Don’t Fear the Statistics – Using OBI for Statistical Analysis Part 2

Nearly every client Edgewater Ranzal partners with uses statistical averages in their analytic and reporting solutions. As far as statistical functions go, it is probably the easiest to understand, however; the limitation of using the average is that it can be difficult to determine how to rate the individual performance of contributors to that average.  Consider the following examples:

  • The average cost of a gallon of milk is $3.20 and the corner convenience store is selling it for $3.45, is that a significant deviation from the average?
  • If the average NFL player’s base salary is $1.86 million and Tennessee Titan’s Marcus Mariota made $5.5 million, is this an exceptional payout? Is the salary significant when his role as the team’s starting Quarterback is considered?
  • Suppose the average gross margin percent for a company’s business units is 58% and one particular business unit’s actual gross margin is 46%. Is that business unit truly underperforming?

It turns out that the average of a particular measurement is very subjective. In this post, we explore how the standard deviation of the average can be used to mitigate subjectivity and how it can be incorporated into data visualizations to identify true outliers.

The NASDAQ-100 is comprised of the largest domestic and international non-financial companies (based on market capitalization) listed on the Nasdaq Stock Exchange. It includes technology giants such as Apple and Alphabet (parent company of Google) along with consumer services such as Bed, Bath, & Beyond.  The quarterly gross margin percent from 2007 to Q3 2016 was downloaded and loaded into a data mart leveraged by Oracle Business Intelligence Enterprise Edition (OBIEE) 12c.  (Q4 2016 data was not available for all companies).  With the exception of Figure 1, the following visualizations were created in OBIEE 12c.

The standard deviation can be thought of as ranges that can be used to classify individual contributors to the average. For instance, the average gross margin percent for the NASDAQ-100 in Q4 2014 was calculated to be 59.9% with a standard deviation of 22.7%.  This can be visualized on a number line as such:

Figure 1 NASDAQ-100 Q4 2014 Gross Margin % Performance Ranges

dont-fear-statistics-part-2-figure-1

Many real world events that have variability follow a predictable distribution pattern. For instance, it is expected that approximately 34.1% of the contributors will fall between the average and one standard deviation up.  From the figure above, it is estimated that approximately 34 of the NASDAQ-100 will have a gross margin percent between 37.2% and 59.9%.  The actual distribution can be visualized as such:

Figure 2 Distribution of NASDAQ-100 Gross Margin %

dont-fear-statistics-part-2-figure-2

The NASDAQ-100 companies do not perfectly follow the distribution; there is a fatter spread into the Negative and Positive buckets (Two Standard Deviations down and up). Other, more advanced statistical methods can be used to redefine ranges, but are beyond the scope of this post.

Of course, this visualization simply confirms statistical theories that were proven over a hundred years ago. The true value of analytics is to take statistical theories and turn them into informative visuals.  One method of visualizing the ranking of companies using the standard distribution in OBIEE 12c is through a Treemap:

Figure 3 NASDAQ-100 Distribution Treemap Visualization

dont-fear-statistics-part-2-figure-3

The size of the box represents the Gross Margin % while the color aligns with the distribution ranking from Figures 1 and 2. This visualization allows the viewer to understand both the rankings and relative performance at a glance.  It is easy to discern the delineation between above and below average (border between yellow and light green) as well as which companies are herding together.

One of the most powerful and essential aspects of business analytics is the ability to dimensionalize data so it can be sliced and diced. One (of many) reasons this is done is to be sure that there is an “apples to apples” comparison.  For instance, comparing the gross margin percent comparison between Qualcomm (QCOM), a semiconductor and telecommunications company, and Ross Stores (ROST), a discount department store, can create misconstrued distributions.  Filtering the visualization in Figure 3 by the NASDAQ industry classifications for Technology companies results in the following Treemap:

Figure 4 NASDAQ-100 Technologies Companies Treemap

dont-fear-statistics-part-2-figure-4

Notice that Qualcomm has slipped from “Moderately Positive” to “Moderately Negative.” Averages and standard deviations can change dramatically when looking at the components of the whole.  To demonstrate this, consider the following visualization comparing the average and deviation spread of the three largest categories (by number of companies) of the NASDAQ-100:

Figure 5 Average and Standard Deviation by Categories

dont-fear-statistics-part-2-figure-5

The border between yellow and light green represents the average while each band represents one standard deviation. Notice that the average gross margin % as well as the standard deviation is higher for Healthcare than for Technology.  Healthcare companies are going to skew the performance perspective of Technology companies.  This skew worsens when comparing against companies classified as Consumer Service.

