Power business innovation with a self-service cloud data warehouse
[MUSIC PLAYING] SCOTT WIESNER: Hi, I'm Scott Wiesner, Senior Director database product management here at Oracle. Welcome to today's virtual summit, where we will explore how Oracle can help power business innovation with a self-service cloud data warehouse. Now, you might know Oracle is the enterprise database company powering mission-critical systems all over the world, and we still do. What you might not know is that we also provide built-in self-service tools to help business users be more productive with data, often without the help of IT. So here's what we're going to cover today. First, Janet George, Group Vice President of autonomous enterprise transformation here at Oracle, will demonstrate how you can extract the maximum value from data, solving for business outcomes.
Then I'll be back later in the program to speak with Eric Probst from Certegy, where we will discuss leveraging the data warehouse to focus on fraud analytics, rather than administration. And then we'll wrap up with next steps to get started today with our self-service cloud data warehouse. So sit back, relax, and discover how the full power of your data is now within reach of your entire organization. JANET GEORGE: Hi, I'm Janet George, GVP of Autonomous in Oracle.
One interesting fact about me is that I am a trained computer scientist. Prior to Oracle, I had the privilege of working in a few tech giants, like Apple, eBay, and Yahoo. Back in Yahoo base, Hadoop was invented and operationalized, along with distributed computing and early cloud technologies in my team.
Last six years, I served [INAUDIBLE] as a fellow and chief data officer for this manufacturing company, taking them through an autonomous transformational journey powered by AI and machine learning. Think lights off, native automation in the factories, driving top-line revenue growth. If a manufacturing company is not immune to this disruptive power of autonomous, no other industry sector is. Let's talk about autonomous. The world is autonomous.
And the world has been autonomous for the last eight years. This trend is here to stay. So now as we think about autonomous, what makes an autonomous enterprise? An autonomous enterprise is an enterprise that has really tackled three pillars very effectively. The first pillar is really modern infrastructure. They have managed to take on and massively adopt cloud and modern distributed computing infrastructures, seamlessly able to scale through using these modern infrastructures. The second pillar that they have tackled really well is modern tools, AI, machine learning, and other technologies.
And the third pillar is the data, the most strategic asset that they can then tap into to convert into knowledge and insights that will drive their top-line revenue growth. OK, so why autonomous? When you think about autonomous, is a prerequisite foundation for a data-driven culture. One has to extract maximum value from the data, while solving for business outcomes.
And autonomous enterprises have figured out how to make their data extremely fluid as it moves through the organization. So they have solved the data mobility problem. Now, autonomous enterprises are powered by machine learning and AI.
These organizations know how to unlock the hidden value of their data. Now, Oracle self-service applications allows everyone to unlock value from their data by deploying machine learning algorithms. Now, let's look at our customers.
Our customers are becoming autonomous. They're all on autonomous journeys. And how do we know this? Because there's massive disruption happening in the industry and our customers are right in the middle of that disruption. They have tried to figure out what to do with their data.
Their data is locked in silos. And they're trying to get rid of these silos and move the data out of the silos. This is a very natural, manual, tedious process. But they want to be able to tackle their data and look at security and governance.
They want to look at data integration. They want to look at the data quality. And then finally, they want to get insights from that data.
So our customers are really trying to figure out how not to be plagued by silos when they are faced with such a disruption. Now, having data silos is quite normal. If you look at most of our customers, many of them, 80% of them, have data silos. And then many of them, 67%, have shadow repositories. There is no single customer view that they can extract.
So they have to overcome this by trying to figure out how to break that gridlock of data silos. Now, one trend we are seeing in the market is, I call it a critical trend. A critical trend we are seeing is the hiring of chief data officers and chief scientific officers. Now, this trend is on the rise. In fact, hiring chief data officers have quadrupled in the last couple of years. And what we found is that the chief data officer has been swept into the C-suite just because the amount of data that organizations are creating and being tapping into have made it a very, very significant hire.
