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ChitChat – Commentary Made Simple

Rittman Mead is excited to announce ChitChat, an innovative new communication tool for OBIEE.

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Rittman Mead’s clients already receive the best Oracle Business Analytics and Data Integration consulting and training. Now, in a major expansion of our business capabilities, our clients can enjoy the benefits of our engineering team’s innovation and expertise.

As an innovator within the world of Oracle enterprise technologies, Rittman Mead sought to design a solution to the lack of commentary features within the business intelligence dashboard. After much work, our best and brightest software engineers developed ChitChat to transform the way you do BI.

ChitChat is a multi-tiered platform that creates a collaborative and dynamic environment for discussion. ChitChat enhances BI capabilities by bringing commenting and documentation functions into the dashboard, increasing ease-of-use and seamlessly integrating with current workflows.

Focus discussion with versatile commenting. Store critical information at the source. Enhance the BI experience. Seamlessly integrate.

Rely on Rittman Mead to get the most out of your business intelligence investments.

To learn more about ChitChat, or to request a demo, click here.

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Corporate Social Responsibility (Where Can We Serve?)

At Rittman Mead, we believe that people are more important than profit.
This manifests itself in two ways. First, we want to impact the world beyond data and analytics, and secondly, we want our employees to be able to contribute to organizations they believe are doing impactful work.

This year, we’ve put a Community Service requirement in place for all of our full-time employees.

We’ll each spend 40 hours this year serving with various nonprofits. Most of our team are already involved with some amazing organizations, and this “requirement” allows us to not only be involved after hours and on the weekends, but even during normal business hours.

We want to highlight a few team members and show how they’ve been using their Community Service hours for good.

Beth deSousa
Beth is our Finance Manager and she has been serving with Sawnee Women’s Club. Most of her work has been around getting sponsorship and donations for their annual silent auction. She’s also helped with upgrading a garden at the local high school, collecting toys and gift wrap for their Holiday House, and collecting prom dresses and accessories for girls in need.

Charles Elliott
Charles is the Managing Director of North America. He recently ran in the Dopey Challenge down at Disney World which means he ran a 5k, 10k, half marathon, and full marathon in 4 days. He did the run to raise funds for Autism Speaks. Charles was recognized as the third largest fundraiser for Autism Speaks at the Dopey Challenge!

David Huey
David is our U.S. Business Development rep. He recently served with the nonprofit Hungry For A Day for their Thanksgiving Outreach. He flew up to Detroit the week of Thanksgiving and helped serve over 8,000 Thanksgiving dinners to the homeless and needy in inner city Detroit.

Andy Rocha

Andy is our Consulting Manager. Andy is a regular volunteer and instructor with Vine City Code Crew. VC3 works with inner city youth in Atlanta to teach them about electronics and coding.

Pete Tamisin

Pete is a Principal Consultant. He is also involved as a volunteer and instructor with the aforementioned Code Crew. Pete has taught a course using Makey Makey electronic kits for VC3.

This is just a sample of what our team has done, but engaging in our local communities is something that Rittman Mead is striving to make an integral piece of our corporate DNA.
We can’t wait to show you how we’ve left our communities better in 2016!

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Action Links in OBIEE 12c – Part 2

Introduction and Scenario

Part 1 of this series on Action Links had us creating a simple navigate action and left us in suspense with the promise of a new means by which to filter one report from another. Now time to deliver! The scenario: say we’ve got a company dashboard that is pretty locked down in terms of adding any new user-facing content, and as a developer you occupy more of an analyst role where you can build analyses all day, but dashboards are a no go. So how then can we drive some of our own detail analyses from dashboard selections? The answer? Hidden driving documents! In a nutshell, these documents store selected dashboad values and act as a filter for our detail analyses, utilizing OBIEE’s “filter on the results of another analysis” filter option. It should be noted that we could simply set the target analysis’s filters to be equal to the presentation variables generated by the source dashboard prompt. However, the following technique is simply to illustrate a possible technique, say when you’re going between two different subject areas, that might be applicable in your current situation or at least spark an idea to get you headed in the right direction towards a possible solution. So, let’s start by getting the pieces to our puzzle together.

Start with the dashboard and prompt. We are going to navigate from a monthly sales trend to a detail report displaying product sales detail.