As a general rule, a single point is not the best indicator of long term performance. Although the average and standard deviation for a single quarter was calculated through the agglomeration of one hundred companies, it should be considered a single data point.  Consider the following visualizations that show a comparative trend for four different companies for the entire date range downloaded:

Figure 6 Gross Margin % Trend for Adobe, Amazon, Electronic Arts, and Priceline

dont-fear-statistics-part-2-figure-6

At a glance, viewers can see that Adobe (upper left) consistently beats the average performance while consumer goods and technology giant Amazon (upper right) has been performing below average until recently. Electronic Arts (lower left), a video game developer, seems to have erratic gross margin % returns; however, looking past the noise, the company is nearly always between moderately positive and moderately negative when compared against other NASDAQ-100 companies.  Finally, Priceline (lower right) has been increasing gross margin % consistently and steadily pulling ahead of other NASDAQ-100 companies.  If Priceline’s gross margin % trend continues and the performance of the other companies remains constant, Priceline will move into the “Extremely Positive” gross margin % ranking in Q4 2016 or Q1 2017.

Returning to the questions posed at the beginning of this post:

  • The average cost of a gallon of milk is $3.20 with a standard deviation of $0.08. The corner grocery store selling milk for $3.44 is three standard deviations above the average!
  • The average NFL base salary is $1.86 million with a standard deviation of $2.80 million. Comparatively, Marcus Mariota’s $5.50 million salary is one standard deviation above average. However, with the average quarterback base salary being $5.69 million with a standard deviation of $7.17 million, he is actually minimally undercompensated.

For the final question, we ask the reader to evaluate his enterprise:

  • Calculate the average gross margin percent for your company’s business units for the quarter and find the business unit that is approximately 10% less than that average. Are they truly underperforming? Are you able to properly classify these business units to gain the greatest insight into relative performance?

Average and standard deviation can be applied to any metric by which a company wishes to evaluate itself. It can be used in combination with external data to create industry benchmarks.  For instance, if you were to plot your company’s gross margin % performance against the trends above, how would it look?

We want to close this post with the same idea that we closed Part 1 of the “Don’t Fear the Statistics” post: statistical analytics is part science/technology and part art.  Reducing statistical calculations to consumable visualizations is the key.  In the visualizations above, references to “standard deviation” were diligently omitted in favor of familiar terms such as “Moderately Negative.”  Approaches such as this help with change management, adoption, and the acceleration from simple reporting to true analytical insight into business process improvement based on data.

Don’t Fear the Statistics – Using OBI for Statistical Analysis Part 1

Recently, Ranzal has been working with a client in the healthcare space implementing Oracle Business Intelligence (OBI), and a requirement surfaced to translate a scorecard report into an OBI dashboard. One of the data elements was simply captioned “Trend” and colored red, yellow, and green.  It was discovered that this Trend was the slope of a linear regression plot (more on what that means in a moment) and the color was based on an arbitrarily chosen number.  This immediately raised some concerns from the Ranzal team who then made some suggestions for more pertinent statistical analysis.

To set the stage, this healthcare client’s summarized (and greatly simplified) income statement divides Revenue into Inpatient and Outpatient and Expenses into Total Labor and Non Labor. Revenue and expenses are the primary focus of much of the analytics at an aggregate level.  A single (seemingly arbitrarily chosen) number was used to determine the colored flags for each of these measures.  This was despite Inpatient Revenue and Non Labor Expenses comprising the majority of the revenue and expense amounts (respectively).  If we were to plot out these categories for the first five months of a fiscal year, we see the following (all data have been altered to preserve client confidentiality without overly affecting the overall analytic output):

figure-1

Figure 1 Revenue and Expense Trend Plot

The trouble with plotting a trend of numbers is that it is sometimes difficult to understand, at a glance, how the organization is performing. In the plots above, clear downward and upward trends can be seen for Inpatient Revenue and Total Labor Expense (respectively).  However, upon closer examination of Outpatient Revenue and Non Labor Expense, there are two upward trending months and two downward trending months.  The overall trend is difficult to discern.