And it's on the rise. Now, let's look at what this chief data officers are chartered. They're actually chartered with re-imagining the business. They chartered with building data-driven foundations. Their main goal is to figure out how to provide seamless data access to everybody within the organization, ability to enable all types, all data types, structured data, unstructured data, all the different schemas, trying to figure out how these schemas can talk to each other, and finally, self-service enablement. They want the organization to be able to look at the data without having to write very long, extensive data queries.
Now, when you think about outcomes, most organizations want to tap into their data to really solve for outcomes. So when you're thinking outcomes, you're thinking, how quickly can I get to the outcome, the role of self-service, and trying to get to the outcome? So the data is in silos. You want to extract the data out of the silos.
You want to apply machine learning techniques. And you want to get to that knowledge. And the ability to do self-service to get to that knowledge quickly saves you time, and energy, and speed. So what we want to do is, again, see how Oracle can help you with the self-service, enablement, and innovation. So I'm going to share with you one example of how we can do this.
In order to be successful, the businesses must have access to comprehensive self-service environments so they're not reliant on IT. They need to be able to work with all the data. They need to be able to test the hypothesis. They need to be able to use different kinds of analytics or machine learning and, ultimately, find ways to keep ahead of the competition.
So what I want to do now is to hand this over to my colleague, Sam, who will show you what that looks like. He's going to take you through a scenario about a movie streaming company whose executives are worried about customer churn. And if they're worried about customer churn, then this is something that the company would want to investigate very quickly.
So Sam, if you can, show us how this could be done. SAM HEINRICH: Thanks for the introduction, Janet. My name is Sam Heinrich. I'm An associate Consultant in the strategy and transformations organization here at Oracle.
Today, I'm going to be taking you through how Oracle self-service applications can help a business analyst solve a complex data problem. In this case, we've got a movie streaming company who's worried about losing customers. Now, as you can see, we have about 1,800 lost customers, which represents 1% of our customer base.
So clearly, this is a big problem. And they've lost these customers in the last month. I'm looking at our corporate dashboard, which shows some of the general information we have about our lost customers.
Now, I want to define exactly what I mean by a lost customer or, as I may refer to it going forward, churned customers. As you can see, these customers made purchases in each of September, October , and November of 2020. But all but one of them did not make any purchases in December of 2020. So this is going to be the definition we'll use going forward. Purchases were made in September, October, and November, but none were made in December.
Now, I want to create a way to predict customer churn. So I want to know, how can we prevent it? And the way I'm going to do that is by getting more data and by implementing a machine learning model. Let's start by loading the data from our corporate data lake. So here I am in the data lake.
And I want to add as much information as I can about our customers. So I'll go to Data Load. And I'm going to pull from our autonomous data warehouse. So I'll go to Cloud Storage. And here is all the data we have in our corporate data lake.
I'm going to pull CustSales because this contains all of the transactions and all of our information we have about our customers. So this will allow us to predict whether a customer will churn or not. It would take a little too long for me to load for this demo, so I'm going to go ahead and look at the data transformations we'll need to do to get this ready for a machine learning algorithm.
So, as you can see, we need to do some complex transformations. But Oracle's Data Integrator makes this easy. There's one less step to do. And I've done everything else so that this is quick for the demo.
But what we need to do is replace our null values. So the data, as we have it, has some blank values. And we want to turn those into zeros.
So I'll go here to Data Preparation and I'll add a data cleansing step. I'll drag this here. And I want to rename this replace null.
Now, I want to do this for all of the columns in all of our data sets. And I want to replace null numeric fields with a zero. Now that I've chosen that, I can drag it here.
And I would then save it and go ahead and run it. It would take a few seconds too long for a demo, so I'm going to jump ahead to where we've built our machine learning algorithms. So Oracle machine learning, machine learning is typically the field of the data scientists.
