Cognos

The Gartner Magic Quadrant is Worthless: Cognos Edition

February 19, 2020 by Ryan Dolley 12 Comments

Be sure to check out an updated version of this blog post for the 2021 Magic Quadrant here!

Another February brings another edition of everyone’s favorite yearly head scratcher – the Gartner Magic Quadrant for Business Intelligence! I have expressed some strong opinions on the worth of the Magic Quadrant as a tool for decision makers in the past and this year will be no different. As always I strongly urge you to read the report rather than just rely on the picture as (once again) vendor positioning on the scatter plot feels extremely disconnected from the analysis contained below it. So let’s take a look at the 2020 Magic Quadrant as it relates to Cognos Analytics.

The 2020 Gartner magic quadrant for BI is much harsher to Cognos than their written analysis — IBM’s position and Gartner’s written analysis are out of sync

A big change to the Magic Quadrant this year is the return of enterprise reporting as key differentiator for what they are now calling ‘ABI platforms.’ The second differentiator is ‘augmented analytics’, which is integrated ML and AI assisted data prep and insight generation. Gartner is now calling visualization capabilities a commodity. What’s old is new again.

The return of enterprise reporting

This should be great news for Cognos Analytics! Cognos is a recognized leader in enterprise reporting. In fact Cognos’ reliance on enterprise reporting was the raison d’etre for knocking it out of the leaders quadrant to begin with.

It’s extremely curious, then, that IBM’s positioning on this quadrant was not more markedly improved. It’s even more curious that Gartner writes of enterprise reporting, ‘At present, these needs are commonly met by older BI products from vendors like…IBM (Cognos, pre-version 11)’. It’s almost as if Gartner is unaware that Cognos 11 meets the same enterprise reporting needs as previous versions. At the very least they seem unwilling to give IBM credit for it on the chart. The write-up tells a different story.

Augmented analytics gain steam

The second differentiator on the Magic Quadrant is also good news for Cognos. The platform’s augmented analytics capabilities have seen tremendous investment in the 11.1 release stream and are legitimately ahead of most vendors I have hands on experience with (Power BI, Tableau, Domo, Incorta being the primary ones.) Observe:

That’s a lot. If you want a comprehensive set of powerful, modern augmented analytics capabilities Cognos is a great choice.

The fact is that Cognos’ strength lines up perfectly with Gartner’s 2020 market differentiators, while it’s only ‘weakness’ – self-service visualization – is now considered a commodity. Again, why are they so poorly represented in the MQ image, and does the actual analysis tell a different story?

What does Gartner say about Cognos Analytics?

This write up is a lot rosier for IBM than the dismal MQ image suggests. I’ve summarized Gartner’s written analysis of IBM for you below:

Strengths

Cognos is one of the few offerings that offers all critical capabilities and differentiators in a single platform
IBM’s roadmap includes AI driven data prep, social media analytics and a long term goal of unifying self-service, enterprise reporting and planning (think Planning Analytics) in a single platform
Cognos can be deployed on-prem or in any cloud, unlike many other vendors

These significant strengths seem totally disconnected from where they have IBM placed on the quadrant. If enterprise reporting and augmented analytics are key differentiators between ABI platforms and Cognos is one of the only offerings that has it in a unified platform, how are they not better represented on the completeness of vision axis? Baffling!

Cautions

It is not often the sole enterprise standard
We think it costs more than other vendors
People don’t call us as much as they used to about Cognos

That last point is the key to unlocking the reality of how the Gartner Magic Quadrant for business intelligence really works. Let’s see why.

Gartner is a self-driven feedback loop

A huge component of ranking on the Gartner Magic Quadrant for Business Intelligence is straight up how often prospective customers call them about various tools. They don’t call asking about Cognos very much, ergo Cognos has a poor ranking. Don’t believe me? Look at my analysis of their MQ for planning platforms to see how survey scores seemed to have no impact on their ranking of Oracle as the market leader – Oracle’s survey scores were horrible!

Ask yourself, would you call Gartner to discuss a BI tool they rank in the bottom third of vendors? You wouldn’t. You call Gartner to talk about Microsoft, Tableau, Qlik and (bafflingly) Thoughtspot. Otherwise you call someone else. As long as this remains a major criteria for ranking Gartner will remain a market distorting self-feedback mechanism.

