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Scaling Data Architecture for the Future - Panel Discussion

Vinoth Kannan
Lead Data Architect at Daimler Truck AG
+ 2 speakers
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Data & AI Summit 2021
February 3, 2021, Online, USA
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About the talk

Scaling Data Architecture for the Future - Panel Discussion

- Vinoth Kannan, Lead Data Architect, Daimler Trucks & Buses

- Paul Moxon, SVP Data Architectures, Denodo

- Moderator - Dave Knifton, Lead Global Data Architect, HSBC

Link to the event: https://www.sofasummits.com/data-and-ai-2021

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About speakers

Vinoth Kannan
Lead Data Architect at Daimler Truck AG
Paul Moxon
SVP Data Architecture and Chief Evangelist at Denodo
Dave Knifton
Lead Global Enterprise Data Architect at CRISIL Global Research & Risk Solutions

An accomplished Data & Cloud Architect and Product Leader with over a decade of hands-on experience with large-scale distributed systems, Big Data, and Cloud technologies. As a result-oriented thought leader, I have a track record of strategizing, designing multiple Advanced Analytics platforms, and building teams that drive value from data investment that matters.

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Accomplished product management and product marketing leader with a proven ability to successfully introduce and grow new products and sustain existing, more mature, product lines. • Successful track record at introducing new products to market and rapidly growing customer base and revenues. • Over 15 years of experience in software product management and product marketing following full product development lifecycle (product ideation through to product retirement) using various development methodologies (waterfall, Agile/Scrum, extreme programming). • Strong business focus – working with sales and external partners to drive product go-to-market strategies and optimize sales and marketing strategies.

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Data defines the modern world. There is almost no area of human endeavour where data does not play a significant role - and its impact on us all increases every day. I believe the role of data architecture is to drive an organisation's success by maximising the benefit it derives from its data. The key enabler for delivering this benefit is to simplify, agree and share the organisation's definitions of its data. From these firm foundations, a data-driven approach can be adopted transforming the organisation to become truly data agile.

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Take me to global global data architect for HSBC, wholesale Bank. Closest galaxy. Okay, so Okay, and that leaves me as the only ones. So call moxon VP of days, Rocks matches on Chief evangelist Donato Technologies, and I'm based in New York. So I'm looking at over the snow and he's 18 inches of snow for the last couple days. I'm looking over the beautiful snowy landscape of New York City. Great. Fantastic. I canceled the time of the meeting is is scanning data architecture to the future. I guess it's quite an interesting title in terms of a

tested idea that they'd rockitecture needs to scale in some way. And maybe we should start there because I'm not quite sure what we mean by scaling infrastructure. The scaling architecture might be interesting. But what's your take on on, on the title? Get off? Maybe I can go and I think we can really see how the data in the past. We had data warehouses that we have single thousand different projects trying to connect. Know what you're seeing. Now this week. We moving away from the data warehousing tomorrow, for the update on Lake, kind of sit up there.

But there is much more flexible in terms of what you can do, but the future that has children of challenges in terms of scalability, not just in infrastructure about in terms of been you trying to bring in so many diverse sources into one place. It becomes highly intense of finding the right thing or do you want to find, you know, how do you write quality understanding about the dead cells? And how do you support? So I think these are the boat. Next, I see we have solved the infrastructure but like that such with

massive Cloud infor. Shutting. No one is worried about adding another server to the, to the Did I can look at them? That's not the problem here. I think the main challenges, really, how do you know our Solutions on top in a way that it could break up? That's my taking this whole topic. I think that that is so spot-on. I think, you know everything about the thing likes, the challenge that we could come into the light, but then you've got the disparate sources now in the same location, in the same tank, and in many instances, they haven't really solved. The problem of, how do you

get scales opportunities out of that? Tell you, if it's still in, in a different tax? I sort of agree with enough but if you think about what's happened over the last few years, use of the data has exploded everybody know, once their machine Learning Adventure. So that they need Richard Asa so they can make better decisions or those algorithms. So it's going to meet the different use cases. The different number of users as well as the different data source in the self. We get a date before mormor

a wide variety of sources. And how do we capture them? How do we make that days and readily available for all these? If he gives cases? I work with manufacturing companies and they they are getting

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Vinoth Kannan
Paul Moxon
Dave Knifton