A problem like this might require multiple calls to IT to make sure we're transforming the data correctly. And then once we actually get to the machine learning, we would need a whole team of data scientists. Oracle machine learning allows business analysts to do complex machine learning experiments with just a few clicks. So I can see the steps that AutoML took in order to create these models, such as algorithm selection, adaptive sampling, and feature selection. And I can see the results of the experiment.
So we've got a list of all the models that AutoML tried and their accuracy. So we can see that the best was the neural network, which had over 90% accuracy. And if we click, we can see which of the features from our data set were the most impactful in determining whether we thought we would lose a customer. Now, I can also use a confusion matrix to better assess the model accuracy. So here we can see that of the 20 actually lost customers, our algorithm correctly predicted 19 of them.
And although there were a number of false positives, that's OK, because we're going to use this data to try to offer some sort of promotion to the customers we think might stop using the service. So if we offer the promotion to a few too many, that's a lot better than not offering it to people who will actually end up stopping using the service. Now, if I had a data scientist who wanted to fine tune the machine learning algorithm or just better understand exactly what the algorithm did, I can click Create Notebook. And that will generate all the code so that a data scientist can easily fine-tune or customize it to their needs.
Now that I have my model, it might be good to enhance it in the future. And we could do that using graph analytics. Here we see a chart of whether customers read other customers reviews of our service and how that might have influenced their purchasing behavior.
So with this, we could easily add it to our machine learning algorithm and enhance our findings. But I'm going to work with the model we have already set. And I want to figure out how we can use this to actually offer a promotion and try to keep some of the customers we lost. So here we see a map of the customers that the model has identified might be likely to stop using our streaming service. It is also color-coded based on their proximity to a nearby pizza restaurant.
So what we can do is we can offer a promotion where the customer receives a discount at the pizza restaurant if they purchase a movie using our streaming service. And hopefully this would entice customers to continue watching movies when they otherwise might not. So now I've taken you through an example where a business analyst can use Oracle's self-service applications in order to create real business insights using machine learning and data transformations without having to call IT or write complex code.
JANET GEORGE: Thank you, Sam. I got two takeaways from that demonstration. First, if you or your colleagues are in the line of business-- and this is something you should take a look at-- it's not just self-service analytics, but actually, it's a whole self-service analytical data warehouse, everything the line of business needs to solve problems and to do so without having to call IT for help. My second key takeaway is if you are in IT, then this is a data warehouse environment that you want your line of businesses to be using. It has everything you need to get that done. And it's very self-reliant, so you can spend time looking at the analytics and let your business not have to deal with data loading and data transformations.
You can do that for them. And you can do the analytics for your business. Now let us take a customer example and look at how we engaged with the customer because I know not all of you want to do self service.
You might want some help. You may not be fluent with machine learning or analytics. And if you want help, then I will demonstrate how we can do that by engaging with Oracle. One of the big strategic customers approached us. And they wanted us to help them with the self-service analytics.
So basically, what they told us is, we have a top-line business strategic use case that we want to solve for. And so we went into a discovery process with our client. And we tried to understand what they wanted us to do. Through the discovery process, we learned that they had a very limited understanding of their customers.
And they wanted to better understand the customer segments. And they wanted to do this by member clustering. And then they wanted to know market basket analysis, what their customers are actually buying and what products influence the purchase of other products so they could get top-line revenue.
And so our customers went through a framework that we engaged with. And we engaged with the C-suite of our customer base. And so we defined the problem. We looked at what they wanted to solve.
We talked about an approach. We understood the outcomes that they wanted to solve for. And then we went into the analysis. So as part of the analysis, we were able to do customer segmentation.
And the outcome-- when we started out on the customer segmentation, we used a multi-dimensional approach. So initially, the customers were looking at their customers just on one dimension, how much money are they spending on our site and what transactions they're doing. But as we started to engage with them, we learned that we could do a multi-dimensional approach.
And so we used three dimensions to look at clustering of their customers. The first dimension was really looking at the transaction, just as they had been doing. The second dimension was to really look at the frequency of the purchase. And then the third dimension was to really look at how recently they purchased.
