Geoffrey Moore, Author of Crossing the Chasm & Inside the Tornado once remarked, “Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.” and quite expectedly, nobody is in favor of being the deer here. Many organizations have, and many more are implementing big data framework by the day to have insights interpolated this way to hold onto a competitive edge.
Following image (based on the Peer Research Report on Big Data analytics) shows how many organizations already have a formal strategy and support system for Big Data, how many are on their way to get it, and how many do not have any plans as of now. It puts in numbers what is the tide of analytics.
This has led us to the shifting balance of power among job roles today. Big data analysts, the people who manage the tsunami of information, identify and isolate patterns and frame conclusions and predictions from this data, are the demigods of today’s business scene.
Also read: Top tools and Techonologies in Big Data!
Why we are calling switching to Big Data analytics, a career change which is guaranteed to materialize in impressive paychecks and lavish benefits, will be clear as day in just a couple of minutes. Sit tight and read on:
Reason 1: Big Data Analysts are sorely needed!
Data is the lifeline of the age of information. However, crude data is more or less useless. Processed data is information, the processed information is knowledge and processed knowledge is wisdom. People who perform this analysis at various stages, are practically running the system.
There are more jobs in big data analysis this year than there were in the previous year.
A new survey conducted by Competitive Edge Research Reports reports that Big Data budgets will rise to an average of over $6 million over the next two years. In the following years, the opportunities for data analysts will also go up, and the talent pool will richen as many IT professionals are ready to invest time and effort in this arena. The following graph from indeep.co.in proves that big data analytics jobs have been growing in the recent years. We expect this trend to continue for quite a long time.
Further, In a study by QuinStreet Inc., Big data analytics came up as a high priority for US businesses. Majority of organizations have already begun or are planning to implement it in the near future.
With this backdrop, we have also observed a higher number of job postings at online platforms like indeed.co.in, dice.com, glassdoor.com, monster.com etc which is the most definitive sign of an increase in the sheer number of job opportunities available in the domain. This the claim by the graph and our earlier discussion about job opportunities in the big data analytics domain.
Reason 2: The Demand-Supply crunch
Big data analytics is a relatively recent job creator. The number of analysts needed is way more than the available talent pool. With the analytics becoming more important, technology professionals who can exploit big data are in high demand. Even more, organizations are implementing the analytics functionality further boosting the demand for experienced Big Data analysts.
As pointed out by Rob Bearden (CEO, HortonWorks, #12 in silicon valley), the talent pool right now is, at best, about 20% of the existing demand. This imbalance is a blessing for novices and people looking to switch to a more lucrative and sustainable job role.
During a time when the advent of AI is threatening certain jobs, Big Data analytics offers opportunities and stability at the same time. More often than not, organizations go to appreciable lengths to satisfy their analysts in terms of remuneration and work-life balance.
Reason 3: Increasing pay for Big Data Analytics
The intense demand and limited talent pool are affecting the paychecks of big data analysts quite positively. Most organizations are shelling out some real lavish packages for the right skill and experience.
According to the data from DataJobs, the salary ranges seem justifiable:
- Data analyst (entry level): $50,000-$75,000
- Data analyst (experienced): $65,000-$110,000
- Data scientist: $85,000-$170,000
- Database administrator (entry level): $50,000-$70,000
- Database administrator (experienced): $70,000-$120,000
- Data engineer (junior/generalist): $70,000-$115,000
- Data engineer (domain expert): $100,000-$165,000
If you are looking for a detailed explanation of job responsibilities that Datajobs took into consideration while developing these salary generalization, you should go here.
Reason 4: Pivotal Position in Decision Making
One of these days, you might feel disappointed or dissatisfied thinking your role in your organization is not good enough, that you do not hold enough authority or your manager doesn’t take you that seriously…
Even though this sounds a lot like a bad job by your manager, this stuff happens and is not good for your morale. However, once you are in analytics, you’ll be right under the spotlight and your bottom-lines will be the bottom-lines that matter, more than you can foresee right now.
As the numbers say today, 77% of top organizations consider data analytics a critical component of business performance. What this means to a big data analyst is that they will be providing the information which would change policies and courses of mightiest of organizations. From customized marketing, healthcare, retail to what-not, the data is on its way to grow from the mildly complex, highly structured data sets to the wild west of data in the near future. Processing it, making sense of such data will be synonymous with a superpower and as we all know, with great power, comes great responsibility.
