Cloud Wars Perspectives on Oracle Analytics
Cloud Wars Perspectives on Oracle Analytics

Oracle Analytics Creating ‘Continuous Analysis Workflow’ with Augmented Analytics

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In today’s data-driven economy, Oracle believes its unique combination of SaaS applications, cloud database technology, and modern AI tools will allow it to deliver higher levels of business value and innovation than competitors can match.

Oracle is looking to became a world leader in that field by pulling together those assets and expertise in a newly invigorated Oracle Analytics business under the direction of group vice president Bruno Aziza. 

I recently had a chance to interview the high-energy Aziza for an episode of my Cloud Wars Live podcast. We discussed a range of topics around customers, the state of the market, and the extraordinary disruptions the cloud and AI are having on the business world today.

Here are some of the most-compelling excerpts from that discussion. (You can watch or stream the entire episode here.)

Where Is the Analytics Business Today?

“If you kind of deconstruct where the industry’s been, the first 10 years were really about centralization and creation of reports and dashboard primarily by the IT organization. That created a lot of frustration with business users who kind of rush to solutions. Back then there were download and desktop tools that would enable business users to self serve to the information. That really created a lot of interest, a lot of attention and great dashboards were built out of that. 

“But right around two years ago, the space realized that maybe the pendulum had swung a little too far and now we had people that were playing with information that either they weren’t supposed to have and sometimes they would leave their company with data they weren’t supposed to have on their desktop, or information that was inaccurate because it was misaligned.”

The Promise of the “Third Phase” of Data and Analytics

“In this third phase, there’s a set of trends that could really help organizations take advantage of the data. The first one is cloud as a disruptive force. The fact that if you are not a cloud first analytics solution, you’re going to have a lot of issues with data provisioning but also data protection, right? 

“It’s one thing to give access to people to a lot of data, but you also want to make sure they authorize for it and you want to make sure that you’ve managed that relationship between data and people themselves. The second aspect of it is now as you start evolving the use of analytics, it’s no longer just the data scientists or maybe the engineers or maybe even the business analysts that need insights in their workflow. I mean, despite all the work that we’ve done over the last 30 years, only 35% of employees can use the data analytics tools that have been produced for them.

“So we have the opportunity to break through that glass ceiling and get information to business leaders, line of business application folks that are maybe in the CRM workflow, maybe in an HCM workflow and an application workflow where you don’t require now for them to become business analysts. They’re using analytics as part of their workflow. And in here AI is a big role to play because as augmentation is pushing this data and these insights, it’s enabling business users to have this what we call continuous analysis workflow.”

The Massive Impact of AI and ML

“We’ve been talking a lot about artificial intelligence and how artificial intelligence is going to impact the world of work, if you will. And there’s a lot of fear about will AI take my job? I think if you read a lot of research from Andy McAfee or Tom Davenport or some of the folks that have really researched what’s going on, the real trend is augmentation. What that means is that we are creating now software that can augment the capabilities of the people that we have working on those hard problems. Doesn’t mean it replaces them, it means it enables them to do certain things a lot faster, a lot simpler.

“And a great example is data preparation. Today, 80% of an analyst’s time is spent on preparing data. If you think about the insight pipeline, just to think that so much data, so much time is spent on just preparing, cleaning the data. It really is using the wrong tactics for the result. What you want to use this business analyst or this line of business person that really has a reporting needs, you want to just get the data to them, integrated, clean, completed and so forth.

“So we have spent a lot of time making sure that AI is integrated into the analytics workflows. So when you use Oracle Analytics Cloud for instance, you’ll see that when you import data, we automatically clean the data for you. We identify what type of data it is, so if it’s social security numbers for instance, we will hide it automatically for you. I mean, we’ll still give you the choice if you want to see it or not, but we’ll show you on the right side you’ll see all the options that we can automate for you in order to make the data ready for analysis.

“And that’s an important step. There are many other examples just like that.

“Like for instance, now that we understand the data and its structure, we can auto-generate dashboards for you. We can suggest areas you can investigate and so forth. And all of this is designed to augment the abilities of folks involved in analysis because the point of data and AI and analytics is enables us to take action faster. Right? We got to stop being in love with building more beautiful dashboards. That’s not what’s driving the bottom line. It’s the actions of people because they have the data that is making results.”

The Value of Being a Full-Stack Provider

“We have a lot of assets on artificial intelligence and we’re making artificial intelligence applied and invisible. You see I’m using A and I here. AI applied and invisible, and the reason for that is because it’s applied to workflow inside the application and the value of it will come to you. You’re not having to buy AI or build something… we are just making that available as part of the applications that you’re looking to use inside the database. Right?

“So the fact that the database gets self maintained and self tuned and so forth, that’s a result of the AI work. The fact that we can clean data, build dashboards for you, that’s the result of AI.

“It’s AI at work if you will. And inside our application business, you know with Oracle applications for applications, or Oracle Analytics for applications we are making now available pre-packaged content automatically because we understand this data. So you have an advantage when you partner with Oracle, you have a vendor that’s not a typical business intelligence vendor. We’re full stack platform and we are in the middle of your database, your data needs. A lot of the corporate and data that enterprises rely on, it runs on the Oracle database. And we’re embedded in your applications and a lot of decisions and workflows are triggered in Oracle applications today.

“That’s why we’re unique for any customer, any prospect looking at solution… The fact that we have a cloud-first and AI-first analytics platform truly differentiates us from anybody else you might be looking at in the market.”

Customer Examples: NHS

“The NHS, the National Health Service in UK, is a fairly large organization. I think it’s one of the top employers in the world. But when they got started with data analytics, they grapple with this issue of well how do we take a team of two DVA’s and make that a turning point on how we use data for the NHS? Well, now they have saved a billion and a half using data analytics.

“The Chief Data Officer, Nina Monckton, has been on stage many times because she has won this year’s Woman in Technology Award for not only saving the organization a lot of money but also being able to do it with a fairly small team and in fraud detection at a scale that was never done before.”

Customer Examples: Daimler-Benz

“Daimler-Benz wanted to do some segmentation and wanted to identify cars and how car owners would be more likely to buy another category of cars. And categories that didn’t even match the segmentation. One of them was a high end car type and the other one was a low end car type. The machine basically scanned through the data and said, ‘There is an area here of cross sell that you could probably build a campaign on that you weren’t thinking about.’ 

“I mean, if you’re following us, you want to watch the video because Mark, who is the person in charge of this initiative not only gives you some good tips on how he did it, but he also introduces this idea that it’s okay to question the machine. So again, going back to the topic we started this with, AI and humans, you know. They’re not replacing each other, they’re collaborating. Sometimes it can be augmented and sometimes humans have particularities of judgment that are required in order to make the machine better.”


Disclosure: Oracle, which at the time of this writing was a client of Cloud Wars Media LLC, sponsored this article.


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