By this same logic, Cognos is the world’s #1 business intelligence tool in the Ryan Dolley Magic Quadrant as it represents 90% of my calls!

Why the 2020 Magic Quadrant should make you feel good about IBM Cognos Analytics

The BI market is shifting once again. Visualization is a commodity, enterprise reporting is king and augmented analytics is on the rise. As I’ve outlined above, IBM Cognos Analytics’ feature set is extremely well positioned to thrive in the landscape Gartner describes, whether or not they recognize it. There simply is no platform that offers the total package of mode 1, mode 2 and augmented analytics like Cognos.

Want to further the conversation? Connect with me on LinkedIn and check out PMsquare’s website for help getting the most out of Cognos.

Read on to learn how to modernize Cognos and become your own leaders quadrant!

The 3 Cognos Query Modes

January 29, 2020 by Ryan Dolley 12 Comments

This is actually a sequel one of the very first blog posts I ever wrote for blueview. While a lot has changed since 2015 understanding the difference between Compatible Query Mode and Dynamic Query Mode is still crucial. With the addition of data sets running on the compute service (aka ‘Flint’) things are even more complicated. My goal for this article is the run back the clock, peel back the onion and give you a historical, technical and practical understanding of the 3 Cognos query modes. Buckle up folks, this is going to get wild.

All 3 cognos query modes are available in Cognos 11.1 — There are many query engines hidden beneath the hood of your 2020 model Cognos

Compatible Query Mode

Compatible Query Mode is the query mode introduced in ReportNet (AKA, my junior year of college…). It is a 32-bit C++ query engine that runs on the Cognos application server as part of the BIBusTKServerMain process. CQM was the default query mode for new models created in Framework Manager up to Cognos 10.2.x, after which Dynamic Query Mode became the default. The majority of FM models I encounter were built in CQM and thus the majority of queries processed by Cognos are CQM. It remains a workhorse.

CQM relies exclusively on the report service to deliver query results — CQM resides within the Report Service

It is, however, an aging workhorse. Query speed is hampered by the limitations of 32-bit processes, particularly as it relates to RAM utilization. CQM does have a query cache but it runs on a per session, per user basis and in my experience causes more problems than it’s worth. Furthermore, Cognos 11 features either don’t work with CQM (data modules) or must simulate DQM when using CQM-based models (dashboards). This almost always works but of course fails whenever you need it most…

CQM works just fine and moving to DQM is not urgent, however I strongly advise you to do all new Framework Manager modeling in DQM (or even better, build data modules) and start seriously considering what a migration might look like.

Dynamic Query Mode and the Query Service

Dynamic Query Mode is the query mode introduced in Cognos 10.1. It is a 64-bit java query engine that runs as one or many java.exe process on the Cognos application server and is managed by the query service. The terms ‘DQM’, ‘query service’ and ‘XQE’ all essentially refer to this java process. All native Cognos Analytics features utilize DQM only – CQM queries execute in simulated DQM as mentioned above. You can see the criteria necessary for this to work here. DQM is both very powerful and very controversial among long time Cognoids. Let’s take a look at why.

DQM uses the query service to deliver results — DQM features dramatically improved query performance

What’s great about DQM?

DQM has a ton going for it. As a 64-bit process it can handle vastly greater amounts of data before dumping to disk. If configured and modeled properly, it features a shared in-memory data and member cache that dramatically improves interactive query performance for all users on the Cognos platform. It even filters cached query results by applying your security rules at run time.

DQM is tuned via Cognos administration and by a large number of governors in Framework manager to optimize join execution, aggregation and sorting. It handles extremely large data volumes, especially when combined with the basically defunct Dynamic Cubes feature. It even combines cached results with live SQL executed against a database on the fly. On its own. Like you don’t have to tell it to do that, it just does. Magic!

What’s not great about DQM?

Unfortunately given the list of excellent attributes above, DQM has some problems. It is very complex to understand, manage and tune and requires DMR models to fully utilize the all the caching features – consider that the DQM Redbook produced by IBM is 106 pages. A standalone tool exists called Query Analyzer dedicated to help you understand what the heck DQM is even doing as it plans and executes queries.

