Supercharging Oracle EBS with Purpose-Built Schemas and Dashboards

Okay, I see some attendees joining the session today so let's get our webinar underway so welcome everybody, this is super charging Oracle EBS with purpose built schemas, dashboards

My name is Nick jewel i'm Incorta's senior director of solutions, marketing and i'm going to be introducing our topic today and i'll also be our moderator for the Q amp a a little bit later on.

A little about myself i've worked in product architecture and delivery in a number of startups as well as in some big financial services firms i've been a technology evangelist with analytics communities data leaders.

And the wider public, I also got a chance to do a PhD in data science back when it wasn't even called data science and in my spare time I love to work with the data science charity data kind to offer charities advice on how to use data for good.

Now i'd also like to introduce our DEMO presenter who's joining us virtually today.

Alex dark isn't in quarter sales engineer based out of Dallas Texas he's got nine years of experience in the bi and analytics software space.

With seven of those years, being in a professional services capacity he's going to be giving us an awesome walkthrough of the end quarter platform just a little later on.

But without further ado let's kick us off so we're going to walk you through a new way to drive real value from your Oracle E business suite through the use of the quarter platform that's going to allow your analysts to build insights from all your data in the platform and beyond.

So we're going to be touching on three big topics in the session today so first of all, will be building foundation on why analytics can help change the trajectory of your organization.

Especially when it comes to making best use of all the data assets that are available inside oracle's E business suite.

Then we'll take a tour through some of the challenges that we all face when working with data from complex business applications.

And how we can rethink many of the parts that we've taken for granted and see how these changes can lead us to supercharged results.

Then we'll put the quarter platform to the test and see many of these concepts in action to provide a faster, more effective way to prepare EBS data for analysis, with no unnecessary data copies.

transformations or aggregations after that you're going to have questions and we're here for you in the Q amp a alright let's get started, and we are going to start with a little scene setting now you've implemented Oracle EBS your ap your ar processes are running.

smoothly now what you'll want to analyze that data, maybe for operational intelligence and what's happening right now.

all the way through to sophisticated forecasting and helping steer next business actions, but how do we get there, what does the journey look like, well, it turns out that we need to be asking the right questions.

Now, you could say that the world of analytics is set on a premise of four levels descriptive diagnostic predictive and prescriptive.

Now this definition, can feel a little tired, but it's valuable to work out where we're actually investing on our data journeys.

And it's also important that organizations don't get stuck in a particular level because of data literacy skill gaps.


People will often gravitate towards the easiest form of analytics that they're going to be capable of rather than asking the bigger questions that often have the biggest payback.

So first up descriptive analytics, meaning that we're trying to describe something that's already happened.

Now to do this we typically build reports dashboards or observations that help our departments know what's happened in the past or what's currently happening.

Now all of us have the ability to look backwards and describe things so descriptive analytics is often a very comfortable place lots of weeping and cheering when somebody designs a gorgeous new dashboard and maybe shares it on social media.

If you didn't know that there were actually four possible levels of analytics you think that you were done right there and many organizations do spend a lot of time and a lot of resources.

Building very pretty dashboards without really moving the needle with smart data informed decisions if people don't know how to progress beyond just looking backwards on what's happened.

They can't diagnose they can't find out the why behind it, but nevertheless, this first level is absolutely vital, we have to build a foundation to be able to move forward just remember it's really the end goal.

So next is diagnostic analysis and a simple definition here would be insights diagnostics is all about discovering root causes learning the drivers of the business and why things happened.

asking why means asking lots of questions that often start with what first and then why it builds on that first layer we talk about democratizing access to data and analytic systems.

What we're really saying is that we want more of our people to be asking what type of questions rapidly, followed by why questions this starts to move the Needle, on our data culture, more than anything else.

So real examples here, well, maybe we get a descriptive dashboard that shows a trend in expenses for a finance team.

Our diagnostic analysis kicks in with questions like, why did the trend line change compared to previous quarters, which segments of the business showed the trend.

Why did the shift occur in certain countries or regions compared to others now these questions really help build out a much stronger awareness of a business than a simple report or dashboard.

Then we move into predictive and eventually prescriptive analytics using our foundational skills.

