Category Archives: Rittman Mead

Action Links in OBIEE 12c – Part 1


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.

The post Action Links in OBIEE 12c – Part 1 appeared first on Rittman Mead Consulting.

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 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 – 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
&val4=“@{11}” – will discuss these quoted parameters later on

*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!


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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Rittman Mead and Oracle Big Data Webcast Series – November 2015

We’re running a set of three webcasts together with Oracle on three popular use-cases for big data within an Oracle context – with the first one running tomorrow, November 3rd 2015 15:00 – 16:00 GMT / 16:00 – 17:00 CET on extending the data warehouse using Hadoop and NoSQL technologies.

The sessions are running over three weeks this month and look at ways we’re seeing Rittman Mead use big data technologies to extend the and capabilities of their data warehouse, create analysis sandpits for analysing customer behaviour, and taking data discovery into the Hadoop era using Oracle Big Data Discovery. All events are free to attend, we’re timing them to suit the UK,Europe and the US, with details of each webcast are as follows:


Extending and Enhancing Your Data Warehouse to Address Big Data

Organizations with data warehouses are increasingly looking at big data technologies to extend the capacity of their platform, offload simple ETL and data processing tasks and add new capabilities to store and process unstructured data along with their existing relational datasets. In this presentation we’ll look at what’s involved in adding Hadoop and other big data technologies to your data warehouse platform, see how tools such as Oracle Data Integrator and Oracle Business Intelligence can be used to process and analyze new “big data” data sources, and look at what’s involved in creating a single query and metadata layer over both sources of data.

Audience: DBAs, DW managers, architects Tuesday 3rd November, 15:00 – 16:00 GMT / 16:00 – 17:00 CET – Click here to register

Audience : DBAs, DW managers, architects

What is Big Data Discovery and how does it complement traditional Business Analytics?

Data Discovery is an analysis technique that complements traditional business analytics, and enables users to combine, explore and analyse disparate datasets to spot opportunities and patterns that lie hidden within your data. Oracle Big Data discovery takes this idea and applies it to your unstructured and big data datasets, giving users a way to catalogue, join and then analyse all types of data across your organization. At the same time Oracle Big Data Discovery reduces the dependency on expensive and often difficult to find Data Scientists, opening up many Big Data tasks to “Citizen” Data Scientists. In this session we’ll look at Oracle Big Data Discovery and how it provides a “visual face” to your big data initiatives, and how it complements and extends the work that you currently do using business analytics tools.

Audience : Data analysts, market analysts, & Big Data project team members Tuesday 10th November, 15:00 – 16:00 GMT / 16:00 – 17:00 CET – Click here to register

Adding Big Data to your Organization to create true 360-Degree Customer Insight

Organisations are increasingly looking to “big data” to create a true, 360-degree view of their customer and market activity. Big data technologies such as Hadoop, NoSQL databases and predictive modelling make it possible now to bring highly granular data from all customer touch-points into a single repository and use that information to make better offers, create more relevant products and predict customer behaviour more accurately. In this session we’ll look at what’s involved in creating a customer 360-degree view using big data technologies on the Oracle platform, see how unstructured and social media sources can be added to more traditional transactional and customer attribute data, and how machine learning and predictive modelling techniques can then be used to classify, cluster and predict customer behaviour.

Audience : MI Managers, CX Managers, CIOs, BI / Analytics Managers Tuesday 24th November, 15:00 – 16:00 GMT / 16:00 – 17:00 CET – Click here to register

Oracle OpenWorld 2015 Roundup Part 2 : Data Integration, and Big Data (in the Cloud…)

In yesterdays part one of our three-part Oracle Openworld 2015 round-up, we looked at the launch of OBIEE12c just before Openworld itself, and the new Data Visualisation Cloud Service that Thomas Kurian demo’d in his mid-week keynote. In part two we’ll look at what happened around data integration both on-premise and in the cloud, along with big data – and as you’ll see they’re too topics that are very much linked this year.

First off, data integration – and like OBIEE12c, ODI 12.2.1 got released a day or so before Openworld as part of the wider Oracle Fusion Middleware 12c Release 2 platform rollout. Some of what was coming in ODI12.2.1 got back-ported to ODI 12.1 earlier in the year in the form of the ODI Enterprise Edition Big Data Options, and we covered the new capabilities it gave ODI in terms of generating Pig and Spark mappings in a series of posts earlier in the year – adding Pig as an execution language gives ODI an ability to create dataflow-style mappings to go with Hive’s set-based transformations, whilst also opening-up access to the wide range of Pig-specific UDF libraries such as DataFu for log analysis. Spark, in the meantime, can be useful for smaller in-memory data transformation jobs and as we’ll see in a moment, lays the foundation for streaming and real-time ingestion capabilities.


