IBM Cognos Report Studio : Using CSS Classes to Manage Report Formatting

Source:http://www.ironsidegroup.com/

May 7, 2013 by Geoffrey Speare 

 

ou have two or more crosstabs on a Report Studio report. All of the crosstabs should have the same formatting, which differs from the Cognos default. While it’s possible to change the formatting for each crosstab separately using the formatting tools of Report Studio, you will have to make the same changes independently for each crosstab. If changes are made in the future, they will have to be made again for each crosstab. Using CSS classes, a report author can standardize all crosstabs within a report without independently setting the formats for all crosstab elements.




Using CSS classes, it’s possible to change the default crosstab formatting for the entire report, which will ensure that each crosstab is formatted the same way with only a single formatting change.
Step 1: Start with any report that has multiple crosstabs. (The report in these examples is taken from the GO Sales sample data, but any report will do.)






Step 2: Select the row members on one of the crosstabs and look at the Classes property (the last property in the list). Note the name of the class, Crosstab member cell.










Step 2: From the View menu, select Classes.








Step 3: In the Global Class Extensions list, find Crosstab member cell.








Step 4: Select Crosstab member cell. Observe that the formatting samples on the right show how a member cell is displayed in the report.
Step 5: Change the properties for Crosstab member cell as listed below.

• Background Image: None
• Background Color: Transparent
• Border: None (click None under Style, then the Apply All Borders button under Preview).

Step 6: Through the Page Explorer, return to the main report page. (Note that you can get to the Classes screen through the Page Explorer as well as the View menu.)







Step 7: Observe the crosstabs. Note the formatting for the rows and the columns of both crosstabs has changed. This is because the column members are part of the same class – you can check this in the Classes property.




Step 8: Click on a fact cell of one of the crosstabs, so that Crosstab Intersection is shown in the Properties window, and look at the Classes property. It will be empty.








Step 9: Right-click on a fact cell and select Select Fact Cells. Now the Classes property will have a value, Crosstab fact cells.









Step 10: Open the Classes screen from either the View menu or Page Explorer. Locate Crosstab fact cell and change the Foreground Color property to red.
Step 11: Return to the report page. Observe that all the fact cells are now red.
Step 12: Click on the row members of one of the crosstabs and create a total row. Note that the formatting for the Total member and its fact cells are different from the other items in the crosstab.








Step 13: Click on the Total member cell, and observe the Classes property: Crosstab member cell (outer member, not nested).
Step 14: Click on the ellipsis in the Classes property to bring up the Classes dialog.






Step 15: Remove the Crosstab member cell (outer member, not nested) class from the Selected classes box, and add Crosstab member cell from the Global classes box. Observe that the formatting for the Total member changes to match the other member cells.
Step 16: Right-click the Total member cell, and select Select Member Fact Cells. Observe the Classes property: Crosstab fact cell (outer total, not nested).










Step 17: Go to the Classes dialog, remove the existing class, and add the Crosstab fact cell class. Observe that all the fact cells are now formatted the same.

Other Applications:

CSS classes can be used for crosstabs, lists, and other report elements in Report Studio. You can modify existing classes or create new ones to apply consist formatting across multiple objects in a report. This makes it much easier to maintain formatting changes for large, complex report.
 

Managing Risk with IBM’s GRC Business Analytics Solutions

Source:http://www.ironsidegroup.com/

April 2, 2013 by David Myers

 

In a world of ever changing government and industry regulations, many organizations are having a challenging experience efficiently meeting compliance requirements. Additionally, as the economy continues to sputter, organizations are exploring new ways to reduce loss, improve decision making around resource allocation, and optimize their business processes. With the latest release of IBM’s Governance, Risk & Compliance (GRC) solutions, which includes: OpenPages and Algorithmics, meeting these requirements and beginning to improve processes internally can become seamless. The painful, long drawn out process of managing market, credit and liquidity risk can soon become an efficient exercise. Integrating your current reporting technologies (IBM or not) will help your organization gain greater risk visibility and be ready for any challenges.

IBM OpenPages Software

Currently, IBM OpenPages offers specific GRC solutions for several highly regulated industries including banking, utilities/energy, insurance, and pharmaceuticals/healthcare. OpenPages is designed to enable C-level leadership to take a proactive approach to managing risk. These prebuilt solutions provide reporting, analytics and document management capabilities that address Key Risk Indicators (KRIs), Issue Management, and Loss Event Management. By leveraging Cognos as the reporting backend, users experience best-in-breed reporting technology with complete intractability including drill down, drill up, dash-boarding and mobile integration. Most importantly, OpenPages will integrate directly with your reporting tools in most cases.