With the introduction of Oracle Business Intelligence Enterprise Edition (OBIEE)12c, a Trendline function was introduced that allows the creation of a linear regression trendline. Once this is applied, the above trend plots can be augmented to get a clearer picture of performance:

figure-2

Figure 2 Revenue and Expense Linear Regression

This trendline uses a simple linear regression formula that is comprised as the slope (commonly represented by the letter m) and Intercept (commonly represented by the letter b) in the following formula:

y = mx + b

In our trend plots, the letter y represents the revenue and expense categories and x represents the fiscal periods.

The intercept is where the trendline crosses the y-axis when x is equal to zero. For most statistical analyses, the intercept is unimportant.  The slope can be thought of the average change over the two parameters.  Using OBI, the slope of each revenue and expense category can be calculated and the dashboard updated:

figure-3

Figure 3 Linear Regression Slope

In the example above, the slope of the Inpatient Revenue can be thought as decreasing an average of $291,000 a month.

One issue with using the slope is that it is subjective. As was mentioned, our healthcare client had chosen a single arbitrary slope for each of the revenue and expense categories.  The slopes in the example above range from 29 thousand to -291 thousand.  Complicating matters, the client wanted the ability to run these Analysis for individual hospitals which can dramatically affect the slope.  For instance, a hospital operating in Kansas City will probably not have the same revenue growth (or shrinkage) as a hospital operating in New York City.  To use the slope as a quantifiable objective properly, a target slope would have to be determined for the enterprise and at each granular level expected to be benchmarked (hospital, department, etc.).  This creates some obvious maintenance issues.

A more objective approach is to use the correlation coefficient, a number on a range from negative one to positive one. A correlation ranking of one indicates a positive correlation while a ranking of negative one indicates a negative correlation.  For instance, for most companies, the number of units sold is often has a high degree of positive correlation to revenue.  This would correspond to a correlation coefficient of close to one.  For many companies working in the commodities market, the more competitor’s revenue increases, the lower the possible market share.  This would be a negative correlation and result in a correlation coefficient calculation of negative one.  A correlation coefficient of zero indicates a lack of any correlation.  For instance, the number of broken arms set in a New York hospital is probably uncorrelated to the number of bowls of soup served by Panera Bread in Kansas City.

It is worth noting that correlation does not mean causation. For example, consider the number of pirate attacks and users of Microsoft Internet Explorer (IE) users:

figure-4

Figure 4 IE Usage and Pirate Attacks

The number of pirate attacks and IE users have both been in decline since 2009. As can be seen by the scatter graph on the right, the more pirate attacks, the greater the use of IE.  Regardless, naval security experts are probably not asking for adoption rate reports from Microsoft.

Returning to the client’s use case, adding the correlation coefficient to the dashboard provides a greater understanding of how the company is objectively performing:

figure-5

Figure 5 Month and Revenue / Expense Category Figure Correlation

Inpatient Revenue has a correlation of -0.69, which is moderately significant for a metric most businesses want to increase. Conversely, the Outpatient Revenue has a slightly negative correlation of -0.36.  While this should be a cause for concern, a “wait and see” approach (or deeper dive into Outpatient Revenue Categories) might be more prudent.  Because the range of the correlation coefficient is negative one to one, filtering this analysis down to a more granular level, such as a hospital or department, will return an objective number that can be subjected to independent interpretation.

There are cases in which the subjectivity of the slope is particularly useful. In the case of our client, a full year budget was prepared at the beginning of the fiscal year and periodically updated as the year progressed. The slope of this budget could be used to generate the average dollar change desired per month.  The advantage of this is that it reduces the possible volatility of a particular month into a single number that can be compared to the benchmark.  As a final addition to the dashboard, a full year budget slope was added:

figure-6

Figure 6 Full Year Budget Slope

With the exception of Non Labor Expenses, this organization is missing the mark on all of their budgetary goals, and the trend indicated by the actual slope and correlation coefficient means this situation is likely to get worse.

A word of warning about statistics in general and the use of slope and correlation coefficient in particular: micro and macro trends can should be considered and extreme outliers can mask actual trends.

