Category Archives: Rittman Mead

OA Summit 2020: OA Roadmap Summary

OA Summit 2020: OA Roadmap Summary

If you are in the Oracle Analytics space, the OA Summit is a great source of content: from Keynotes, customer stories, Partners deep dives; the website is a collection of stories about Oracle Analytics. We've been part of the initial episode talking about how you can start your journey to Data Science with Oracle Analytics!

In Tuesday's session, Gabby Rubin, VP of Product Management, and Jacques Vigeant, Senior Director Product Strategy, shared a series of insights about the product roadmap that we'll cover in this blog.

Public Roadmap & IdeaLab

This is quite big news in the Oracle space, now there is a clear vision about what's coming in the product, accessible by everyone on a public website.

OA Summit 2020: OA Roadmap Summary
OA Roadmap and IdeaLab

The public roadmap works well also in conjunction with IdeaLab: a place where everyone in the OA community can suggest new product features and up/downvote or add comments on other people's ideas. These hints are reviewed by Product Managers and, if considered valid, included in future releases of the product!

Converged Analytics

The state of the art in the Analytics space offers a neat separation between self-service tools and centralized IT-governed ones. Even for Oracle Analytics, we have two separate approaches as of now: self-service data preparation via Data Visualization vs enterprise IT-driven via RPD.

OA Summit 2020: OA Roadmap Summary

What was announced at the OA Summit is that the two approaches will converge: there will be the option to self-create a multi-table and multi-source federated dataset which can be shared. This approach empowers the end-user and works on top of the usual self-service data-source definition workflow enabling for each identity in the diagram, ML-based transformations and enrichment recommendations as well as caching setting definitions.

Empowering the end-user also means enabling best software development practices like versioning, certification and promotion capabilities on top of the asset created. The multi-table federated dataset created by end-users will seamlessly transition into enterprise-level semantic models without IT intervention.

OA Summit 2020: OA Roadmap Summary

From the enterprise side, as announced during OOW19, we'll see a new web-based semantic modeler which will substitute good old Admin Tool. Unlike the current "Lite Data Modeler", the new product will enable the same level of definition complexity we can find in today's RPDs thus will be compatible with every OAC/OAS prebuilt repository. The new web-based semantic modeler is not only a pure replacement of the Windows-based admin tool, but it also offers a native source control integration with Git and in-built options for Data Lineage explorations.

OA Summit 2020: OA Roadmap Summary

As mentioned the new tool is web-based; if you're an expert RPD developer and worried about different development methodology slowing down the build process, well, there is big news for you! You will be able to edit Repositories via the new JSON based Semantic Modeling Markup Language! Yep, you'll be able to define all the layers of an RPD in JSON syntax, even outside the web-based semantic modeler tool itself. This opens a huge variety of opportunities for source-control, CI/CD pipelines, as well as automatic (coded) builds of RPD artifacts.

Oracle Analytics as Enterprise Keystone

As briefly described in my Data Virtualization blog post, Oracle Analytics can (and should) be considered the Analytics Keystone in the enterprise: the convergence of IT-driven and self-service metadata models can already be exposed via ODBC in OAS enabling the downstream application to access the data inheriting the security settings defined in the Repository. The OA team is working to offer the equivalent in JDBC format for all Oracle Analytics products!

OA Summit 2020: OA Roadmap Summary

Oracle Analytics is also enhancing the number of source-system connectors available: we'll soon have the option to connect directly to Oracle EPM Cloud from Data Visualization, and similar connectors are arriving for JDBC sources and Google's Big Query.

OA Summit 2020: OA Roadmap Summary

Machine Learning in OA

Augmented Analytics and Machine Learning capabilities have existed for a long time in Oracle Analytics. A new important improvement in this area will enable Data Flows to use Machine Learning models created outside OA via the usual GUI.

OA Summit 2020: OA Roadmap Summary

Data Scientists will be able to use their favourite environment and toolset in order to analyse the data and build models. The models built in external systems like Oracle Databases, Oracle Data Science Services, or 3rd-party services could then be registered within OA and used to score data in Data Flows making the Data Scientist and Data Analyst collaboration much easier increasing the ML ubiquity in enterprises.

Predictions explainability will also be possible directly in OA, with details of each model exposed appropriately depending on the model type.

