Women in Big Data (WiBD) at the WiDS Silicon Valley@SAP 2019

The Event Theme: Data Science Empowering Business

L. to R.: Regina Karson, Radhika Rangarajan, Elvira Wallis, and Gayle Sheppard

This event was one of more than 150 ambassador-driven regional events under the umbrella of the Women in Data Science (WiDS) program. SAP was a sponsor and WiBD was a community partner. The agenda included panels, keynotes and breakout sessions.  WiBD was happy to be able to contribute.

Tina TangSr. Director Product Marketing, SAP HANA for machine learning & advanced analytics and esports, was on the Event organizing committee and led some creative ice breakers to connect participants.

Regina Karson, Marketing Strategist & Business Development for Machine Learning/+, Aventi Group, moderated a panel discussion titled “The impact of data science and diversity on business”. Panelists included:

Regina asked the three panelists four questions:

Question 1: How has data science changed in the last 36 months? For example, there has been a lot of discussion about democratizing data science to bridge the data science talent gap and giving rise to the citizen data scientist.

Gayle: Data science is different. In the past three–five years, we’ve progressed data science roles to professional, defined roles throughout commercial, government and NFP organizations. Universities have produced wide range of data science curricula. Organizations such as WIBD and WIDS were formed, providing access to expertise, jobs and mentoring. Universities and commercial organizations have launched a variety of platforms, both open source and for fee, to accelerate machine learning development.

The cloud has been a significant enabler for democratizing access to tools and encouraging the development of platforms, including data ingestion and NLP pipeline tools, methods of organizing information such as dynamic graphs, visualization tools for consumption of outcomes, and of course ML recipes for solving specific problems. Cloud Service providers for the Enterprise (e.g., AWS, Azure, etc.) packaged capabilities include data management, NLP, ML and visualization tools—so the data scientist and engineer spends much less time working to build bespoke environments and has many tools to assemble for development.

When it comes to platforms, the enterprise perspective leans to AWS or Azure environments. In Health Care, firms are transitioning to the cloud, and governments are also in transition to the cloud. So, cloud platforms became the great enabler.

It is also noteworthy to view how terminology has changed over the last decade; for instance:

  • 2010: Data Mining/Analytics
  • 2012: Analytics, Data Mining, Big Data
  • 2013: Data Science picks up, Big Data, Analytics
  • 2015: Perceptual Computing, Cognitive Vision
  • 2018: Decision Intelligence
  • 2019: Little Data, Contextual Adaptation

Elvira: How do we democratize data? As companies think about data, data can be overwhelming to Citizen X. What does that mean to those that supply data and stewards and ethics? Data experience: As we interface with chatbots, consider voice-language. These are great opportunities for women in data science.

Question 2: As industry experts, what are some data science use cases that you would like to share?

Regina: SAP Innovation Awards government use cases:

  • IA’18 winner – OSR, Brisbane, Australia for real estate tax default treatment.
  • IA’19 Winner – State of Arkansas for prison recidivism challenge: Predict the likelihood of recidivism and prescribe actions to reduce recidivism at the individual level (such as getting a GED) and at the group level, to inform policy decisions and individuals.

Elvira:

  • SAP IoT PoCs – Global Ocean Race – AKXONoble sailors had to measure bios and did analyzes for sailors to pick crew/time, and a real time count of calories to reduce weight of ship, etc.
  • Hiliti Product as a Service: Hole as a service is a construction co. offering; handicap and retired people buy a service; and more.

Radhika: Referenced World Bank; what is the minimum amount money needed to put a meal on table?

Gayle: A favorite recent use case was a focus on customer risk in Financial Services. It dealt with the ability to personalize peer review analysis by customer to understand where anomalies exist, as well as novel or potentially emergent trends in customer behavior. Peers were established based on an unsupervised similarity algorithm with no a-priori attribute selection.

