This article was originally posted on LinkedIn Pulse by Jigsaw Academy CEO Gaurav Vohra. In it, he explores the trends that emerged in the analytics industry in 2018, and what we can expect to see in 2019.
In a conversation last year with a few industry stalwarts, I observed something quite interesting. Several of the people I spoke to seemed to have a good understanding of how analytics training methods were changing within their own companies – specifically, how their internal training techniques had evolved and been refined over time. However, a lot of them found that it was more difficult to get a macro view of how analytics training practices were developing and evolving in each of their respective industries as a whole.
This conversation helped highlight the unique position that we are in at Jigsaw Academy, as a result of our regular interactions with numerous companies from a wide range of different industries. Given the range of domains we are exposed to, we are well placed to provide an industry-level picture of the latest trends in analytics training, how these practices have evolved, as well their future prospects. This article is an attempt to trace how analytics training trends changed in 2018, and how they are poised to evolve in the year to come.
Trends we noticed in 2018
Organizations want to become “data-smart”
While companies across all domains continue to become more data-driven in their business practices, becoming data-smart is an entirely different prospect. As I’d detailed in my post on LinkedIn, it involves a top-down approach, that helps ingrain a data-led approach into the very DNA of the entire company. By evaluating the analytics capabilities of every team in the office and imparting the appropriate training, data is incorporated into all organizational processes, and made integral to most of the functions within the company.
The move by more companies to ramp up efforts in this area became especially apparent to us at Jigsaw in the 2nd half of the year, as we saw a substantial spike in demand for training programs. There has been a specific focus on upskilling existing non technical teams, something that has time again proven to be the most effective route for companies, when compared to everything that goes into hiring new talent.
Most significantly, analytics training appears to be reaching a new scale. The collective push by companies in the country is evidenced by the 33.5% compounded annual growth rate of the analytics industry in India, currently valued at an estimated $2.71 billion in annual revenue. The rise in demand has been seen across almost all sectors, in everything from Finance & Banking to E-commerce, and it doesn’t seem likely to slow down any time soon.
Several reports by industry experts have predicted that the analytics industry, particularly in India, will continue to scale new heights over the next few years. Just about every company in every sector relies on data at some level, and the business world as a whole has begun working harder to ensure that the full power of available data is tapped into, and harnessed to drive growth.
Machine Learning in financial services
As technology grows more sophisticated day by day, Machine Learning in particular is becoming a dominant force in the world of analytics. Almost any Financial Service you use today likely makes use of a complex Machine Learning algorithm to make the process significantly easier. It especially has numerous uses in marketing, along with AI, and has been adopted by banks and credit card unions to make life simpler for both the service itself, through increased ROI, as well as for the customer.
This has been made easier by the rapid growth of online banking in India. According to a survey conducted last year by Avaya, about 26% of Indians prefer to use online payment options, rather than dealing with cash. In pure numbers, the total strength of online banking users in the country, is estimated to reach about 150 million by the year 2020. While this growth in digitized banking continues at a furious pace, customers still expect customer service to be commensurate with an in-person experience. As a result, financial companies have begun using user-generated data to ensure that customer service remains at a high standard. The multiple interactions that any user has with a bank’s website or app can provide numerous touch-points for the companies to use, to customize services for that particular customer. This includes additional services they may offer, as well as targeting ads more specifically to certain users.
AI algorithms have also become very common when it comes to trading. Automated systems are used to make millions of trades each day, and are heavily relied upon, as they provide arguably more accurate assessments of the market. With the sheer amount of data available, the algorithms are able to negate the potential errors that human calculation can bring in, hence making this a more reliable trading method.
Open Source tools in financial services
Along with an increased adoption of Machine Learning and Artificial Intelligence techniques, financial companies have also begun leaning very heavily on open source tools for their work. The most obvious benefit of these tools is the vast cost benefit, while still maintaining the same quality that proprietary softwares can provide. Beyond that, several open source softwares can be used across multiple platforms – this drastically cuts down the need for increased training, or having different staff members take over specific projects. For companies that have incredibly high volumes of these functions, using open source tools would help them make massive savings.