IBM Algorithmics Software

Within the financial industries, identifying credit and market risk proactively will ensure that all organizations are able to identify the strongest opportunities for profit, as well as the largest areas of risk or loss. By utilizing Algorithmics, asset management firms are able to run simulations to learn what risk is present in any investment, in many different markets. Additionally, Algorithmics has an automated data management system that enables users to directly reduce operational risks. Algorithmics also offers a web-based reporting front-end that can also tightly integrate directly with other reporting tools and source systems.

TM1 Cell-level Security, Custom Security Cubes and Rules

Source:http://www.ironsidegroup.com/

May 7, 2013 by Jere Haigh 

 

TM1 version 9 introduced the concept of cell-level security, allowing users to secure individual cells without the need for a complicated matrix of element-level security cubes. Cell-level security can quickly become cumbersome, as each cell within a cube requires its own security setting. This article demonstrates how to use rules and a custom security cube to utilize this feature effectively. The IBM sample server GO_New_Stores will be used as an example.
Cell security works by an administrator creating a cell-level security control cube that contains all of the source cube’s dimensions, with the “groups” dimensions added. This new cube allows security to be applied for any specific cell or grouping of cells. By default, the cube contains no security settings and therefore does not apply any security for any cell(s). This gives users whatever access has been applied by the cube and element-level security models, while the cell-level cube security does nothing.
Look at the view below from the Store Sales Plan cube. In this example, we would like to prevent users from updating the months Jan through Mar for Budget version 1, and all input for all months to Budget version 2 while at the same time, still allowing security based on the cube or other dimensions to apply.
What happens if we would like to prevent users from updating Jan through Mar for Budget version 1, but allow all months to be input for any other version, at the same time still allowing any other security to apply?

The simplest way to address this is to create a cell-level security cube for Store Sales Plan. To do so, the steps are as follows:

1. Right click on the cube GO_New_Stores.
2. Select Security > Create Cell Security Cube.
3. Display Control Objects, under View, if you have not already done so.
4. Locate cube }CellSecurity _Store Sales Plan.

Once that cube has been located, create a rule file for it and enter the following:

[{'Jan','Feb','Mar'},'Budget version 1']=S:’READ’;

Note that because this is a security cube Skipcheck and Feeders are not needed.
Now when the security cube is queried, the results should look like the image below. Note, where the security cells are blank, cell-level security is not applied.

Now when a user queries the cube they will see:

Notice how Jan through Mar is now not available for input for Budget version 1.

Custom Cube Scenario

However, what happens if users would like to secure multiple cubes across multiple dimensions? A custom security cube can be created that all applicable cubes can reference to update and manage their cell level security. To do so, the steps are as follows:
1. Chose the dimensions you would like to secure (the example below uses Month, Budget version, and Products).
2. Create a cube with the chosen dimensions and a measure dimensions with a string input element. The name of the cube is irrelevant, but the example uses Security.

3. The new cube can be populated like any other cell-level security cube, with READ, WRITE, or ADMIN.

• Now the rule file for }CellSecurity _Store Sales Plan will be updated with:

[]=S:DB(‘Security’,!Budget version,!Month,!Products,’Text’);

The cube will now be referenced by the new cube and updated for any changes made in the security cube. Notice how the Store Sales Plan cube now has different security by product, version, and period as defined in the security cube.

This new cube could include any number or combinations of dimensions. It would even be possible to include the }cube control dimension to use one cube to manage different security for different cubes. You could also include the }groups dimensions and manage the security at the group level within your custom cube.

 

Introducing IBM Cognos Disclosure Management

Source:http://www.ironsidegroup.com/

January 1, 2013 by Rich Sepe

 

The worlds of financial and operational analytics have always diverged, creating a disconnected analytics environment that segregates decision-making. That’s why The Ironside Group is thrilled with IBM’s recent release of IBM Cognos Disclosure Management (CDM), a solution that brings together the power of finance and operational analytics, with the ability to easily annotate your results when you need more than just numbers alone. IBM Cognos Disclosure Management is a reporting and process automation solution designed to merge financial and operational analytics with narrative analysis in a controlled, auditable environment. You may have experienced the need to produce “board books”, or narrative reports, within Cognos, but had no way to accomplish the job other than custom, one-off reports. This has the potential to be a powerful solution for the Office of Finance.

Easily Combine and Narrate Your Most Accurate BI & PM Data

Cognos Disclosure Management marries financial, operational, and statistical data with narrative analysis in a fluid and dynamic environment. Finance professionals can tie-in Cognos business intelligence with their financial data and value-add analysis, and make it available to others in a controlled, secure collaboration to strengthen analysis and decision-making.
This includes the capability to link report data directly to enterprise data sources. Users can create dynamic reports that update automatically when the numbers in underlying data sources change—ensuring a single, consistent version of the truth throughout the office of finance.