Migrating from CQM to DQM is often a complex project to evaluate and execute. I once provided a customer an LEO estimate of 8 – 32 weeks to complete a migration project. I have seen migrations take almost a year. I’ve seen things you people wouldn’t believe…

The purpose of this blog is not to push professional services but this is one instance where I think you really should contact PMsquare for help. But let’s say you have a ton of CQM models and don’t have the time to migrate them all. Is there a shortcut to high performance on large(ish) data volumes? Why yes, yes there is.

Data Sets and the Compute Service (aka ‘Flint’)

Data sets are an in-memory data processing engine first introduced in Cognos 11.0 and greatly enhanced in 11.1. Cognos 11.1 data sets run on the compute service aka ‘Flint’. The compute service is a 64-bit spark-sql process that is created and managed by the same query service that manages DQM, so it’s not really an independent cognos query mode. I will write a more in-depth article about data sets and Flint in the future, but let’s take a super quick look at how they work before we get into why they are amazing.

The compute service uses Spark SQL to deliver results — The compute service is a modern in-memory compute engine

How do data sets and the compute service work?

Data sets are not live connections to the underlying data like CQM or DQM – rather, they are a data extraction that is stored in a parquet file and loaded into the Cognos application server memory when needed for query processing. It works like this:

An end user creates a data set from an existing package, cube or data module OR uploads an excel file (the process is the same!)
Cognos fetches the necessary data and loads it into an Apache parquet file
The parquet file persists in the content store and is available to all application servers
When the query service on an application server requires a data set for query processing, it first checks to see if it has a local and up-to-date copy of the parquet file
If not, it fetches one
In either case, the parquet file is then loaded into the memory of the application server
Data is processed by the compute service using Spark SQL and results are returned to the query service
The query service receives results from the compute service and may perform additional processing if necessary
The results are then passed to the report service or batch report service for presentation

What makes data sets great?

They’re easy to build, easy to join and manipulate in data modules, easy to schedule and the performance is great. Once loaded into memory a data set is shared between users on the same application server. I have done multiple projects where I accomplish weeks or even months of ETL by getting fancy with data sets and data modules. No wonder they are my favorite of the Cognos query modes.

What’s even better is how data sets provide a radically shorter path to high performance, DQM and Spark based queries for your existing CQM models without having to commit to a full conversion. You simply use a CQM FM package as the basis for a data set, then utilize that data set as a source in a data module. Once complete, you’ve unlocked the full set of incredible data module and dashboard capabilities like forecasting without having to do an 8 to 32 week project.

Which Cognos Query Mode is right for me?

Okay that was a ton of data, some of it pretty technical. Which of the Cognos query modes should you choose and how do you learn more?

TLDR

Immediately cease all development of new Framework Manager models using CQM
Consider migrating existing CQM Framework Manager models to DQM models or to data modules (PMsquare can help with this)
Data sets are your ‘get out of CQM free’ card; they vastly improve the performance of most CQM queries and simplify presentaiton for end users

References

Read on to up your Cognos game

Relative Time in Framework Manager

December 31, 2019 by Ryan Dolley 6 Comments

Kamil asks an excellent question about relative time in framework manager in response to my Framework Manager vs Data Modules article:

Great article. I have one question, is it possible to use relative dates with packages from framework manager?
– Kamil

Like all questions in Cognos, the short answer is ‘no’ and the long answer is ‘yes!’ Let’s take a quick dive into relative time in Framework Manaager.

Relative time in FM? No!

The enrich package screen of Cognos — The Enrich Package interface

There is no ‘easy button’ for using the new relative time functionality with Framework Manager, unfortunately. I was briefly hopeful that this is possible using the enrich package functionality but it’s not there.

Enrich package is an important piece of the Cognos pie so it’s worth talking about briefly. Enriching a package provides needed context that allows Dashboards, Explore and the AI Assistant to do their thing. Most notably, enriching a package will allow Cognos to properly display time and geographic data types and will collect the sample data that allows the AI assistant and Explore to function properly. Enriching a package taxes your system so consider restricting the query subject or running it off hours.