To target more advanced analytics towards specific business challenges like predicting business or customer behavior or working to optimize processes to grow our top line or reduce costs and the supply chain.

Now, at this level, the analytics are often quite advanced but don't often need to be developed by large groups inside an organization.

However, data literacy is needed to be able to interpret and use the information to make smarter more informed decisions.

And this touches heavily on the definition of data literacy from my favorite industry expert in this area, Jordan morrow who says that data literacy is the ability to read work with.

analyze and especially communicate with data across all of these different levels.

Now, the effect of a good data literacy initiative touches many different disciplines within an organization from.

Setting the analytical strategy through to implementing data science or data visualization programs defining what an analytic culture means to accompany.

Even the simple statement that we're moving away from the phrase I reckon.

To backing up our positions with data and basically into the process of managing data quality data governance and the implications of using data.

In real world projects, considering the biases the blind spots, the consequences of misuse.

So i'm going to finish this section on digital literacy, with the three c's of data literacy according to Jordan morrow.

Firstly, curiosity Now it may have killed the cat but the dictionary definition is the urge that you feel to know more about something.

We use curiosity to open doors and our analytics work and it's all about that second level in our analytics stack diagnostics and asking why.

Second, we talk about creativity so creativity thinking beyond the boundaries that the working world, often puts in front of us will a new perspective on a data set help unlock a particularly gnarly business problem.

Maybe a new data visualization or data story, maybe it's the simple act of having access to all the data top to bottom, that gives us that creative spark.

Now, with this one think about the anti pattern to creativity everything gets served up in exactly the same way, a static report.

A dashboard with predefined drill through it's inflexible it's brittle it's fragile.

And then, our final see well that's for critical thinking.

A practice of being disciplined rational and informed by evidence it's really a shift away from the Hippo the highest paid person's opinion, maybe in a meeting, maybe in your department.

it's a way from gut feel from executing a sales play simply because that's how we've always done that.

So, really, this is our framework for analytics, how can we apply this to an EBS environment with its deep reach into the hearts of our businesses.

So let's set the scene for how we unlock the amazing data assets inside our EBS world by looking at the landscape that we find ourselves working in.

Now, historically speaking, there have been very few analytics options in the EBS ecosystem that are quick to deploy that give performance when you need to analyze over all those underlying tables.

And that actually allows you to deal with the challenge of multiple data sources and then, of course, to be able to deploy it in a cost efficient manner.

Now, with EBS you can consider it to be a single application or data source, but really under the covers there's well over 100 subject areas well over 50,000 tables.

So it's about as complex as any multi source environment when you consider trying to build comprehensive analytics over such a complex system.

And of course Oracle EBS doesn't exist in isolation, for companies there's salesforce or any number of other off the shelf for custom applications likely to be in your environments.

And let's not forget that every step in the data pipeline that feeds both the EBS system, but also all those other systems is hugely costly to maintain.

For every application there's data engineering administration architects and modelers and, of course, a whole industry of bi developers trying to carve value out of this deployment.

So, for example, take just a simple income statement and have a look here on screen at the cost of sales line that i've highlighted.

Now you might say, hey this number is way off in terms of what we forecasted and, of course, that would be descriptive analytics at play.

But our next level asking why that's The case is what captures the imagination.

Now, in the world of bi we might call this root cause analysis drill through reporting or drill to detail.

Ultimately it's all about getting to the lower levels of data that roll into these numbers and getting to the truth of what's actually driving maybe this interesting anomaly in our metrics.

Once you reach this point, knowing that it's possible to trace a figure from a top line KPI all the way down to transactional details.

It builds trust in the data and in the data literacy process so that we can move towards higher value outcomes.

let's take a look at these numbers in a slightly different way from the view of the analyst that has to navigate the underlying system.

and, specifically, looking at just the colors all these different boxes representing the different subject areas inside ABS, or maybe similar systems.

So you take a common metric in your financial reports, maybe like net income you quickly realize it's not coming from any one of these boxes or these tables.

Instead it's drawn from individual metrics that are really amalgamated together from across diagrams just like this.

And it's critical to be able to reach down into the individual transactional records from any and all of these tables when we need to ask our why questions we can't always just take that high level metric at face value.