For an example of micro and macro trends, consider JCPenney, a retailor that has been struggling since 2010. The following visualization (created using Oracle Data Visualization Desktop) charts the quarterly revenue from 2004 Q3 to 2016 Q4 along with the trendline for the entire period.  The bars represent the correlation coefficient to that particular quarter (i.e. the first bar is the correlation between 2004 Q3 and 2004 Q4 while the second bar is the correlation between 2004 Q3, 2004 Q4, and 2005 Q1, etc.):

figure-7

Figure 7 JCPenney Revenue Trend and Correlation

Notice that the first correlation bar is equal to one. When there are only two data points, the correlation coefficient will be equal to one, negative one, or zero.  The next data point and correlation for 2005 Q1 (JCPenney recognizes holiday revenue in Q1 of each year) continues the high correlation streak, however, the following quarter drops the correlation down to 0.35.  The correlation fluctuates quarterly until about 2012 Q2 when the definite downward trend is established.

A savvy analyst will break JCPenney’s performance during this time range into three distinct trends. Upward trending from 2004 to 2008 Q1, diminished upward trend from 2008 Q2 to 2012 Q1, and then a flat, but greatly reduced revenue from there:

figure-8

Figure 8 JCPenney Distinct Trends

As an example of how an extreme outlier can affect statistical analysis, consider GTx Incorporated, a pharmaceutical drug developer. In December 2010, GTx recognized $49.9 million dollars in revenue from a partnership with Merck& Co., Inc., which spiked GTx’s revenue (previously averaging $2 million a quarter) to $56.7 million dollars:

figure-93

Figure 9 GTx Incorporated Revenue Trend

In the visualization above, the orange projected trendline was calculated using revenue from 2004 Q1 through 2009 Q4. The purple trendline is the projected calculated using 2010 Q1, which includes the huge revenue spike.  Obviously, the orange trendline is the more accurate due exclusion of the extreme data point.

Statistical analytics is part science/technology and part art. As with any data and visualizations, a certain degree of intelligent interpretation is needed to determine what it all really means.  Functional users should be focused on what the various statistical interpretations mean and not be distracted on the complexity of the underlying mathematical functions.  Trend visualizations can aid users in understanding how to interpret these statistical calculations.  Many organizations miss opportunities because of individuals unwilling to embrace statistical methods due to the lack of solid education and guidance about what these numbers really mean.  Training, change management, and the creation of rich visualizations can help enterprises harness the capabilities of statistical analysis and extend the role of their business intelligence systems.

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.

OBIEE and Essbase – Defining OLAP Integration

In this second part of our OBIEE series, the integration between OBIEE and Essbase is a seamless transition from our OLAP cube to the OBIEE suite managed by using OBIEE’s Administration Tool.

OBIEE Administration Tool view

OBIEE Administration Tool view

This Administration tool has been designed with wizards, utilities, and interface design elements to help administrators work more efficiently.

Essbase test outline

Essbase test outline

Using an existing Essbase outline called ‘test’, this outline can be used to import an OLAP connection to OBIEE.

From the Administration Tool, select

File | Import | from Multi-dimensional

 
Enter the provider type, Essbase Server name, and its login credentials. The physical layer table, connection pooling, etc. will be automated and established once the import completes. You can also manually set each individual component in the physical layer if you want this level of control.

obiee-import

obiee-import-login

 

When the Physical layer has been established, simply drag and drop the folder of your Essbase outline from the Physical layer to the Business Model and Mapping layer to define a mapping between the business model and the physical layer schemas.

Physical Layer in Administration Tool

Physical Layer in Administration Tool

 

Once the business model mapping has been established, move the business model to the Presentation layer to make it available for user views.

Business Model & Mapping Layer in Administration Tool

Business Model & Mapping Layer in Administration Tool

 

This Presentation layer allows the Administration tool to present customized views of the business model to users. The business models can be managed in this presentation layer by removing unwanted or unneeded columns, restrict certain columns from view, or maybe rename a column to a more user-friendly name.

Presentation Layer in Administration Tool

Presentation Layer in Administration Tool

 

Once adjustments to column views have been completed and ready in the presentation layer, it can be made available in the Subject Areas for users to develop reports using the Answers component of OBIEE.

Subject Areas in OBIEE Answers

OBIEE Subject Area in the Answers component of OBIEE

 

So the three layers within the OBIEE Administration tool are defined as follows:

  • Physical layer – Represents the physical structure of the data sources to which the Oracle BI Server submits queries. This layer is displayed in the right pane of the Administration Tool.
  • Business Model and Mapping layer – Represents the logical structure of the information in the repository. The business models contain logical columns arranged in logical tables, logical joins, and dimensional hierarchy definitions. This layer also contains the mappings from the logical columns to the source data in the Physical layer. It is displayed in the middle pane of the Administration Tool.
  • Presentation layer – Represents the presentation structure of the repository. This layer allows you to present a view different from the Business Model and Mapping layer to users. It is displayed in the left pane of the Administration Tool.