If easy relative time does come to FM this is where I’d expect it to go. It’s worth reiterating that FM itself will receive no changes going forward so it’s time to start making the change to data modules. It’s easier than you think!

Relative time in FM? Yes!

Here’s where things get a little trickier and using relative time with your FM model becomes possible. To make this work we’re going to need to use https://ibmblueview.com/what-are-cognos-data-modules/Data Modules, custom tables and the lookup reference feature.

Step 1: Add a package source to a data module

Adding a package as a source to data modules — A package has been added as a data source to this data module

Click the ‘new’ icon and select data module
Navigate to your package in the folder structure, click on it and click ‘OK’
The data module screen will open with the package visible in the data tray

Step 2: Create a custom time table

Building a custom table in Cognos — Building a custom time table from a package

Click the ‘Custom tables’ tab and click ‘Create custom table’
Click ‘select tables’ and click the package source. All the tables in the package are displayed on the left.
Click ‘create a view of tables’ and click ‘Next’
Don’t forget to give your custom table a new name!
Click ‘invert’ then select only the table with which you want to use relative time
Click finish. Your custom time table will appear in the data tray

Step 3: Add relative time functionality

Adding relative time to the custom table in Cognos — Relative time can be added to your custom time table

Click the ‘Add sources and tables’ button and select ‘Add more sources’
Navigate to the ‘Calendars’ folder in the samples and select the ‘Fiscal calendar’ data module
Click ‘OK’. The FiscalCalendar table will appear in the data tray, hidden by default
Expand your custom time table, click the date you wish to use for relative time and click the ‘properties’ button in the upper right. The properties window will open.
In the properties window, select ‘FiscalCalendar’ in the ‘Lookup reference’ drop down menu.
You now have relative time functionality in your data module!
Rinse and repeat for any additional time or measure fields that require this functionality

Step 4: Join the custom time table to the package

Joining a custom table to a framework manager package — The custom time table can now be joined to the package

Click your custom time table and choose ‘New… Relationship’ in the pop up menu
Select the appropriate table to relate the custom time table to the rest of the package. Oftentimes this is a fact table.
Select the appropriate field(s), cardinality and join type for the join.
Click ‘OK’

There you have it! Relative time in Framework Manager (sort of)

At this point you can save and use your data module, which is made of your pre-existing package plus one or more custom tables. This doesn’t solve the obvious problem that your existing content references the package and not the new data module, but new content can be built off this module. The module will even inherit changes made to the package. And there you have it – relative time in Framework Manager… sort of!

Read on to level up your Cognos skills!

Cognos Analytics Data Servers

December 30, 2019 by Ryan Dolley 2 Comments

Joe Schmoe asks a fairly common question about Cognos Analytics data servers in the comments to my Framework Manager vs Data Modules article:

In Cognos Analytics 11.1.5, I don’t see a way to use a data source to feed a data module. It looks like in order to use data from a database, you need to use a package — which means you need to use Framework Manager anyway. Am I missing something?

Let’s take a look at how data servers work in Cognos Analytics to (eventually) answer Mr. Schmoe’s question.

What is a data server?

A data server is simply a connection to a database that has been defined within Cognos Analytics. This definition contains the database url, connection parameters, username and password necessary for Cognos to authenticate with, issue queries to and receive data from the desired database. A data server can be re-used by an infinite number of data modules to provide self-service users and report authors controlled access to desired data while maintaining high quality row-level security. Longtime Cognoids will no doubt say ‘Ryan, that sounds exactly like what we used to call a data source!’ Yes. Yes it does.

Cognos Analytics data server vs Cognos 10 data source

Cognos Analytics data servers and Cognos 10 data sources are fundamentally the same while having some differences in where and how they are configured. If you’re familiar with Cognos 10 you can breath a sigh of relief as your existing knowledge is almost 100% applicable. You just need to learn a slightly new UI.

Cognos Analytics data server

Introduced in Cognos Analytics
Provides a definition which Cognos uses to connect to a database
Configured via the manage menu
Requires JDBC drivers and uses exclusively JDBC connectivity
Uses Dynamic Query Mode exclusively
One data server can be used by infinite data modules
Source for data modules only – no Framework Manager [Editors note: Commentor Jerzy points out below that you can use data servers as a source for FM for DQM models only. I didn’t know that! Thanks Jerzy!]

