing machine learning.

Apart from the unstructured data, another big hurdle that a data scientist faces is the poor infrastructure. Just imagine a scenario wherein you are stuck in a jammed highway filled with potholes & your job is to navigate these conditions at the lowest possible time.

There may be a situation where you face a database that is not optimized as per your requirement. Or it is difficult for you to analyze the source through its data lineage. You end up waiting for days or even weeks to get access to the database. Or you may be stuck with poor infrastructure just because some people are afraid to work on it because of the fear of breaking everything. There is also a chance that you end up with multiple dashboards, past data science work with no repo, or absence of centralized data store.

Furthermore, it can make you wait for hours just to work with a dataset. All these issues can affect the quality of your work. If you encounter a poor infrastructure then it is better to discuss with the concerned person as early as possible. Again you can document the issue & work with the DevOps team to resolve the issue. It will be better if you learn DevOps to some extent as well.

At times, data science can be unethical

Yes, you read it right. There will be several occasions when data work can be profoundly unethical, and for many data scientists, it is the scariest part of all. Often people will be involved in shady practices during collecting or analyzing the private data of their customers.

These data can be private messages, the heat map for the user behavior on the app, or even the customer interaction with the app. All these things may be practiced in the name of knowing the customer for providing a better customer experience.

Not only the data, but even the recommendations of machine learning algorithms can be unethical. For instance, you might be asked to work with the algorithm that is created & trained with the data based on flawed practices in order to get a better result. Not 100%, but there may be a case when you might be asked to figure out about any particular customer even if that customer doesn’t want you to know it.

Before wrapping this article let’s go through some more statistics about the bizarre consequences of data science.

• Less than 0.2% of all data we create is ever used or analyzed.
• Nearly 80% of the photos are taken on smartphones. A majority of these will become searchable data online.
• Bad data costs US businesses $600 Billion each year.
• Around 70% of data is being created by individuals, but it is enterprises that are responsible for storing & managing 80% of the data.
• Data can be misused in terms of politics & for discrimination.

What did we learn?

No doubt data science is the new hope for all the organizations looking to increase their profit & market dominance. And when people say that data is the new oil they might be correct. But the question is that do they really understand data science to its core?

Yes, data can be the game-changer when it comes to deriving insights from the people’s past record. Yes, data can help in predicting outcomes in various industries. Yes, data can help in retaining customers at the rate achieved never before. Yes, data can also increase profit. And yes, being a data scientist does come with a high payroll.

But what I want you to ask yourself is do you really want to have a data science career because of the high demand or high pay scale.

Or do you really want to become a data scientist? Do playing with the data to have meaningful insights fascinate you? If yes- then data science can be a really great career option for you.

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