Automating Enterprise Planning with EPBCS: A Case Study Featuring Sims Metal Management

Enterprise Planning and Budgeting Cloud ServiceIn using Enterprise Planning & Budgeting Cloud Service (EPBCS) to support annual budgeting and forecasting processes, organizations are choosing solutions that allow them to leverage the financials, projects, capital and workforce business processes necessary to provide a driver-based solution that links expected intake to revenues and costs. In turn, they are able to more efficiently produce integrated income statements, balance sheets and cash flow statements.

Featuring Jim Clark of Sims Metal Management, Our Special Guest

 Our August 16, 2017 webinar, featuring Jim Clark, Group Manager of FP&A at Sims Metal Management, takes a detailed look at how one organization automated enterprise planning to streamline processes and produce better results.

Within a real-world scenario, this means that whether using EPBCS out of the box or as a “hybrid” of OOTB with customized extensions, companies like Sims are able to adjust sales forecasts—throughout the year and through sales cycles—to better match the actual costs and needs in areas such as raw materials and labor.

A Better Approach To Performance Management

Using this integrated approach to Performance Management, companies are, in effect, bringing actual performance numbers, on a monthly basis, into their models.

As a result, changes and adjustments can be fine-tuned and incorporated into the mix.  Forecasts can be based more on actual numbers and less on assumptions, thus leading to a balance sheet that matches projections. From a planning perspective, companies can be more nimble and, ultimately, create their models with greater accuracy.

Whether you are participating live or via a recording, this webinar will illustrate how organizations like Sims are leveraging EPBCS in ways that allow them to: 

  • Gain insight to increase efficiency and improve outcomes
  • Better understand how organizations like yours can make standardization and centralization a top priority
  • See how an integrated solution works not just in theory, but actually in practice
  • Follow the processes to results that include improved accuracy and increased efficiency across the enterprise

For More Information

No matter where your team or your organization is along your EPBCS journey, this webinar is certain to provide you with valuable insight and context that can help you to implement changes that lead to greater efficiency and a more streamlined forecasting process overall.

Register for our “Automating Enterprise Planning with EPBCS: A Case Study Featuring Sims Metal Management ” webinar:

Missed the webinar? View Recording Here.


A Comparison of Oracle Business Intelligence, Data Visualization, and Visual Analyzer

We recently authored The Role of Oracle Data Visualizer in the Modern Enterprise in which we had referred to both Data Visualization (DV) and Visual Analyzer (VA) as Data Visualizer.  This post addresses readers’ inquiries about the differences between DV and VA as well as a comparison to that of Oracle Business Intelligence (OBI).  The following sections provide details of the solutions for the OBI and DV/VA products as well as a matrix to compare each solution’s capabilities.  Finally, some use cases for DV/VA projects versus OBI will be outlined.

For the purposes of this post, OBI will be considered the parent solution for both on premise Oracle Business Intelligence solutions (including Enterprise Edition (OBIEE), Foundation Services (BIFS), and Standard Edition (OBSE)) as well as Business Intelligence Cloud Service (BICS). OBI is the platform thousands of Oracle customers have become familiar with to provide robust visualizations and dashboard solutions from nearly any data source.  While the on premise solutions are currently the most mature products, at some point in the future, BICS is expected to become the flagship product for Oracle at which time all features are expected to be available.

Likewise, DV/VA will be used to refer collectively to Visual Analyzer packaged with BICS (VA BICS), Visual Analyzer packaged with OBI 12c (VA 12c), Data Visualization Desktop (DVD), and Data Visualization Cloud Service (DVCS). VA was initially introduced as part of the BICS package, but has since become available as part of OBIEE 12c (the latest on premise version).  DVD was released early in 2016 as a stand-alone product that can be downloaded and installed on a local machine.  Recently, DVCS has been released as the cloud-based version of DVD.  All of these products offer similar data visualization capabilities as OBI but feature significant enhancements to the manner in which users interact with their data.  Compared to OBI, the interface is even more simplified and intuitive to use which is an accomplishment for Oracle considering how easy OBI is to use.  Reusable and business process-centric dashboards are available in DV/VA but are referred to as DV or VA Projects.  Perhaps the most powerful feature is the ability for users to mash up data from different sources (including Excel) to quickly gain insight they might have spent days or weeks manually assembling in Excel or Access.  These mashups can be used to create reusable DV/VA Projects that can be refreshed through new data loads in the source system and by uploading updated Excel spreadsheets into DV/VA.