So what ends up happening, if you don't have this capability teams start to move to anti patterns of analysis they start extracting raw data manually they start trying to stitch it all together typically in excel.

And the veracity the fidelity of the data starts to fall apart in the process we end up with that classic juuling spreadsheets that happened all the time in finance meetings.
Now, in quarter fundamentally flips the table on the old way of doing things, especially around EBS and its complexity.

We opened up the data and it's direct source format no change request tickets know ingesting missing data no complex time consuming remodeling or transformation of the data.

And no need for extra tools or technology to present that data back to end users just simply a user with a question.

And the ability to navigate all the data elements of EBS to get to insights faster delivered end to end in hours, and certainly not months, and certainly not years.

but also new questions can be turned around significantly faster than legacy approaches business queries that once took weeks, can now be answered in minutes, simply because the whole dataset is ready to be analyzed.

So we get flexibility to the business considerably lower maintenance costs for the data and it teams who manage and support the back end.

Now, in fact, the cracks have been showing and so called modern data architectures for some time.

we've reached this states where multiple layers get developed, each with their own cottage industry in terms of supporting tools and technologies.

Maybe we start off with data from source systems over on the left hand side landing into data leaks often simple raw extracts from those sorts systems.

And then progressively refined through enterprise data warehouses and often into business data marts those star schemas that we always hear about.

When data gets presented to the business in simplified terms each layer, resulting in a significant loss and data integrity.

Data decisions have stripped away upwards of 90% of the original data to reach those aggregates, and we end up with the creation of multiple copies of data in silos across the organization.

So how about simplifying this whole process, what if you could take the business application data, as is ingest it enrich it.

deliver it so that users can work with all levels of that data from fundamental transactional levels, all the way upwards as quickly as possible.

And this means that you get access to operational analytics at that most granular level in the same platform.

As your typical sales or marketing analytics that might slice by product category location or other features and again.

By taking the data, as is and landing it into the quarter platform we're making everything.

available for analysis, not just that final 10% roll up of the data, but the whole data set so effectively future proofing a data process for our end users.

So whatever questions get thrown at the data everything from the underlying business applications is present, and this most fundamental level.

Without the need to step back to write new scripts to load or move data.

And to make this as simple as possible in quarter at the concept of a self service layer

Over all of this data that allows users to create business friendly views from that underlying data without compromising the security of the original source systems.

So these so called a business schemas are easy to create directly through a browser with the whole experience really focused on getting critical data elements prepared and enriched ready for exploration and analysis.

Now, at this point, the analysis happens natively inside in quarter again directly through a web browser with dozens of rich customizable charts and, of course, extensive ability.

or by making those business views available to popular bi tools like tablo power bi and many more so that analysts are instantly productive offering all the power, all the performance but not requiring someone to learn a new interface or a new way of working.

So, want to see what this looks like in the real world well here's an example for you broadcom finance teams lived in that world of silos workday Oracle era P, and of course excel.

Any requirement for a new report or dashboard took them between six and 12 weeks now they've invested in all the traditional data architecture.

warehouse data marts bi tools, but they still faced a huge delay between a business request an actual delivery.

The results after super charging within quarter, it was a huge transformation in agility in responsiveness and total cost of ownership.

They moved from an inconsistent nightly refresh to the data warehouse to 96 refreshes every single day.

And that became a game changer in terms of providing accurate up to the minute information for descriptive and diagnostic decision making.

And this new ability to ask questions and get near instant answers, by going direct to the source meant a heightened focus on their bottom line.

A dramatic reduction in maintaining legacy technology and the teams needed to support them.

So, as we wrap up the slides before the DEMO let's take a look at the world of Oracle EBS and in quarter offering seamless instant access to all your critical business data.

Without an expensive fragile data pipeline industry sitting behind the scenes and without a cast of hundreds of IT staff.

All of which are on peak hourly contract rates, instead, we have a direct to source data mapping that doesn't require heavy duty atl provides the data at full fidelity to your analysts and your decision makers.

And to supercharge this even further well as part of the quarter platform we offer pre built dashboards and business schemas that provide a huge head start.

versus the cold start that most analytics teams face when working with EBS.

