Ravi Korlimarla: Driving Enterprise Software Innovation with Cutting-Edge Technologies
Workday is a leading provider of enterprise cloud applications for finance, HR, and planning. The company was founded in 2005 and is focused on providing solutions related to financial management, human capital management and analytics applications designed for the largest enterprises, educational institutions and government agencies around the world. world. Several organizations ranging from mid-size businesses to Fortune 50 companies have chosen Workday to meet their needs for their promising solutions.
A passionate chef
Ravi Korlimarla is director of enterprise data science and advanced analytics at Workday. He has interacted with data teams and led data projects for the past 15 years, which has sparked in him an interest and passion for data and analytics. Ravi comes from a background in computer engineering and ERP. Therefore, it is widely exposed to those systems and processes that create those data structures. According to him, the next natural progression in the space of AI, ML and Big Data would be to apply algorithms to predict, diagnose and forecast.
Past experiences that helped to learn
There are a few lessons Ravi learned from his past experiences that he still follows today. Above all, it is about preparing the company and an ecosystem to capture advanced analytical results. “It’s very important to educate leaders and make them aware of the possibilities of using AI and ML models in workflows,” he adds. The second of them is this: learn to never overestimate the impact of AI and ML projects. He also points out that the benefits come slowly at first, but get worse over time, so it’s always important to start slow, prove the benefits on a small canvas, and grow from there.
Turn challenges into opportunities
It’s been a while since Ravi went from a data and business SME to a leader in data science and his journey has not been easy at all. He says part of his challenge was finding opportunities to apply his newly learned data science skills in a real-world business scenario, and he didn’t have the luxury of doing that every time. Initially, whatever small opportunities presented themselves to him, he seized them all to deploy his models, and thus Ravi was able to generate business impact.
Most of his models were groundbreaking and the first of their kind in the organization, however, his transition was long and unnecessarily painful. But Ravi never looked back and always made a point of turning challenges into opportunities to learn, research and explore.
Attributes that Make a Successful Data Science Leader
Ravi says that leading data science teams is both exciting and challenging for him. “Recently there has been a growing interest in this area, so hiring talent is not an issue but retaining talent is,” he points out. Ravi expresses that a good data science leader recognizes this fact and carefully fosters a creative, fun, and friendly environment. An effective leader leads by example, and this applies to data science leaders as well. Be the thought leadership beacon of new model building approaches and adopt best practices in implementing a frictionless MLOps process while constantly positioning the team for high business impact, inspires and motivates the team .
“I used this approach and as a result, I always maintained teams with attrition rates well below industry averages,” he concludes.
Embedding Innovation to Leverage Success
Ravi believes in innovation that flows organically from the problem-solving thought process. It intends to take an in-depth look at the current state of business, in a multidimensional way, focusing on people, process, technology and data. Typically, gaps can be multiple with many facets to optimize. An innovative solution fills most of these gaps with the least amount of disruption and effort and AI and ML solutions are ideal for such situations. So he thinks the best disruption always changes the underlying paradigm, with the target audience hardly noticing the change.
Disruptive technologies that have an impact
Ravi says big data, AI and ML solutions can be hugely disruptive if executed with vision and targeted for impact. Companies today are rushing into this space feeling left behind, often without a clear vision, commitment or strategy.
An effective growth model like propensity to buy alone has the potential to add 5-8% to company revenue. Other such models can add millions to the company’s bottom line. According to him, the key for future leaders is to establish the vision and get commitment from the bottom of the company. This should be followed by a well-thought-out strategy that articulates a well-orchestrated and connected AI and ML model plan for the business that has the potential to give millions back to the business while staying ahead of the curve. on the competition.
A future full of opportunities
Ravi says leveraging AI/ML to drive growth, reduce OpEx, drive employee engagement, and optimize community investment will create opportunities for the industry. Some companies are already leading in some of these areas, while others are actively investing. “It will be an exciting journey for the industry as it embarks on some of these ambitious programs to improve business revenue and results and for the benefit of the global community,” says Ravi. .
Words of Enlightenment to Aspiring Leaders
Enlightening budding leaders Ravi says the big data, artificial intelligence and machine learning spaces are growing faster than the universe, so space is revealing blind spots that have the potential to distort or distort. expand knowledge and intuition. “Extreme caution should be exercised when adopting open source concepts. It is important to learn, but given the insane speed of progress, it is also important to possess precise wisdom and to apply newly acquired knowledge in a multidimensional way,” he says. He goes on to say to stay away from buzzwords and fancy concepts and stick to the things that will benefit the leader, the team, the company and the community.