Analytics and Data Science are emerging fields which are now being integrated with all the industries across all sectors. Be it Fortune 500 name or local startups — everyone is using analytics to garner insights from big data. The entire industry has seen a sharp increase in demand for highly-skilled professionals as well as systems. But this month we decided to find out what are the challenges faced by this thriving sector.
Be it organisational challenges, educational questions or operational quandaries — we dug deep to uncover what’s stopping India from becoming a superpower in this the analytics and data science sectors. We asked our readers an array of questions, as comprehensive as possible, to get a complete understanding of the problems faced in this sector.
About The Study:
For this study, we asked our readers to tell us more about the challenges they faced — mostly at an organisational level — in the analytics and data science sector. We took opinions from all those who practice data science — from professionals with less than two years of work experience to CXOs — to get a thorough idea of the issues they faced in this swiftly-developing sector.
Our survey was met with much enthusiasm — and we got great insights from it. Some of them were expected, and many of them were real eye-openers. So here are the key problems and challenges faced by the analytics and data science industry as a whole:
1. What is the biggest challenge faced by the analytics and data science sector in India?
This key question was aimed at the organisational cadre. Clearly, understanding of the analytics and data science sector by clients/stakeholders/management was the chief problem faced by 22% of the respondents.
Interestingly enough, shortage in talent was also an important problem faced by 19% of the organisation of all magnitude. This came as a surprise to us, seeing how the number of institutes, MOOCs and online resources that offer education in analytics and data science is increasing steadily. The lack of standardisation of processes and techniques and inflated expectations from stakeholders was another key problem faced by 15% of all the companies and data science and analytics practitioners.
2. What do you think is the best way to increase the talent pool in analytics?
As we discussed earlier, the best way to increase the talent pool in analytics, according to 48% of our respondents, is regular upskilling. It can either be sponsored by companies or employees themselves. In an era where so many resources are available at the fingertips of the users, the answer to this challenge seems obvious.
However, our respondents have also voiced their opinion on the quality of education in India. 36% of our readers have said that the best way to increase the talent pool in analytics is to improve the quality of education — especially the courses, the degree programmes and the teachers and instructors.
Only 10% of the respondents thought that talent pool was not an issue they faced in their respective companies or careers.
3. How can we create or increase awareness about Analytics at a management level?
This question gave us a clear insight into the attitude of the corporates working or beginning to work with analytics. For 37% of respondents felt that making the C-level management aware of how analytics can help optimise ROI was one of the keys to smooth adoption and upgradation in the company.
On the other hand, 32% of the respondents thought that educating the management about benefits of analytics through road shows, events and on other platforms would help them increase awareness about this industry.24% of the respondents thought that rather than educating or teaching their peers, it would be easier if they could demonstrate the benefits of analytics with the help of effective use cases.
4. What data problems do you face the most?
24% of the respondents said that one of the key challenges in the analytics and data science sector was the fact that data sources are many times too complex or siloed. Only 7% of the respondents said that the got no data to work with.
5. How do you think that standardisation can be brought to the Analytics ecosystem?
This was one of the questions where we got a very interesting (if fragmented) answer by our respondents. When asked about the level of standardisation in the sector, 35% of our respondents said that analytics and data science sector will work smoothly only if a central authority was created to craft policy and standards.
32% of the respondents were quite pessimistic about the process — they admitted to the fact that they did not think that standardisation was possible at all in analytics and data science fields.
Only 28% of the respondents said that standardised tools for everyone’s use would help in streamlining the sector.
6. Do you think Analytics has left many open-ended definitions? How can this be fixed?
One of the many qualms about this sector is that it has many open-ended definitions and jargons. Interestingly, to 34% of our respondents said that if academics pitch in for creating more solid definitions, the problems would be solved.
In line with the question above, 32% of the respondents said that if a central body were to create definitions that everyone in the industry adhered to, the problem would be wiped out entirely.
Interestingly, 17% of our readers felt that open-ended definitions and jargons were not a problem at all in the analytics and data science industry.
7. How can we deal with the problem of inflated expectations from stakeholders?
Analytics and data science is such a sector where most managers think that the system is in place, it will work wonders. Many times it backfires and works against the sector itself. It, therefore, comes as no surprise that 40% of our respondents said that educating stakeholders about the working of the analytics department — workflow, timeframe, results, etc — would help in streamlining the process.
24% of the respondents said that showcasing workflow through successful use cases, even from other companies or projects, would help with the problem of inflated expectations from stakeholders.
Only 21% of our readers said that regular demonstration of the development of products and services would help understand the stakeholders better.
8. What ROI problem do you face in your Analytics firm?
As mentioned earlier, telling the time, date and quantity of the return on investment in any analytics or data science department can be tricky. That is why 57% of our respondents (an overwhelming majority) said that quantify ROI was the basic problem they faced. 20% of the respondents felt that the stakeholders or managers did not wait for the tuning period to see the fruits of the labour from the analytics and data science department.
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