Digital edge: Turning big data into a competitive advantage
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Digital edge: Turning big data into a competitive advantage

Mark Samuels — March 2021

Every C-suite executive understands the potential power of big data, but not everyone knows how to turn those vast raw resources into game-changing insight. Three digital leaders from around the globe explore how their organizations are creating a competitive advantage through large-scale data analytics.

Our virtual panel:
• Sudaman Thoppan Mohanchandralal, chief data and analytics officer for the Benelux region at insurance giant Allianz
• David Walmsley, chief digital and omnichannel officer at jewelry firm Pandora
• Paul Coby, group CIO at industrial catalysts market leader Johnson Matthey.



Allianz Benelux: Using artificial intelligence to transform the organization

Sudaman Thoppan Mohanchandralal, chief data and analytics officer at the Benelux unit of finance services giant Allianz, says one factor is crucial above all others when it comes to making the most of information: “Be hyper-relevant to your business. Data is not an IT topic, it’s a business topic.”

And nowhere is the potential business impact of big data greater today than in the field of artificial intelligence (AI). Sudaman recognizes that there is a huge amount of hype — as well as potential — around the possible impact of AI.

However, in the case of Allianz — best known for its insurance products — the potential of AI is already being realized; his team has so far delivered 70-plus data-powered AI products. Sudaman says the good news, in terms of replacing hype with reality, is that these technologies have transformed how Allianz Benelux runs its business.
Creating an advantage

One example is the pricing of finance products for customers, which was traditionally completed using linear models that relied on a limited range of variables. Now, the company is able to run hybrid models, which use a combination of techniques from AI subfields.

Allianz Benelux uses this deeper analysis to ensure the ‘explainability’ of its product pricing. Experts recognize that explainability is one of the keys to building trust in AI-based automated decision-making. Many AI systems exist as a black box, producing results that customers receive but without providing an understanding of how the decision was reached. Sudaman’s data initiatives have helped Allianz Benelux overcome that.

 

“Sudaman Thoppan Mohanchandralal, chief data and analytics officer, Allianz Benelux=
Sudaman Thoppan Mohanchandralal, chief data and analytics officer, Allianz Benelux


“We have to understand the process of how we get to the output,” he says. “That’s especially true in pricing products for our customers. All data chiefs need to ensure the system avoids any gender- or race-related bias, for example. In that context, explainability is both critical and important. We have to be able to prove that there is no way that the bias is creeping in.”

Sudaman says the company also uses big data and AI to build products for other business uses, including fraud prevention. Fraud might be a relatively rare event, but when it is analyzed across a company’s finance portfolio, it can become a significant total — and one that ultimately adds to the insurance premiums customers pay.

“Understanding the risk of our customers, predicting the risk, and even preparing them for that risk is also another application area — and we build products using machine learning and data analytics to do those.”

While AI creates “an amazing advantage” for the business, digital must develop a strong understanding of the problems that can be solved with AI — and those that cannot, says Sudaman.

“That makes the education in this area critical,” he says. “Not only at the technical level or the business level, but at the executive level. Sometimes, with their exposure to [sensationalist] magazine articles and consulting propaganda, executives who are driving the AI agenda can put almost unrealistic expectations on data scientists. That needs to be avoided.”

Yet Sudaman also believes the advances he’s seeing in his own organization demonstrate that data-led AI can create huge benefits for the business and its customers. The task now is for all digital leaders to help their boards turn potential into capability.

Pandora: Building a digital hub to help create rich customer experiences

It’s not just data chiefs that have to care passionately about turning data into actionable information — it’s every executive’s business. Take David Walmsley, chief digital and omnichannel officer at jewelry company Pandora, who says effective use of information is a vital element of his role.

So much so, in fact, that he’s spent much of the past 18 months developing a digital innovation hub where the business could make the most of the data it holds to create compelling customer services.

After joining Pandora in April 2019, Walmsley worked with his C-suite colleagues to firm up plans for the hub, bringing together the company’s top tech talent in a single location close to the firm’s global headquarters in Copenhagen. The facility, which opened in July 2020, now has a 120-strong team developing data-led innovations.