The responsibilities will indeed be humongous. People who will be working in this field in coming five years, will indeed have starry packages but also are prone to be overworked. Freelancing could be a solution to maintaining your work-life balance. We will discuss freelancing and its applicability in Big Data analytics shortly.
Reason 5: Diverse Jobs, Multiple roles
Metrics and Analytics Specialist, Data Analyst, Big Data Engineer, Data Analytics Consultant are some of the multiple job positions within big data analytics. It is a given that everyone working in big data analytics will have to deal with a plethora of information from various sources and thus you will develop a taste for almost all kinds of data.
Big Data Analytics: Usage across Industries(Source: Peer Research)
Big Data analytics is a complex process and you can choose one of the following subsets of it:
- Predictive Analytic:
Here, we predict future events with the help of advanced data mining, segregation techniques in association with AI. The following image will distinguish predictive analysis from the prescriptive analysis.
- Prescriptive Analytics
Prescriptive analytics deal with finding the best course of action in a certain situation. It can very much be understood like the arrangements you would make for reaching somewhere in case your train reservation isn’t confirmed by the time of boarding. In a more serious case, the directions for vacating a building in case of fire are essentially prescriptive analytics at work.
- Descriptive Analytics:
These are the activities carried out in the first stage of analysis. The objective here is to create a summary of historical or recorded data to produce some useful information. If the data can be further processed, it would be good to prepare it for further analysis in this stage. The techniques data aggregation, data mining, querying, reporting and data visualization are utilized here to form patterns and identify relationships in seemingly unrelated data.
- Diagnostic Analytics
Using techniques like drill-down, data discovery, data mining and correlations, this stream of analytics tries to find the contributing factors behind an incident or event.
In a nutshell,
|Descriptive analytics: What happened?Diagnostic analytics: Why did it happen?Predictive analytics: What could happen in the future?Prescriptive analytics: How should we respond to these potential future events?|
However, which part of the process you contribute to is a choice you should make early on. As the experience piles up, it will be harder for you on a personal level to switch or quit a job that lucrative. Most probably even your manager won’t let you go.
Reason 6: The Win-Win that Freelancing is
The millennials are a distinctive generation. Contrary to the post-war old-school workaholics, today’s workforce focuses more on balancing their work life and personal life. Today instead of a single, daily job, we prefer an arrangement which diversifies our funds. One could credit this attitude to the Great American Depression and similar economic collapses that happened along the time.
However, since ‘diversify’ is the new smart, it is almost unreal to assume that one would like to be tied up with one employer. Freelancing is an advancing trend and will soon be the general standard of the industry.
On this pretext, let’s examine what the scene of big data analytics might turn out as. It’s not always a good ROI decision for a company to keep a big data analysts on a full payroll, specially for smaller to medium-sized enterprises. Such enterprises also outnumber the larger organization and MNCs.
Now, a big data analyst will soon become vital for a business to retain its edge. Hence, when the pay standard skyrockets, many will find it convenient to hire an expert on a project to project basis. The hiree will get to decide the hours he works (that’s basically the first benefit of freelancing) and most will be working from his base of operations (home, in general). On top of it all, most of our work can be performed online, the need of locomotion has summarily been ousted.
Reason 7: Clear Growth Path
Unlike most other jobs, big data offers you a rather expedited growth path. Considering how valuable experience is to this booming industry, a background in big data analytics ranging from five to eleven years will land you a top-tier job. The following image depicts the general career path for a person who has been consistently performing in the big data analytics industry.
To us, this seems like a pretty sweet deal. Taking into account the demand-supply crunch, as we discussed earlier, the growth path gets easier as the numbers in your experience add up.
Just like all other good things in life, being good at big data analyst won’t come easy. If you are a novice to data science, you will have to work your way up from the scratch. If mathematics makes you wince (which it really shouldn’t, considering maths is almost interesting!), this might not be an especially easy ride for you. You’ll have to master statistical analysis, probability theory, linear algebra along with Python/R (depending on which job role you take up in the said domain). To be able of representing data well visually, you will have to utilize both your creativity and your technical expertise.
As to why you would work that hard for a job switch, we believe the seven reasons above make a pretty good case in favor of big data analytics.