While the six products mentioned can be grouped nicely into two categories, the following matrix outlines the differences between each product. The following sections will provide some commentary to some of the features.

Table 1

Table 1:  Product Capability Matrix

Advanced Analytics provides integrated statistical capabilities based on the R programming language and includes the following functions:

  • Trendline – This function provides a linear or exponential plot through noisy data to indicate a general pattern or direction for time series data. For instance, while there is a noisy fluctuation of revenue over these three years, a slowly increasing general trend can be detected by the Trendline plot:
Figure 1

Figure 1:  Trendline Analysis


  • Clusters – This function attempts to classify scattered data into related groups. Users are able to determine the number of clusters and other grouping attributes. For instance, these clusters were generated using Revenue versus Billed Quantity by Month:
Figure 2

Figure 2:  Cluster Analysis


  • Outliers – This function detects exceptions in the sample data. For instance, given the previous scatter plot, four outliers can be detected:
Figure 3

Figure 3:  Outlier Analysis


  • Regression – This function is similar to the Trendline function but correlates relationships between two measures and does not require a time series. This is often used to help create or determine forecasts. Using the previous Revenue versus Billed Quantity, the following Regression series can be detected:
Figure 4

Figure 4:  Regression Analysis


Insights provide users the ability to embed commentary within DV/VA projects (except for VA 12c). Users take a “snapshot” of their data at a certain intersection and make an Insight comment.  These Insights can then be associated with each other to tell a story about the data and then shared with others or assembled into a presentation.  For those readers familiar with the Hyperion Planning capabilities, Insights are analogous to Cell Comments.  OBI 12c (as well as 11g) offers the ability to write comments back to a relational table; however, this capability is not as flexible or robust as Insights and requires intervention by the BI support team to implement.

Figure 5

Figure 5:  Insights Assembled into a Story


Direct connections to a Relational Database Management System (RDBMS) such as an enterprise data warehouse are now possible using some of the DV/VA products. (For the purpose of this post, inserting a semantic or logical layer between the database and user is not considered a direct connection).  For the cloud-based versions (VA BICS and DVCS), only connections to other cloud databases are available while DVD allows users to connect to an on premise or cloud database.  This capability will typically be created and configured either by the IT support team or analysts familiar with the data model of the target data source as well as SQL concepts such as creating joins between relational tables.  (Direct connections using OBI are technically possible; however, they require the users to manually write the SQL to extract the data for their analysis).  Once these connections are created and the correct joins are configured between tables, users can further augment their data with data mashups.  VA 12c currently requires a Subject Area connected to a RDBMS to create projects.

Leveraging OLAP data sources such as Essbase is currently only available in OBI 12c (as well as 11g) and VA 12c. These data sources require that the OLAP cube be exposed as a Subject Area in the Presentation layer (in other words, no direct connection to OLAP data sources).  OBI is considered very mature and offers robust mechanisms for interacting with the cube, including the ability to use drillable hierarchical columns in Analysis.  VA 12c currently exposes a flattened list of hierarchical columns without a drillable hierarchical column.  As with direct connections, users are able to mashup their data with the cubes to create custom data models.

While the capabilities of the DV/VA product set are impressive, the solution currently lacks some key capabilities of OBI Analysis and Dashboards. A few of the most noticeable gaps between the capabilities of DV/VA and OBI Dashboards are the inability to:

  • Create the functional equivalent of Action Links which allows users to drill down or across from an Analysis
  • Schedule and/or deliver reports
  • Customize graphs, charts, and other data visualizations to the extent offered by OBI
  • Create Alerts which can perform conditionally-based actions such as pushing information to users
  • Use drillable hierarchical columns

At this time, OBI should continue to be used as the centerpiece for enterprise-wide analytical solutions that require complex dashboards and other capabilities. DV/VA will be more suited for analysts who need to unify discrete data sources in a repeatable and presentation-friendly format using DV/VA Projects.  As mentioned, DV/VA is even easier to use than OBI which makes it ideal for users who wish to have an analytics tool that rapidly allows them to pull together ad hoc analysis.  As was discussed in The Role of Oracle Data Visualizer in the Modern Enterprise, enterprises that are reaching for new game-changing analytic capabilities should give the DV/VA product set a thorough evaluation.  Oracle releases regular upgrades to the entire DV/VA product set, and we anticipate many of the noted gaps will be closed at some point in the future.

