Sl No |
Model |
Description |
Examples |
1 |
Subscription |
Disrupts through “lock-in” by taking a product or service that is traditionally purchased on an ad hoc basis, and locking-in repeat customer by charging a subscription fee for continued access to the product/service |
Netflix, Dollar Shave Club, Apple Music |
2 |
Freemium |
Disrupts through digital sampling, where users pay for a basic service or product with their data or ‘eyeballs’ rather than money, and then charged to upgrade to their full offer. Works where marginal cost for extra units and distribution are lower than advertising revenue or the sale of personal data |
Spotify, LinkedIn, Dropbox |
3 |
Free |
Disrupts with an ‘if-you’re-not-paying-for-the-product you-are-the-product’ model that involves selling personal data or ‘advertising eyeballs’ harvested by offering consumers a ‘free’ product or service that captures their data/attention |
Google, Facebook |
4 |
Market Place |
Disrupts with the provision of a digital marketplace that brings together buyers and sellers directly, in return for a transaction or placement fee or commission |
eBay, iTunes, App Store, Uber, AirBnB |
5 |
Access-overOwnership |
Disrupts by providing temporary access to goods and services traditionally only available through purchase. Includes ‘Sharing Economy’ disruptors, which takes a commission from people monetizing their assets (home, car, capital) by lending them to ‘borrower’ |
Zipcar, Peerbuy, AirBnB |
6 |
Hyper Market |
Disrupts by ‘brand bombing |
Amazon, Apple |
7 |
On Demand Model |
Disrupts by monetizing time and selling instant-access at a premium. |
Uber, Taskrabbit |
To summarise, AI techniques such as continuous customer mapping, estimation of logistics, disruptive technology models etc. have helped e-commerce companies in reshaping their business processes and enhancing efficiencies, thereby adding substantial value and making their contribution quite significant. Use of AI systems in e-commerce sector currently accounts for USD 317.8 Mil in AI market value and 5.0% in AI market share (Deloitte-CII, ibid).
Brief analysis of other Business Sectors
In addition to the e-commerce sector, many other sectors have started using AI based solutions to reshape their business processes and enhance their efficiencies. Some of the sectors are:-
- Predictive maintenance- Here the power of machine learning -to detect anomalies and analyse very large amounts of high-dimensional data- has helped in raising the bar for preventive maintenance systems to new levels. Industries with high maintenance requirements (including airlines) have immensely benefitted from this. To cite an example-Sense Hawk-a pioneering company offering Life Cycle Management for Solar Power Plants, has helped solar power sector in improving its viability (Ganguly, September 20, 2020)
- Airport Security- Artificial Intelligence powered systems are now being used for baggage screening at airports. AIM (AIM, January 14, 2020) has reported that AI-powered systems have been found effective in strengthening security efforts at the airport, by Airports Authority of India.
- Education- India has one of the largest student populations in the world. There are approximately 290 million students in schools, with more than half that number, enrolled in government schools across the country. However, providing quality education has not been an easy task. AI has been a great tool for the education sector to address this problem (Indiaai, August 10, 2021). To cite an example, TagHive has introduced ‘Class Saathi’- a quiz-based EdTech solution- for in-class and at-home learning, for use by students, teachers, parents, and administrators, through an AI-powered smartphone app (TAGHIVE, n.d.),
- Healthcare- Health care sector has also immensely benefitted for AI/CT applications especially in activities like patient monitoring, on-line consultations, treatment management, new drug research etc. To cite an example, a team of experts from a renowned technology institution in India, working alongside a Kolkata based medical centre, has devised an AI-assisted model for automatically grading the aggressiveness of breast cancer. The solution relies on deep learning algorithms to identify high-risk and normal tumour types, overcoming the human error (ASSOCHAM-PWC, April,2018)
- Computer Applications- CA Technologies India Pvt Ltd and Bizzflo India Pvt Limited have employed AI and CT tools to improve their software business processes and efficiencies (Applied Roots, n.d.)
Based on the capabilities of AI and CT for improving the business processes, NASSCOM (NASSCOM, 2021) has opined that India as of now has just scratched the surface in this field, and its contribution to India’s GDP is expected to rise to ~ $ 500 billion, by 2025.
It is important to note that with increasing use of data science, demand for data scientists has been rising at an unprecedented rate, across the sectors. The demand is most among BFSI (38%), followed by Energy (13%), Pharma and Healthcare (12%), and E-Commerce (11%), among others (NASSCOM-FICCI-E&Y, September 2017). The analysis above undoubtedly shows the impact of AI and CT on various business sectors in India. Despite this phenomenal impact the wide spread Data Science adaptation has been hampered by many factors. Some generic challenges and remedial measures have been highlighted in the succeeding paragraphs.
Generic Challenges
Challenges related to Critical Shortage of Data Scientists in India
India has been unable to meet the huge demand for qualified manpower creating an appreciable ‘demand and supply’ gap, thereby making the task of ?nding qualified data scientists quite strenuous. Studies carried out by Dialani (Dialani, August 05, 2020a) and Kumar (Kumar, December 04, 2020) had examined various reasons for shortage of qualified human resources. Some of the important reasons were: -
- Data Scientists have a fairly steep learning curve, in that they need to learn the underlying mathematics, statistics, and computer science and not just the syntax of the related languages.