The other key feature that ODI12.2.1 provides though is better integration with external source control systems. ODI already has some element of version control built in, but as it’s based around ODI’s own repository database tables it’s hard to integrate with more commonly-used enterprise source control tools such as Subversion or Git, and there’s no standard way to handle development concepts like branching, merging and so on. ODI 12.2.1 adds these concepts into core ODI and initially focuses on SVN as the external source control tool, with Git support planned in the near future.


Updates to GoldenGate, Enterprise Data Quality and Enterprise Metadata Management were also announced, whilst Oracle Big Data Preparation Cloud Service got its first proper outing since release earlier in the year. Big Data Preparation Cloud Service (BDP for short) to my mind suffers a bit from confusion over what it does and what market it serves – at some point it’s been positioned as a tool for the “citizen data scientist” as it enables data domain experts to wrangle and prepare data for loading into Hadoop, whilst at other times it’s labelled a tool for production data transformation jobs under the control of IT. What is misleading is the “big data” label – it runs on Hadoop and Spark but it’s not limited to big data use-cases, and as the slides below show it’s a great option for loading data into BI Cloud Service as an alternative to more IT-centric tools such as ODI.


It was another announcement though at Openworld that made Big Data Prep Service suddenly make a lot more sense – the announcement of a new initiative called Dataflow ML, something Oracle describe as “ETL 2.0” with an entirely cloud-based architecture and heavy use of machine learning (the “ML” in “Dataflow ML”) to automate much of the profiling and discovery process – the key innovation on Big Data Prep Service.


It’s early days for Dataflow ML but clearly this is the direction Oracle will want to take as applications and platforms move to the cloud – I called-out ODI’s unsuitability for running in the cloud a couple of years ago and contrasted its architecture with that of cloud-native tools such as Snaplogic, and Dataflow ML is obviously Oracle’s bid to move data integration into the cloud – coupling that with innovations around Spark as the data processing platform and machine-learming to automate routine tasks and it sounds like it could be a winner – watch this space as they say.

So the other area I wanted to cover in this second of three update pieces was on big data. All of the key big data announcements from Oracle came in last year’s Openworld – Big Data Discovery, Big Data SQL, Big Data Prep Service (or Oracle Data Enrichment Cloud Service as it was called back then) and this year saw updates to Big Data SQL (Storage Indexes), Big Data Discovery (general fit-and-finish enhancements) announced at this event. What is probably more significant though is the imminent availability of all this – plus Oracle Big Data Appliance – in Oracle’s Public Cloud.


Most big data PoCs I see outside of Oracle start on Amazon AWS and build-out from there – starting at very low-cost and moving from Amazon Elastic MapReduce to Cloudera CDH (via Cloudera Director), for example, or going from cloud to on-premise as the project moves into production. Oracle’s Big Data Cloud Service takes a different approach – instead of using a shared cloud infrastructure and potentially missing the point of Hadoop (single user access to lots of machines, vs. cloud’s timeshared access to slices of machines) Oracle instead effectively lease you a Big Data Appliance along with a bundle of software; the benefits being around performance but with quite a high startup cost vs. starting small with AWS.

The market will tell which approach over time gets most traction, but where Big Data Cloud Service does help tools like Big Data Discovery is that theres much more opportunities for integration and customers will be much more open to an Oracle tool solution compared to those building on commodity hardware and community Hadoop distributions – to my mind every Big Data Cloud Service customer ought to buy BDD and most probably Big Data Prep Service, so as customers adopt cloud as a platform option for big data projects I’d expect an uptick in sales of Oracle’s big data tools.

On a related topic and looping back to Oracle Data Integration, the other announcement in this area that was interesting was around Spark Streaming support in Oracle Data Integrator 12c.


ODI12c has got some great batch-style capabilities around Hadoop but as I talked about earlier in the year in an article on Flume, Morphines and Cloudera Search the market is all about real-time data ingestion now, batch is more for one-off historical data loads. Again like Dataflow ML this feature is in beta and probably won’t be out for many months, but when it comes out it’ll complete ODI’s capabilities around big data ingestion – we’re hoping to take part in the beta so keep an eye on the blog for news as it comes out.

So that’s it for part 2 of our Oracle Openworld 2015 update – we’ll complete the series tomorrow with a look at Oracle BI Applications, Oracle Database 12cR2 “sharding” and something very interesting planned for a future Oracle 12c database release – “Analytic Views”.