The Role of Oracle Data Visualizer in the Modern Enterprise

Chess as a metaphor for strategic competition is not a novel concept, and it remains one of the most respected due to the intellectual and strategic demand it places on competitors. The sheer combination of moves in a chess game (estimated to be more than the number of atoms in the universe) means that it is entirely possible that no two people have unintentionally played the same game.  Of course, many of these combinations result in a draw and many more set a player down the path of an inevitable loss after only a few moves.  It is no surprise that chess has pushed the limits of computational analytics which in turn has pushed the limits of players.  Claude Shannon, the father of information theory, was the first to state the advantages of the human and computer competitor attempting to wrest control of opposing kings from each other:

The computer is:

  1. Very fast at making calculations;
  2. Unable to make mistakes (unless the mistakes are part of the programmatic DNA);
  3. Diligent in fully analyzing a position or all possible moves;
  4. Unemotional in assessing current conditions and unencumbered by prior wins or losses.

The human, on the other hand, is:

  1. Flexible and able to deviate from a given pattern (or code);
  2. Imaginative;
  3. Able to reason;
  4. Able to learn [1].

The application of business analytics is the perfect convergence of this chess metaphor, powerful computations, and the people involved. Of course, the chess metaphor breaks down a bit since we have human and machine working together against competing partnerships of humans and machines (rather than human against machine).

Oracle Business Intelligence (along with implementation partners such as Edgewater Ranzal) has long provided enterprises with the ability to balance this convergence. Regardless of the robustness of the tool, the excellence of the implementation, the expertise of the users, and the responsiveness of the technical support team, there has been one weakness:  No organization can resolve data integration logic mistakes or incorporate new data as quickly as users request changes.  As a result, the second and third computer advantages above are hindered.  Computers making mistakes due to their programmatic DNA will continue to make these mistakes until corrective action can be implemented (which can take days, weeks, or months).  Likewise, all possible positions or moves cannot be analyzed due to missing data elements.  Exacerbating the problem, all of the human advantages stated previously can be handicapped; increasingly so depending on the variability, robustness, and depth of the missing or wrongly calculated data set.

With the introduction of Visual Analyzer (VA) and Data Visualization (DV), Oracle has made enormous strides in overcoming this weakness. Users now have the ability to perform data mashups between local data and centralized repositories of data such as data warehouses/marts and cubes.  No longer does the computer have to make data analysis without the availability of all possible data.  No longer does the user have to make educated guesses about how centralized and localized data sets correlate and how it will affect overall trends or predictions.  Used properly, users and enterprises can leverage VA/DV to iteratively refine and redefine the analytical component that contributes to their strategic goals.  Of course, all new technologies and capabilities come with their own challenges.

The first challenge is how an organization can present these new views of data and compare and contrast them with the organizational “one version of the truth”. Enterprise data repositories are a popular and useful asset because they enable organizations to slice, dice, pivot, and drill down into this centralized data while minimizing subjectivity.  Allowing users to introduce their own data creates a situation where they can increase data subjectivity.  If VA/DV is to be part of your organization’s analytics strategy, processes must be in place to validate the result of these new data models.  The level of effort that should be applied to this validation should increase according to the following factors:

  • The amount of manual manipulation the user performed on the data before performing the mashup with existing data models;
  • The reputability of the data source. Combining data from an internal ERP or CRM system is different from downloading and aligning outside data (e.g. US Census Bureau or Google results);
  • The depth and width of data. In layman’s terms, this corresponds to how many rows and columns (respectively) the data set has;
  • The expertise and experience of the individual performing the data mashup.

