OPA! The Future of Cloud Integration – Important Updates Are Coming

Much to the chagrin of Product Management, I often abbreviate Cloud Data Management to CDM.  Why do they not like that I do this?  Well there is a master data management tool for Customer data that you can guess also uses the same acronym.  While I understand the potential confusion, since I’m telling you up front, there should be no confusion when I use CDM throughout this post.

I recently had the opportunity to meet with Oracle Product Management and Development for FDMEE/CDM to get a preview of what’s coming to the product and offer feedback for additional functionality that would benefit the user community.  We generally get together about once a year; however, it’s been a bit longer than that since our last meeting, so I was excited to hear what interesting things Oracle’s been working on and what we may see in the product in the future.

Now any good Oracle roadmap update would not be complete without a safe harbor reminder.  What you read here is based on functionality that does not yet exist.  The planned features described may or may not ever be available in the application – at the sole discretion of Oracle. No buying decisions should be made based on the information contained in this post.

Ok, now that we have that out of the way, let’s get into the fun stuff.  There are a number of enhancements coming and planned, but today I am going to focus on two significant ones:  performance and ground to cloud integration.

Performance Enhancements

We’re all friends here, so we can be honest with each other.  CDM (and FDMEE) isn’t an ETL tool in the truest sense of the word. It is not designed to handle the massive data volumes that more traditional ETL can and does.  You might think to yourself thanks for the info there Tony, but we all know that, and you wouldn’t be wrong, but I like to set the stage a bit.

If you know the history of FDMEE, you know that it was originally designed to integrate with Hyperion Enterprise and then HFM.  Essbase and Planning became targets later.  Integrating G/L data is far different than the more operational data that is often needed by targets like EPBCS and PCMCS.  While CDM (and FDMEE) can technically handle the volume of data with this more granular data, the performance of those integrations are sometimes less than optimal.  This dynamic has plagued users of CDM for years.  It has only been exacerbated when integrations are built that do not have a deep understanding of how to tune CDM (and FDMEE) processes to achieve the highest level of performance within the constructs of the application. As CDM has grown in popularity (owing to the growth of Oracle EPM Cloud), the problem of performance has become more visible.

To address performance concerns, Oracle is planning to support 3 workflow methods:

  • Full – No change from legacy process
  • Full, No Archive – Same workflow as today but data is deleted from the data table (tDataseg) after a successful export.  This means the data table will contain less rows and should allow new rows to be added faster (inserts during the workflow process).  The downside of this method is that drill through is not available.
  • Simple – Same workflow as today but data is never moved from the staging/processing table (tDataSeg_T) to the data storage table (tDataSeg).  This is the most expensive (in terms of time) action in the workflow process so eliminating it will certainly improve performance. The downside is that data can never be viewed in the Workbench and Drill Through is not available.

Oracle has begun testing and has seen performance improvements in the range of 50% in data sets as large as 2 million rows.  To achieve that metric required the full complement of the new features of Data Integrations (i.e., Expressions) to be utilized. That said, this opens up a world of possibility for how CDM can potentially be used.

If you have integrations that are currently less than optimal in terms of performance, continue monitoring for this enhancement.  If you need assistance, feel free to reach out to us to connect with our team of data integration experts.

On-Premise Agent

Ground to cloud integration is one of the most important capabilities to consider when implementing Oracle EPM Cloud.  As the Oracle EPM Cloud has evolved, so too has the complexity of the solutions deployed within it which has steadily increased the complexity of the integrations needed to support solutions.  While integration with on-premises has always been supported through EPM Automate, this requires a flat file to be generated by the system from which data will be sourced. The file is then loaded to the cloud and processed by CDM.  This is very much a push approach to data integration.

The ability of the cloud to pull data from on-premises systems simply did not exist. For integrations with this requirement, FDMEE (or some other application) was needed. Well as the old saying goes, the only thing constant is change.

Opa! – a common Greek emotional expression. It is frequently used during celebrations.  Well it’s time to celebrate because Oracle will soon (CY19) be introducing an on-premises agent (OPA) for CDM!