Intelligent Process Design and Automation

Disclosure Management also provides intelligent process design and automation, transforming previously manual processes and enhancing controls well beyond the corporate financial reporting process and across other corporate finance processes. Disclosure Management includes workflow and task management capabilities allowing finance professionals to gain insight into critical performance management and reporting processes through dashboards and alerts. It also optimizes controls and auditability with standard review and validation procedures and ensures they are performed at each level of the organization—from department to entity to corporate headquarters.

Familiar Authoring Environment

Lastly, Cognos Disclosure Management automates the creation of management and executive PowerPoint presentations. Users can take advantage of their expertise with the calculation capabilities of Microsoft Excel, and their knowledge of formatting in Microsoft PowerPoint and Microsoft Word, while utilizing the additional functionality and capabilities of an enterprise database driven solution

Key Features at a Glance

• Easily combine business intelligence and performance management information in a single secure environment.
• Finally create those time consuming, manually annotated reports in a centralized, governed and automated environment with 100% accurate supporting data.
• Simple user interface based in Microsoft Office
• Direct Microsoft PowerPoint integration
• Easy to use wizard for adding queries from your live and accurate BI and PM data sources
• Simple management of reports, workflows and user based security
• Supports Popular Data Sources
  • OLAP (TM1/Controller (FAP), Essbase, MSAS)
  • HFM
  • External Excel File
  • Relational (Oracle, DB2, SQL)
  • Cognos Go Office, Cognos Café (limited – no dynamic refresh)
  • TM1 Perspective Excel Add-in (limited – no dynamic refresh)
  • Controller Excel add-in (limited – no dynamic refresh)
• Supports Microsoft Office 2003, 2007 and 2010

 

IBM Cognos TM1 – Freezing Old Data

Source:http://www.ironsidegroup.com/

November 6, 2012 by Jere Haig 

Many TM1 users have cubes with rules associated with historical data or completed plan versions and are needlessly recalculating their data on a continuous basis. This creates an unnecessary performance drain, and to make matters worse, what happens when the business rules change? Do you change history? Do you make rules overly complex by adding a time or version component to all of your rules? Using a few simple steps, this data could be “frozen” or made static, resulting in faster response time, less RAM consumption, and alleviating issues when business rules change. In this article, we will explain how to go about freezing this data.
The first step is to identify the dimension(s) that we will use to determine what data we need to freeze. It could be one dimension or the intersection of multiple dimensions, such as freezing certain months for certain versions. For this example, we will use the “Version” dimension. This dimension contains Actuals, Budgets, and various plans. You will need to create a simple numeric attribute that identifies the elements as active or inactive. We will call this attribute “Inactive” and set the inactive value to 1; this will indicate the value should be frozen.


Next, any rule files for any cubes that should be frozen will need to be updated with a line to exclude any “Version” elements that have an inactive flag value of 1. Just be aware, you may want to leave some rules active, such as rules to overwrite consolidations for items like headcount, or percentages. Simply put these rules above the line to exclude elements with the flag.
[]=N:if(attrn(‘version’,!version,’inactive’)=1,STET,CONTINUE);
The next and final step has several components. A TI process will need to be written to export the frozen values to a flat file. This process should include all rule calculated values, but should exclude consolidated elements. Next, the process should set the attribute value to 1. If the data to be frozen spans multiple cubes, simply add multiple exports to the same process, but create one file per cube.



Finally, a second process will need to be created to reload the values from the flat file into the cube from which they were exported. Again, if the data spans multiple cubes, create one process per cube and use the unique files created in each process above. It is possible to use one process to load from multiple cubes, but this makes the TI processes more difficult to create.


These formally calculated values will not show as inputtable non-calculated values. Just be aware trace calculation will no longer function, removing the audit trail of how the value was derived.
Before:

After:

To ensure maximum performance gain, conditional feeders should be used to exclude the frozen value from being fed by the rule file. Even if the conditional feeders are in place, the memory will remain consumed until the server is restarted or the cube in question is unloaded and reloaded.
=>DB(‘Income Statement’,!Account,!currency,!Market,!Period,if(attrn(‘version’,!version,’inactive’)=1,’ ‘,!Version),!Measure);
If you would ever like to reactivate a version, simply reset the flag back to zero, reprocess the feeders, and the value will be recalculated based on the current rules in place. The trace calculation functionality will also now be enabled. If the rules have changed, the calculated values could differ from the previously frozen values

