Data quality, COVID response, saving the coral reefs and more during Transform’s Data, Analytics, & Intelligent Automation Summit

The Data, Analytics, & Intelligent Automation Summit at Transform 2021, presented by Accenture, took a deep dive into how data, analytics, and intelligent automation can help the greater good, the bottom line, and more.

The day kicked off with the Big Bytes in AI & Data breakfast, presented by Accenture. Leaders from Accenture, American Express, Opendoor, Evernorth, and Google ultimately agreed that the quality of the data under their AI solutions is non-negotiable.

As Valerie Nygaard, product lead at Google Duplex, said, “You can make tons of tech innovations, but so much of the time they rely on the quality of the data, that accuracy, the normalization, the processing, and the handling.”

The American Express credit and fraud risk group uses models powered by machine learning to monitor $1.2 trillion in charges annually around the world, and return 8 billion risk decisions in real time, said Anjali Dewan, vice president of risk management, consumer marketing and enterprise personalization decision science at American Express.

“Having the discipline to make sure that the quality of that data is consistent, starting from evaluation when you put it into production, is a key competitive advantage,” she explained.

Opendoor’s valuation models, which service more than 90,000 customers, and enable more than $10 billion in real estate over 30 markets, are only as worthwhile as their data input, said co-founder and CTO Ian Wong. To ensure coverage and accuracy, they’ve built custom inspector apps that use a human expert to collect first-party data and then input it back into their central repository in real time.

It takes time to collect and manage data, ensure it is high quality and governed, and then organize it to drive insights, said Mark Clare, enterprise head of data strategy and enablement at Evernorth/Cigna. But the new agile, collaborative processes and visual-based discovery they helped one financial services company implement led to the company’s global head of business discovering an eight-figure attrition risk in less than 30 minutes.

One big takeaway for Ahmed Chakraborty, global managing director, applied intelligence North America lead at Accenture, is that when you take a data-driven journey in the enterprise, it’s a change journey, and a big part of the change is to drive adoption.

“I call this the last-mile connection,” he said. “Literacy around data is critical. Elevating the entire acumen of your enterprise to understand data, understand what you can do with data, is so critical in the long-term journey to drive adoption and the change in your culture.”

“Cloud to survive. AI to thrive: How CXOs are navigating the path to data-driven reinvention” 

The Summit’s keynote featured Hari Sivaraman, head of AI content strategy at VentureBeat, in conversation with Accenture’s Sanjeev Vohra, global lead – applied intelligence.

Post-pandemic, there’s been a massive shift toward data, AI, and cloud to create greater good, greater revenue, and greater efficiencies.

Vohra identified four key fundamental changes he and his team have seen, particularly in the past year. First, is that cloud and data have come together as superpowers. On the one side, he explained, is the proliferation of cloud which provides much higher levels of compute power and the flexibility to scale up and scale down, depending on the need. That’s combined with vast amounts of data now available both inside companies or obtained from third-parties.

“Data and cloud are a huge trend we see powering the entire planet and it has really advanced during the pandemic,” he said.

The second trend is that the C-suite from companies across industries are now actually interested in these technologies and how they can be used to derive business value. “It has  has moved out of the experimentation zone, or pilot zone,” he said, “to be used for scale.”

Speed is the third trend. As Vohra explained, “Nobody wants to spend two years, three years trying to drive value. [Business leaders] are really getting serious about saying what can be done in six months.”

The last trend is talent. It’s scarce, and the demand is coming from everywhere. So companies now are having to make important decisions about how much investment is required for building staff, and what portion is focused on building internally versus recruiting from the outside.

Later in the discussion, Vohra shared one of the projects he is particularly excited about. Along with Intel and the Philippines-based Sulubaaï Environmental Foundation, Accenture is saving the coral reef with AI and edge computing that monitors, characterizes, and analyzes coral reef resiliency. Accenture’s Applied Intelligence Video Analytics Services Platform (VASP) detects and classifies marine life, and the data is then sent to a surface dashboard. With analytics and trends in real-time, researchers make data-driven decisions that are helping the reef progress even as we speak (or as you read).

Cigna C-suite executives discuss the impact of AI and digital interactions in transforming the health of their customers

During the AI in health panel, Gina Papush, global chief data and analytics officer at Evernorth/Cigna, had a conversation with Joe Depa, global managing director at Accenture, about how they’re using actionable intelligence to make health care more predictable, efficient, and most importantly, effective.

Their major focus over the past year and a half has been been understanding the impact of COVID geographically and across different population segments.

“One of the things we’ve uncovered is that without a doubt, there are differences in terms of how COVID is impacting different groups of customers, and particularly Black and Hispanic customers,” she said.

The organization partnered with their clinical and customer experience teams, working with employers locally in those markets, to bring a concerted, data-driven efforts to drive outreach. They proactively distributed PPE and education about preventing infections, and managing illness. And as vaccinations rolled out, they worked with customer employers to get these to vaccination sites.

Once they shifted focus to studying post-COVID effects, particularly long-haul COVID, they found that in patients with long-haul COVID, many customers have pre-existing chronic conditions such as heart inflammation and heart disease, which are prevalent at higher rates in communities of color. Now they’re focused on identifying risks, and data science teams are building models and applying models to identify those who may be at risk post-COVID for severe complications.

“It’s critical that post-COVID care continues, and our predictive analytics enable us to be more pinpointed in driving that care to the right folks,” Papush said.

Understanding consumer behaviour with big data & delivering AI powered products that offer personalized recommendations

This AI in retail panel, presented by Accenture, unpacked the ultra-personalization trend with AI leaders from DoorDash, Nike, and Accenture.

“It became more apparent every day that the post-pandemic acceleration of digitization has changed the way people consume and interact with products in all categories,” said Lan Guan, applied intelligence global solutions AI lead at Accenture. “AI has leapfrogged to indulge consumer demand for exactly what they want, when they still want it. That’s what ultra-personalization is all about.”

For DoorDash, this personalization centers around what the company calls “the restaurant selection problem,” explained Alok Gupta, head of data science and machine learning at DoorDash.

Consumers come to DoorDash with a specific food in mind. Their data scientists are focused on understanding what that desire is, and identify potential new restaurant partners that can help make the DoorDash app’s restaurant and food selection as robust as possible.

With digital demand exploding at Nike, their whole model had to shift, said Emily White, Nike VP of enterprise data and analytics. The company used AI and machine learning to automate internal processes to gain speed and launch a new distribution facility to fully support their growing digital demand.

Her team created a replenishment engine to read the signal, identify available inventory across all of Nike’s distribution centers and stores, and determine which products should be allocated to the Adapt facility in Tennessee to best serve the southeast region. It’s their largest distribution center worldwide, built to distribute the company’s Nike and Jordan products to individual consumers, wholesale customers, and Nike’s retail channels as efficiently as possible in the new digital-first world.

“The outcome of this is decreasing transportation time and cost, improving our sustainability, and helping us react faster to our local demand,” she said.

One of Accenture’s clients, a fashion brand, used AI and an ultra-personalization approach to go from passively offering just a few clothing collections a year to responding to what’s still hot in the market. They collect real-time consumer feedback from across social media platforms with AI and machine learning. Within just a couple of hours, designers translate this information into product ideas and send them to micro-studios for experimental production.

“Two quick results here,” Guan said. “25% growth in yearly revenue, and 29%-plus increase in revenue-per-visit, all because of that ultra-personalization.”

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