 

Some of the features of the Administration tool make management of metadata and data much less complicated. The change management feature makes it easy to change multiple object names, text, case, and adding prefixes and suffixes. This allows for drag and drop capabilities from the physical to the business model layer.

Organization of metadata is straightforward using a feature called metadata administration. This feature grants users the ability to create folders to manage dimension tables and hierarchies.

The multi-user collaboration feature regulates the off-line/on-line modes for read only or to take effect immediately. This enables metadata repositories to be checked out or checked in and authorizes multiple administrators to work on a repository concurrently.

The Export/Import feature supports the export and import of metadata to move systems from staging to production and provide documentation.

Defining how OLAP is presented to OBIEE has been explained in basic format within this blog article but readers should know that this Administration Tool is much more powerful and can allow for more focused control within each of its layer process managing metadata and data. It is integrated and is flexible and its goal is to help move disparate source data to the OBIEE suite.

The end result can be a visual dashboard that makes sense of data utilizing charts, graphs, stop lighting, embedded images, tickers, etc. to organize and present data in a manner your audience will embrace and use.

Dashboard created with Answers from Essbase test outline

Dashboard created with Answers from Essbase test outline

 

This concludes part 2 of the OBIEE & Essbase integration. Keep an eye out for my next article where I’ll review RDBMS integrated with OBIEE and how it can be used in conjunction with Essbase in Answers reporting.

Reporting with OBIEE & Essbase

OBIEE + Essbase

OBIEE + Essbase

Oracle Hyperion’s Essbase is a fast and flexible multidimensional database and has been widely used for this reason.  Similarly, reporting against Essbase has been in top demand because of the speed and efficiency of Essbase.  However, there has been no single front-end reporting application that is integrated with Essbase to the extent of the Oracle Business Intelligence Enterprise Edition – OBIEE (BI Answers, BI Interactive BI Dashboard, BI Scheduler, and BI Publisher components).  This combination may be the answer for many users who have seen this disparity in reporting.

Traditionally, those users who have worked with reporting tools realize that their complete need for reporting can’t be handled with a single application.  Users either develop multiple front-end reporting applications to integrate into their business decision making or opt to go with less reporting.  The trade off of doing more with different applications cost time and money while doing less doesn’t give them fully utilized analysis of their data nor do they get a full return on their investment.  In the Hyperion world, users are asked to create reporting views on a Hyperion Reports application for mid-level managers – a group who understands detailed data where they can pivot dimensions and see alternate views.  But for senior management or C-level executives, a Web Analysis canned reporting view is a must because they are less familiar with the detailed data.  While it has been acceptable and necessary to create reporting views of the same data on different applications in the past, OBIEE may solve this issue for the future.

OBIEE is a powerful reporting application that can also be utilized as a middleware tool to manage Essbase data and provide the same or similar reporting capabilities like Web Analysis, Reports, Interactive Reporting, and Crystal Reports all rolled into a single package.  Within OBIEE, reports can be created for different types of users where data can be presented in many different layers for viewing but managed within a single application.

Options within OBIEE provide more robust capabilities that weren’t possible before.  Users find that they spend their development time creating a work around more often than not because their application can’t do this or is limited to that.  This diminishes reporting empowerment for the user and also limits their full use of relevant data.

Oracle has managed to find a way to merge different technologies from different companies that make sense of reporting development while adhering to the demands and needs of users.  The sooner users leverage these hybrid offerings, the sooner their data and their return on investment can be fully realized.

I’m open to your comments. Look for the next OBIEE article on how this integration between Oracle BI Enterprise Edition and Essbase is accomplished.

Contributed by:
Michael Duong, Lead Consultant
Hyperion Essbase Certified
Ranzal & Associates
mduong@ranzal.com

Welcome to Ranzal & Associates’ Blog!

Ranzal specializes in Business Intelligence and Business Performance Management with a concentration in Oracle/Hyperion’s toolkit. Ranzal works closely with corporate executives, line-of-business management, end users, and information systems departments alike to address the business issues and challenges inherent in data gathering, management, and dissemination. Organizations from various industries have engaged Ranzal with outstanding results.

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