- Despite huge influx of data scientists every year, very few of them have proper expertise in the subject. In a world of self-taught coders, most data scientists tend to have formal degrees in computer science, statistics, or mathematics- which cover only one aspect of Data Science.
- Only a limited number of Data Scientist have proper knowledge of Python or the other most commonly used languages in data science and data analysis.
- India is way behind the western world in terms of universities and boot-camps offering degrees in data science. As a result, not many knowledgeable data scientists are available in India, to meet even the existing demand.
In addition to above, many researchers like Deoras (Deoras, February 25, 2019), Analytics Data Magazine research teams (AIM, February 27, 2020, and March 05, 2021) had analysed various challenges, including those for industry and data scientists. A gist of those challenges is presented below: -
Industry Level Challenges
- Ability of Finding the Right Data & Right Data Sizing- As data science applications are still comparatively in nascent stage, most common problem currently being faced by the industry pertains to availability of right data -a crucial requirement for building the right model. Further where appropriate data is available, data scientist in India are constrained due to their limited ability for processing large volume and velocity of data, for arriving at profitable business decisions.
- Ability for Consolidation of information- Industries in India currently possess voluminous but scattered data. Consolidation of information thus remains one of the biggest challenges.
- Lack of Public Awareness- Despite growing importance of analytics and data science, there is still a need to educate the industry about immense utility of accumulating and analysing the right data.
- Lack of Stakeholder Commitment- In spite of the fact that data analytics solutions help enterprises in business process transformations, unfortunately there is lack of involvement and commitment from the key stakeholders. This is crucial for moving a project in the right direction and delivers the right business impact.
- Utilisations of Data Science Models – Currently the data scientists in India find entire process of adoption of data science solutions to execution quite intimidating. It requires professionals with a strong problem-solving capability to make that happen, root cause going back to back to non-availability of right talent, coupled with archaic education system in India and availability of limited training infrastructure.
- Identifying Appropriate Analytics Use Cases - As the analytics industry is still evolving non-availability of appropriate cases studies, which serve as a potent decision making tool for the stake holders, is also one of the biggest problems. It is a challenge to identify correct data for the appropriate analytics use case. Preparing sound simulation models and use of other statistical tools could be a solution, till enough case studies become available.
- Lack of Agility - Currently due to limited data availability, the analytics functions are being structured in a way that restricts interaction with the end user. Many experts believe that for analytics to become more user friendly, it needs to be more agile and in sync with businesses, during the decision-making process.
- Apprehensions about Security of Data- As analytics demands handling huge volume of data, which could be company or sector sensitive, ensuring security of the data remains a big challenge- especially in today’s world. Thus availability of wherewithal for ensuring privacy and making data as safe as possible from any wrong use is a must.
Data Scientists Level Challenges
Lack of appropriate skills with the Data scientist in India have crated many difficulties for them in relation to application of data science. Some major ones are:-
- Getting Data from Multiple Sources- While building the models or anomaly detection system, data scientists get bogged down with huge amount of data coming from different sources/databases. The biggest challenge the face is finding ways to consider all forms of data and convert it into one single format to centralize the observation. A real-time querying production database also poses a problem. A probable solution could be inculcating ability to acquire unstructured data and convert it into one meaningful database.
- Unlocking value out of Unstructured Text Data- It has been observed that major chunk of data that is currently being stored by enterprises around the world-including India, is unstructured text data. Traditionally, an enormous amount of time, effort and resources are required to be spent by analysts in data processing by transforming unstructured text data into a standardized format to find insights out of it. Sometimes lack of right expertise results in benefits being outweighed by the cost. Fortunately, enterprises have realized the impact of using Ontologies (a set of concepts and categories in a subject area or domain that shows their properties and the relations between them) in reducing the burden on data processing thereby making data analysts more efficient.
- Inability of Setting up the Infrastructure/Tools for handling High Velocity of Data- Data Scientists also face challenge in setting up the infrastructure/tools for handling modern data requirements (especially streaming), due to high volumes and velocity of data. This issue can be efficiently handled by using data streaming cloud PaaS services like Azure Stream Analytics and Azure Databricks.
- Understanding the Quality based on the Semantics of Data- Enterprises use Big Data Analytics by integrating data from both internal and external sources of data -including structured, semi-structured (weblogs) and unstructured data (Social media feeds), to enhance customer satisfaction. But due to lack of expertise, data scientists in India find it difficult to do Sentiment Analysis using Customer Feedback arising from the processing of unstructured data coming from call logs or chat-bots. These provide vital insights into why a customer is not happy about the services provided or a product and the feedback can be used to improve service quality and enhance customer satisfaction. The data scientists thus need to be trained for this capability.
Other Challenges
- Lack of Training Infrastructure- As data science field is still evolving in India the training infrastructure is inadequate to meet the requirements. Though beginning has been made lot more needs to be done. Due to its criticality, this issue will be examined separately later.
