Career paths in Data Analytics
Buckle up and read along to know what are the different career paths that you can take in the future to build a long-lasting career in data analytics.
There are millions of businesses out there in the world and all of these businesses collect data in quintillions of bytes every passing second.
What do they do with this mammoth amount of data?
They collect this data, structure it quickly and utilise the same in an efficient manner to tailor their products or services specifically to the needs of every customer that resides in their funnel.
So a person who has just come to know about a certain product might get to see a certain ad that is very different from the one that is shown to the user that has known about that same product for months.
In order to make this possible, it is required for businesses to gather consumer data that is unstructured, structure it to derive meaning from it and use the same for customer segmentation, measure their lifetime value, and then tailor strategies down to the T for every one of these segments.
Putting this much data into perspective requires manpower which makes the job of data analysis one of the sexiest jobs of the 21st century (Harvard Business Review).
Sounds fascinating. Isn’t it?
If it does then this article is for you. Buckle up and read along to know what are the different career paths that you can take up in the future to build a long-lasting career in data analytics that can be used to.
What Do Data Analysts Do?
The answer to this question depends a lot on the kind of organisation in question and the data's involvement in making key business decisions.
The role of a data analyst in any organisation is similar to that of Sherlock Holmes who displayed an innate ability to deduce answers to difficult questions just by observing patterns and connecting them to major incidents that took place over a period of time in the recentpast.
At Masai, we have a team of data analysts who take the business data (student-centric as well as the data pertaining to the hiring partners) and use them to draw out insights that can be important for other teams.
For example, the placements team at Masai might need a list of students who are eligible to sit for the hiring process of a company that requires candidates with a certain level of proficiency which they made clear with a specific set of numerical performance parameters.
This criteria has multiple layers and thus going through a database of hundreds of students and finding out the ones who are perfectly suited to sit for the hiring process is an uphill task for anyone from the placements team. This is where data analysts come in and do what they do.
Similarly in another scenario, the curriculum team might need to find out the scope for improvement in the curriculum in a quest to improve it. The amount of data here would come from the student evaluation scores, their session presence, and various other factors. This is a very complex problem for the curriculum team to solve all by themselves. Thanks to the data team, all of this can be done in a whiff.
Now if we dive deeper into the details of the process that help analysts make business decisions easier, we can draw out the following are the major responsibilities of a data analyst-
- Data mining from primary and secondary sources
- Data cleaning and dissection to remove irrelevant information
- Using statistical tools and techniques to analyse and interpret results
- Identifying trends and patterns in large data sets
- Identifying new process improvement opportunities
- Providing management with data reports
- Creating, designing, and maintaining databases and data systems
- Troubleshooting code and data issues
If this intrigued you to know more about the daily life of a data analyst, you can refer to our blog and gain more insights into the life of a data analyst.
Beginning of a data analyst career path
The stepping stone to kickstart a career in any domain are entry-level jobs. These are the jobs that get you through the door.
For data analysts, such jobs come with the designation of Junior Analyst, Analyst 1, Analyst 2, etc.
It is essential to note here that these jobs require candidates to have a good grip on all the processes involved in data analysis- from raw data preparation and analysis to creating visualisations and sharing observations.
All of this would also need candidates to have a strong understanding of the ins and outs of various programming languages such as Python, SQL, and R along with hands-on proficiency in tools such as Excel and Tableau.
Once you have gathered a fair bit of experience (say 1-2 years in an entry-level job), where do you go? Or more importantly, what do you do?
Well, it is very important in times such as today where people stay connected to each other every second of the day to market themselves. The availability of various platforms such as GitHub and LinkedIn can help analysts gear up their skills to gain a direction that would help them advance their careers.
See, it is easier said than done. So, even if the framework for advancing a career after starting out in entry-level data analyst roles might have diluted into simpler words above, it is not as easy as it seems. A lot of commitment, dedication, and hard work is required if you are someone who aspires to get to the next level badly.
Ascension on the corporate ladder
As is the pattern in many industries, people tend to look for greener pastures after having spent a considerable time learning the tradecraft in entry-level roles. The same applies to data analytics.
For analysts who get to a position where they are well equipped with the ins and outs of data analytics jobs in an entry-level role, it is acceptable to look forward to opportunities that come with bigger responsibilities.
This is a critical point in the life of data analysts where they need to make a decision.
Typically, data analysts with a vast amount of knowledge and good leadership skills tend to go for the roles of senior data analysts or analytics managers.
Alternatively, some analysts choose to veer off the beaten path and choose to become specialists in certain domains. They go on to work with data in niched-out fields such as healthcare, finance, cybersecurity, automation, manufacturing, etc.
They end up taking jobs roles such as:
- Financial Analyst
- Business Analyst
- Security Analyst
- System Analyst
- Digital marketing Analyst
- Marketing Analyst
- Insurance Analyst
What makes specialisation great in the field of data analytics is that it lets analysts completely absorb the knowledge in a domain they might have had a keen interest in at some point in their life and that makes working as a data analyst more rewarding, satisfying, and fulfilling.