This agent will allow a workflow to be initiated from CDM, communicate back to the on-premises systems, initialize and then upload an extract to the cloud. The extract will be natively imported by CDM.  This approach is similar to how the FDMEE SAP adaptor currently works.  From an end user perspective, they click Import on the Data Load Workbench and after some time, data appears in the application. What’s happening in the background is that the adaptor is initializing an extract from SAP and writing the results to a flat file which is then imported by the application. OPA will function in an almost identical way.

OPA is a light weight JAVA utility that requires no additional software (other than JAVA) that will be installed on local systems. It will support both Microsoft and Linux operating systems. Like all Oracle on-premises utilities (e.g., EPM Automate), password encryption will be supported. The only port(s) which are required to be opened are 80 (HTTP) or 443 (HTTPS).  A customer can then use an externally facing web server to redirect to an internal port for the agent to receive the request.  This is true only if the customer wants to run the agent on a port other than 80 or 443 and do not want to open that port on their enterprise firewall.  If the customer wants to run the agent on port 80 or 443 and either of those ports are open, then no firewall action would be required.

The on-premises agent will have native support for Oracle EBS and PeopleSoft GL – meaning the queries are prebuilt by Oracle.  Additionally, OPA will support connecting to on-premises relational data sources.  Currently Oracle, SQL Server and MySQL drivers are bundled natively but additional drivers can be deployed as needed meaning systems such as Teradata will be able to be leveraged as data sources.

OPA will also provide an ability to execute scripts (currently planned for JAVA but discussions for Groovy and Jython are in flight) before and after the on-premises extract process.  This is similar to how the BefImport and AftImport event scripts are currently used in FDMEE.  This will allow the agent to perform pre and post processing such as running a stored procedure to populate a data view from which CDM will source data.

The pre and post events of OPA really open up a world of opportunity and lay the foundation for CDM to support scripting.  How you might ask?  In v1.0, OPA is intended to provide a mechanism to load on-premises data to the cloud.  But in theory, CDM could make a call to OPA at the normal workflow events (of FDMEE) and instead of waiting for a data file, simply wait for an on-premises script to return an execution code.  This construct would eliminate the security concerns that prevented scripting from being deployed in CDM as the scripts would execute locally instead of on the cloud.

The OPA framework is really a game changer and will greatly enhance the capability of CDM to provide Oracle EPM Cloud customers a true “all cloud” deployment.  I am thrilled and can’t wait to get my hands on OPA for beta testing.  I’ll share my updates once I get through testing over the next couple of months.  I’ll also be updating the white paper I authored back in December of 2017 once OPA is released to the general public.  Stay tuned folks and feel free to let out a little exclamation about these exciting coming enhancements…OPA!

Implementing Zero-Based Budgeting: The Requirements

A Culture Change and a Centralized System

The first post in this 3-post series – Implementing Zero-Based Budgeting: Benefits, Myths, and Goals – covers the benefits of zero-based budgeting. To summarize, it enables you to achieve long-term savings that result in sustainable growth and holds your financial analysts accountable for the cost figures they approve and how they are managing the overall budget. This allows more effective recognition of any unwanted costs and how you that money can be shifted into other growth areas within the company.

However, to reap the benefits of a zero-based budgeting program, a culture change is needed first at certain levels within the company. The goal is to eventually have the entire company complete this culture shift, but it is best to start small. Along with a change in culture, a centralized reporting system needs to be created as well to provide teams the ability to share real-time numbers with each other to achieve the goals of this new budgeting program.

Better Than a Quick Fix

What exactly is meant by a culture change? This means starting small and fostering this culture change in other departments starting with Finance. To be successful with this new program, other departments will eventually have to jump on board with this new budgeting approach. These departments will need to step up in analyzing their own costs and how they can save more without diminishing their capabilities.

For example, while financial analysts talk to the shop floor to see where costs can be reduced, the HR department should work with Finance to determine how it can become leaner. Moreover, the IT department should take the lead on negotiating with its vendors to find any areas that can be saved. These are just a few examples of how different departments can step up to the plate; implementing a successful zero-based budgeting program will requires team effort.

