Oracle Cloud Chief explains why autonomous features will further blur enterprise lines

Derek du Preez Profile picture for user ddpreez January 17, 2019
At last year’s Oracle OpenWorld the company announced its second generation cloud, which came packed with a whole host of autonomous features. Oracle’s Steve Daheb explains the impact on the enterprise lines of business.

Steve Daheb, Oracle SVP Cloud
Steve Daheb, Oracle SVP Cloud

Oracle founder and CTO Larry Ellison used his keynote at last year’s Oracle OpenWorld event in San Francisco to announce the company’s second generation cloud platform, which claims to have been re-architected from the ground up and includes a whole host of autonomous features. The announcement came off the back of Oracle also announcing its new autonomous - or self-driving - database.

Ellison’s keynote primarily focused on how the AI/ML capabilities made data protection and security enhancements easier for the enterprise, but also highlighted the impact on improved downtime. And whilst it’s unsurprising that Oracle is pushing ‘autonomous’ as a theme, given the hype around AI, it’s worth exploring further how the vendor sees this playing out for enterprise buyers.

I got the chance to sit down with Steve Daheb, SVP of Oracle Cloud, at the company’s European leg of its OpenWorld events, in London this week. Our conversation was interesting in that Daheb articulated how Oracle’s autonomous proposition - or the idea of applied autonomy in general - could have an impact on lines of business, who need to be more tightly integrated to better serve the customer.

We’ve all heard about how buyers strive for the holy grail of a ‘360 view of the customer’ - a message vendors have capitalised on for years. However, Daheb’s pitch makes more practical sense, in my view, as it brings to the fore how businesses could be better focusing on outcomes.

For example, instead of data dumps to better try and analyse and obtain a holistic view of the customer. Ideally, enterprises would like to be able to simply ask questions, such as - ‘Can you give me an update on X customer this year?’. Without needing to understand what’s happening behind the scenes, providing said company is operating in the cloud and integrations are in place, autonomous features could enable a response. This is a far superior approach than what traditionally occurred, whereby a customer service exec, for example, would need to obtain data from sales, marketing, IT, and whoever else, to piece together an answer.

Daheb explains:

I think it winds up being a natural part of the discussion. At some point things became very segmented - my ERP is over here, my HCM is over there, my sales systems are here, my customer systems are there. At some point, and as you use machine learning, and as you automate all of this, you’re just asking business questions. How’s my business performing? How are my customers doing? How are we thinking about a customer?

A customer could span legacy revenue, which sits in your ERP. Customers could be sitting in your service system, because they’re an ongoing customer. Customers could have active deals, which are sitting in a sales system. Customers could be attending events or responding to demand, which could sit in a marketing system.

Historically you’d paste things together. Today I could combine all that data and ask simple questions and get insights. Where does one application stop and another begin? I think there’s a really interesting opportunity there. And I think for Oracle we have those pieces. That’s what makes it really interesting for us.

The opportunity of the stack

Oracle’s pitch on this, unsurprisingly, is that it has a fully integrated stack to offer. The Frankenstein of Clouds problem has become increasingly relevant for buyers in recent years - which is why the likes of Salesforce are investing in integration platforms, to make that easier. Daheb notes that whilst Oracle has an integration play, it’s full cloud stack enables the above scenario to become possible (and obviously isn’t going to be bad for Oracle’s balance sheet, either).

Daheb says:

There’s obviously things we can do naturally given we have the breadth of the applications, and we have that stack, which is platformed, which allows us to connect and extend and enrich. And then do the machine learning to get these insights. I don’t know if we are talking about that enough. I think we are just starting to realise that that’s sort of a big deal. It’s interesting to think about how that unfolds.

Daheb adds that the companies he’s worked at, and the ones he works with, that are able to do this, are the ones that understand and operate a ‘customer care model’. Whilst that has been particularly challenging on-prem, and to be fair, is still a challenge in a digital enterprise, the cloud does enable easier access to this core data. Combine that with integrated processes and autonomous call-collect features and new possibilities open up. Daheb says:

I think there’s still discrete things you focus on, but it definitely makes it easier to work together and makes every group better at what they do. I think understanding the customer in a more holistic way helps us all do our jobs better.

Interestingly, Daheb also elaborated on Oracle’s recent autonomous database and second gen cloud announcements, explaining why the vendor may see new opportunities emerge. Obviously buyers will be looking to AI and ML as ways to enable efficiencies and explore new service models, but it also could allow Oracle to see more ‘gravitational pull’ around the Oracle ecosystem, according to Daheb. He says:

Autonomous is very interesting because there’s this gravitational effect with it. If I’m using autonomous, it’s going to pull integration, because how do I pull all the different pieces? It’s going to pull analytics. As I think about moving to the cloud, how do I think about my security? So it pulls with it some of the security elements as well. So even if what they’re doing is looking at something like an autonomous data warehouse, it winds up being a bigger conversation, just because there’s those different things that have to happen to be able to do analytics in the cloud.

If I’m deploying an autonomous data warehouse, it’s because I want to get insights into something. So it would involve integration of different data, it would involve securing that data, would involve the visual analytics with respect to that warehouse. It’s a really interesting entry point.

My take

I found this conversation with Daheb interesting because it provided more nuance and insight into the impact of AI/ML/autonomation than you typically get from a vendor exec. The focus wasn’t just on the ‘dream big’ benefits that automation could have on the bottom line or customer experience, but it considered how enterprises need to think differently about how they operate in these environments. It’s no good doing a lift and shift and carrying on with the status quo, silos still in place. Companies need to think about what is possible, thanks to the improving capabilities of these technologies.

A caveat - I’m yet to meet an enterprise doing this in reality. It’s all very much still in a phase where it is the art of the possible. And that’s okay, you’ve got to look ahead to make progress. However, we still don’t have a blueprint for how enterprises get from this A to B in reality. And that’s because there isn’t one. Legacy still shackles and companies are targeting low hanging fruit. However, nonetheless, the opportunity is there.

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