The Solution

Now that we’ve determined the dashboard analysis from which we want to navigate, we can set up our driving document. Again, the overall concept of a driving document is to act as an intermediary container for selected values that then get passed from one report to another. So let’s set this up. Given that our trend is prompted on Year, Company, Region, we need to place three dummy columns on our driving report to store the presentation variables generated by each prompt. I used the same three columns in my driving report for consistency, however any column will do. Note that the columns in the picture have custom names already are not the native columns from their respective tables.

Edit each column to store the presentation variables generated by your dashboard prompt. If the dashboard prompt does not have a presentation variable for each column prompt, go ahead and get those set up before completing this step. My year column variable in this example is appropriately titled SALES_YEAR. Be sure to put single quotes around your variable as in ‘@{SALES_YEAR}’ when entering it in the Edit Column dialogue.

You can now save your driving report and place it on your dashboard. Right away you should see that your driving report picks up the values in each column prompt. Note the use of a default value {2012}. Using these is ideal so that if we want to edit our driving document, we at least have some values to pass it, as opposed to it throwing an error on the Results tab.

 

Now for some bad news. You might have noticed an obvious limitation to this approach, which is the driving report’s limited selection of only one column value from each column prompt. Nevertheless, let’s run this thing through to the end and see what happens. The next piece of the puzzle is setting up your target analysis.

Our analyst in this scenario wants to set up two reports to be driven off of our dashboard selection. The first will be a top 10 report for products based on the selected month. The second will be another top 10, but for sales reps instead. Navigating to our desired subject area, let’s bring over the needed columns, formatting as needed. In this example, we’re going to include a custom calc which gives us the percent variance to the prior period which has also been conditionally formatted to indicate positive or negative growth.
We have placed the columns necessary to build each report and also added filters, as seen in the proceeding picture.

Now comes the crux of this neat trick. We need to add the proper filters to the analysis so that any values we pick from both the dashboard prompt and the source analysis itself will drive our target analysis.

In the target report, we added a filter for each column that we’d like our analysis filtered on and likewise, comes from our source report.

To set this filter, simply apply the ‘is based on results of another analysis’ filter to each column. Then, browse for the driving report we made and apply the ‘is equal to any’ Relationship option. As our driving report values will only have one value for each of the filtered columns, our target report columns should filter on each of these. Lastly, use the drop down to choose the corresponding column from the source analysis. In the following pic we are selecting the  Region column from our driving report so that our target report will be filtered on whatever Region is selected on our source dashboard.

As the last steps in our target analysis, make sure you’ve set all the filter columns to respond to the driver analysis and that you select the ‘is prompted’ filter for any additional dimension columns on your report that are not driven by a dashboard prompt. In the example, this is going to be the Month column. This will ensure that our target analysis “listens” to whatever month is selected in our source analysis, that is, our sales trend.

Don’t worry if your Results tab shows no values as this is expected behavior (we haven’t sent any values over yet!). If you’d like to set it up so that your target report actually yields results without clicking on the action link, simply apply default values to the presentation variables in your driving document, as described above in the Year example. The last piece of this puzzle is finally assigning the action link to our respective column. Follow the instructions outlined in the first part of this post in order to navigate to our target analysis. In our example, this is going to be our Sales column.

Now that we’ve got all the pieces of the puzzle, let’s put it all together and see what happens. Clicking on any sales point on our trend line, we should be taken to the target analysis which should be filtered on both our dashboard prompt values as well as our Month. In this example, I’ve applied a filters object to the target analysis to make sure the target analysis responded to all applied filters.

Looks like everything works great! At this point you can hide the driver report on your dashboard and get rid of the filters object on your target analysis. I’d like to hear about some neat ways people have used this technique or modified it to suit their particular business/use case. Feel free to post in the comments! Stay tuned for part three of this series on Action Links where we go in depth about the GoURL syntax and how it can be a powerful and customizable tool in navigating to web pages and OBIEE 12c content.