Cognos 10 data source

Introduced in Cognos 8? 7? Reportnet? I was in college, it’s all very hazy
Provides a definition which Cognos uses to connect to a database
Configured via legacy administration console
Uses a wide variety of connection types including JDBC, ODBC and others
Uses Dynamic Query Mode or Classic Query Mode
One data source can be used by infinite Framework Manager packages
Source for Framework Manager only – no data modules (we’ll cover a simple workaround later in this article)

As you can see, data servers and data sources serve the same function within Cognos – defining a reusable database connection for Cognos – and have many of the same features. They differ primarily in the types of databases to which they can connect, the connectivity standard they use and how they are configured within Cognos.

Configuring data servers

Configuring a data server is easy and is – for the most part – a one time, ‘set it and forget it’ task most frequently done by Cognos administrators. Like everything in Cognos you have a few options for how to proceed. Before we dig into that though, let’s take a look at the info we need regardless of how we’re configuring data servers.

Data server configuration checklist

Identify which type of database you want to connect to
Make sure you have permission to connect to it!
Check to make sure it’s supported by Cognos
Check to see if there are any special considerations for that database
Acquire the relevant JDBC driver and install it in the ‘drivers’ folder of the Cognos install directory
Ask your DBA for the following information:
- URL for connecting to the database
- Database name
- Schema name
- Username for authentication
- Password for authentication

Configuring a data server from scratch

Now that we have our ducks in a row we can build the data server. This is incredibly easy, though you might not know it from reading these instructions. Let me break it down for you:

Click the manage icon then select ‘Data server connections’
Click the plus button in the upper right of the data server connections window
Select your database type from the list that appears
The data server configuration window will automatically open for you to input your connection url from above.
In authentication method, select ‘use the following signon’ and input your username and password
Click ‘Test’ and ensure Cognos can connect to your database
Click on the ‘Schemas’ tab and set the following:
- In ‘Load options’, choose whether or not you want Cognos to retrieve statistical samples from the database. I generally leave this alone. Turning this off will disable certain advanced features of Cognos – like auto joins in data modules
- In ‘Tables’, select which tables you want to expose via the data server. I highly recommend you select only the tables which you need. Allowing Cognos to profile your entire production database during work hours is an extremely bad idea…
Click ‘Save.’ Your data server is ready for use in data modules!

Converting Cognos 10 data sources to Cognos Analytics data servers

While it’s true that a data source configured in Cognos 10 cannot directly feed a data module, you can easily convert a data source to a data server provided the administrator has configured a jdbc connection for the data source. To do so follow these steps:

Click the manage icon then ‘Administration Console’
Click ‘Configuration’ tab and ‘Data Source Connections’
Click the ‘set Properties’ icon for the data source you wish to convert
Check the ‘Connection’ tab
Check the box next to ‘Allow web-based modeling’
Click ‘OK’
Exit the administration console and open ‘Data Servers’ in the ‘Manage’ menu. You should see your data source list as a data server.

Using data servers in Cognos analytics

By following the process outlined above we can easily use data servers to get our data into data modules and then into reports, dashboards or the new exploration feature without ever needing to reference Framework Manager. I hope this satisfies Mr. Schmoe and as always, contact me if you have any questions!

Read on to level up your Cognos skills!

Cognos Analytics 11.1.5 What’s New

December 21, 2019 by Ryan Dolley 5 Comments

It’s the holiday season and IBM has given us an unexpected gift in the waning days of 2019. No, I am not referencing the fact that Cognos Analytics is currently half-off! IBM rather suddenly dropped a new Cognos release chocked full of stocking stuffer features for all the good little Cognoids across the land. So let’s read on to learn what’s new in Cognos 11.1.5.

Schematics

The schematic feature of Cognos Analytics 11.1.5 enables interactive custom maps for things like stadium or airplane seating charts — Schematics allow you to turn anything into an interactive map

Schematics are undoubtedly the eye catching new feature in Cognos Analytics 11.1.5. Schematics allow Cognos to dynamically visualize data on an image – think a stadium or airplane seating chart, a hospital floor plan or a diagram of a machine. The schematic is interactive and plays nicely with other features.

