Mar 15–18, 2020

Data science + Domain Experts = Exponentially better Products

Jeffrey Vah (Dell Technologies), Gayathri Rau (Dell Technologies)
9:05am9:30am Monday, March 16, 2020
Location: LL20A
Secondary topics:  Data Quality

Who is this presentation for?

Non-technical or Business audience




To deliver industry best-in class data science products, multiple groups will need to collaborate to not only design and develop an ecosystem, but also revamp operations processes and develop work flow models. The availability of information and its influence on customer and bottom line impacting business transformation is fast making today’s companies leaders important change agents of the digital age.

Among top computer makers, on average, 1 in 3 Laptops will fail over a 3 year period, many of which will be sent to a repair shop for deeper diagnostics and issue resolution. If parts are replaced, those parts are sent for failure analysis to determine what, if anything malfunctioned. During all of these engagements, and many more, data on each unit or part is being gathered, entered into a system in fields and free text and stored. At some point, depending on the question(s) being addressed, certain data may be accessed and used as part of a targeted analysis. However, the analysis is usually focused on a specific question or incident. Our data science product envisions combining this data in a way that identifies patterns and predicts outcomes, which can be used to create the ability to predict the repair before the unit arrives at the repair depot or, perhaps, before the customer is even aware that a fault exists.

In addition to using data in creative ways, company leaders are, by extension, driving operational process improvements and workforce transformation to shift their thinking on how they can best marry technology with process to improve how they deliver exponentially better data science products. In this session, we will share our experience throughout the product lifecycle journey we took as we integrated business expertise with the data scientists and technologists.

Prerequisite knowledge

General understanding of today’s business problems and basic data science concepts.

What you'll learn

Guidelines, best practices and pitfalls to avoid on the journey of digitally transforming business data through AI and machine learning. Example lifecycle of a data science product exemplifying machine learning using conversational AI, Natural language processing & Deep Reinforcement learning.
Photo of Jeffrey Vah

Jeffrey Vah

Dell Technologies

Sr. Principal Test Engineer, DellEMC Inc, One Dell Way, Round Rock, TX
26+ years expertise in Test Engineering, 7 years in Engineering Management, Test Design, Tools & Automation.
Publications: Co-authored article, CIOReview, Nov, 2018, Patents: Co-Inventor of (4) pending patents for innovative use of machine learning models at Dell Technologies; Co-Inventor of US patent grant #9050529 for Innovative Hardware Design at Microsoft (2012).

Photo of Gayathri Rau

Gayathri Rau

Dell Technologies

19+ Years in Data and technology field with experience in collaborating with business & technology architecture teams and enabling platform capabilities & innovation on enterprise data platform. Currently managing a team of product owners, data scientists and BI developers to build AI & Machine learning products to help solve customer & business problems.

Patents: Co-inventor of Patent pending for innovative use of Machine learning models at Dell technologies.

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