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Action Links in OBIEE 12c – Part 1

Introduction

With the release of OBIEE 12c, let’s take a look at Action Links and how things may be different compared to the previous release, 11g. Over this three part blog series, we’re going to cover the more popular link types, which are navigating to BI content, and navigating to a web page. However, to sweeten the deal, I’ll also include some tricks for your tool belt which well enable you to do the following:

 

  • Navigate to a target report, while filtering it on parameters chosen in the source
  • Pass filter parameters via the GoURL syntax from a source report to another, target report
  • Become familiar with the GoURL structure and how to apply it to your business case

 

In the first installment of this three part series, we’re going look at how to navigate to other reports and dashboards in your catalog through the ‘Navigate to BI Content’ action. This will set you up for parts 2 and 3, wherein we show you some tricks using Action Links.

 

1. The Action Link UI

By now, there are likely lots of blogs talking about the new look and features of OBIEE 12c, so we can keep this bit short. Suffice to say it got a much needed face lift, with both changes in overall skinning and in its portfolio of icons. While this change in graphics may induce a bit of frustration on part of the developer, I believe this approach to design will end up being a good long term strategy to handle later releases of the product as trends in UX seem to have their feet firmly planted in the stripped down, the clean, and the subdued. Even with this shift, however, the basic processes and series of steps to implement most any of the features in Answers remains the same, Action Links being no different. Just follow these simple steps below to set up your Action Link! After you’ve got a hold of the basics, look to future posts in this series for some tips and tricks using Action Links.

 

In chosen column, go to the Column Properties menu:

 

Next, click on the Interaction tab:

 

Select ‘Action Links’ as the Primary Interaction value and click on the ‘+’ icon. This will display another dialogue box where we will set up the actual properties of the Action Link. Click on the running man icon (this little guy seems to be more intuitive than the green gear):

 

 

2. Navigate to BI Content

For the first example, we’re going to select the ‘Navigate to BI Content’ option. This simply allows us to go to another report or dashboard, as though you were clicking on a link in a web page. To implement this on your report, simply follow the steps above and then refer to the steps below.

After clicking on the running man icon, select the ‘Navigate to BI Content’ option. This will be followed by a dialogue box allowing you to select the object to which you want to navigate.

 

Confirm your selection and then click ‘OK’, not once, not twice, but thrice, at which point you’re taken back to the Criteria tab. From now on, this column will take you to the selected report.

And that’s it! Take a look back here for part 2 on Action Links in OBIEE 12c, which will outline a neat technique on how to implement what’s called a ‘driving document’ to filter values between disparate reports using the navigate action.

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OBIEE 11g and Essbase – Faking Federation Using the GoURL

This blog is going to address what happens when we can’t take advantage of the Admin tool’s powerful vertical federation capabilities when integrating relational stars and Essbase cubes. In the Admin tool, synonymously referred to as the RPD, vertical federation is the process of integrating an aggregate data source, in this case Essbase, with a detail level source from a data mart. This technique not only has the ability to increase query efficiency and decrease query time, it also has the added benefit of bringing together two powerful and dynamic reporting tools. But like most things, there is a pretty big caveat to this approach. But, before I jump into what that is, some housework. To start, let’s make sure things don’t get lost in translation when going back and forth between Essbase and OBIEE jargon. In Essbase speak, dimensions can be thought of as tables in a relational structure, whereas Essbase generations can be thought of as columns in each table, and members are the values in each column. Housework done, now the caveat. Often, dimensions in Essbase cubes are built in such a way as to not neatly support federation; that is, they are arranged so as to have an uneven number of generations relative to their corresponding relational dimension. It should be noted at this point that while federation is possible with a ragged hierarchical structure, it can get kind of messy, essentially ending up in a final product that doesn’t really look like something an Essbase-centric user community would readily and eagerly adopt. So what then, can we do when federation is out of the question? Let’s frame the solution in the form of a not-atypical client scenario. Say we’ve got a requirement per a large finance institution of a client to bring together their Essbase cubes they’ve used thus far for their standardized reporting, i.e. balance sheets, income statements and the like, with their relational source in order to drill to account detail information behind the numbers they’re seeing on said reports. They’ve got a pretty large user base that’s fairly entrenched and happy with their Smart View and Excel in getting what they want from their cubes. And why shouldn’t they be? OBIEE simply can’t support this level of functionality when reporting on an Essbase source, in most cases. And, in addition to these pretty big user adoption barriers to an OBIEE solution, now we’ve got technology limitations to contend with. So what are our options then when faced with this dilemma? How can we wow these skeptical users with near seamless functionality between sources? The secret lies with URL Action Links! And while this solution is great to go from summary level data in Essbase to its relational counterpart, it is also a great way to simply pass values from one subject area to another. There are definitely some tricks to set this up, but more on those later. Read on.