Changing the culture doesn’t happen overnight. Senior leaders should take the lead in fostering this change. To ensure that everyone is on the same page, managers need advocate the new approach within their respective departments.

Incentives also help teams to buy into this new budgeting approach.  Although incentives for growth metrics may already exist, additional incentives can effectively encourage staff to find ways to reduce costs for the metrics they manage.

Some examples of incentive metrics are the realized ROI based on the requested capital expenditure and the total cost saving dollars resulting from a zero-based budgeting program. For the former, this can mean moving to the Cloud to save money or reducing redundant tasks by introducing centralized software. For the latter, it can be exemplified by achieving a 10% cost reduction per phone.

Best Practice to Achieve Success

A crucial component of the success of a zero-based budgeting program is an officer who governs the entire process from start to finish. This individual (or team) should contain deep knowledge of the budgeting process. Naturally, s/he will not know the ins and outs of each department, so that is why s/he needs to be an ambassador to department leaders. The officer will also provide oversight to ensure that past bad habits of budgeting do not return to plague this new program. And lastly, s/he must be dedicated to the craft of continuous improvement which means seeking outside counsel when needed.

As mentioned earlier in the post, a culture change needs to be accompanied by a centralized reporting system. Alithya has helped clients implement Oracle Planning and Budgeting Cloud Service (PBCS) and Enterprise Planning and Budgeting Cloud Service (EPBCS) and overcome the deficiencies of Excel-based models. These models lose sight of what the true cost numbers are because past budgets are simple anchors of history rather than detailed breakdowns of cost. Moreover, these numbers become siloed within the vast library of Excel models. With Oracle PBCS or EPBCS, budgets can be highly surgical and help leaders in the company pinpoint reductions.

A centralized system allows the capture of all changes in a single location in real-time, and it provides insight into how effectively managers seek cost savings. This can be used as a key indicator to determine if their actions are in line with this new methodology.

Furthermore, centralization not only holds managers more accountable, but it also empowers them to create innovative cost-saving solutions. Driven by incentives, staff will burn with a clear purpose to find new ways to achieve sustainable growth for the company and be rewarded for hard work.

Recapping What It Takes to Achieve ZBB Success

The goal is to create a cost savings culture that allows more capital to be invested into growing parts of the company. To be successful, follow the best practices outlined, starting with a culture change within the company and giving your teams a centralized PBCS and EPBCS system to more clearly see all data points. The hard work does not stop here, though! The next post delves into setting up a zero-based budgeting system.

An Exploration of the EDMCS REST API

Recently my team and I had the opportunity to implement Oracle’s newest offering – Enterprise Data Management Cloud Service (EDMCS). EDMCS for those of you who are not familiar provides a cloud-based solution for managing master data (also referred to as metadata) across the organization.  Some like to refer to EDMCS as Data Relationship Manager (DRM) in the Cloud, but the truth is, EDMCS is not DRM in the Cloud.

EDMCS is a completely new vision of what master data management can and should be. The architect of this new cloud offering is the same person who founded Razza Solutions which was the company that developed the product now known as DRM.  That is important to know because it ensures that the best of what DRM has to offer is brought forward.  But, more importantly, it ensures that the learnings and wish list of capabilities that DRM should have are in the forefront of the developers’ minds.

Ok, now let’s get back to fun stuff. In the 18.05 patch for EDMCS, the REST API (v1) was exposed for public usage.  The documentation for the REST API can be found here:

https://docs.oracle.com/en/cloud/saas/enterprise-data-management-cloud/edmra/rest-endpoints.html

As I highlighted in the previous post Troubleshooting Cloud Data Management Metadata Load Errors, I had developed an automation routine to upload EDMCS extracts to both PBCS and FCCS using FDMEE and Cloud Data Management.  We had been eagerly awaiting the REST API for EDMCS to finalize this automation routine and provide a true end-to-end process that can be scheduled or initialized via a single action.

Let’s take a quick look back at the automation routine developed for this customer. After the metadata has been exported to a flat file from EDMCS, the automation would upload a copy to the PBCS and FCCS pods, launch Cloud Data Management data load rules which would process the EDMCS metadata extracts, run a restructure of the database after all dimensions had been loaded, and then send a status email alerting the administrator of the result.  While elegant, I considered this to be incomplete.