If you have an existing centralized data repository, you have probably already gone through data validation exercises. Reexamine and apply the data and a metadata governance processes you went through when the data repository was created (and hopefully maintained and updated).

The next challenge is integrating the data into the data repository. Fortunately, users may have already defined the process of extracting and transforming data when they assembled the VA/DV project.  Evaluating and leveraging the process the user has already defined can shorten the development cycle for enhancing existing data models and the Extract, Transform, and Load (ETL) process.  The data validation factors above can also provide a rough order of magnitude of the level of effort needed to incorporate this data.  The more difficult task may be determining how to prioritize data integration projects within an (often) overburdened IT department.  Time, scope, and cost are familiar benchmarks when determining prioritization, but it is important to take revenue into account.  Organizations that have become analytics savvy and have users demanding VA/DV data mashup capabilities have often moved beyond simple reporting and onto leveraging data to create opportunities.  Are salespeople asking to incorporate external data to gain customer insight?  Are product managers pulling in data from a system the organization never got around to integrating?  Are functional managers manipulating and re-integrating data to cut costs and boost margins?

To round out this chess metaphor, a game that seems to be nearly a draw or a loss can breathe new life by promoting a pawn to a lost queen. Many of your competitors already have a business intelligence solution; your organization can only find data differentiation through the type of data you have and how quickly it can be incorporated at an enterprise level.  Providing VA/DV to the individuals within your organization with a deep knowledge of the data they need, how to get it, and how to deploy it can be the queen that checkmates the king.

[1] Shannon, C. E. (1950). XXII. Programming a computer for playing chess. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 41(314), 256-275. doi:10.1080/14786445008521796

Oracle Business Intelligence – Synchronizing Hierarchical Structures to Enable Federation

More and more Oracle customers are finding value in federating their EPM cubes with existing relational data stores such as data marts and data warehouses (for brevity, data warehouse will refer to all relational data stores). This post explains the concept of federation, explores the consequences of allowing hierarchical structures to get out of synchronization, and shares options to enable this synchronization.

In OBI, federation is the integration of distinct data sources to allow end users to perform analytical tasks without having to consider where the data is coming from. There are two types of federation to consider when using EPM and data warehouse sources:  vertical and horizontal.  Vertical federation allows users to drill down a hierarchy and switch data sources when moving from an aggregate data source to a more detailed one.  Most often, this occurs in the Time dimension whereby the EPM cube stores data for year, quarter, and month, and the relational data sources have details on daily transactions.  Horizontal federation allows users to combine different measures from the distinct data sources naturally in an OBI analysis, rather than extracting the data and building a unified report in another tool.

Federation makes it imperative that the common hierarchical structures are kept in sync. To demonstrate issues that can occur during vertical federation when the data sources are not synchronized, take the following hierarchies in an EMP application and a data warehouse:

Figure 1: Unsynchronized Hierarchies

Jason Hodson Blog Figure 1.jpg

Notice that Colorado falls under the Western region in the EPM application, but under the Southwestern region in the data warehouse. Also notice that the data warehouse contains an additional level (or granularity) in the form of cities for each region.  Assume that both data sources contain revenue data.  An OBI analysis such as this would route the query to the EPM cube and return these results:

Figure 2: EPM Analysis – Vertical Federation

Jason Hodson Blog Figure 2

However, if the user were to expand the state of Washington to see the results for each city, OBI would route the query to the data warehouse. When the results return, the user would be confronted with different revenue figures for the Southwest and West regions:

Figure 3: Data Warehouse – Vertical Federation

Jason Hodson Blog Figure 3

When the hierarchical structures are not aligned between the two data sources, irreconcilable differences can occur when switching between the sources. Many times, end users are not aware that they are switching between EPM and a data warehouse, and will simply experience a confusing reorganization in their analysis.

To demonstrate issues that occur in horizontal federation, assume the same hierarchies as in Figure 1 above, but the EPM application contains data on budget revenue while the data warehouse contains details on actual revenue. An analysis such as this could be created to query each source simultaneously and combine the budget and actual data along the common dimension:

Figure 4: Horizontal Federation

Jason Hodson Blog Figure 4

However, drilling into the West and Southwest regions will result in Colorado becoming an erroneously “shared” member:

Figure 5: Colorado as a “Shared” Member

Jason Hodson Blog Figure 5

In actuality, the mocked up analysis above would more than likely result in an error since OBI would not be able to match the hierarchical structures during query generation.