The Scenario

In order to best demonstrate this solution, let’s set up a dashboard with two pages, one for each report, and a corresponding dashboard prompt. The primary, source report, out of Essbase, will be something that could easily resemble a typical financial report, if not at least in structure. From this high-level chart, or similar summary level analysis, we’ll be able to drill to a detail report, out of a relational source, to identify the drivers behind any figures present on the analysis. In this example, we’re going to be using the 11.1.1.9 Sample App, Sample Essbase subject area to go to the equivalent relational area, Sample Sales. Yes, you could federate these two, as they’ve done in Sample App, however they’ll serve well to demonstrate how the following concept could work for financial reporting against ragged or parent-child structures. Values for Product Type, in the following instance, could just as well be the descendants or children of a specific account, as an example. As well, there is no equivalent relational subject area to use for the sake of the SampleApp Essbase GL subject area. In the example below, we have a summary, month level pivot table giving us a monthly sales trend. The user, in the following example, can prompt on the Year and Customer segment through a dashboard prompt, but as you’ll see, this could easily be any number of prompts for your given scenario.

Monthly Trend Summary:

Solution 1:

In the sales trend example above, we are going to enable our user to click on a value for a revenue figure and then navigate to a detail report that shows products sold for the month by date. Again, this all must be done while passing any chosen parameters from both the dashboard prompt and analysis along to the detail analysis.

Proof of Concept

First, let’s start with the guts of the report example above. As you can see, there is quite a bit more under the hood than meets the eye. Let’s go over the approach piece by piece to help build a more thorough understanding of the method.

Step 1: Include the Columns!

So the idea here is that we want to pass any and all dimensional information associated with the revenue figure that we pick to a detail level report that will be filtered on the set of parameters at the chosen intersection. We can hide these columns later, so your report won’t be a mess. I’ll add here that you might want to set any promoted values to be equal to the presentation variable on its respective dashboard prompt with a default value set, as seen below. This will help to make the report digestible on the compound layout. The following picture shows the prompted values to drive our summary report on Year and Customer Segment. You can do this in the filters pane on the criteria tab with the following syntax:

 

                            All column values we want to pass need to be represented on the report:

 

                           Values that will be passed to detail report (in this case, BizTech, Communication, Active Singles, 2012, and 2012 / 11):

Step 2: More Columns!

In addition to the columns that comprise the report, we need to add an additional iteration of every column for all of those added to the report in the first place. In the pictures above, you can see that these are the columns titled with the ‘URL’ prefix. In the column editor, concatenate quotes to the column values by attaching the following string (this is a single quote followed by a double quote and another single quote w/ NO spaces between them):

‘ ” ‘ || “Table”.”Column Name” || ‘ ” ‘

While this step may seem extemporaneous, you’ll see a bit later that this step is all too necessary to successfully pass our column values through our URL Action Links. After you’ve created the custom columns, just group them along with their counterpart in the report, as in the pics above.

Step 3: An Approach to Handling Hierarchies

In the previous pictures, you can see the products hierarchy that comprises the rows to the report. In order to pass any value from the hierarchy as well as its members we are going to have to include its respective generations in the rows as well. For our example, we’re going to use Brand, LOB, and Product Type. In this way, a user can select any sales value and have all three of these values passed as filter parameters to the detail analysis through a URL. You’ll notice that we haven’t given these columns a counterpart wrapped in quotes as you were told to do previously. This is quite on purpose, as we’ll see later. These columns will provide for another example on how to pass values without having to implement a second column for the purpose of wrapping the value in quotes.

 

When first placing the hierarchy on your analysis and expanding it to where you’d like it for the sake of the report, you can simply select all the column values, right click and then select ‘Keep Only’. This will establish a selection step under the Products Hierarchy to ensure that the report always opens to the specified structure from now on. So, that’s good for now, let’s get to the magic of this approach.

 

Step 4. Set up the Action Link

In this case, we’re going to ‘drill’ off of the Sales column in our table, but we could really ‘drill’ off of anything, as you’ll see. So, pop open the Interaction tab for the column and select Action Links as our primary interaction. Edit that guy as follows (see URL procedure below). It used to be that we could do this via the ‘P’ parameters, however this method seems to be mostly deprecated in favor of the col/val method, as we shall utilize below.

