It is rightly said that data is the new oil. And while the value of the latter fluctuates widely, data will consistently remain a critical asset. This has been indubitably demonstrated as the world collectively battles a deadly viral outbreak.
We may still be months – or years – away from developing a cure, but datasets around the novel coronavirus have helped authorities manage the crisis better and mitigate its impact. Artificial intelligence (AI)-based data analytics and predictive modelling have enabled us to understand the disease better, paving the way for a better response mechanism in the event of another emergency.
And healthcare is not the only sector that can benefit from leveraging big data capabilities. Almost every industry has woken up to the possibilities and future scope that big data and its cohorts can play in improving their bottom line and maximising profits.
But how did big data analytics emerge as a top priority for businesses?
Why Companies Are Turning To Big Data
As the name suggests, big data involves extracting, analysing and processing a vast swath of data using various techniques. With the proliferation of online enterprise applications across industries and geographies, a large amount of data is generated every day from different sources.
This entails dealing with massive sets of sensitive and unstructured data, which albeit challenging for companies, if done right, can potentially help improve their businesses. Be it creating better frameworks to respond to customer and client demands, or launching new business models, a thorough understanding of the business can create greater efficiencies and give them an edge over competitors in a data-driven world.
And the convergence of several other technologies have accelerated this progress.
Recent advances in AI, machine learning (ML) and deep learning (DL) has been pushing the envelope of these data-driven capabilities. Sophisticated algorithms trained on large volumes of data have helped companies solve problems by delivering better insights, and are further fueling more disruptions.
For instance, big tech companies like Amazon, Google and Facebook have long been turning to applications of big data to gain a competitive advantage in a data-driven environment. All three have a large collection of data at their disposal – be it through the products customers buy, their online search habits, or even the posts they share.
But what is important to note here is that a lot of data does not necessarily translate into actionable insights. This is where technologies like AI and ML can help examine large amounts of data to help create strategies unique to a company.
Earlier, with limited storage capacities, maintaining data was more cumbersome than beneficial to companies. Moreover, deriving insights from this data manually was no mean feat. However, AI and Cloud technologies have created new opportunities to use the scope of big data analytics in India more effectively, as indicated above.
This means that analysing and processing data has become quicker, while delivering better outcomes. As a result, many companies have started investing heavily in improving data integration capabilities into their day-to-day functions. And this is likely to see increased adoption by firms that have, up until now, been reluctant to join the digital economy.
With the breadth of internet access and web presence expanding – especially in India – more and more micro, small and medium enterprises (MSMEs) are embracing emerging technologies to catch up to peers who have been leveraging its benefits for the better part of the last decade.
This influx is likely to spike the variety and volume of data going forward. In fact, according to a McKinsey report, the amount of data generated will double every three years. This has created a demand for data analytics skills across industries, with companies willing to onboard big data experts in droves. What is more, organisations will also do well to use data to inform new strategies to survive the oncoming recession.
This is because any economic downturn should signal to businesses that it must analyse every source of revenue with the scope of big data analytics. This can help alleviate risks by estimating which parts of their business offerings are more likely to prosper given the circumstances. This will enable sound and informed decision making, the only shield against a poor economy.
In other words, applications of big data can help organisations mitigate the impact of the oncoming recession by helping them target the right audience, make economic decisions, and minimise losses. What is more, advanced analytics tools can also help determine which functions can be automated, helping companies operate lean.
All this indicates that there has never been a better time to jump on the big data bandwagon and learn big data, and more and more companies are coming to this realisation.
Shortage Of Big Data Analytics Talent
According to the aforementioned report, most companies are currently extracting only a fraction of the potential value from the introduction of big data analytics. While the challenges in integrating data-driven insights into mainstream business processes and existing operations may be many, attracting the right talent has been a common grievance across companies, industries and even geographies.
Many organisations and enterprises we spoke to also reported that recruiting analytical talent is quite difficult given the breadth of responsibilities they are meant to take on. While some companies struggle to find candidates with advanced technical skills, others fail to meet people who truly understand the ‘rhythm of data’. Explains Manoj Karanth, VP-Digital at Mindtree:
“In big data analytics, nearly 80% of the initial time goes in preparing data, analysing the different slices of data, and understanding the rhythm of the underlying data. These steps are critical before you can apply algorithms and techniques.”
Delving into data involves many processes, each of which demand unique skill sets. Additionally, many companies also report challenges in retaining this talent, since the demand for data scientists and other roles that require knowledge of big data analytics are at an all-time high.
This scarcity of data science experts have led companies to acquire startups and smaller firms where many skilled professionals flock to for experience. But despite these efforts, the demand for data scientists is likely to far outstrip supply, creating a big deficit in the big data analytics talent market.
But the travesty here is that there is actually no dearth of candidates seeking jobs in big data and analytics. However, the level of data literacy – the ability to deftly handle and work with data – does not match the proficiency required for many of these jobs.
This makes quality courses and programs for analytics job seekers to upskill themselves the need of the hour.
