In this blog post
The Essentials to Data Science Success
While the need to leverage AI to tap into data insights is widely acknowledged by organizations, their path to successful AI adoption comes with several challenges. Taking a business-centric approach, hiring the right resources, and building digital data over time are some factors that could heavily influence outcomes. With the need for industries to work in tandem with academia to solve technology challenges, businesses must also focus on augmenting human intelligence with AI by analyzing key success factors, project categorizations, choosing the right approach, hiring, and nurturing the requisite skillet, and getting proactive with predictive and prescriptive intelligence.
GAVS conducted a very insightful webinar on the paths to success for data science. This blog captures some of the key discussion points and takeaways from the webinar titled ‘The Essentials to Data Science Success within Organizations.‘ The link to the entire webinar is available at the end of the blog.
Mr. Balaji Uppili, Chief Customer Success Officer at GAVS Technologies, moderated the session. He has over 23 years of experience in the IT industry. The panelists were: Mr. Vineet Raina, Chief Data Scientist from GS Lab who has over 17 years of IT experience; Mr. Srinath Krishnamurty, Principal Architect, also from GS Lab who has worked across verticals including CRM (retail/finance), Life Sciences, and Healthcare; and Prof. Nandan Sudarsanam who is an Associate professor at IIT Madras and Core Member at the Robert Bosch Center for AI and Data Science.
AI, ML, and Data Science
Artificial Intelligence is intelligent behavior exhibited by machines – similar to how human beings subjectively describe a scenario, react to situations, and carry out actions. AI can be either rule-driven or data-driven. If it is rule-driven, the intelligent behavior that an artificial agent personifies is subjectively human-defined. An example would be the categorization of emails. To detect and categorize a mail as spam, users can code some rules to identify if the mail is from a certain email address, domain, or keywords. In a data science approach, emails are classified based on past data.
Data science is a broader umbrella that comprises everything related to data. Machine Learning is seen as more of an applied subset of Artificial Intelligence. It is very specific to the fact that an algorithm reacts to data instead of traditional rule-based programming. In ML, the initial steps involve data capturing, data preparation, and then eventually, after machine learning, the models need to be deployed in production.
In the last few years, the possibilities of using data science have immensely increased. People use the umbrella of data mining to analyze data to influence business strategies. There is increasing adoption of these data-driven methods across industries to optimize business processes. These technologies are also helpful to technology leaders to become more aware of new possibilities of growth and new risks.
With the advent of technology, it would be difficult to point at any domain or vertical where AI has not had an influence. Technologies such as image recognition and voice identification also use data science. Some practical examples of data science include its usage in the healthcare industry to predict health conditions based on just a few seconds of the patient’s voice sample. Similarly, businesses are looking to optimize their processes based on predictions from models built using past data. An ideal example of this would be sales at gas stations that can intelligently use past data to plan inventory replenishment.
Reproducibility in Data Science
Reproducibility is one factor that needs attention while building data science models. If a model is built in a certain way, a few housekeeping items, such as data storage, will need to be looked into to help reproduce it. In the context of businesses, one of the things to be observed is that although data science is a scientific discipline, the focus on the reproducibility of experiments and peer reviews is within an industry. In the past couple of years, several tools have come up to enable the reproducibility of experiments. However, there are still a few challenges when it comes to client data as there are no defined situations.
Maturity of AI
Some argue that AI is not mature enough to solve complex business problems. However, the beauty of these AI/ML models lies in their ability to learn and adapt continuously. While AI cannot be used as a proxy for data-driven decision-making, it can be used to facilitate the same. To that end, businesses should identify areas where AI can be applied and not the other way around. Although AI models will never feel emotions, they can be trained the right way to make the right decisions. To that end, Reinforcement Learning (RL) can be used where the model learns through rewards and penalties. If a data-driven solution is unable to solve the problem, it might mean that the problem statement was not framed correctly.
Future of Data Science
Evidently, the technology is here to stay. Many organizations embrace the evolving technologies based on the current uses of AI and ML. To that end, academia and industry leaders expect these technologies to become more ubiquitous. As a result, data science which was only used by data scientists, is now democratized, thus making it available to a larger population. Many software engineers and people from various backgrounds are likely to start applying AI techniques in ways unimagined. It would also be interesting to see how these technologies become applicable in real life.
The panelists Mr. Vineet Raina and Mr. Srinath Krishnamurty have authored a book titled ‘Building an Effective Data Science Practice: A Framework to Bootstrap and Manage a Successful Data Science Practice’. The book uses real examples to give insights that will help data science teams remain productive and aligned with their business goals. The authors have covered everything from fundamentals of data science to deeper explanations around building a professional reference operating model for a data science function in an organization.
This blog offers only a high-level gist of the webinar. You can watch the entire discussion, including the poll questions, and the experts’ take on audience questions here.
GAVS periodically organizes insightful webinars with GAVS’ tech leaders, the leadership team, and industry thought leaders to explore current and emerging trends. To watch all of our webinar recordings, please visit https://www.gavstech.com/videos/.