OA Summit 2020: OA Roadmap Summary

In addition to the above, OA will also allow the usage of Advanced Database Analytics like Sampling, Un-pivoting, Clustering, Anomaly Detection or tokenization with more to come. This allows the usage of already existing functions (avoiding to reinvent the wheel) that can perform securely on massive amounts of data in the Oracle Database.

Data Visualization New Features

There is also some news which has already been shipped in the latest product or will be available soon. Some examples are:

  • Adaptive content: we can enable content scrolling in Data Visualization projects, allowing proper spacing of otherwise crowded visualizations
  • Canvas Filter control: a prompt that can filter only a subset of analysis registered to it (similar to the Master-details concept in Analysis)
  • OAC Embedding Framework: That allows to integrate OA capabilities into external web applications
  • Digitalized Map Layers: create an infographic on top of any image, all directly in OAC
OA Summit 2020: OA Roadmap Summary

OA on Mobile

What about consuming OA content in mobile devices? Data Visualization content is adaptive, the visualization size and appearance will change depending on screen type, so all created content could simply be accessed via mobile browser. Oracle is also investing in Day by Day, which acts as a personal proactive Data Assistant and now enables the threshold-based notifications with more visualization types coming later in the year.

OA Summit 2020: OA Roadmap Summary

The new announcement regarding mobile is the new Oracle Analytics Mobile App which substitutes the Oracle BI Mobile HD and will provide a way to use, search and collaborate on curated content exposed by OA with an experience in line with modern apps.

OA Summit 2020: OA Roadmap Summary

More on this, All the Oracle Analytics Apps will enable collaboration: several people will be able to access and comment on visualizations and data avoiding the need to switch to other tools like emails.

A whole new wave of capabilities is in the Oracle Analytics roadmap, for any doubt or questions feel free to reach us!

Data Virtualization: What is it About?

Data Virtualization: What is it About?

The fast growth of company's data, data-sources and data-formats is driving an increasing interest in the field of Data Virtualization; what is it about? And what tools can provide this functionality?

Data Virtualization defines an approach to expose data coming from disparate data sources via a common interface to downstream applications.

Data Virtualization: What is it About?

Do you have your sales data in an Oracle Database and client peculiar information in some Cloud App? Data Virtualization will show them as an unique data source while, in the backend, will fire the proper queries to source-systems, retrieve the data and apply the correct joining conditions. Data Virtualization abstracts the technical aspects of the datasources from the consumer. Unlike the ETL approach,  there is no data copy or movement, with source systems accessed in real-time when the query is executed (also called Query in Place (QIP)).

To be clear: Data Virtualization is NOT a replacement of ETL, both paradigms are valid in specific use-cases. Data Virtualization is a perfect scenario when reduced amounts of data coming from various systems need to be joined and exposed. On the other side, when massive amounts of data need to be parsed, transformed and joined and data-retrieval speed is the key, then an ETL (or ELT) approach is still the way to go.

Data Virtualization Components?

So far we had a look at the theory and goals of Data Virtualization, but what are the main components of such a tool? We can summarize them in four main points in our source-to-user path: Data Access Optimization, Business Logic, Data Exposure and Security.

Data Virtualization: What is it About?

What should each layer do? Let's see them in detail.

Data Access Optimization

Data Virtualization: What is it About?

In this layer, the connection to data-sources needs to be defined, tuning parameters set and data-points of interest determinate. This query should be responsible for optimizing the query pushdown to source systems in order to retrieve the minimal row-set that needs to be displayed or joined with other datasources.

The Data Access Optimization layer should also be able to handle different datasources with different capabilities (e.g. aggregation functions) and, in case pushdown is not possible, perform transformations after data load. Finally, to ease the stress on the source system, caching options should be available in this layer.

This layer should also dictate the methodology of datapoints access, defining (with the help of the Security layer) if a certain column can be read-only or also written.

Business Logic

Data Virtualization: What is it About?

The Business Logic Layer should be responsible for translating data-points to company-defined metrics or attributes. Datapoints can come from various data-sources so it should also contain the definitions of joining conditions and data federations.

Metrics and attributes need to have aggregations and hierarchies defined in order to be used by downstream applications. Hierarchies can also help with vertical data federation, defining exactly the granularity of each datapoint, thus enabling query optimization when more aggregated datasources are available.

