Saves up to 80% of time in model building, cuts 90% of the learning curve time &delivers 20%-40% more accurate and stable models.
By Srinivasan Rengarajan, VP & Global Head – Data Science and Analytics, 3i Infotech
According to a recent Indian jobs study, data science is one of the topmost and fastest growing field in India and its relevance is increasing in almost every sector.Reports from NASSCOM suggests that India’s data industry would reach $16 billion by 2025 from the present level of $2 billion.At the core of it, data science is the science of examining raw data and applying statistical techniques for the purpose of drawing business related conclusions and predicting business outcomes.In every organization, there are opportunities to implement data science and transform the way business is carried out.
Leading analysts like Gartner and Forrester have quoted 2018 as a milestone year for organizations, with over 70% of them expected to leverage data science for Business Optimization. It is one of the most talked about topics in the CxO community.
In today’s era, all small and large corporates are sitting on a gold mine of data, however, the biggest challenge they are facing is to use these data to get business insights which they can implement to make effective business decisions and optimize their business. In the Indian context, below are the industries adapting data science to gain the competitive advantage:
- Financial institutions are optimizing price, improving customer satisfactions, predicting risk of defaults, optimizing underwriting process
- Hospitals are increasing diagnoses accuracy, providing physicians with accurate sickness’s causes for individual patients, preventing patient readmissions, predicting risk of infections
- Retail chains are increasing occasional and loyal customer satisfaction, optimizing campaigns, offering the right price for products, preventing inventory shortage
- Manufacturing organizations are predicting machine failures, providing predictive safety alerts, building accurate pro-active maintenance plan
However, applying traditional data science methods to real-world business problems is time-consuming, resource-intensive, and challenging. It also requires experts in the several disciplines, including data scientists.
Enterprises leveraging Automated Data Science to achieve efficiencies:
Automated data Science represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Automated data science platforms are bringing the advanced AI techniques into reach for the mainstream. Organizations are finding that with automated data science they can make progress in AI without hiring new data scientists or embarking on expensive, time-consuming training for their employees. Instead, almost anyone with domain experience and a familiarity with data can build predictive models without writing a single line of code or having deep knowledge of machine learning algorithms.
With automated data science, AI innovation is not just exclusively in the realm of the data scientists, but can now be shared with those that best understand the business needs. The main obstacle to AI success is no longer capability, but rather a refusal to embrace new methods and new approaches. Automated data science platforms removes many of these obstacles, and with a sound data science strategy will accelerate your success.
Automated data science saves up to 80% of time in model building, cuts 90% of the learning curve time, delivers 20%-40% more accurate and stable models and lastly zero preparation time for production deployment of models, thereby giving the utmost advantage to organizations to adapt data science.
The world is being disrupted by visionaries. Combining the power of AI and automated data science with a sound strategy is helping build a future that is smarter, more efficient, and fairer for everyone. The companies that take advantage of automated data science will succeed and prosper. Those that don’t will be left behind.
Corporate Comm India(CCI Newswire)