“That advanced analytics and data engineering unit has generated wins in terms of personalized experiences and targeted emails for customers,” he says.
Joint efforts

Walmsley recognizes that some of this work is table stakes — targeting an email, he says, is not necessarily rocket science. However, it’s the behind-the-scenes work around big data that makes this kind of initiative successful. And the establishment of bespoke technical foundations has been a big piece of work for the in-house digital team.

“We think it’s very important to build intellectual property around our own data,” he says. On top of a data layer and fundamental customer management system, Pandora’s digital team is adding third-party data analytics products in order to help turn information into insight.

 

“David Walmsley, chief digital and omnichannel officer at jewelry firm Pandora=
David Walmsley, chief digital and omnichannel officer, Pandora


Pandora is working with Salesforce.com, for example, to support its data-led e-commerce initiatives. Another key partner is Adobe — Pandora uses its campaign management and audience management tools to hone and target the right customers with the right messaging.

Marketing is now a data game, he argues. “Our chief marketing officer is data-driven and pushing us all the time in terms of how we how we create that data landscape,” he says.

But the digital team works with partners around the business to ensure that investments in information-led initiatives — such as linking store data with web data — produce tangible results. “You need to create use cases in a language that everyone can understand — just saying that we need to link store data and e-commerce data can be a fairly obscure conversation for most of the business. Such as: if we know someone really loves our Harry Potter charms and jewelry, then we should increasingly put Harry Potter charms and jewelry in front of them when they come to our website or when they receive an email,” he says.

“Maintaining that discipline not only focuses our investment on things that are really going to matter, it helps people in the business have a way into this discussion and dialogue.”


Johnson Matthey: Running data-led projects with a strong business case

Highly experienced IT leader Paul Coby knows the value of big data better than most. Having formerly held CIO positions at UK retailer John Lewis Partnership and British Airways, he joined industrial catalysts maker Johnson Matthey in April 2018, establishing a priority to better exploit the business’s information.

“One of the key opportunities we’ve got here is data,” he says. “Everybody knows it’s a real asset, but how to really understand it and make use it — that’s the big opportunity.”
Making sense of the data

Johnson Matthey increasingly sees many of its innovations emerging from analytics. And to aid that creative process, Coby has established a data office designed to help employees turn information into game-changing insight.

Establishing the right approach for this office has been crucial. It’s not for data specialists to sift through information and tell other parts of the organization how to do their jobs better. Instead, the data office — in Coby’s words — “enables the business.”

 

“Ian Bradbury of Fujitsu=
Paul Coby, group CIO at Johnson Matthey

He gives the example of the company’s vehicle catalyst plant in Macedonia. The people working in there are experts in how their facility works. But what they need is information to help the plant work more efficiently and effectively — and that’s where the data office comes in. Our team can’t tell them how things interrelate and what needs to be done — only they can do that,” he says. “But what we can do is ensure that the data [relating to the plant’s operation] is consistent and clean, and they have the toolsets and visualization capabilities that enable them to make optimal use of the information.”

In the case of the Macedonia plant, the data office uses an AWS cloud to host its numerous  data feeds, such as production-line performance. Coby says a broad range of tools available in AWS enables workers to analyze the resulting information in different ways. The impact has been transformative.

“The people leading that initiative said that the classic spreadsheet approach took them literally days to understand what’s going on. With all the [operational] data in the cloud, they can now use effective front-end tools to understand what’s going on across the plant,” he says.

“They can visualize it, they can see the trends, they can make the connections, and, because the data is in one place, things that took them five days to work out can now be seen in five minutes,” he says.

Coby says the Macedonian example helped his organization to create the formal processes that underlie successful data initiatives. His team has introduced a Competency Centre for Advanced Analytics, which shares best practice and helps the company avoid embarking on big data projects without a clear business case.

“Every month, we’ll get everybody around the virtual table and then decide which proofs of concept we’re going to go after. If they work, then they have to be paid for by the business to go mainstream. The fact that these proofs of concepts are reviewed by both IT and by key stakeholders from around the business, who really challenge them, ensures that there’s a bar to clear,” he says.
First published March 2021
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