So using these three dimensions, we started to segment their customers. And when we got to the outcome, the C-suite was really flabbergasted. They were like, wow. Literally, of the one billion customers they had, more than 50% of those customers were low-revenue contributing customers.
In fact, pretty much 90% of the revenue was generated by less than 30% of their customers, a huge eye opener. So the C-suite felt like they were running the business kind of blindly. They did not have a really good understanding of their customers on multiple dimensions. And they were spending money, marketing costs, to really figure out how to market to these customers without having a clear understanding of how the customers were spending time or money on their website.
Next, they wanted us to do market basket analysis, which means when a customer walks into the store in the retail, what are they buying? What are the purchase behavior patterns? What are the correlations between the purchase behaviors? Are they buying beer with beer? What else are they buying? Are they buying candy? Are they buying cigarettes? What do these purchase behaviors look like? And they wanted to have an understanding of how these purchases are made. Are they made sequentially? Are they made based on the season? Are they made based on item numbers? And what are some of the correlations? So we were able to deploy graph analytics. And through graph analytics, we were able to classify the top five categories which were key product influencers to product purchases.
So cigarettes, fuel, soda, sweets, and pastries bubbled up to the top nodes of the graph and became the top products that influenced key purchases. So graph and market basket analysis were the two outcomes we solved for one of our big customers using our Oracle analytics capabilities and our Oracle analytics stack. So Oracle has a full stack of products that we offer to our customers for the enterprise.
And so we have data engineering. We allow your data to flow through batch or streaming. We have data management products. And so we allow you to manage your data through your data warehouse or through your data lake.
We have data analytics products, so you can do analytics or you can do data science through a data science platform. And we also allow you to do visualization on our stack. So Oracle has the complete stack for all customers that are on this autonomous journey. Let me conclude by sharing with you three to four key takeaways. When we are on an autonomous journey, when our clients are on an autonomous journey, they want to tap into fully autonomous capabilities.
Fully autonomous capabilities allows for limitless scale, that means no over-provisioning or no under-provisioning of your clusters, no bursting of capacity. You're able to grow to your demand in workload seamlessly. So your second key takeaway is autonomy allows for 99.5% availability through redundant architectures. That means you can actually future-proof your architecture.
And you can move from architectural rigidity to operational agility. And number three is your data access. So autonomous allows you to have data access, which means you can go through your data provisioning, you can go through your data processing, you can go through your data persistence, and you can go through your data transformation all autonomously. Next key takeaway is autonomous security. And this is a very important one because you don't want downtime on your clusters.
You want self-patching. It saves you time. It saves you energy. And most importantly, it saves you a lot of patching errors, human errors that occur from manual patching for every new security patch that is released out in the market. And the last takeaway, I will say, is cloud computing allows you managing complexity, allows you to manage complexity.
And when you look at distributed computing, you're looking at quadratic levels of complexity. Now, Oracle does all this for you. Oracle has the technology to allow you to manage distributed computing and cloud computing complexity.
Thank you. SCOTT WIESNER: I'm joined by Eric Probst, who's a Senior Manager of Fraud Prevention at Certegy Payment Solutions. Welcome, Eric. ERIC PROBST: Thank you, Scott. Thank you for having me. SAM HEINRICH: Tell us a little bit about Certegy Payment Solutions.
ERIC PROBST: Certegy is a payment solution company that's been in the industry for 60 years. We work in the retail, check cashing, and gaming settings. We have over 4,000 merchants on service.
And we're looking to expand into the ever-growing contactless market with our bank pay product. We have the most comprehensive risk management service in the industry, which allows us to offer ACH services for a fraction of the cost of interchange fees. Last year, we processed about $40 billion in check volume, including US government stimulus checks for the COVID payments. We have helped those affected by hurricanes and flooding get their checks cashed and money in their hands when they need it the most sometimes, when the banks are closed. SAM HEINRICH: OK, Thanks, Eric. Let's talk a little bit about your business challenges and objectives.