So predefined templates and logic that get you directly to the data that matters from deep within EBS itself offering key metrics sample reports.

Data visualizations analytical self service all based on our deep experience of working with EBS customers.

And the results of using these data Apps can often be dramatic customers deploy a data APP as part of their solution they're often working with all their data for accounts payable.

General ledger inventory management end to end within the same day.

So it's about time we showed you what a supercharged EBS environment actually looks like and remember please drop any questions that you have into the Q amp a window we'll do our best to answer these after the DEMO.


But now it's my great pleasure to introduce Alec stark who's out virtual se who sadly can't join us directly today but he's going to give you a little taste of what high performance high fidelity analytics looks like.

Thank you, it is definitely my pleasure today to walk you guys through a quick little DEMO of in Florida so i'm going to take over the screen here.


You should be seeing a login screen so as I was saying, when I first logged into a quarter, you will be navigated to the content, page where you can see all your dashboards reports or.


In my case, I have a favorite a dashboard with accounts receivable data which i'll be showing you guys today.


And so the first step here we're going to take a look at our schema page on the quarter platform, this represents all visible data sets that you have brought.

into the quarter platform at a specific note we're going to look at accounts receivable.


So those schemas are basically all the physical tables you've brought into the platform and how you've chosen to organize those so in the case of our Oracle blueprint.

it's one data source, with many different schemas where we've organized it by different modules and subject areas like.


accounts receivable hcm and so on, now in this example environment we brought in a smaller subset of the accounts receivable data at Oracle consisting of.


14 tables 34 joins and about 2 billion records of data now if I click on the diagram, we can see visually what this data looks like so when we bring data into in quarter.


we're bringing it, as is from that source system, so those who are familiar with you know the Oracle tables you'll see tables, such as you know, our a customer transaction lines all which has all of our detailed transaction level information about invoices and so on.

And so, in this table alone we have almost half a billion records, you can see in this diagram how all the tables are related to each other, using our direct data mapping technology, so how we enable.


The ability to report on this data without all the transformation, creating star schemas is that this direct data map just maps out all the joins relationships.


Across all those data sources, and you can see that doesn't that doesn't just happen within that silo.


Of the accounts receivable schema, but you can see cross data source and cross schema joins in this diagram where in the in one case we have customer accounts and Oracle being joined to the account table in salesforce your CRM.


So you're able to query across those data sources in a single report.

Incorta Admin: So let's close out of this diagram and what I can do is I can click on explore data to immediately begin building the dashboard off the data in the schema.


This will bring up the analyzer tool just how we build our insights.


And since pivot tables tend to be pretty calculation and sensitive and slow tools i'm going to start there i'm going to change my insight type.


From a listing table to a pivot table, which will require me to define some row column and measure fields so First things first in our customer transaction lines all i'm going to drag and drop a few measures so we've got quantity ordered invoiced and revenue.


Next, for my rows of data i'm going to search for a field i'm looking for called sales channel and drag and drop that one.


And that is a filter as well, because you know there's a little bit of dirty data in here we don't want this negative one or undefined channel information.

So i'm going to go ahead and select everything but that and then another row that I would like to see is something called party type.


And i'll bring that in as a row as well.


Now, last but not least, I need a column so i'm going to go with month names.


And just to ensure that it's sorted properly i'm going to drive the month number as sort by field.


Now as i'm dragging these in once I have all three you know row column and measure to find it immediately generated in preview of that data now, this is a subset of the data at this moment.

But once we save this it'll query that full half a billion record set and you'll be able to see just how fast and perform it in court.


If I had it safe.


To just put this into dashboard so we'll call this webinar.

And now, is querying that full dataset.

Incorta Admin: Now from here one of the beautiful benefits of a quarter, is that we also have access to that rod transaction level detail so if I wanted to drill into some of these.


Incorta Admin: You know pieces of information, like sales channel I can create a ride detailed transaction table I can do that as simply as duplicating This insight.


Going into edit it.


And then changing what type of data we're tape on table we're looking at, so I can go to the listing table.

And then i'm going to clear out.


Some of these fields and i'm just going to drag the entire transaction right all table into the measures.


