Ravi Korlimarla: Driving Innovation in Enterprise Software 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 it focuses on delivering solutions related to financial management, human capital management, and analytics applications designed for the world’s largest companies, educational institutions, and government agencies. Several organizations ranging from medium-sized businesses to Fortune 50 enterprises have selected Workday to address their needs for their promising solutions.
A Passionate Leader
Ravi Korlimarla is the Director of enterprise data science and advanced analytics at Workday. He has been interacting with data teams and leading data projects over the past 15 years, which has imbibed a certain interest and passion for data and analytics in him. Ravi hails from a computer engineering and ERP background. Therefore, he has ample exposure to these systems and processes that create these data structures. According to him, the next natural progression in the AI, ML, and big data space would be to apply algorithms to predict, diagnose, and forecast.
Past Experiences that Helped in Learning
There are a few lessons that Ravi learned from his past experiences that he still follows even today. First and foremost, them is to prepare the business and an ecosystem to catch advanced analytical outputs. “It is very important to educate leaders and to increase awareness around the possibilities of utilizing AI and ML models in the workflows” he adds. The second of them is that—to learn to never overestimate the impact of AI and ML projects. He also stresses that benefits come in slowly at first but compound over some time and hence it is always important to start slow, prove the benefits on a small canvas and expand from there.
Turning Challenges into Opportunities
It has been some time since Ravi took a transition from data and business SME to a data science leader and his journey was not at all smooth. He says that a part of his challenge was to find opportunities to apply his newly acquired data science chops in a real business scenario, and he did not get the luxury of doing that, every time. Initially, whatever little opportunities met his way, he seized them all to deploy his models, and this way Ravi was able to generate a business impact.
Most of his models were path-breaking and first-of-its-kind in the organization, however, his transition was long and needlessly painful. But Ravi never looked back and always made a point to turn in challenges into opportunities to learn, seek and explore.
Attributes that Make a Successful Data Science Leader
Ravi mentions that leading data science teams is exciting and challenging at the same time for him. “Recently, there has been a growing interest in this field, so hiring talent is not a problem but retaining the talent is” he stresses. Ravi expresses that a good data science leader recognizes this fact and fosters a creative, fun, and friendly environment with care. An efficient leader leads by example, and this applies to data science leaders as well. Being the lighthouse of thought leadership of new approaches to model building and adopting best practices in implementing a frictionless MLOps process while consistently positioning the team to deliver high business impact, inspires and motivates the team.
“I have used this approach, and as a result, I have consistently maintained teams with attrition rates, well below industry averages” he concludes.
Embedding Innovation to Leverage Success
Ravi believes in innovation that arises organically from the thought process that is directed towards solving a problem. He intends to deeply examine the current state of the business, multi-dimensionally, focusing on people, process, technology, and data. Typically, gaps can be multi-fold with many facets to be optimized. An innovative solution addresses most of these gaps with the least disruption and effort and AI and ML solutions wonderfully suit such situations and hence he believes that the best disruption always changes the underlying paradigm, with the target audience hardly noticing the change at all.
Disruptive Technologies that Drive an Impact
Ravi asserts that big data, AI, and ML solutions can be hugely disruptive if they are executed with a vision and targeted for an impact. Companies today are hastily jumping into this space, with a sense of being left behind, often with no clear vision, commitment, or strategy.
An effective growth model like propensity to buy alone has the potential of adding 5 to 8% to the company’s top line. Other such models can add millions back to the company’s bottom line. In his opinion, the key for future leaders is to establish the vision and gain commitment from the rank and file of the company. This needs to be followed with a well-thought-out strategy that articulates a well-orchestrated and connected AI and ML model blueprint for the company which has the potential of giving back millions to the business while staying ahead of the competition.
A Future Filled with Opportunities
Ravi states that leveraging AI/ML to drive growth, reduce OpEx, drive employee engagement, and optimize community investments will create some opportunity areas for the industry. Some companies are already leading in some of these areas, while others are actively investing. “It is going to be an exciting journey for the industry as they embark on some of these ambitious programs for the betterment of the business top and bottom lines and for the benefit of the world community” states Ravi.
Words of Enlightenment to the Aspiring Leaders
Enlightening the aspiring leaders Ravi says that big data, AI, and machine learning spaces are expanding faster than the universe, so the space reveals blind spots that have the potential to both warps or expand, one’s knowledge and intuition. “Extreme caution needs to be exercised in adopting open-source concepts. It is important to learn, but given the maddening speed of advancements, it is also important to possess accurate wisdom and apply the newly acquired knowledge multi-dimensionally” he claims. He goes on to say that it wi to stay away from buzz words and fancy concepts and stick to the elements that will benefit the leader, the team, the company, and the community.