Schematic Views

One interesting feature of Schematics is the ability to create ‘views.’ Views enable end users to automatically filter the schematic to render only those sections that interest them. In the example above I created two views of the United States – one titled ‘Midwest’ and one titled ‘Rest.’ You can see that unselecting ‘Midwest’ hides those states. For a more complex example, imagine a schematic with a detailed diagram of a car. Users could interactively choose to view only the suspension, transmission, power train, etc… Very powerful!

Schematic Management

Managing Schematics is not exactly straight forward but IBM has provided excellent instructions. Multiple schematics can be bundled together into a package, which allows report authors to select from a library of related schematics. These packages are managed just like the custom visualizations that debuted in Cognos Analytics 11.1.4.

Schematics are currently only available in reporting.

Data Modules

You guys know I love data modules. Well in Cognos Analytics 11.1.5 the changes to Data Modules are so, so, SO GOOD. Just ridiculously good. I got a huge smile of my face using it for the first time. You have no excuse at this point, they’re just too awesome!

Custom Tables

IBM has taken all of the scattered virtual table/view building functionality and very smartly bundled it together into a new, clean and totally kick ass interface called ‘Custom Tables.’ From custom tables you can easily blend tables or spreadsheets from any source together while understanding at a glance how all that blended data is related. I cannot tell you how crucial this feature is as I and my customers build increasingly complex data flows in Cognos.

Members in the data tree

Cognos Analytics 11.1.5 adds the ability for members to appear in the data tree regardless of source type — Members are now visible in the data tree regardless of source type

As of Cognos Analytics 11.1.5, members are now visible in the data tree regardless of source type. Yes, you read that correctly – even for relational sources. You can drag and drop individual members into visualizations, use them to build sets, etc. Even against excel spreadsheets. Holy cow! This change applies across the entire UI, not just data modules.

Show generated SQL and query control

Another huge quality of life upgrade – you can now see generated SQL directly in data modules! And you can now use a property called ‘item list‘ to determine whether the data module fetches the entire table or uses minimized SQL, just like you could in FM.

Dashboards

Dashboards received a host of small quality of life changes plus one big, frequently requested enhancement that I know you’ll love. So let’s start there.

Dashboard to dashboard drill through

Dashboards can now drill through to dashboards — Dashboard to dashboard drill through – it finally happened

Dashboards can now utilize other dashboards as drill-through targets. IBM did a great job of making this extremely straightforward so end users should have no problem with it. It works like this:

Create two dashboards that share at least one data source
Establish a Drill-through definition (pictured above)
Notice there is no mapping of parameters or anything like that!
In the source dashboard, select a visualization element and choose the drill-through icon
The target dashboard will open and a new filter will appear in the ‘All tabs’ filter section
The target dashboard has been filtered by the values passed from the source

Customize tabs

Cognos 11.1.5 brings significant new tab controls

You can now customize the location an appearance of Dashboard tabs. In the example above, I moved the tabs to the left side and formatted the fourth tab to appear in red with some cool spectacles as an icon.

Various and sundry improvements

Here is a summary of additional enhancements to Dashboards:

Show and hide rows and columns in crosstabs
Customize missing values in visualizations
Assistant-suggested questions based on context
Enhancements to forecasting (as in it’s more accurate)

Administration

I don’t write a ton about administration on this blog. Mostly that’s because it’s boring. However there are some important changes that I want to highlight for you.

Save reports to cloud storage

Save to cloud is a new capability that allows you to, well, save reports directly to 3rd party object storage services. This allows you to archive your 10-years-and-running collection of daily PDFs to Amazon S3 instead of in your content store. I think this is hugely helpful, especially for customers on Cognos cloud where archival and bursting capabilities have been severely restricted. It also portends good things as far as integration with 3rd party cloud vendors going forward.

AI Capability

The new AI capability controls who can use the AI Assistant in Dashboards and Explore. I know the ability to turn this on and off is something many Cognos teams have been asking for. My advice to you is to just leave it on! But if you absolutely must lock it away, now you can.