Oracle OpenWorld 2015 Roundup Part 1 : OBIEE12c and Data Visualisation Cloud Service

Last week saw Oracle Openworld 2015 running in San Francisco, USA, with Rittman Mead delivering a number of sessions around BI, data integration, Big Data and cloud. Several of us took part in Partner Advisory Councils on the Friday before Openworld itself, and along with the ACE Director briefings earlier that week we went into Openworld with a pretty good idea already on what was being announced – but as ever there were a few surprises and some sessions hidden away that were actually very significant in terms of where Oracle might be going – so let’s go through what we thought were the key announcements first, then we’ll get onto the more interesting stuff at the end.

And of course the key announcement for us and our customers was the general availability of OBIEE12c 12.2.1, which we described in a blog post at the time as being focused primarily on business agility and self-service – the primary drivers of BI license spend today. OBIEE12c came out the Friday before Openworld with availability across all supported Unix platforms as well as Linux and Windows, with this initial release not seeming massively different to 11g for developers and end-users at least at first glance – RPD development through the BI Administration tool is largely the same as 11g, at least for now; Answers and Dashboards has had a face-lift and uses a new flatter UI style called “Oracle Alta” but otherwise is recognisably similar to 11g, and the installer lays down Essbase and BI Publisher alongside OBIEE.


Under the covers though there are some key differences and improvements that will only become apparent after a while, or are really a foundation for much wider changes and improvements coming later in the 12c product timeline. The way you upload RPDs gives some hint of what’s to come – with 11g we used Enterprise Manager to upload new RPDs to the BI Server which then had to be restarted to pick-up the new repository, whereas 12c has a separate utility for uploading RPDs and they’re not stored in quite the same way as before (more on this to come…). In addition there’s no longer any need to restart the BI Server (or cluster of BI Servers) to use the new repository, and the back-end has been simplified in lots of different ways all designed to enable cloning, provisioning and portability between on-premise and cloud based around two new concepts of “service instances” and “BI Modules” – expect to hear more about these over the next few years, and with the diagram below outlining 12c’s product architecture at a high-level.


Of course there are two very obvious new front-end features in OBIEE12c, Visual Analyzer and data-mashups, but they require an extra net-new license on-top of BI Foundation Suite to use in production. Visual Analyzer is Oracle’s answer to Tableau and adds data analysis, managed data discovery and data visualisation to OBIEE’s existing capabilities, but crucially uses OBIEE’s RPD as the primary data source for users’ analysis – in other words providing Tableau-like functionality but  with a trusted, managed single source of data managed and curated centrally. Visual Analyzer is all about self-service and exploring datasets, and it’s here that the new data-mashup feature is really aimed at – users can upload spreadsheets of additional measures and attributes to the core dataset used in their Visual Analyzer project, and blend or “mash-up” their data to create their own unique visualizations, as shown in the screenshot below:


Data Mashups are also available for the core Answers product as well but they’re primarily aimed at VA, and for more casual users where data visualisation is all they want and cloud is their ideal delivery platform, Oracle also released Data Visualisation Cloud Service (DVCS)– aka Visual-Analyzer-in-the-cloud.


To see DVCS in action, the Youtube video below shows just the business analytics part of Thomas Kurian’s session where DVCS links to Oracle’s Social Network Cloud Service to provide instant data visualisation and mashup capabilities all from the browser – pretty compelling if you ignore the Oracle Social Network part (is that ever used outside of Oracle?)

Think of DVCS as BICS with Answers, Dashboards and the RPD Model Builder stripped-out, all data instead uploaded from spreadsheets, half the price of BICS and first-in-line for new VA features as they become available. This “cloud first” strategy goes across the board for Oracle now – partly incentive to move to the cloud, mostly a reflection of how much easier it is to ship new features out when Oracle controls the installation, DVCS and BICS will see updates on a more or less monthly cycle now (see this MOS document that details new features added to BICS since initial availability, and this blog post from ourselves announcing VA and data mashups on BICS well before they became available on on-premise. In fact we’re almost at the point now where it’s conceivable that whole on-premise OBIEE systems can be moved into Oracle Cloud now, with my main Openworld session on just this topic – the primary end-user benefit being first to access the usability, self-service and data viz capabilities Oracle are now adding to their BI platform.


Moreover, DVCS is probably just the start of a number of standalone, on-premise and cloud VA derivates trying to capture the Tableau / Excel / PowerBI market – pricing is more competitive than with BICS but as Oracle move more downmarket with VA it’ll end-up competing more head-to-head with Tableau on features, and PowerBI is just a tenth of the cost of DVCS – I see it more as a “land-and-expand” play with the aim being to trade the customer up to full BICS, or at least capture the segment of the market who’d otherwise go to Excel or Tableau desktop – it’ll be interesting to see how this one plays out.

So that’s it for Part 1 of our Oracle Openworld 2015 roundup – tomorrow we’ll look at data integration and big data.