Automation, in my view, is a process that can be executed without user interaction. While an automation routine certainly has parameters that must be generally maintained, once those parameters are set/updated, the automation cycle should not be dependent on user input or action.  In the aforementioned solution, we were beholden to the fact that EDMCS exports had to be run interactively; however, with the introduction of the publicly exposed REST API in the 18.05 EDMCS patch, we are now able to automate the extract of metadata from EDMCS.  That means we can finally complete our fully automated, end-to-end solution for loading metadata.  Let’s review the EDMCS REST API and how we did it.

The REST API for EDMCS is structured similar to other Oracle EPM REST APIs. By this, I mean that multiple REST commands may need to be executed to achieve a functional result.  For example, when executing a Cloud Data Management data load rule via the Data Management REST API, the actual execution of the data load rule is handled by a POST call to the jobs function with the required payload (e.g. DLR name, start period, etc.).  This call is just one portion of a functional requirement.  To achieve an actual data load, a file may need to be uploaded to the cloud, the data load rule initialized, and then the status of the data load rule be retrieved.  To achieve this functional result, three unique REST API executions would need to occur.

To export metadata from EDMCS to a flat file using the REST API, the following needs to be executed:

  1. Get the dimension information for the EDMCS application from which metadata will be exported
  2. Execute an export of the dimension(s)
  3. Determine the status of export
  4. Download the export to a flat file

Let’s explore each of these in a little more detail. First, we need to get the dimension IDs for the application from which we will be downloading metadata.  This is accessed from the applications function.

https://docs.oracle.com/en/cloud/saas/enterprise-data-management-cloud/edmra/op-v1-applications-get.html

When executing this function, the JSON object return includes all applications that exist in EDMCS (including those archived). So the JSON needs to be iterated to find the record that relates to the application from which metadata needs to be exported.  In this case, the name of the application is unique and can be used to locate the appropriate record.  Next, we need to query the JSON object to get the actual dimension id (circled in red).  The dimension ID is used in subsequent calls to actually export the dimension.

Great, now we have the dimension ID. Next, we need to execute the REST API call to export the dimension.

Automated Metadata 1.docx

https://docs.oracle.com/en/cloud/saas/enterprise-data-management-cloud/edmra/op-v1-dimensions-dimensionid-export-download-post.html

You will notice that when you access this POST method, the dimension ID from the previous step is required:

/epm/rest/v1/dimensions/{dimensionId}/export/download

The JSON object returned from this execution contains minimal information. It simply provides the URL to the next required REST API execution which will provide the status of the execution.

Automated Metadata 2.docx

With this information, we can check the status of the export using the jobRuns function

https://docs.oracle.com/en/cloud/saas/enterprise-data-management-cloud/edmra/op-v1-jobruns-jobrunid-get.html

The JSON object returned here provides us the status of the export invoked in the prior step (in yellow) as well as a URL to the actual file to download which is our last step in the process.

Automated Metadata 3.docx

Once the export job is complete, the files can be streamed using the URL provided by the REST execution in the prior step.

https://docs.oracle.com/en/cloud/saas/enterprise-data-management-cloud/edmra/op-v1-files-temp-fileid-get.html

And there you have it, a fully automated solution to download metadata to flat files from EDMCS. Those files are then provided to the existing automation routine and our end-to-end process is truly complete.

And for my next trick…let’s explore some of the different REST API tools that are available to help you in your journey with the EPM REST APIs.

 

Troubleshooting Cloud Data Management Metadata Load Errors

In my last post, I highlighted a solution that was recently deployed for a customer that leveraged Enterprise Data Management Cloud Service (EDMCS), Financial Data Quality Management Enterprise Edition (FDMEE), and Cloud Data Management (CDM) to create an automated metadata integration process for both Planning and Budgeting Cloud Service (PBCS) and Financial Close and Consolidation Cloud Service (FCCS). In this post, I want to take a bit of a deeper dive into the technical build and share some important learnings.