Other new features of Cognos Analytics 11.1.5

There are a host of other small changes that I’m not going to cover here. Many of the changes to Report Authoring, Explorations, etc… are covered above as there are quite a few UI spanning updates in this release. Overall I’m extremely pleased with this one. Schematics are a great new feature while the changes to Data Modules are exceptionally good. I suggest -as always – that you update to 11.1.5 as soon as possible. And as always, hit me up with any questions you may have.

Read on to level up your Cognos skills!

Framework Manager vs Data Modules

November 20, 2019 by Ryan Dolley 22 Comments

I have spent a lot of time showcasing data modules to audiences across the world in-person and in livestreams, helping people understand how and why to use them as part of Cognos Analytics. The most consistent question I receive – by far – is about understanding framework manager vs data modules. There are a lot of outdated opinions and outright misconceptions floating around so let me outline the exact feature differences between framework manager and data modules as of Cognos 11.1.4.

What do data modules and framework manager have in common?

The answer is ‘a lot’ but this wasn’t always the case. In the 11.0 releases data modules were missing many essential framework manager features and didn’t offer compelling reasons to switch. Of course that has changed. As of 11.1.4 framework manager and data modules both:

Produce data models that can be used with all Cognos 11 features
Join dozens or hundreds of tables across multiple databases
Execute cross-grain fact queries (aka the dreaded determinants)
Build simple or complex calculations and filters
Build alias, view, union and join virtual tables
Secure data by groups, roles and users
Create OLAP-like dimensional hierarchies
Offer enterprise governance, auditablity and security

Oftentimes people washed their hands of data modules a couple years ago and are surprised to see virtual tables, cross-grain fact queries and security by groups. These features may exist in both but the implementation in data modules is superior from a usability perspective.

Column dependencies can handle degenerate dimensions, unlike framework manager — Column dependencies go beyond what was possible using determinants in FM

What do data modules offer that framework manager does not?

Again, the answer is ‘a lot’. The 11.1. release takes data modules beyond what is possible in FM with a host of powerful capabilities and quality of life enhancements. The following features are either exclusive to data modules or done infinitely better in data modules.

Natural-language and Ai powered auto-modeling
Automatic join detection
Easy integration of excel data
Ability to easily clean data
Flexible hierarchies that go up, down and across (navigation paths)
Easy measure binning and attribute grouping
Easy extraction of year, month, day, etc… from data data types (split)
Automatic creation of relative time filters (YTD, MTD, PYMTD, etc…)
Automatic creation of relative time measures (YTD actuals, PYTD actuals, etc…)
In-memory materialized views within Cognos Analytics
In-memory query cache
Easy multi-model inheritance for single source of truth
Degenerate dimension aggregation (column dependencies)

Some of these features are absolute game changers for how I craft highly performant, easy to use and self-service friendly data models. Consider ~~the coconut~~ relative time; because this was such a titanic brain buster in framework manager only the most skilled developers could deliver. Now it takes minutes for end users to implement.

Building a dozen relative time filters can be done in as little as five clicks. — It took five clicks to build the relative time filters that take ~1 trillion years in FM

What are data modules missing?

There are still some things data modules lack:

Object level security
DMR capabilities
Parameter maps
Multiple connections for data servers

If I’m being honest, I don’t really recommend you use many of these features for new development in 2019 unless you absolutely have to, particularly DMRs. DMRs are very powerful for those who know MDX but a true maintenance and self-service nightmare in the long run. I cannot count the number of clients who are stranded with critical DMR based reports they cannot understand. In any case, a little bird told me that DMR-like functionality will grace data modules soon.

Going beyond the feature list

Comparing framework manager vs data modules feature for feature, we can see how data modules have few shortcomings and offer huge advantages. While this is a common way for IT folks to think (and I would know, I’m one of them!), I argue that it badly misses the point. By using data modules an IT professional can do weeks of FM work in an afternoon while a self-service user can easily accomplish tasks that will otherwise be done in Power BI. I repeat it often but I’ll say it again – data modules are the key to modernizing your Cognos Analytics environment and delivering content with the speed modern users demand.

What do you recommend?

I’ll parrot Cognos offering manager Jason Tavoularis and say, ‘use data modules unless you can’t.’ And as you can see above, the list of reasons you can’t has become quite short. I start ALL consulting engagements under the assumption that we’ll be building data modules and I’m always happy with the results.