Question 3: As AI advances, it is essential that we trust the systems that inform our decisions. This trust extends to government, business and data science. Trusted AI is the convergence of explainable AI, bias, ethics and diversity.

Gayle: This is the big topic for the next decade of intelligent and autonomous systems. Without trust, explanation, transparency, and AI will stall prior to adoption. Public, private, and academic organizations are focusing here, and we are beginning to see guidelines and recommendations from Europe, technology vendors, technology and trade associations, and academia. This is an exciting time for discussion, understanding, and codification of standards.

Elvira: Trust v. governance is a key challenge.

Radhika: Be aware of bias as you create models. Data distribution should reflect the audience.

Question 4: What career advice would you give to the audience or yourself?

Regina: Advocate for others and for yourself.

Radhika: Think big little things…big objectives, little things that take it to next level…regular goals plus boundary on stretch goals. Reposition how goals are expressed on your team; use a tiered and bundled approach to goals that illuminates points of success vs. bundled (where incremental successes are masked).

Gayle: I agree with Regina: Don’t wait to be asked, ask for what you want, stretch, let the past inform you but not define you. It’s good to understand your strengths and weaknesses and, instead of limiting yourself, make both work for yourself as a member of a team, as a leader, as a citizen. In life, be a mentor and be mentored.

Elvira: Be a lifelong learner. Pursue this by changing jobs and challenge yourself in different ways. Develop versatility.


In addition to the panel discussion, Jen Guenther, Sr Analytics Engineer at Netflix, and Fang Duan, Sr Data Scientist at Netflix partner and payments team, ran the break out session: “A Glimpse into the Day of a Data Scientist.” Jen and Fang shared highlights from a few A/B experiments, and discussed what they do to ensure that customers get the best possible experience.

 Thank you WiBD, for a valuable exchange at this community event!

Event homepage


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Tina Tang, Sr. Director Product Marketing, SAP HANA for machine learning & advanced analytics and esports. Tina was on the Event organizing committee and led some creative ice breakers to further connect participants. Tina is global product marketing lead at SAP for digital platform advanced analytics and machine learning. Tina is co-founder of Womeninbigdata.org, a 9000+ strong volunteer organization spanning 5 continents.

Regina Karson, Marketing Strategist & Business Development for Machine Learning/+, Aventi Group. Regina currently works with Fortune 500 early stage companies on their technical marketing and business development needs with a data science focus. Regina was 2017-WiBD-MVP and 2018-WiBD-Volunteer-of-the-Year.

 

Radhika Rangarajan, Director Data Analytics & AI, Intel. Radhika leads a team focused on Customer Success, and Cloud and Community collaborations leveraging open source software innovations in Advanced Analytics and AI. Radhika is also the Co-founder and Director of Women in Big Data, a 9000+ global community focused on strengthening diversity in tech and championing the success of Women in Big Data and Analytics.

Gayle Sheppard, CEO & Founder, global technology executive. Gayle has founded, created, or contributed to start-up and Fortune 100 companies focused on Artificial intelligence, Digitization of business, and International business. She recently led the Saffron acquisition at Intel and ran the Saffron Group at Intel.

 

Elvira Wallis, SVP Global Head IoT, SAP-diversity & inclusion advocate. Elvira is responsible for ideating, defining, delivering, and taking to market IoT business solutions to increase revenue, adoption, and thought leadership. 

 

Jen Guenther, Sr Analytics Engineer at Netflix. Jen provides analytics for and builds data products about Netflix’s payments processing systems. She has more than a decade of experience in analytics, business intelligence, and data management. Jen has participated in several Women in Big Data events hosted by Netflix supporting women in tech.

Fang Duan, Sr Data Scientist at Netflix partner and payments team. Fang’s focus area includes A/B testing and machine learning techniques that improve user acquisition. Previously in the financial industry, she has rich experience in developing model architecture for customer lifetime value, detecting e-commerce fraudulent activity, and providing personalized recommendations in customer service areas.

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