And it's a.


So our dashboards are pretty dynamic, all I have to do at this point if I want to limit what set of data on looking at at the bottom, I can click on a channel like consumer electronics.


As it as a filter at the top and now we're looking strictly at the consumer electronics transaction line information.


For the more, of course, our visualization has many different chart types and visuals you can use So if I wanted to copy this chart again.


I can create something more visual off of that information.


So, in order to organize my data here i'm going to drag and drop this up to the top.


Or at least try to edit this.


And an insight visualization types of dive bar charts line charts and such i'm going to do a quick tree map chart here.


delete some of the extraneous data that we don't need here.


Save that and this chart will also be interactive with the rest of my dashboard.


So the clear out the electronics from the top, I reset this back to scratch.


I could then go in here and I could look for I want to see consumer electronics and March I click there and I can filter by sales channel and month name just sales channel just my thing and i'll go ahead and do both and so that's who will filter my detailed transaction level data.


So that's just kind of a simplified example I want to take a step now over to the actual Oracle EBS blueprint kind of show you guys how that works under the hood so i'm going to go to this other tab here.


And as you can see i'm logged in to the homepage.


Of the blueprint environment, we have a huge number of pre built reports that you can use as a starting point.

keeping in mind the blueprint, we like to think of it more as an accelerator, we all know, you have customized data on your platform and you may want to make your own custom reports, but just as a good starting point.


We have dozens of different dashboards from financial supply chain and so on, but i'm going to go back to the schema page from earlier.


And, since this is a real you know live test environment of the Oracle blueprint, you can see the many different schemas we have ap ar you know hcm and so on, and if I look at this example.


I mentioned earlier, that my DEMO was based on a subset, this is the real data now, in this case we've got 33 tables and 151 joins and then, if you look at the diagram.


When you include those cross schema cross data source joins you end up with North of 80 objects, in the spring.


Showing those hundred and 51 joins the Oracle data is immensely complex and with our blueprint we've already map that out for you, so all you have to do is getting poured.


deploy the blueprint connect to Oracle and load the data and then all the hard work has already been done for you to get you started on reporting on that data i've got the diagram pulled up and, as you can see it's pretty intense.


Now, another feature, I want to call out here is this concept of a business schema.


I think it's pretty reasonable to assume that you know those end users your business users they're not really going to understand the had really complicated raw Oracle data.


So the business schema is kind of our our semantic or abstraction layer you can curate the data down to a smaller subset for those users, based on use cases and different moral modules.


You can rename and we label those fields to user friendly terminology and you can even create business calculations and kpis so I clicked on a our cash receipts and then this this is schema we have two different views.

And if I open up cash receipts, you can see, you got something like EBS ar errors, the little applications amount applied has just been renamed something simple like i'm not applied.


We also have some calculated formulas here to where you know you can have a common set of kpis that are all defined the same way for all audiences and they're using the same calculation.


So nobody's creating different definitions of what is our net profit or or whatever those kpis might be.

Lastly. If I go to content that's going to show you one quick dashboard here i've got one called GL journals to ar.


And so, just like we have those pre built schemas for your users to build your own reports these pre existing reports are also quite usable and you can.


Use them right out of the box we've got some GL information visualized in here and we even have some built in drill downs.


So if I go to the journal details I could click on a journal header ID and I actually have a drill down to another dashboard where you can then look at that information in more detail.

So I hope that was pretty easy to understand, I believe now it's time for us to shift over to Q amp a, so I will hand it back over to Ben.

Fantastic Thank you very much Alex for that remote DEMO let's head over to some Q amp a now.

And remember it's not too late, be curious arts are some questions in the Q amp a panel, I can see a few here on my admin screen so i'll go through these while you're getting warmed up.

First of all, so EBS does in quarter handle all of the mappings for EBS out of the box.

So, yes that's essentially what we've just seen in the DEMO from Alex it basically has all the standard tables all of the relationships and.

Obviously, I guess, where you have customizations in your own EBS environment we deploy the blueprint or data APP and then adjust for those customizations directly in in quarter so thanks for that question.

let's take a look at a couple more where does the data physically reside inside in quarter and what happens when it gets copied over so.