A lot of our viewers here have probably similar challenges. And they're trying to get better in their business. And for you honing in on fraud analytics and making check cashing easier and more specific.
Walk us through some of your challenges and where you wanted to get to. ERIC PROBST: Sure. So we were a Hyperion Interactive Reporting company for 20 years.
We run the queries that amount to spreadsheets. We wanted to modernize to the cloud and joined the 20th century. We have multiple data sources. We wanted better integration. We also had a lot of manual processes.
We want to automate these processes to free up the analyst's time. We want to do statistical modeling, machine learning, graph studio, stuff like that. We wanted a better performance. I mean, from what I understand, the cloud performs better than on-prem servers.
And we were a legacy Excel spreadsheet company. We would print out the wall of numbers, show them to our customers, and get them through the data. Now we have the opportunity to use high-fidelity charts and graphs to show our story and show how the performance is going. SAM HEINRICH: Fantastic. So for what I'm gathering here is-- and again, a lot of viewers probably in a similar situation or even gone a little bit way in this journey, which is, you've got some legacy stuff that you inherited. And you use that opportunity to, hey, now's the time to look at some of the modern tools, what's in the cloud, to kind of get away from all the manual administrative tasks you had to do and get to the cloud where you've got performance and with the system-- that we'll talk about in minute-- automation which lends itself to focusing more on the business and less on just the administrative IT tasks.
ERIC PROBST: Correct. It's easier to create something and analyze it, as opposed to spinning it out into Excel format and getting it in the right shape so you can look at it and analyze it. SAM HEINRICH: Right. And then I would imagine that look, unless you're a statistics person, a business person would rather see high-fidelity charts than that wall of numbers, so definitely appreciate that. Customer service is first and foremost, sounds like.
ERIC PROBST: Absolutely. It helps us keep our merchants a better opportunity to approve more people. SAM HEINRICH: So, Eric, tell us a little bit about the solution that you landed on and kind of walk us through the components in the process, if you would.
ERIC PROBST: Well, we landed on the Oracle Analytical Cloud and ADW with ODI. SAM HEINRICH: OK. And generally speaking, some of these from your previous roles, this could take a while, right? So your expectation was about how long do you think this was going to take? ERIC PROBST: Yeah, so in my experience, god, it took about 18 months to implement a new system.
In our case, in this system with Oracle, our end users need to be ready in six months. SAM HEINRICH: Wow, OK. So you had an expectation, like, wow, this is going to take a while to get all these things started up in a new system from previous history.
And now with all the automation and the ease of starting and combining these things kind of better together message, it only took about six months. That's good. So it's good time to market. No one wants to wait for that stuff. Now, we talked to a lot of companies about setting up new systems.
And they've got various database administrators to kind of cue this thing up. How many database administrators did it take to kind of set this system up for you? ERIC PROBST: Well, it was one full-time and, it was about one and a half. SAM HEINRICH: That's amazing. So you can appreciate that not only was it fast, but you didn't have to have a whole slew of people just to get these things up and running.
And I believe your system that you landed on, today, is about 6 OCPUs and that's about 4 terabytes where we're landing data for those analytics. ERIC PROBST: That is correct, with auto-scaling. SAM HEINRICH: With auto-scaling, yes, let's not forget about that. So we have a cost efficiency there for those spikes in use then. Thanks for mentioning that. So again, thanks for choosing Oracle.
It sounds like you got things up and running quickly. Why did you choose Oracle, if you don't mind me asking? ERIC PROBST: Well, Scott, we needed automated reporting for our customers. We need a weekly, biweekly, and monthly report sent out. We also needed that in the form of Excel.
And we needed everything to be automated and scheduled. SAM HEINRICH: And what kind of performance? I mean, one of your objectives was you needed things to be faster. Are you seeing some good performance out of that as well? ERIC PROBST: We are. I mean, the reports go out on time, as scheduled.