There are a number of options to enable the synchronization of hierarchical structures across EPM applications and data warehouses. Many organizations are manually maintaining their hierarchical structures in spreadsheets and text files, often located on an individual’s desktop.  It is possible to continue this manual maintenance; however, these dispersed files should be centralized, a governance processes defined, and the EPM metadata management and data warehouse ETL process redesigned to pick up these centralized files.  This method is still subject to errors and is inherently difficult to properly govern and audit.  For organizations that are already using Enterprise Performance Management Architect (EPMA), a scripting process can be implemented that extracts the hierarchical structures in flat files.  A follow on ETL process to move these hierarchies into the data warehouse will also have to be implemented.

The best practices solution is to use Hyperion Data Relationship Management (DRM) to manage these hierarchical structures. DRM boasts robust metadata management capabilities coupled with a system-agnostic approach to exporting this metadata.  DRM’s most valuable export method allows pushing directly to a relational database.  If a data warehouse is built in tandem with an EPM application, DRM can push directly to a dimensional table that can then be accessed by OBI.  If there is a data warehouse already in place, existing ETL processes may have to be modified or a dimensional table devoted to the dimension hierarchy created.  Ranzal has a DRM accelerator package to enable the synchronization of hierarchical structures between EPM and data warehouses that is designed to work with our existing EPM application DRM implementation accelerators.  Using these accelerators, Ranzal can perform an implementation in as little as six weeks that provides metadata management for the EPM application, establishes a process for maintaining hierarchical structure synchronization between EPM and the data warehouse, and federation of the data source.

While the federation of EPM and data warehouse sources has been the primary focus, it is worth noting that two EPM cubes or two data warehouses could be federated in OBI. For many of the reasons discussed previously, data synchronization processes will have to be in place to enable this federation.  The previous solutions for maintaining metadata synchronization may be able to be adapted to enable this federation.

The federation of EPM and data warehouse sources allows an enterprise to create a more tightly integrated analytical solution. This tight integration allows users to transverse the organization’s data, gain insight, and answer business essential questions at the speed of thought.  As demonstrated, mismanaging hierarchical structures can result in an analytical solution that produces unexpected results that can harm user confidence.  Enterprise solutions often need enterprise approaches to governance; therefore, it is often imperative to understand and address shortcomings in hierarchical structure management.  Ranzal has a deep knowledge of EPM, DRM, and OBIEE, and how these systems can be implemented to tightly work together to address an organization’s analytical and reporting needs.

Using Data Visualization and Usability to enhance end user reporting – Part 4: Tying it all together

Now that the foundations have been set in my last three posts, in this final post I’ll share how we can create reports, leveraging:

• Standard definitions and metrics
• The understanding of how users  will consume data and interact with the system

To effectively create reports, make sure to follow these key best practices:

1. Reduce the data presented by focusing on the important information. For example, rather than showing two lines for revenue actuals and revenue budget, try showing one for the difference. Users can identify trends much more quickly when there are fewer objects to focus on.

2. Concentrate on important data and consolidate it into chunks. If you have two charts, use the same color for revenue on both of them. This makes it easier to interpret and see trends between them

3. Remove non-data items, especially the images, unnecessary lines and graphics. This helps the user focus on the actual data, so they can see trends and information rather than clutter.

Here is an example of two reports with the same data. The first provides a table with various colors, bold fonts and line. The second report highlights the important areas/regions. Your eyes are immediately drawn to those areas needing attention. Table two allows the user to draw accurate conclusions more effectively and in a much shorter timeframe.

These are some general practices which can be applied in most cases and will give users a much more positive experience with your reporting system. If you need help making sense of your reporting requirements, creating a coherent reporting strategy or implementing enterprise reporting, please contact us at

Using Data Visualization and Usability to Enhance End User Reporting – Part 3: The Balance between Data and Visual Appeal

In part three of my blog series, I’ll provide an overview of the important balance between data and visual appeal when creating reports, including some of the latest research and findings.