Cloud Data Management introduced the ability to load metadata from a flat file to the Oracle EPM Cloud Services in the 17.11 patch. This functionality provides customers the ability to leverage a common platform for loading both data and metadata within the Cloud.  Equally important, CDM allows metadata to be transformed using its familiar mapping functionality.

As noted, this customer deployed both PBCS and FCCS. Within the PBCS application, four plan types are active while FCCS has the default two plan types.  A design decision was made for EDMCS to create a single custom application type that would store the metadata for both cloud applications.  This decision was not reached without significant thought as well as counsel with Oracle development.  While the pros and cons of the decision are outside the scope of this post, the choice to use a custom application registration in EDMCS ensured that metadata was input a single time but still fed to both cloud applications.  As a result of the EDMCS design decision, a single metadata file (per dimension) was supplied with properties necessary to support each plan type.

CDM leverages its 23 “dimensions” to store metadata information for processing. Exactly like data, metadata is imported using an import format into the CDM relational repository.  Each field from a metadata file is aligned to a CDM dimension field.  The CDM Account dimension always represents the target application member name and the CDM Entity dimension represents the parent of the member.  All other fields can be aligned to any of the remaining 21 dimensions.  CDM Attribute dimensions can be utilized in the import and mapping process but are not available for exporting to the cloud application.  This becomes an important constraint especially in a multi-plan type deployment.  These 21 fields can be used to store any of the properties required to successfully load metadata to the target plan type.

Let’s consider this case study for a moment. The PBCS application has four plan types.  If a process were to be built to load all plan types from a single CDM data load rule, then we would not be able to have more than five plan type specific attributes or properties because we would not have enough CDM fields/dimensions to store the relevant information.  This leads to an important design approach.  Instead of a single CDM data load rule to load all plan types, a data load rule was created for each plan type.  This greatly increased the number of metadata properties and attributes that could be loaded by CDM and ensured that future growth could be accommodated without a redesign of the integration process.

It is important to understand that CDM utilizes the Planning Outline Load Utility (OLU) to actually perform the metadata load to the cloud application. The OLU loads metadata using merge (yes Planning experts, I realize that I am not discovering fire) which is important to understand especially when processing multiple metadata loads for a single application.  When loading metadata, there are certain properties that are Application level.  I like to think of these as being global.  Additionally, there are plan type specific attributes that can align (or not align) to the application level value/setting.  I like to think of these as local.

Why is this important? Well when loading a metadata file, if certain global properties are excluded from the metadata load file, the local properties (if specified) are utilized to default the global properties. Since metadata is loading using merge, this only becomes problematic when a new member is being added to the outline.

In this particular example, an alternate hierarchy with shared members was specified in one of the plan types. The storage property of the alternates was obviously set as Shared; however, when attempting the metadata load, the following error was encountered:

A Base Member cannot be changed to a Shared Member.

After much investigation (details to follow), I discovered that the global property should also be included in the metadata load.

While CDM utilizes the OLU to load metadata, it does not provide as much verbosity in the error information as the PBCS web interface (which also uses OLU) when loading metadata. Below is an example of the error in the CDM process log.  As a tangent, I’d love to check the logs without needing to open a Service Request.  Maybe Oracle will build an enhancement that allows that in the future (hint, hint, wink, wink to my friends at Oracle).

Baha Mar - Error Handling 1

So where do I go from here? Well, what do we know about CDM loading metadata to the cloud application?  We know that CDM uses the OLU to load a flat file generated by CDM.  Bingo!  The metadata file output by CDM is a good starting point.  That file is in the Outbox of the CDM application and can be downloaded in several different ways – CDM Import process (get creative folks), CDM process details, or EPM Automate.  Now we have the metadata file and can test to determine if the error is caused by CDM or the metadata itself.  It’s all about ruling out variables.  So, we take the metadata file and import it manually within the PBCS web interface and are able to replicate the error.  But now we have an important new data point – the line number from the metadata file that is causing the error.

Baha Mar - Error Handling 2

Now that we have actionable information, we can review each property and start isolating and eliminating different variables. We determined that this error was only occurring for new alternate hierarchy parents being added to the outline.  As a test, we added the global storage property and voila, the metadata load completed successfully.  Face palm!