URL Procedure

http://sampleapp.rittmanmead.com:7780/analytics/saw.dll? – Server URL*
Portal&Path=@{1} – path to dashboard
&Page=@{2} – dashboard page
&Action=@{3} – action to perform, in this case navigate (there are others)
&col1=@{4} – column from target analysis we wish to manipulate (our sales detail analysis)
&val1=@{5} – column from source analysis with which we are going to pass a filter parameter to target
&col2=@{6}
&val2=@{7}
&col3=@{8}
&val3=@{9}
&col4=@{10}
&val4=“@{11}” – will discuss these quoted parameters later on
&col5=@{12}
&val5=”@{13}”

*Note that this value can be made into a variable in order to be moved to different environments (DEV/TEST, etc…) while maintaining link integrity

The picture above details how to set up the URL link as described above. The col1 value is the column from the target analysis we want to filter using the value (val1) from our source. Be sure to qualify this column from the subject area from which it originates, in this case “A – Sample Sales”.

Ex: “A – Sample Sales”.”Time”.”T05 Per Name Year”

Val1, as these parameters exist in ‘sets’, is the column from our source analysis we want to use to filter the target analysis. This is where our custom, quoted columns come into play. Instead of using the original column from our analysis, we’re going to use its quoted counterpart. This will ensure that any values passed through the URL will be enclosed in quotes, as is required buy the URL. Note that we’re not using a value parameter in this case, but a column instead (the dropdown to the left of the text box).

Ex: ‘ ” ‘ || “Time”.”T05 Per Name Year” || ‘ ” ‘

You can proceed this way to pass as many values as you’d like to your detail analysis, with this coln, valn method. Again, just be sure that your columns are included in the source analysis or the values won’t get ported over. Once you’ve got all your columns and values set up, go ahead and enter them into the URL field in the Edit Action dialogue box, as above. Make sure you reference your variables using the proper syntax (similar to a presentation variable w/ an @ sign):

Ex: col1=@{4} – ‘4’ being the variable name (note that these can be named most anything)

Quoting Parameters

As an alternative to including an extra iteration of each column for the sake of passing quoted column values, we can instead, put quotes around the parameter in our URL, as in the example above. The limitation to this method, however, is that you can only pass a singular value, as in Year, for example. In later posts, we’ll address how to handle passing multiple values, as you might through a dashboard prompt.

Step 5. Set Up the Detail Analysis

For our detail analysis we’re going to set it up in much the same way as our summary. That is, we need to include the columns we want to filter on in the target report as. Unfortunately, our target report won’t simply pick them up as filters as you might put on your filters pane, without including them on the actual analysis. Again, any columns we don’t want visible to a user can be hidden. Below, we simply want to see the Calendar Date, Product, and Revenue, but filtered by all of our source analysis columns.

In the criteria view for our target, detail analysis, we need to make sure that we’re also setting any filtered columns to ‘is prompted’. This will ensure that our target analysis listens to any filter parameters passed through the URL from our source, summary analysis. As a last step, we must again fully qualify our filters, as in the picture below.

This picture shows our Year ‘is prompted’ filter on our target, detail analysis. Note that this column is also a column, albeit hidden, on this report as well. This will act as a filter on the analysis. It is being ‘prompted’ not by a dashboard prompt, in this instance, but by our source, summary analysis.

Step 6. Testing it All Out

Now that we’ve got all the pieces of the puzzle together, let’s see if it works! To QA this thing, let’s put a filter object on the target, detail analysis to make sure that the report is picking up on any values passed. So if we click on a sales value, we should be taken to the target analysis and see that all the parameters we set up were passed. The picture below confirms this!

Conclusion

Hopefully this can be one more trick to keep in the tool belt when faced with a similar scenario. If you have any hiccups in your implementation of this solution or other questions, please feel free to respond to this post. Stay tuned for additional articles related to this topic that go much more in depth. How do you handle passing multiple column values? How do I keep my report query time low with all those extra columns? How do I pass values using the presentation variable syntax? Can I use the Evaluate function to extract the descendants of a filtered column?

 

 

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