The Business Logic Layer is also responsible for decoupling Business Logic from datasources: when a change in a datasource happens (e.g. the creation of a datamart or a database vendor change) the metrics and attributes layout will not be altered, all the changes will happen in the Data Access Optimization and hidden in the Business Logic Layer definitions.

Data Exposure

Data Virtualization: What is it About?

The Data Exposure Layer is the one facing downstream applications or users, in this layer metrics and attributes should be organized in business-driven structures and made accessible via various methods like web-interfaces, ODBC, SOAP or REST.

Datapoints defined at this layer should contain business descriptions and be researchable via a data catalog allowing the re-usage of pre-built content.


Data Virtualization: What is it About?

Exposing data to downstream applications needs to happen securely, thus datapoint access rules need to be defined. Data Virtualization systems should integrate with the company's Identity management tools to identify and dictate who can access any particular datapoint.

Security should not only work as ON/OFF, but also allow the access to subsets of data based on privileges: e.g. a country manager should only see data from his/her own country.

Security is not only about defining boundaries but also about auditing the correct access to data (GDPR?). Data virtualization systems should allow security evaluations or provide pre-built security checks.

Data Virtualization in Oracle

As you might understand, Data Virtualization is a new, hot and growing topic; what has Oracle to offer since there isn't an official Oracle Data Virtualization tool? Well, check the picture below:

Data Virtualization: What is it About?

This is an Oracle Analytics repository (RPD)! A tool created in 1998, acquired by Oracle in 2007 and with a long history of successful implementations! The reality is that Data Virtualization is not a new topic (we talked about it in 2008), but simply an old capability, with a new name!

Oracle Analytics RPD's three-layer concept matches exactly the Data Access Optimization, Business Logic and Data Exposure layers mentioned above providing the same functionality. The Security is a key component across the whole Oracle Analytics Platform and successfully integrates with all major identity providers.

Oracle Analytics for Data Virtualization

Oracle Analytics is a tool born for data analytics but solves successfully the problem of Data Virtualization: data from different source systems can be queried, joined and exposed.

OA is capable of firing queries based on fields selected by the downstream application and optimized for the datasource selected. Query pushdown is enabled by default and specific datasource features can be turned ON/OFF via configuration files. Specific transformations can happen in OA's memory if the datasource doesn't allow the pushdown. Results of queries can be cached and served for faster response.

Data Virtualization: What is it About?

In scenarios where massive amounts of data need to be sourced, probably Oracle Big Data SQL or Cloud SQL could be used, pushing part of the virtualization at the database level.

Vertical and horizontal Data Federation can be defined, so data can span across various tables and aggregated datasources can be used for faster response. Metrics, attributes, hierarchies and joins are defined in the model thus related complexity is hidden from downstream applications.

Data exposed can be accessed via a web browser using the traditional Answers and Dashboard or the innovative Data Visualization. Data can also be extracted via SOAP APIs and via ODBC when the related component is exposed. Data Sources defined in Data Visualization and BI Publisher can also be extracted via REST APIs. There is a plan to extend JDBC access also to RPD defined Subject Areas.

Data Virtualization: What is it About?

A utility, called Metadata Dictionary, automatically generates a Data Catalog which can be used to expose and share what datapoints are available. In the future, Oracle Analytics Cloud datapoints will also be available via Oracle Data Catalog, a specific offering around this area.

The security included with the tool allows datapoint access definition to specific roles as well as limits on data exports size or access times. Security settings can be exported and audited via external tools. The platform usage can also easily be monitored for performance and security reasons.

Self-service Data Virtualization

Data Virtualization: What is it About?
Self-service Data Virtualization

All we discussed so far describes the IT-driven Data Virtualization where the access to data is only managed by a restricted group of people (usually the IT department). Oracle Analytics enables it alongside Self-Service Data Virtualization, where new datasources, joins and security layers can be defined directly by Business Users enabling a faster and secure data sharing process which can still be audited and controlled.

Do you want to expose the business unit's forecast data in your Excel file alongside the revenue coming from your datamart? With Oracle Analytics this is only few clicks away including the security options controlling the audience.

Oracle Analytics offers both top-down and bottom-up approaches to Data Virtualization at the same time! IT-Driven, highly secured and controlled data-sources can coexist with user-defined ones. Joins and metadata models can be built from both sides and promoted to be visible by wider audience, all within one environment that can be tightly secured, controlled and audited.