- Last Mile Connectivity Limitations- This is due to basic limitations of the telecom sector in India. Government needs to take cognisance of this factor and initiate remedial measures.
- Relatively high costs related to deployment of Data Science – As the use of Data Science is still evolving the initial costs for deployment of Data Science are relatively high but these are expected to come down as uses increases, thus patience would be key.
Infrastructural Challenges
IBM Chief Ginni Rometty has rightly pointed out problems being faced by Indian data scientists due to lack of skills, by stating that ‘the Indian IT workers are getting affected because of their massive skill gaps’. To close that gap, employees would need to be well-versed in various technologies related to data (PTI, March 13, 2019). Imparting that deep knowledge would, in turn, demand basic skills set for the aspirants along with availability of qualified instructors and adequate infrastructure (Kumar, December 04, 2020a).
Many reputed institutions like IIM Calcutta and IIT Kharagpur have started offering courses related to Data Science, amongst others (DST, n.d.). In addition, Tata Consultancy Services (TCS) has partnered with four colleges across the country that would offer courses in Big Data Analytics (AIM, July 09, 2016). Though the above measures have helped in mitigating the training challenges, they are still inadequate and much more needs to be done, to meet the emerging requirements.
Reformative Measures
To address the above challenges appropriate reformative measures would need to be put in place, that too on priority. The enormity and complexity of these reformative measures would demand joint efforts by the Government, Industry, Academia and Individuals, to be effective. Some of the segment wise reformative measures required for improving the situation need to cover:
- Government
- Policy- The governmental policies need to offer incentives to companies entering data science field for efficiency enhancement, creating better working conditions, creating new skilled jobs. It should also provide support for leveraging latest technologies through technology transfer during FDI deals in key sectors.
- Skilling and Re-skilling Initiatives- Data Science usage demands availability of highly skilled personnel. Government needs to provide adequate facilities for technical skill upgradation. It may also collaborate with and incentivize industry for offering their infrastructure for skilling the Indian personnel in across the country. This would enable the MSME sector, backbone of Indian economy, to adopt Data Science techniques.
- Establishing Centers of excellence (CoES)- Government may establish CoEs in AI and CT which would help in transforming unorganized sectors to organized ones.
- Industry
- Vision-Formulate vision for industry 4.0- Create a vision for exponential technologies for industries ( including small scale) both in organised and unorganised sectors
- Gig Economy- Encourage and use the gig economy approach- to leverage the competencies of the existing workforce, by reskilling them.
- Ecosystem- Create collaborative learning ecosystems for each industry /Develop workforce re-training programs across organization levels/Partnering with Government for various reformative measures.
- Academia
- Focus on cognitive/judgment-driven skills
- Offer tailored courses with flexible completion timings will enhance students’ inclination towards learning
- Offer appropriate courses for existing workforce- for reskilling
- Individuals
- Understand multifaceted skill requirements for working in data science field and upskill accordingly.
- Be prepared to follow a long learning curve, while on job
- Be prepared to embrace the gig economy due to dynamics of the emerging market
Note: Gig Economy relates to a labour market characterized by the prevalence of short-term contracts or freelance work as opposed to permanent jobs, requiring frequent re-skilling (Madell, 2021)
In summary, it is clear that India needs to adapt Data Science, in a big way, sooner than later, to ensure progress and competitiveness in the global arena. In the words of Moore “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” (DataFlair, n.d. b)
Conclusion and Recommendations
Data Science with its abilities has become powerful tool for transformation for governments as well as businesses. Many companies/organisations in UK, USA and Europe have adapted Data Science to improve their decision making abilities and enhance their efficiencies.
India too has become cognisant of Data Science field and Government (Department of Science and Technology) and authorities like NITI Aayog have started seriously looking into it. Many sectors like Health, Education, Manufacturing, E-Commerce, Banking and Finance, Power, Transportation, MRO have imbibed AI and CT to improve their business processes and in turn efficiencies. There is now a huge demand for qualified Data scientists, with expertise in various domains/associated languages and same is expected to increase exponentially, which is unlikely to be met with the current wherewithal.
India thus needs to put various remedial measures in place, to overcome the limitations. As India has already created its own niche in the IT field, effectively entering the Data Science field should not be a difficult task. Some specific thoughts that could be considered are:
- Government, industry and academia together should create public awareness about Data Science and its prowess, channelize efforts for not only introducing but propagating Data Science in various sectors and provide incentives /adequate funding.
- Government, industry and academia should consider creation of Centers of Excellence and institutions. It would be important for imparting quality training in the Data Science field and making training affordable.
- Government should consider incentivising telecom companies stepping into 5G Network technology, Further, as wide spread data science applications would demand excellent network connectivity,
Some additional requirements would cover:-
- Recognising and rewarding institutions and individuals making substantial contribution to the field.
- Simplifying processes related to approvals/monitoring/reporting and creating a dedicated channel for resolution of problems.
To conclude, collective efforts- by all the above stakeholders- would be necessary for India, for making its mark felt in Data Science arena.
Acknowledgment
The author would like to thank Dr R Srinivasan for his valuable guidance during compilation of this research Paper.
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