Augmented Analytics, Business Intelligence, Deep Dive, Uncategorized

Novartis: Delivering Augmented Intelligence at Scale

At the Chief Data & Analytics Officer Fall virtual conference, Axel Goris, Global Visual Analytics Lead at Novartis presented their perspective on how to deliver augmented intelligence at scale, in a session hosted by Tellius. Axel focused on many of Novartis’ practices within the world of AI and analytics.

Some of these practices included:

• How Novartis employs augmented intelligence to accelerate the journey from hindsight to insight to foresight
• How Novartis focuses on business and user needs to support a multi-platform strategy
• How Novartis scales AI-assisted analytics to the enterprise for maximum impact
• How Novartis tackles the challenge of analytics-ready data through a structured approach of reusable data assets

Today’s Challenges in Supporting Business Analytics Needs

The main challenges that Novartis faces today in supporting business analytics needs are Agility and Scale to Meet Demand, Technology Strategy, Safe & Compliant Delivery, Ongoing Quality Monitoring & The Everchanging Role of IT. He mentions that businesses going big on Data and Digital drive significant demand and desire to move at speed with autonomy, taking advantage of the latest technology. This creates a challenge for IT to scale and respond fast enough, creating the need to change their delivery approach.

Personas And Needs

For Axel, building a strategy for insights platforms starts with clarity on personas and needs. He explained that there are 4 key personas they monitor.

-First, there are Business Consumers (who take up the largest portion of users). When looking at AI tools, the Business Consumers are often looking for something that can provide Business Monitoring, Business Alerting, Ad-hoc Q&A, and Insight Suggestions.

-Next are the Business Analysts (2nd largest). Business Analysts are looking for Insight Exploration, Assisted Data Prep & Advanced Analytics, What-If Scenarios & Data Storytelling.

-Then come the Developers (3rd largest). With Developers, we start to see the shift in complexity for their wants & needs for AI tools. Developers are looking for Advanced Dashboarding, Data Modeling and ETL & Low-code & Auto ML/Data Science.

-Lastly, we have our smallest and most technical group, the Data Scientists, who are looking for Full code ML & Data Science.

From Data to Insights

The session took a moment to discuss the process from Data to Insights. Where questions like “What happened? Why is this happening? What will happen?” and, “How can we make it happen?” are all answered. Again, these sections are divided into personas and groups where those different needs (explained in the section above) are put into a more clear place through the data to insights process.

I highly encourage all to watch the full session from the CDAO Fall Virtual Conference and to share your thoughts with us on Augmented Analytics & Intelligence on our LinkedIn & Twitter


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