With a whole gamut of developers also making the leap to data analytics, financial organizations are able to hire talent that thoroughly understands the ins and outs of these softwares. More importantly, the standards of open source tools are very high, and these are continuously driven by the developer community. As a result, companies are almost certain to have analysts who are fully updated about the latest advancements in the technology, and how it can be used to further streamline their operations.
Longer courses, for longer-term benefit
Another interesting trend we spotted was that companies have begun waking up to the fact that high-intensity, short-term courses serve an extremely limited purpose. Very often, topics may be skimmed over, to the detriment of a certain team in the organization, which would in turn hamper the progress of the entire company. Shorter courses also tend to be more rigid in their structure, as a lot needs to be covered in a relatively limited period of time. As a result, certain groups of employees may miss out on vital elements of the course material, due to scheduling conflicts, which again does more harm than anything else.
Continuous learning is something that has become more popular in recent times. Quite often, there may be some amount of training that is done in-person, but this is substantially supplemented by plenty of learning that the employee does on their own time. This lets them acquire a deeper understanding of the subject matter, at a more comfortable pace, while also being able to keep up with any further developments in the analytics world. More importantly, they can gain industry insights, which will only help them in their decision making going forward.
As an extension of the previous point, virtual learning has become hugely popular among young working professionals, particularly when it comes to analytics. It has proved to be an excellent way for employees to add on to their regular workload, but at their own convenience, enabling them to pick up new skills whenever they find the time. Various studies have shown that the learning time can be reduced by 25-60% when compared to the more traditional medium. It also provides a win-win situation for the company, as along with upskilling its workforce, online training is also highly cost-effective when compared to in-person sessions.
The use of videos, slides, and more interactive media channels, help learners retain the material far better. Studies have shown that the visual medium is far more effective and impactful when it comes to teaching, versus in-person lectures. Some e-learning services, such as Jigsaw, also offer a mix of the two for those who might require it. While in-person lectures can lay the foundation, the online lessons are what help learners deepen their knowledge of a particular area of expertise.
Measuring the impact
A lot of companies have also realized that simply encouraging further learning and training for their employees is only useful up to a point. Calculating the impact that this training has is essential, to ensure that it is impacting the company in a positive way, particularly from a business perspective. While these training programs are mutually beneficial to the organization as well as the individuals in it, there needs to be adequate ROI from them to justify undertaking similar initiatives in the future.
Analyzing the impact of the training sessions is often done by setting specific targets for the employees, once the training has been completed. Their KPIs may be realigned to take into account the new skills they have been trained in, and managers can track each individual’s progress in these new areas. Effective upskilling is also a major factor in talent retention. Companies that make a concerted effort to help employees add to their skill-sets are more often than not likely to be repaid with loyalty from their workforce. At the end of the day, the aim is to build organizational capability, and maintain a high quality of work throughout the entire setup.
Trends for 2019
Given the nature of the industry, and how fast things can change, we can fully expect a number of trends to gain prominence in 2019 – some of them picking up from what we observed last year.
Developing analytics skills organization-wide
We began to see an upswing in this in 2018 itself, and we believe there will be a continued effort being made across the board at organizations to enhance the analytics skills of employees. While the top-down approach to instate data practices is still used, managers at the every level have begun to take more initiative to make this happen. Taking the essential first steps to train employees in different teams, sets the tone and considerably speeds up the efforts to increase the data literacy of the entire organization. More importantly, the training can be customized to the specific needs of a particular team, and the focus can be on the most important tools and techniques.
