Whirlpool Corporation [NYSE: WHR]

Yusuf Abbasi is the Global Sr Director Data and Analytics - Center of Excellence at Whirlpool Corporation. He is responsible for leading the data and analytics strategy, governance, and execution for the global organization. With a diverse background encompassing engineering, business, and data science, Yusuf brings a wealth of expertise to his responsibilities. His experience spans digital transformation, machine learning, cloud computing, and business intelligence.

Please tell us about the journey that you've had so far and your roles and responsibilities at Whirlpool Corporation.

My professional journey started around a decade ago at Caterpillar, where I delved into analytics as part of a central excellence team spanning all verticals. The focus was on driving top-line growth and bottom-line efficiency to save cost in improvement. My primary areas of expertise were pricing and marketing analytics, with subsequent exploration into digital analytics.

Transitioning to Kraft Heinz, a consumer packaged goods (CPG) company, I immersed myself in consumer analytics and direct-to-consumer (D2C) digital transformation efforts. The emphasis shifted to analytics at the commercial level within the CPG industry.

Later, at L'Oreal, my focusshifted to product development for the company. In my current role at Whirlpool, I am primarily tasked with steering strategy, organizational formation, and the implementation of technology to establish a data-driven culture. The overarching goal is to enhance data fluency within the organization and build a robust foundation for a data-driven future.

​ Understanding the role of cloud computing in business and technology is crucial because emerging technologies are like building blocks, and many are either complimentary or reside on the cloud. It's essential to revere and comprehend how cloud computing can shape and enhance infrastructure, as it forms the foundation for technological innovation in your platform. 

What are some of the major challengesin insight engine space today in implementing the right technology?

The crucial consideration when building insights around tech stack is identifying the specific type of insights we aim to derive. A decade ago, simply having a dashboard was a significant achievement, eagerly adopted by users. However, today, two distinct user preferences have emerged. Some users are inclined towards diagnostic insights, utilizing technology to delve deeper into the data. Others seek to expedite the analysis process to reach their desired insights more efficiently.

The shift towards diagnostic insights is gaining prominence, as relying solely on traditional BI dashboards can result in a digital lag. Currently, there are limited technologies addressing this need. Looker, available on Google, stands out as an analysis and visualization tool, but the challenge lies in modernizing and adapting to meet our evolving requirements.

Have you introduced any proprietary technology or methodology that has yielded significant positive outcomes, ensuring that the acquired data is both insightful and beneficial for the end user?

Currently, our focus lies in employing kinetic dashboards that leverage generative AI capabilities. These dashboards diverge from traditional static ones by allowing users to glean insights dynamically. Instead of pre-built dashboards centered around specific KPIs, kinetic dashboards respond to user inquiries or explorations of metrics and trends. Once a question is addressed or a user finishes their inquiry, these dashboards are ephemeral, ensuring relevance and efficiency.

Simultaneously, we are actively implementing measures to streamline the development of visualization tools. While static dashboards remain essential, our goal is to empower users to adopt a more diagnostic approach, minimizing time spent on static creations and encouraging more dynamic interaction with the data.

In the upcoming 12 to 18 months, are there any specific technologies or industry advancements that you you'd really like to draw attention to?

The primary focus for the next 12 months or so is the implementation of localized Learning Management Systems (LLMS) atop various data domains. This move is crucial as many companies, including ours, are still in the testing phase of incorporating generative AI. We aim to explore the limits of this technology and assess the feasibility of deploying a localized LLMS on our data sources. This has the potential to revolutionize how we extract insights from our data and enhance its utility with previously unavailable technologies.

In addition, we are actively exploring other content generation technologies such as Amid Journey and Adobe Firefly. Given the rapid pace of technological advancements, it's anticipated that within six months, new tools for content generation may emerge, further expanding our capabilities in this area. The evolving landscape suggests a continuous influx of innovative options that may further enhance our content creation capabilities.

Any specific piece of advice that you'd like to share with your fellow peers or other industry leaders?

A key emphasis, both from my team and personally, has been the importance of curiosity in our work. Curiosity serves as the catalyst for upskilling, innovation, and the pursuit of best-in-class practices. Despite the seemingly daunting pace of change, my decade-long experience in analytics has shown that change is constant. The one thing I advocate for is a deep understanding of how cloud computing aligns with business and technology, considering that many emerging technologies are either complementary to or hosted on the cloud. This understanding is crucial as cloud technology forms the foundation, much like building blocks, for various advancements.

Furthermore, a vital aspect is the ability to be a proficient analytical translator. This involves effectively converting business inquiries, whether for insights, remedies, hypotheses, or root causes, into actionable findings using data and analytics. Building advanced models is valuable, but without the ability to translate their significance to the business and propose improvements, their impact remains limited.