Data Governance in the Cloud: An Integrated Strategy; A Unified Solution

Are you tasked with making organizational decisions that have placed you in a major dilemma? As a decision-maker in today’s fast-paced economy, you must wonder how you can cut costs, improve the bottom line, and still maintain the data quality necessary to make strategic decisions.

Take heart because it IS possible to achieve a balance of on-premise and off-premise Enterprise Performance Management (EPM) software while maintaining integrity and control of your data to provide the quality and data assurance needed for success – AND benefit financially from new Cloud technologies.

Success is a combination of understanding what each data tract requires and creating an integration strategy consisting of the necessary business processes and software tools that deliver consistency and integrity of your EPM strategic data.

Past trends called for a tight on-premise coupling of all EPM software to achieve the best results. This strategy required maintenance of a large hardware and software infrastructure and related personnel to keep everything running smoothly.  The new Cloud “POD” subscriptions are geared toward reducing the high costs of infrastructure which is a financial benefit. As in all things in life, there is a consequence of moving to Cloud technology.   An unexpected consequence of Pod technology is the creation of isolated silos of information, but there is an easy resolution!  The key to overcoming this limitation is to gain an understanding of what each component offers and demands, and creating an integration strategy to bridge that gap.

If you are interested in learning how to create this strategy to bring the various pieces together as a unified solution or if your organization plans to migrate to the EPM Cloud platform in the future, this whitepaper helps to define a process to pre-build the integration strategy and make moving to the Cloud easier with reduced time to migrate.

Download our whitepaper: Data Relationship Management (DRM) for Cloud-Based Technologies:  Using DRM for Data Governance in the Cloud

Don’t Let Incremental Overtime Plague Your Healthcare Organization!

Get to the Root Cause: Increase Productivity and Patient Care While Reducing Labor Costs

The Causes and Consequences of Incremental Overtime

Incremental overtime may be costing your healthcare organization thousands of dollars unnecessarily and result in decreased employee morale and poor productivity, so it’s important to understand its root causes by gaining the ability to track overtime. A Labor Productivity/Labor Management solution that delivers key analytics provides specific answers to the root causes of incremental overtime.  Common causes include:

  • Early clock-in/late clock-out
  • Inability to complete required tasks by end of shift
  • Shift transition conflicts (i.e. last minute attending to patient needs or handoff not yet completed)

The Solution and its Benefits

A Labor Productivity solution provides data for labor hours so that ratios can be derived based on each organization’s definition of incremental overtime, and this leads to a clear understanding of the root causes of incremental overtime so that corrective action can be taken, including:

  • Ensure management visibility at change of shifts
  • Employee coaching/staff meetings to aid time management skills
  • Provide daily reports/analysis to managers to establish protocol for handling incremental overtime risks
  • Designate a synchronized clock that employees should rely on (i.e. department wall clock)
  • Educate employees on OT authorizations – cite repeated behavior in performance evaluations

Incremental Overtime 1

By addressing the causes of incremental overtime using data provided by a Labor Productivity solution, providers can deliver great patient care while decreasing labor costs by thousands of dollars and increasing productivity.

Incremental Overtime 2.jpg

 

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

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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 %

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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

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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

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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

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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

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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.

Oracle Business Intelligence Cloud Service (BICS) September Update

The latest upgrade for BICS happened last week and, while there are no new end user features, it is now easier to integrate data. New to this version is the ability to connect to JDBC data sources through the Data Sync tool.  This allows customers to set up automated data pulls from Salesforce, Redshift, and Hive among others.  In addition to these connections, Oracle RightNow CRM customers have the ability to pull directly from RightNow reports using Oracle Data Sync.  Finally, connections to on premise databases and BICS can be secured using Secure Socket Layer (SSL) certifications.

After developing a customer script using API calls to pull data from Salesforce, I am excited about the ability to connect directly to Salesforce with Data Sync. Direct connections to the Salesforce database allows you to search and browse for relevant tables and import the definitions with ease:

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Once the definitions have been imported, standard querying clauses can create the ability to include only relevant data, perform incremental ETLs, and further manipulate the data.

While there are no new features for end users, this is a powerful update when it comes to data integration. Using APIs to extract data from Salesforce meant that each extraction query had to be written by hand which was time consuming and prone to error.  With these new data extraction processes, BICS implementations and integrating data becomes much faster, furthering the promise of Oracle Cloud technologies.

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

Upgrading Oracle Business Intelligence to 12c

Ranzal was recently invited to participate in a number of chalk talks for the Healthcare Industry User Group (HIUG) in San Antonio, TX. One of these chalk talks covered how an organization should prepare for and execute an upgrade to Oracle Business Intelligence (OBI) 12c.  Since the technical steps are already covered in numerous blog posts as well as Oracle documentation, our conversation focused on a strategic approach to the upgrade.  Our conversation essentially came down to four topics:

  1. An overview of the simplicity of the upgrade from 11g to 12c
  2. Organizational mindset while preparing for the upgrade
  3. The new technical infrastructure and the implications for the organization
  4. How to introduce users to the new features and how this might impact governance

We wanted to formally document this lively and fast-paced discussion to help other organizations as well as the HIUG chalk talk participants who were furiously scribbling notes and may not have had the opportunity to take it all in.