Learning Big Data Online: Emergence Of Courses
Covid-19 may have emerged as a curse for new graduates, but they can turn it around by using this time to educate themselves in skills that can help them secure a lucrative career in big data. With many educational institutions and edtech platforms launching courses or giving wider access to existing programs, students must use this time to prepare for jobs that would still be in demand during the recession. What is more, since many of these courses are targeted at digital audiences, it could not have been at a more opportune time.
With lockdowns enforced in many places across the globe, learning has found a place within homes as students enrol in droves to utilise the extra time they have on their hands. While many universities have added full-time data and analytics programs to their curriculum which can be completed remotely, edtech firms have partnered with some of these institutions to offer both long-term and short-term courses on various subjects.
In fact, the report referenced above estimated that the number of graduates from data science programs could grow by 7% every year. And with the demand for these programs going up, this number is bound to increase.
This augurs well for learners who are likely to be better positioned than their non-data science counterparts who may not be able to rebound from recession the same way. According to a research conducted by AIM, a negative impact on revenues triggered by an economic downturn may not directly affect the analytics functions of IT firms. It also states that the impact will be further cushioned by government-backed stimulus packages, and relatively quick resumption of businesses and consumption, once the recession ends.
This indicates that while different sectors will experience varying impacts of the recession on their respective businesses, professionals seeking a change of industry will do well to dive into data and analytics for a relatively high job security in the time to come.
What is more, these educational initiatives are also targeted at data science professionals, who although may be lucky enough to have a job at the moment, are likely to get replaced by candidates who show more promise, are better skilled, and up-to-date with all the latest developments in the field.
This means that the road is not smooth even for seasoned data scientists, and that the onus of continuous improvement is on everyone in this field.
This is because data science is accelerating at a rapid pace, with new tools and platforms emerging periodically. Although no one can realistically master them all, keeping abreast with top developments in the field will demand continuous learning, which can be accrued with a solid academic background with the help of these courses.
As companies lay even more emphasis on data, talented data scientists will see more demand than ever before. This will prompt organisations to increasingly create specialised positions for candidates who combine a strong understanding of data with various other capabilities.
Analytixlabs’ Big Data Courses
Having established the fact that the future scope of big data analytics in India holds a lot of promise for both students and working professionals, it may be a good time to shed some light on how to navigate this field through the complex web of online courses.
Although not impossible to acquire, the skill sets dispersed through these courses entails a long-term game. While some may instantly prepare you for practical implementation within various business functions, others may just give you a leg up to enable you to understand more advanced courses within that subject more easily.
Whatever the case may be, the gains are seldom instantaneous and demands a disciplined approach that involves careful planning, consistent action and relentless motivation.
While there are many offerings to learn big data online currently available, few capture a comprehensive curriculum for the benefit of the learner. With skills to adept learners to take advantage of lucrative opportunities in the IT/ ITES field, Analytixlabs’ Big Data course is tailored for you.
Covering big data concepts like SQL, NoSQL (MongoDB), Hadoop, Spark, Cloud Computing, HDFS, Sqoop, Hive, Impala, and more, it offers you practical skills on each of these tools. In fact, this is one of the few Big Data analytics courses that focuses on multiple dimensions, helping you leverage data sources for advanced analytics, AI and ML models for predictive analytics.
At varying prices and learning modes – including demo classes, instructor-led live classes, and video-based self-paced sessions – it encapsulates 120 hours of learning and offers an extensive and flexible training.
Spread over 10 weekends, you will get video recordings of the sessions for self study purposes, weekly assignments, additional study material, module-wise case studies and projects, specially curated sample questions, as well as career guidance and support.
Aimed at professionals in IT/ITES, Business Intelligence (BI), or computer science graduates, it will guide you through advanced training for more demanding data engineering roles. At the end of the program and on successful completion of assignments and projects, you will be awarded certification from one of India’s top rated institutes.
Another course offered by Analytixlabs focuses on more advanced Big Data Science and is directed at students coming from IT, Software, as well as those belonging to a Datawarehouse background and seeking to get into the Big Data Analytics domain.
Going beyond introduction to big data concepts, this training will help you gain detailed practical skill sets on Hadoop, as well as a chance to learn advanced big data concepts through Python, Spark, and more. For extensive hands-on practice, you will also get access to the virtual lab and several other assignments and projects.
To sum it, this course should be able to equip you with all that is necessary to be a data scientist, since it captures the essence of key tools like Python, Hadoop, and Spark with Machine Learning. Spread over 240 hours across 40 classes, it is slightly longer at 18 weekends.
Just like the previous course, it includes demo classes, instructor-led live classes, and video-based self-paced sessions, along with other resources for self study purposes. This includes video recordings of class sessions, weekly assignments, module-wise case studies and projects, and career guidance and support. On completion of this course as well, you will stand to earn an Advance Data Science Certification to add to your resume post the training period.
Although learning big data courses may come with certain prerequisites like industry-domain expertise and knowledge of some commonly-used tools, it will prepare you for multiple job opportunities across various industries and domains. While the job market may look bleak from an oncoming recession, you can stave off some of the negative impact by embracing the scope of big data that is likely to rebound from this period quickly, and even thrive in these trying times.
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Anu is a writer who stews in existential angst and actively seeks what’s broken. Lover of avant-garde films and BoJack Horseman fan theories, she has previously worked for Economic Times. Contact: email@example.com