I guess, first of all, we don't query in analytics against the source system against dbs directly for for lots of different reasons, one of which is performance we don't want to slow that source system down in an operational environment.

In environments like Oracle cloud they actually don't allow direct querying So in fact there they have a tool called bi CC that publishes the data out for consumption.

Within quarter, we take a full physical copy out to the quarter platform we hold that in park a format, which is a columnar data format.

And we run our direct data mapping On top of this environment which is basically that innovation that essentially removes the complexity from any queries that you want to run on the data, so you saw that in alex's DEMO just now.

it's equivalent to basically having to skip the step, where you join all of those tables, but without the cost or the efforts to actually reshape or aggregate the data beforehand.

And after that first initial load over to the quarter platform we typically use incremental loads to get data into in quarter much faster, so you know broadcom case study a refresh 96 times a day versus what they had before, which was a traditional nightly refresh.

let's have a look and see if there's any more, yes, so is there an assumption that the data is in perfect condition as it arrives in in quarter, how does encoder handle data cleansing data processing steps that are needed for for real world data.

Well, we have features and capabilities to clean up to improve that data once it lands in in quarter.

We probably remove about 90% of the traditional data engineering that's needed, but if cleaning is still required we've got built in formulas, as we saw in the DEMO we can manipulate and fix the data that way.

Or we can go deeper and actually use coded languages Python our scala etc against Apache spark, which is actually part of the quarter platform as well to generate results for more complex scenarios.

cool we have another question here, what about major source system upgrades is it going to be reflected automatically in in quarter for the.

For the predefined model, thank you very much for that question and basically as Alec showed in the DEMO you can head over to the schema tab and in quarter.

To bring in changes from the underlying structure from that source system, so if EBS or other systems do get upgraded it's a relatively simple process to refresh the structure or the Meta data behind the scenes.

And then load in to bring in those additional tables and columns so quite quite gracefully and elegantly handled as part of that schema definition inside in quarter thanks for that one.

last question coming through how flexible is in quarter in terms of deployments so we have on premises, or we can work with just about any flavor of cloud service provider to set up an environment.

Again, you don't have to use our visualization components it's very easy to work with tablo power bi click just about any other existing bi tool to talk to in quarter, as if it were a postgres database.

So another question coming through on the chat, thank you for your questions, keep them coming.

I can't see manufacturing as reporting area for analysis is that available inside in quarter.

Absolutely, it is so if you're visiting in quarter.com website, you can head over to the solutions area and we will have a manufacturing session, so please.

Go and go there data data Apps are available for manufacturing lots of pre built intelligence so schemas business schemas and visualization content available for that area.

Okay, what is the breakdown that our customers have deployed into.

I don't have any hard numbers for this right now but i'm going to say primarily our customers are using the big three so aws G CP and azure if you sign up to use the quarter SAS service that currently is based on Google cloud platform.

Okay anymore let's have a look, if a customer is working with multiple er PS so, for example, Oracle EBS and jd edwards or EBS and netsuite How would that work well, the answer is very well.

Actually companies like broadcom that we heard about earlier on i've got lots of the rp systems they do a lot of corporate m&a activity, they bring lots of companies.

under one larger umbrella, so they have to work out how to make all of these earpiece systems talk together to be able to generate those financial statements.

and actually in quarter data Apps work really well here to define a schema for each environment and it's relatively trivial to bring these data sources together in a business view and then we get that cross application visibility.

Well i've got time for just one more question I think let's have a look through.

What other connections are available so within the quarter platform we've obviously talked extensively about Oracle EBS today.

Within in quarter, we can bring in everything from data files databases big data environments we've got application connectors as we've seen in the DEMO.

We can connect to a slew of different data lake technologies query services file services dropbox etc.

And then also custom connections, where we basically got a partnership with see data, and that includes hundreds of connectors so chances are we send a pretty good chance of reaching your data wherever it resides.

So with that i'd like to say thank you very much for attending today, if you are now ready to supercharge your Oracle EBS environment.

head over to cloud.in quarter.com slash sign up to start your free trial today and explore the in quarter platform and what it has to offer Thank you so much for attending have a great rest of your day.