We started our discussion with how relatively easy Oracle has made this upgrade. For those of you who experienced the difficult and buggy process of upgrading from OBI 10g to 11g and may be dreading the upgrade to 12c, we have some advice:  relax.  First, the upgrade to 12c is an “in place upgrade” which means your 11g environment remains intact while the metadata and configuration gets “lifted and shifted” into 12c.  Speaking of “lift and shift,” 12c comes with a tool that extracts the metadata and much of the configuration from 11g into one tidy package that is then pushed into 12c.  There is a small amount of manual configuration that has to occur; however, this will only slow down customers with highly customized environments.  Once this lift and shift has occurred, an Oracle validation tool checks that your Dashboards and Analysis are working and alerts you to potential issues.  While there are bugs with 12c (what new or old software does not have bugs?), we have not found any major issues that will cause a full stop for an organization.

So how does an organization prepare for this upgrade? First, we encourage clients to view this upgrade as an opportunity to “clean house,” especially for customers that have been building OBI assets for years.  Used properly, analytic tools lend themselves to experimentation and evolution.  Experimentation can result in partially formed or broken logical objects such as presentation columns and facts, Analysis, or entire Dashboards.  The developers of these objects have the best intention of coming back to either fix or complete development on these objects or, at the very least, delete them.  Developers are busy people though, and eventually the existence of these objects is forgotten.  Evolution of both the tool and the focus on analytics results in objects becoming stale and/or obsolete.  Take this opportunity to clean house on your OBI environment.  Run the consistency check in the BI Administration tool and resolve those lingering issues.  Evaluate your usage statistics and determine if unused Analysis and Dashboards are still needed.  Fix or discard broken Analysis and Dashboards.  As with any technology tool, have a process in place to document, communicate, archive, backup, and restore, if necessary.

The most substantial change to come with OBI 12c is the underlying technical architecture. Fusion Middleware and Enterprise Management has a new look and feel.  Additionally, some actions are no longer performed within these tools.  For instance, deploying the RPD is no longer done through Enterprise Manager (in fact, the RPD is now the BAR file).  The directory has significantly changed which is a bit unfortunate for those of us who like to go directly to the log files in the directory rather than relying on Enterprise Manager and Session Manager.  Finally, many of the RPD . . . that is . . . BAR functions, such as deploying and copying, are done through a new command line interface.  So, while most of your users will be able to log into 12c and quickly adapt to the new look and feel, your OBI support team will have some learning to do.  Again, the goal of this post is to provide an overarching upgrade strategy, so we will not delve any deeper into these changes.  There is plenty of quality content online regarding these changes and you should always review Oracle documentation before performing implementation or upgrades.

End users will initially feel that, with the exception of a new look and feel, nothing much has changed. However, with new graphing options, statistical analytics capabilities based on R, as well as Visual Analyzer, users have an opportunity to expand their analytical capacity.  The organizational challenge is to go back to the change management playbook that was used when OBI was initially introduced, re-evaluate, and update so that end users can get the most out of this upgrade.  Evaluate how to train users on where to properly use the new graphs and charts.  Determine (or re-determine) who your power users are who need or want the new statistical capabilities.  Review existing Dashboards and Analysis and make appropriate upgrades.

Potentially the biggest challenge will be evaluating and understanding the capabilities of the new Visual Analyzer tool which, among other features, allows you to perform data mashups. This new tool will require that your organization determines some use cases and user groups as well as some additional training.  While users uploading data into the OBI system and combining it with existing data models opens up entirely new possibilities for insight, it also creates a governance challenge.  How do you separate and maintain the organizational “one version of the truth” while encouraging and properly promoting new analytic insight?  How will security be handled and users trained to adhere to this model?  How will you handle the archiving and deletion of potentially huge numbers of Excel spreadsheets uploaded onto the OBI server?  While this all sounds intimidating, keep in mind that your organization has already been through these exercises once during the original OBI implementation.  Adapt your existing knowledge.

Thus far, Ranzal has had a positive experience with the 12c upgrade. The underlying technical architecture has resulted in some real gains in performance, especially when leveraging EPM as a data source.  The upgrade is well thought out and simple, especially if you go through a system checkup and resolve issues.  While your technical BI support team will have some homework and learning to do to continue to fill that role, your users will be able to jump right into using 12c.  Despite this ease of user adoption, be sure to have a change management plan in place and take advantage of the new features and capabilities of 12c.

If you are thinking of doing an upgrade and have questions, feel free to reach out to us. Also, keep an eye out for an upcoming webcast on upgrading to 12c with an interactive question and answer session.