Many users believe that once you have the metrics in place and understand what data users want, the next step is to create the reports.

In reality, a lot of thought and a careful eye are required when making design considerations to create charts, grids and tables that convey the details in the simplest terms for user understanding. The right design choices enable users to see easily the trend, outliers, or items needing attention.

Many people think that the more data they can cram in, the better. However, studies have shown that the average person can only store 6 chunks of information at a time.  Depending on how flashy and distracting your graphics and marketing logos are, you may have already used up half of your brain’s capacity, without getting to any reports or dashboards.

Graphic overload may make one consider removing all distracting graphics, highlights, bolds and visual clutter to show the data – novel concept right?

But this is not the solution. There has been lots of visualization studies and research done over the past century that have uncovered that eliminating graphics altogether is not the solution to this dilemma.

In fact, there are several leading experts on this topic, including three key people, who are leading the charge against clutter and visual distraction, cheering for more measured and thoughtful chart and dashboard visual design. These individuals are:

·         Edward R. Tufte

·         Colin Ware

·         Stephen Few

All three have published several books explaining how we interpret visual data, including what makes our eyes drawn to color and form, and what aids understanding. It also explains “chart junk” – a term first coined by Tufte in 1983. Tufte defines “chart junk” as simply:

Conventional graphic paraphernalia routinely added to every display that passes by: over-busy grid lines and excess ticks, redundant representations of the simplest data, the debris of computer plotting, and many of the devices generating design variation.”

The key concept of “chart junk” leads into another of Tufte’s mantras called the “Data Ink” ratio. The idea here is that by minimizing the non-data ink you are maximizing the data ink.  In other words,  that you can achieve the ideal balance of data and design by removing borders, underlines, shading and other ink elements which don’t convey any messages

There are a lot of available resources out there on this topic by these authors and others.

Stay tuned for my final blog post, in which I will demonstrate how to effectively put these concepts  into practice when creating reports.

Using Data Visualization and Usability to Enhance End User Reporting – Part 2: Usability

In this second part of my blog series, I’ll be looking at usability and what it really means for report design.

Usability takes a step back and looks at the interactions users have with reports. This includes how users actually use the reports, what they do next, and where they go. If users refer to another report to compare values or look at trends, they should think about condensing these reports into a single report or even create a dashboard report with key metrics. This way, users have a clear vision of what they need or what Oracle calls “actionable insight”. From there, users can provide other users with guided navigation paths based on where they actually go today.

With improved usability, users can review an initial report and easily pull up additional reports, possibly from a different system or by logging into the general ledger/order entry system to find the detail behind the values/volumes. With careful design, this functionality can be built into reporting and planning applications, to provide a single interface and simplify the user interactions.

Here is a real world example of how improved usability can benefit users on a daily basis: Often a user will open a web browser and an item is highlighted as a clickable link. Normally if you click on the link, it will open up in the same window, causing you to lose the original site that you visited. By clicking the back button, you can also lose the first site that you visited. With improved usability, clicking on a link would result in a new pop-up window, so when finished users are able to choose which windows to close and return to the original window.

The challenge with achieving improved usability, is that many organizations lack visibility into how users actually use reports, especially with users spread all over the world. One possible solution is for organizations to ask users about their daily activities. The issue here is that often users are uncomfortable discussing what they do and where they go online. Companies can overcome this challenge by enforcing sessions where they can ask leading questions including why users feel uncomfortable sharing their daily activities. These types of sessions can help organizations uncover the root causes/issues, giving them the insight to delve deeper to understand what lies behind the report request.

One common scenario where you could apply this approach is when users ask for a full P&L for their business units, so they can compare and ring anyone over budget.  By having a session to understand the users’ specific needs/daily activities, organizations can instead produce a dashboard that highlights the discrepancies by region. With this dashboard, there is no need to compare and analyze; users can open the dashboard and see the indicators with a click of a button. Users can drill down for more information while placing that call!

In conclusion, improved usability means helping users get to the answer quicker, without having to do a lot of unnecessary steps. The old adage is true – KISS – Keep It Simple Stupid!