“We really need to get smart on end-to-end processes and end-to-end data,” says Sheila Jordan of Honeywell. 


Honeywell’s CTO highlights the next evolution for companies to think about

Conferences

Honeywell’s CTO highlights next evolution for companies

the next, the next thing we all have to solve for is it's so interesting because I've been thinking about this for a while but every organization is functionally optimized sales engineering, marketing supply chain. Each function is optimized. But when you step back and think about digital, digital is horizontal. Digital is a custom majority that spans across all those different functions. So your whole point of I think the next thing for all of us to think about is we really do need to get smart on end to end processes and end to end data because it's not it's not it's not okay anymore to solve for say marketing, we have a great brand but we look at any of these customer experiences, whether it's amazon or United app or anywhere. When you book your airline and you go through the process without pulling out a credit card or loyalty number, that data flow is happening throughout the whole experience and you really are touching six or seven different functions, marketing, branding, packaging, pricing, finance to process your credit card. So I think the next evolution for all of us is how we really do organize your never going to change. I don't think the structure of the company. But how do you think about training programs, acceleration programs, moving people around so that they get the breath of this end to end experience because guess what, That's how our customers and partners are interacting with us. It's uh, I know we are probably taking all right, but this is super important. I mean when we thought of, okay, how should we organize the company around those capabilities and we made the decision that you know, the strategy unit, the transformation of the unit that did studies unit the data and ai unit we sit under one organization so they can be close to the business. The number two we looked at is when it comes to cross functional problems to your point and processes this will be managed by those things because we want, you know supply chain to think out of the box. We want commercial to think out of the box. So how do you do that if you actually give them and then to end control of their own silent? No, you take it out of them, you put it in a truly cross functional organization very close to them and allow the people that drive those solutions to think broad. Sure. I fully agree. Well let's talk about clean data um how big of an issue is it for you? Sheila? Uh Not a problem at all. I take it. Oh no, no no. The quality of data is a challenge and will continue to be a challenge because of what we just said. You're bringing these legacy older technologies that served a purpose for a transaction. E R P C R M you know, adobe for content, you know, each one of those things serves a purpose and so what you now need to do is to up level that experience. So you've got to make sure one that that that that I'll just say the skew has to follow all the way through, like you're gonna make sure that that quality of data is important. It becomes even more important than those of you who are in e commerce. I mean think about your commercial and customer experience this as a as a consumer. I mean they don't they have, they call you the wrong name or they recommend something that you have no reason to be recommended a product. I mean what are you gonna do? You just don't go back? So the quality of data and the interaction and the usage of that information is super, super to report. And I still think it's the biggest challenge we have. Yeah mm We are a predominantly bit a big company who also does B two C and B two B two C. We are kind of cutting across the value chain, so understandably, you know, the data when you don't go to the end consumer directly, it's not as strange as you would like. Right. So as I said, we have the right data management capabilities that the governance capabilities. Uh we have a framework both around data governance and responsible ai on how we develop invest today, transform and mana data. So our data upstream, work again, curate manage, build sustained monitor. The models are modeling framework ai ops framework. Those two are the bible. You know, if they don't if you don't go through the framework, if you don't go through this process is if you don't meet the criteria, you don't go through any type of environment that comes from testing M V. P. To production. And I would say kind of, we were like so far because of its for the priority that matter to us as Phase one at the quality of the data that we have is good enough for us to drive transformation. But we are very thoughtful to do a pre assessment and analysis on exactly what is the data quality and the process quality before we move forward to any type of investment.