Maybe this would have been obvious to folks with a lot of Planning experience, but there are plenty of folks learning the intricacies of Planning and Essbase (including our friends converting from HFM to FCCS), so I wanted to share a lesson learned in my journey of metadata integration using CDM.

CDM functionality for metadata represents two of the three primary operations of ETL. In my next post, we’ll dive deeper into how the extract component of ETL was accomplished to provide a seamless end- to-end ETL solution for metadata.

Cloud Data Management (CDM) and Financial Data Quality Management Enterprise Edition (FDMEE): A Case Study in Working Together

Why buy Financial Data Quality Management Enterprise Edition (FDMEE) when Cloud Data Management (CDM) is free?  As outlined in my recent white paper – FDMEE vs. Cloud Data Management – there are myriad factors that can drive the decision.  This blog post highlights how one customer gained a highly flexible and automated solution for data and master data management with an on-premise deployment of FDMEE in conjunction with Cloud Data Management.

This customer adopted a pure Cloud strategy as it relates to Enterprise Performance Management (EPM) procuring subscriptions to Planning and Budgeting Cloud Service (PBCS), Financial Close & Consolidation Cloud Service (FCCS), and Account Reconciliation Cloud Service (ARCS).  A diverse business, the customer has many unique operational systems with varying formats and charts of accounts.  So far, no reason why Cloud Data Management (CDM) can’t handle this requirement, right?  This is what CDM does – uses import formats and maps to consume and transform data – right?  Sure, but with caveats.  Notice that I used the word consume and not extract.  CDM does not provide the ability to link with on-premise systems to extract data.  Additionally, flat file data extracts that lack a consistent structure often cannot be natively consumed by CDM.

In this case, data needs to be loaded each day from numerous sources to support daily operational reporting.  The systems are a blend of on-premise, hosted, and Cloud applications.  The customer requirement dictated that any on-premise system should be connected directly to eliminate the need for a flat file extract to be generated daily.  Additionally, the hosted and Cloud applications are very industry specific and, in some cases, provided by very niche vendors.  The ability to modify extract formats was cost prohibitive or simply not supported.  As a result, several of these data feeds were not consumable by CDM without preprocessing/modification.

In light of the above requirements, the customer procured and deployed FDMEE on-premise.  The power of FDMEE allows a solution to be deployed that provides a direct connection to multiple on-premise systems as well as consume the flat file extracts from hosted and Cloud applications including Excel files (not in the required FDMEE/CDM format) and XML.  Because FDMEE on-premise supports scripting, we were able to greatly enrich the data integration cycle with full end-to-end automation including FTP downloading of hosted data, enhancement of the data integration cycle to detect data mapped to members not yet in PBCS or FCCS, dynamically setting substitution variables based on the processing day, running calculations in PBCS, and sending email status alerts to outline the success or failure of a data load cycle.

Although I am a huge FDMEE advocate, I recognize the value of Cloud Data Management and the benefits it provides in a case like this one.  This customer was one of just three participants in the Oracle Enterprise Data Management Cloud Service (EDMCS) program.  This means that they were able to use the software before it was publicly available – otherwise known as GA.  To participate in this program, one must recognize the absence of certain features and functions with the software.  The program allows the customer (and partner) to offer Oracle development and product management valuable input about the software and in some ways drive what features are prioritized within the product roadmap.

EDMCS currently lacks native connections to FCCS, but this will change over time.  So how does CDM help with loading metadata to FCCS?  In a recent update to CDM, Oracle included the ability to import a flat file into CDM and load metadata to a registered target application such as PBCS or FCCS.  John Goodwin gives a detailed overview of the technical setup.

FDMEE and CDM have come together in this case to provide a fully automated data integration process and an automated master data integration process.  Within EDMCS, a Custom application type was created.  The required properties for FCCS were built and attached to the multiple dimensions being mastered, and flat file exports were generated for FCCS.  We knew we were going to use CDM to manage the master data load process, but we had a decision to make – do we leverage EPM Automate or FDMEE as our automation hub?