As mentioned earlier, there is a large movement to make organizations more “data-smart” from top to bottom. This is being quite clearly seen in the hiring trends for analytics in the last year. The industry is growing at a tremendous pace, and is estimated to be worth a whopping $16 billion by the year 2025. Pay scales have increased dramatically, as have the sheer number of people taking up jobs in analytics. Depending on the mix of skills they possess, the average Machine Learning and Big Data scientist could earn up to ₹13 lakhs per annum. The number of jobs themselves almost doubled from April 2017 to April 2018, showing that companies in India are taking the analytics boom very seriously. With more than 50,000 jobs currently open in the country, it’s also clear that there is a need for more talent, in numerous functions across the board.
Recruiters have also begun to face a special emphasis on “Big Data Unicorns” – analysts with a unique blend of skills, that would make them incredibly valuable assets to any organization. To become a Big Data Unicorn, one would need to learn Hadoop, in combination with Spark and Tableau, as well as knowledge of Mongo, Cassandra, and other popular databases. An analyst who specializes in all of these is a rare breed, one that is highly valued by companies across the board, as these skills would add value to almost any domain.
As the overall adoption of analytics continues to scale up, there is only likely to be a greater demand for data experts in the next few years.
As I had highlighted in the post about becoming a data-smart organization, the follow-up to analytics training is every bit as important as the training itself. Developments are constantly happening in the analytics space, and it’s essential for companies to be in tune with these advancements. A heartening trend we have noticed in the past six months, is that organizations have increased their demand for post-training material, such as mentorship programs and projects. We have received several requests ourselves, and the aim they have largely expressed is to use live projects to train their employees, while continuing to create value for their business. Training employees is not being seen purely as the end goal, and there has been a sharp increase in the focus companies place on what to implement after this training is complete. The training itself is being mapped to specific roles that employees have to take on, as well as projects that will be taken up for the longer-term benefit of the organization. And so data training is taking on a more holistic approach, and we’re seeing better planning far beyond the essential training sessions.
Deep learning and AI
In the past couple of years, with the rapid evolution of sophisticated analytics technology, Machine Learning and Artificial Intelligence have become almost essential to the business world. Disruptive technologies such as face-recognition software and self-driving cars rely heavily on deep learning algorithms, and as these technologies continue to evolve, it’s natural that the demand for the skills that drive them would continue apace. Marketing is also becoming more machine-led, with digital experts using a more research-oriented approach to their campaigns, rather than traditional content marketing methods.
More domain-focused analytics training
As analytics gets more deeply entrenched in the overall functions of a company, their needs are becoming more focused. For instance, the demand for HR analytics in particular has been very high, as companies have realized its importance, along with the fact that it’s been a neglected area for a long time. Analytics can be used by HR teams to predict employee attrition, track employee performances, as well as a lot more. As a result, companies are focusing on ensuring their HR teams are equipped with the requisite analytics skills, which can benefit the entire organization in the long run. As analytics continues to grow, and companies become more cognizant of what each domain brings, there is every chance that this very specific type of training will become more ubiquitous.
Storytelling with Data
With data’s presence ever-increasing in just about every industry and domain, this is a skill that is gaining more and more importance, and we can fully expect to see an increase in demand for it in 2019. Storytelling is especially significant for the “last mile” of any organization’s data delivery. Given the sheer quantity of data that is generated and analyzed daily around the world, there are countless valuable insights that are constantly being derived from this data. However, if an insight isn’t understood, it cannot drive change of any sort. Thus, being able to communicate these insights in the most effective manner, especially for someone who is more business-oriented and with limited technical knowledge, is an important skill. And it is one that organizations have realized is not to be taken for granted. With the increase in more “self-service” analytics systems in companies, the need for effective storytellers will only continue to go up.
Analytics training techniques have been evolving constantly over the last several years. Technology is evolving at a furious pace, and the wants and needs of everyone who uses analytics will naturally change. The changes have also been in a variety of forms, be it in the learning techniques themselves, or the way in which companies have approached analytics training as a whole or the domain / sector level as well as the company level evolution. Given how fast things are moving, the signs are very encouraging indeed.