Data Virtualization is a hot topic indeed, with the ever-increasing number of different datasources in a company's portfolio we'll see the rise of data abstraction layers. But don't look at new shiny tools to solve an old and common problem!

As described above, Oracle Analytics (Cloud or Server) can successfully be used in Data Virtualization contexts. Deployed in conjunction with Oracle Big Data SQL or Cloud SQL and Oracle Data Catalog offers an extremely compelling solution with a set of tools and knowledge already available in the majority of companies.

Getting Smart View to work with OAC

I wanted to demonstrate what I thought would be a simple task to a client today, however it turned out to be a little more complex than I first anticipated, so I thought I would publish here. All I needed to do was get Smart View connecting to OAC.


  1. Excel installed
  2. OAC up, running and available


  1. Download Smart View (see here) - for reference I downloaded version
  2. Double click to install and accept the defaults

Establishing a Connection

When I last did this (not sure how long ago that was), I think the OBIEE extension was installed by default, so I put in the OAC connection string and then couldn't work out why I kept getting an error. Fortunately a colleague of mine pointed out the OBIEE extension wasn't actually installed by default any more, so that's what I needed to do next.

Install the Extension

  • Open Excel
  • Create a Blank Workbook
  • Select the Smart View menu item
  • Select Options

Go to Extensions

The OBIEE extension is no longer installed out of the box

You need to check Check for updates when Microsoft Office starts, then you should get a link above saying Check for Updates, New Installs and Uninstalls

Select the OBIEE Extension to install, once successful you should see

Now you can create a connection to OAC

Create a Connection

  • Select the Panel

  • Choose Private Connection, then Create new connection from the bottom of the screen

Choose OBIEE for the connection type

Enter the URL in the following format:


My example was

You'll be asked to logon

You can then give the connection a name to save it as

Then you should see the Catalog displayed on the right

If you select a View you will get a number of options of how to display it, the simplest is insert.

Once selected, this will load the data into the sheet.

Oracle Analytics: Everything you always wanted to know (But were afraid to ask)

Oracle Analytics: Everything you always wanted to know (But were afraid to ask)

The release of Oracle Analytics Server (OAS) on 31st January 2020 has left many OBIEE users wondering what it means for them. Common questions we were asked at Oracle OpenWorld Europe last week included:

  • what’s the difference between OAS, OAC and OBIEE?
  • where does DV fit into this?
  • should I be migrating and if so, when; what are the benefits?

This blog post aims to answer all these questions!
First of all, let’s define each product in order to compare them.

Oracle Analytics: Everything you always wanted to know (But were afraid to ask)

Oracle Analytics Cloud (OAC)

Oracle’s strategic analytics platform, hosted in the Oracle Cloud.

Oracle Analytics Server (OAS)

OAS is the new on-prem version of OAC, and is intended as a bridge between legacy OBIEE platforms and Cloud. It has almost complete feature parity with OAC, including AI-powered, modern and self-service analytics capabilities for data preparation, visualisation, enterprise reporting, augmented analysis and natural language processing and search. If you are an OBIEE customer, you are automatically licensed for OAS, and Oracle Data Visualisation (DV) is included at no extra cost.


The main difference between OAS and OAC is related to hosting and administration. OAC is hosted and managed by Oracle, while OAS needs to be installed, configured and patched by you in your datacenter. This also defines the level of control and customisation: with OAS you have the full control over config files, styles, custom function etc, while in OAC you’ll be able to change only what’s exposed in the cloud console by Oracle.
OAC will receive more frequent updates and new features, with OAS scheduled to have an annual release bringing the cloud features to on-premise customers.
So the choice between the two depends on the amount of customisations needed vs the time spent on supporting the platform.


OAS was developed to replace OBIEE, however the two products are not exactly the same. There are one or two OBIEE features that are deprecated in OAS, such as BISQLProvider or Act As, but they are still present in the tool, and they’ll not go away until a proper replacement is in place. On the other side, If you were using Scorecards, this tool is no longer shipped with OAS.
OAS on the other hand, brings almost functional parity with OAC, providing a huge amount of new features especially in the self-service area, more info in the dedicated post.

Can I connect to a Database in my Datacenter with OAC?