We chose FDMEE.  Why?  Simply because a lot of automation assets had already been developed in FDMEE that could readily be reused for this process including execution of EPM Automate commands, a framework for leveraging the REST API (for PBCS and FCCS), and email alerting.  Additionally, we found the capabilities of EPM Automate to be somewhat limited.

For example, when you execute a CDM data load rule from EPM Automate, the process ID associated with the execution is not returned.  Why is that important?  Because in the event of a failure, I’d want to download the process log and attach it to the email so the user has information to address the issue.  Could I use the ListFiles command of EPM Automate to get the process log? Possibly, but it doesn’t account for potential concurrency, and I am not doing my job as a consultant if I build a process that can’t handle concurrent operations.  For reasons such as these, we leveraged EPM Automate when possible and the REST API as needed, and we wrapped it all together with an FDMEE process that could be executed on a scheduled basis or on demand simply by using the Script Execution functionality.

Let’s review the end-to-end solution.  In EDMCS, metadata is maintained for PBCS and FCCS.  The metadata is extracted to a flat file (.csv) after maintenance is completed and saved to a network folder.  From FDMEE, the master data integration process is initiated to upload the metadata files to FCCS and PBCS.  Cloud Data Management data load rules are initialized to process the metadata extracts.  In the event of an error, the CDM process log is downloaded.  Finally, an email is generated to alert the administrator of the data integration process status.

There you have it – EDMCS, FDMEE, and CDM working in concert to provide a seamless and elegant solution to data and master data integration for a customer that adopted a Cloud EPM strategy.  If you want to learn how you can enhance your Oracle EPM integration processes, contact us and we’ll be happy to discuss your options.

Undocumented Data Export Feature in Oracle Hyperion PBCS (Planning and Budgeting Cloud Service)

In response to companies looking for more decentralized services with less IT overhead, Oracle has launched the Planning and Budgeting Cloud Service (PBCS). PBCS is a hosted version of the Oracle Hyperion Planning and Data Management/Integration (FDMEE) tools with a particular focus on a completely online-based interface. For additional information on PBCS, please click HERE.

From a functional perspective, this is an ideal situation: to have near-full capabilities of an on-premise solution without the infrastructure maintenance concerns. Practically, though, there are some holes to fill as Oracle perfects and grows the solution.

One of the main areas for concern has been the integration of data into and out of PBCS. Data Management (a version of FDMEE) is the recommended tool for loading flat file data into the system, while there is also the ability to load directly to Essbase with perfect files. Getting files out of the system, on the other hand, has not been so straightforward. Without access to the Essbase server, exporting files proves impractical. Companies often need data exports from Essbase for backups, integrations into other systems, or for review. PBCS does not seem to have a native method of being able to extract Level Zero (Lv0) data on a regular basis that could be easily copied out of the system and used elsewhere.

Despite this, the DATAEXPORT command still exists in the PBCS world. How, then, could it be used to get a needed file?

It actually begins as with a normal on-premise application by creating a Business Rule to do a data export. This can be done manually, but it is recommended to use the System Template to make sure everything is set up perfectly.

JP_ScreenShot_2015_03_09_09_10_35

When setting up the location to export the file to, it should be set up as:

 “/u03/lcm/[File_Name.txt]”

JP_ScreenShot_2015_03_09_09_30_59

When this is done, a user can then navigate over to the Inbox/Outbox Explorer and see the file in there:

JP_ScreenShot_2015_03_09_09_42_39

JP_ScreenShot_2015_03_09_09_44_01

And that is really all there is to it! With a business rule in place, the entire process can be automated using EPMAutomate (EPMAutomate and recommendations for an automation engine/methodology will be discussed in a later post) and a batch scripting client to do a process that:

  • Deletes the old file
  • Runs the business rule to do the data export
  • Copy the file off of PBCS and to a local location
  • Push the file to any other needed location

The one important thing to note is that as of PBCS 11.1.2.3.606 (April 2015 patch), all files in the Inbox/Outbox Explorer — along with any files in Application Management (LCM) — that are older than two months will be automatically deleted. As such, if these files are being kept for archive purposes, they must be backed up offline in order to be preserved.