But this doesn't mean that they cannot switch fields, they always can, but usually, as a person grows in their career - they choose to specialise in areas where their interest lies.
Moving towards data science
One more route that data analysts can take up in their careers is the shift toward data science.
To begin with, it is essential to clarify that data analysis and data science are two completely different career paths. Data analytics serve as the perfect point of entry into the world of data science.
A lot of data professionals dive into the world of data science once they achieve excellence in data analytics with an aim to add more sophisticated skills to their arsenal.
Difference between a data analyst and a data scientist
Despite the fact that data analytics is a specialised role, it is only one discipline within the larger field of data science.
The role of data analysts generally requires them to work with data- collect it, then analyse it and then go on to use this data to make informed decisions that can help drive the wheel of business growth.
Data science on the other hand is a much broader domain that deals with the scientific aspect of data and the ways in which it is gathered and applied for further processing by analysts.
While data analytics makes use of data that is highly structured with an aim to draw meaningful conclusions from it, a data scientist’s job on the other hand is concerned with the methods of data collection and the types of data that are needed to be collected.
The transition from data analytics to data science is not as smooth as people would think since this requires people to dive into advanced concepts such as machine learning, model building, predictive analysis, building algorithms, and learning more advanced languages such as Python and R.
After making the shift to data science, professionals mostly take up senior positions such as senior data scientist, machine learning engineer, or even larger roles such as chief data officer.
Detailed descriptions of the latter two are as follows.
Machine Learning Engineer
Consider a day in your life when you watch a short 20-30 second cricket reel on Instagram and then forget it as soon as you swipe up to the next reel in line which is nothing but a pure imitation of a trend doing the rounds. After 15 minutes of endless and aimless scrolling, you doze off to sleep and then wake up the next morning.
Now in your quest to catch up with the things that took place while you were away the first thing you do is scroll through your Instagram feed. This is when you come across another cricket reel and then scroll down with the hopes of finding something other than cricket. But to your surprise, you come across another cricket reel that is even more entertaining than the previous one, and before you know it, you are hooked to cricket content.
The mechanism at the back end that made all of this possible is machine learning and the people who develop programs/models that make all of this possible are machine learning engineers.
A machine learning engineer is generally a subset of a large data science team that works in close collaboration with the team of data scientists, analysts, data administrators, and architects.
Machine learning engineers design and develop AI algorithms capable of learning and making predictions, which is what machine learning is all about. A typical day in the life of a machine learning engineer generally involves creating predictive models by using the power of research and artificial intelligence systems.
Chief Data Officer (CDO)
This is one of the senior-most roles in the field of data science.
The chief data officer is a senior executive who understands the company's strategy and direction and lays down the regulations on the ways it can be supported with data.
A CDO generally takes care of the organisation-wide data and closely monitors it from the lens of information strategy, governance, control, policy development, and effective exploitation.
Speaking strictly in a layman’s terms, a Chief Data Officer is responsible for the governance of data i.e, the way data is exploited or manipulated, data lifecycle management, its quality, protection, as well as privacy.
Becoming a consultant
The least amount of experience required to get to a level where data analysts can be called a consultant is normally six or seven years.
A data analytics consultant is someone who specialises in data analytics.
A data analytics consultant's primary responsibility is to draw conclusions from data. They are more interested in the outcomes and less concerned with the mechanics or processes of data analysis.
The findings will be explained to company executives by data analytics consultants. As a result, business leaders can devise an effective long-term strategy.
Data analytics consultants find a wide variety of jobs in a wide variety of capacities. They may be attached to a consulting firm or a development company, or they might also be found working independently. They may also be found working as a part of a big team or independently.
Their job responsibilities are also very specific and focussed. A data analytics consultant might be found mining data for patterns, predicting future trends, or synthesising information and using the same to devise strategies for the business to which they are attached with.
When working on a project, they may split their time between their offices and the client's office to complete tasks.
And this is what brings in the concept of flexibility in the field of data analytics. But for someone who is currently serving as an entry-level employee, flexibility should not even feature in the list of expectations. It is advised for them to gain hands-on experience in various roles in data analysis to get to a level where they can enjoy the fruits of flexibility.
The last but the most important career path on this list.
The responsibilities of a data engineer are mainly concerned with the creation, optimisation, collection, transformation, and access of data. For data to be processed in a way that ensures it is in a good enough condition to be used by data scientists and data analysts, certain models need to be created and this is where the expertise of a data engineer is required.
Data engineers handle the aspects of data that require knowledge of software engineering and data science.
Software engineering principles help develop algorithms that automate the data flow process. They also work with data scientists to build machine learning and analytics infrastructure from the ground up, from testing to deployment.
When it comes to outlining your data analytics career path, there is no one-size-fits-all solution. You can specialise and continue to add more complex skills to your arsenal, or you can become a subject matter expert—or a hybrid of the two.
You can design a career that speaks to both your interests and your talents once you've mastered the fundamentals of data analysis. Regardless, every data analyst's career path begins with the same steps: learning the essential tools, skills, and processes, as well as developing a professional portfolio