SAM HEINRICH: Perfect. Thank you for that. And I would expect that, since you had some familiar with Oracle in the past, that your Oracle knowledge helped come into play as well. We have plenty of folks who are watching this who have some Oracle expertise. And they can probably leverage that. And it seems like that was your case as well.
ERIC PROBST: It was. I mean, we have legacy Oracle databases, so it was easy to integrate. SAM HEINRICH: Great. Thanks, Eric. Thanks for walking us through the solution and why you chose Oracle.
Let's talk a little bit about the business benefits of this system. Now you've got 25 risk analysts. I should say there's some data scientist in there, as well, processing over 800 million records.
What are some of those benefits that you're seeing from the system now? ERIC PROBST: So some of the benefits that we see from Oracle Analytical Cloud and ADW is we project a 10% reduction in fraud because we have more time to do the analysis, as opposed to formatting and splitting stuff out into Excel. We also can use this to improve our customer service. So we're making better decisions and approving better people. SAM HEINRICH: Perfect. So look, everybody wants to sell more to our customers.
Sounds like the platform allows you to focus on the business aspects, versus the administrative tasks that you once had to do with the manual processes. ERIC PROBST: That is correct. It gives us a lot more time to analyze and prevent fraud. SAM HEINRICH: So this is great. Thanks for walking us through this. I wanted to ask you one more thing because often we think about, OK, I don't have a lot of this admin thing, these administrative things that I need to do.
And I'm in the business of, like your business is, is reducing risk and predictive insights. What does that allow you to do? Kind of, what's next? Now that you've got the system set up and there's a lot of automation built in, what does that now allow you to do? Can you tell us about what's next in your vision of where this is going? ERIC PROBST: Sure, Scott. OAC comes with a lot of great tools. What we've been learning are graph studio analysis, which can help us track good patterns versus bad patterns, Oracle machine learning, which our data scientists are used to create random floors, and then link analysis to connect accounts with other information we may have that will show the good people and the bad people. SAM HEINRICH: So out of the system, then, you're able to do a lot more to enhance your analysis.
I think you said once before that a lot of this was individuals brains of how to do this. And now that you can clone that in the system to provide graph relationships, Jason, your data scientist, was now leveraging machine learning in the platform. And you had told a story once before about how Nate, your super-user, and Jason were working on a machine learning project. It was almost a little competition where Jason was doing it in the system, itself. And Nate actually accomplished the same goal faster, actually, out of Oracle Analytics Cloud.
ERIC PROBST: Yeah, that's correct. I mean, there was a little competition there. You got the data scientist who, in the background, is writing all this code to get these great results.
Meanwhile, our super-user, Nate, was writing it in OAC with code to loop and analyze transactions as they came in. SAM HEINRICH: Yeah, a much better scenario to do that, versus hand-coding and manual processes to get that done. So now you can leverage the power of the platform to do those things. So I appreciate that.
So we talked a little bit about your implementation time. It didn't' take that long. I would imagine that, as we work together, it became more of a partnership. Tell us a little bit about that. ERIC PROBST: In early 2020, we rolled Oracle Analytical Cloud out to the analysts.
We were self-taught. We had collaboration. And we basically went through the system and tried to learn how to use it.
We were given a deadline of July, where on-prem servers that host at Hyperion were going to be decommissioned. So we needed to learn this quickly. And what that forced us to do was meet with Oracle more often. We met with them so much, we met with you so much, that we thought we should have been put on the payroll. SAM HEINRICH: That's a true partnership, Eric. ERIC PROBST: Yes, sir.
SAM HEINRICH: All right, Eric, thank you very much, again, for being a customer. Thank you for telling us your story, and all the best. We're looking for more fraud prevention from Certegy in the future. ERIC PROBST: Thank you, Scott.
And thank you, Oracle, for having us. SAM HEINRICH: Thank you, Eric, for sharing your journey with us. And thank you, Janet, for your insights. Oracle's commitment to you is to leverage our years of enterprise data management experience to provide an autonomous self-service cloud data warehouse to help you see data and new ways, discover new insights, and unlock endless possibilities.
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