Oracle Analytics: Everything you always wanted to know (But were afraid to ask)

Yes you can, Data Visualization offers the option to connect to any datasource which is reachable from the Cloud. If you don’t want to expose your database directly, Oracle Data Gateway enables the connection from OAC (including RPD based connections) to on-prem data-sources without the need to open any firewall port.

Where does DV come into this?

Oracle Analytics: Everything you always wanted to know (But were afraid to ask)

Data Visualization (formerly known as Visual Analyzer) is Oracle’s self-service tool. If you’re an OBIEE 12c user, you may be paying extra license fees to use DV. If you’re already using OAC, you may have noticed DV is included with your license, and this will also be the case for OAS.  

Ultimately, Oracle Analytics’ aim is to provide a mix and match offering, where you can choose which components are Cloud and on-prem. For example, you can upgrade to OAC and point it to your on-prem database. Or if you’re Cloud averse for whatever reason, you can migrate to OAS and utilise many of OAC’s features.

What does Rittman Mead recommend you do next?

There is probably a different answer for everyone, depending on where you are in your Oracle Analytics journey, so we’d recommend contacting us for an initial chat to go through your options. Broadly speaking, if you’re using OBIEE onwards and you’re considering upgrading to 12c, you should factor OAS or OAC into your decision making process.

To help you decide which product is best for you, we are offering a FREE two-day Oracle Analytics assessment which aims to help you create a business case for migrating to OAC or OAS based on your pain points and anticipated usage. Contact us for more information.

Rittman Mead also provides OAC, OAS and OBIEE training. Our next OAC bootcamp is taking place in London, March 23rd - 26th 2020. For more information go to our training page or contact Dan Garrod.

Oracle Analytics Server is here

Oracle Analytics Server is here

Oracle has been talking about this product for months and we’re pleased to tell you the wait is over (for Linux users anyway), and Oracle Analytics Server (OAS) 5.5.0 is now available for download on edelivery(search Oracle Analytics Server).

Oracle Analytics Server is here
Oracle Fusion Middleware needs to be downloaded separately

If your organisation uses Oracle technology for data analytics, then the likelihood is you’ve heard of Oracle Analytics Cloud (OAC). You may have also come across the latest addition to the family, OAS, the new on-prem version of OAC, set to eventually replace OBIEE.

The umbrella term, Oracle Analytics, now includes:

Oracle Analytics Server is here
  • Oracle Analytics Server (OAS)
  • Oracle Analytics Cloud (OAC)
  • Oracle Analytics for Applications (OAX)

Whilst OAC is the jewel in the crown, and will receive regular quarterly updates, these updates will be reflected in OAS. You’ll be pleased to hear current OBIEE users will be automatically licensed for OAS - the logic behind this is that OAS becomes a stepping stone in your journey to using Cloud. If you’re buying OAS new, the licensing model is the same as the current OBIEE model.

OAS looks almost the same as OAC, minus some features, like the Natural Language Generator. This feature generates explanations of your visualisations in 28 different languages and will probably be included in a later version of the tool.  

How does OAS compare to OBIEE and OAC?

  • Licensing: OAS now includes options like Data Visualization (DV) and Mobile which were previously considered extra.
  • Data Visualization: Oracle’s self-service visualisation tool does what it says on the tin. Allows you to decipher your enterprise data with intelligent visuals. Now it includes almost all the new features available in OAC. A big step forward compared to the DV version available in the latest OBIEE
  • Data Flows: Clean and Transform your data via a GUI based tool without leaving your analytical platform.
  • Machine Learning: All the goodies related to “one-click forecast” or  “Explain” and the full ML capabilities are now included in the on-prem Oracle Analytics Edition!
  • Configuration Options: OAS provides the "OBIEE"-type configuration options, where you can tweak each componend individually

Oracle’s aim is for users to achieve “100% data literacy” and plan to do this via their vision for analytics: augmented, collaborative and integrated. OAS really plays into this strategy, allowing users to employ data science and machine learning techniques to both analyse current trends and predict future ones (find out more in this blog post)

Oracle Analytics Server is here

Talk to us about how to migrate from OBIEE to OAS or OAC. We can help you with every deployment scenario including on-prem, hybrid, full public cloud, or a mix and match of these suited to your needs. Email us: to arrange a chat with one of our team.