Introduction to Predictive Modeling

Source:http://www.ironsidegroup.com/

August 3, 2012 by Jorge Vulej

 

Business intelligence focuses on the identification and understanding of events that happened in the past; that is “what’s happened?” and “why has it happened?” Business imperatives and other constraints are increasingly driving organizations to look forward and use their data to ensure decisions that are being made today will lead to wins in the future. Predictive analytics has often been viewed with hesitation and intimidation and seen as only relevant to statistical specialists, but with the advent and evolution of the IBM SPSS solutions, the power of predictive analytics can be harnessed by a much broader group of users. This newsletter article describes what predictive analytics is, how it is used, when it is used, and how to integrate it into your organization.

What is Predictive Analytics?

Three terms go hand-in-hand when talking about predictive analytics:

1. Predictive analytics: the area of data mining focused in forecasting probabilities and trends.
2. Data mining: the process that attempts to discover patterns in large data sets and transform those discoveries into an understandable structure for further use.
3. Predictive modeling: a process used to create statistical models of future behavior.

Some Applications of Predictive Modeling and Data Mining

Every organization can benefit from predictive modeling and data mining. A few examples include the following:
Analytical Customer Relationship Management (CRM) – Direct Marketing (Database) Marketing

• New customers acquisition
• Customers retention
• LTV (Life Term Value)
• Segmentation & segment imputation
• Revenue optimization using up-sell, cross-sell, uplift, next best offer and contact channel selection models

Risk / Fraud

• Insurance (claims & claims cost)
• Loans and credit risk
• Crime and national security
• QA failures and service
• Health and disease risks
• Audit and investigation targeting

Pharmacological, Genetics and Health Research

• Research of secondary effects in drug development to refine the definition of the target population for the new drug
• Prediction of diseases based on genes, lifestyle and environment and medical history
• Prediction of disease rates and epidemics

Time Series Forecasting 
(Models that predict future values based on previously observed values measured at uniform time intervals)

• Sales
• Inventory levels
• Capability demand
• Natural phenomena
• Employees attrition
• Prices
• Other economy related measures

The Most Comprehensive View of Your Organization

The development and integration of powerful hardware and software systems can deliver a true and complete 360 degree view of your organization in virtually “real time” fashion. The usage of predictive analytics will provide invaluable glimpses into the future, assisting your organization in creating both proactive and preventive strategies.
Predictive modeling and data mining methodologies can be applied both to internal organizational data and customer data related to current, former, and potentially future clients.
We can broadly rank data source types by predictive power, from stronger to the weaker as follows:
Behavioral data (consumer behavior): Orders, transactions, payment and usage history, etc.
Interaction data: E-mail/chat transcripts, call center contacts and notes, web click-streams, real-time interactions and social media.
Attitudinal data: Opinions, attitudes and values, preferences, needs & desires, satisfaction.
Descriptive data: Attributes and characteristics (psycho and socio graphics), self-declared information and geo-demographics.

The Integrative Implementation of Predictive Analytics

The seamless integration between IBM SPSS and the IBM BI and data management solutions, such as Cognos and Netezza, deliver the opportunity to capture continuously updating data from a multitude of sources, and analyze that data with one of the richest modeling tool sets in the industry.
IBM SPSS Modeler can read and write directly from and to Cognos data files. It also supports integration with IBM Netezza Analytics, which provides the ability to run advanced analytics on IBM Netezza servers. These features can be accessed through the SPSS Modeler graphical user interface and workflow-oriented development environment, allowing you to run the data mining algorithms directly in the IBM Netezza environment. With Netezza Analytics you can run powerful predictive and data mining algorithms, such as Decision Trees, K-Means, Bayes Net, Naive Bayes, KNN, Divisive Clustering, PCA, Regression Tree and Linear Regression. The IBM SPSS Modeler also can be integrated similarly with IBM InfoSphere Warehouse, Microsoft Analysis Services and Oracle Data Mining.
Using the IBM SPSS and the other IBM technologies, the knowledge created by this process can be continuously deployed at every delivery point in the organization where it improves your decision making, supports strategies and tactics, planning and technology improvements, etc.

The IBM SPSS Toolset

IBM SPSS products cover the whole predictive Modeling cycle and are organized into four families:

• Data Collection: with a feature-rich suite of survey research software, IBM SPSS Data Collection provides a detailed view of customer attitudes and opinions
• Statistics: IBM SPSS Statistics and its modules that include basic to the most advanced and sophisticated procedures.
• Modeling: IBM SPSS Modeler Professional for data mining and IBM SPSS Modeler Premium, which adds the most advanced text analytics capabilities.
• Deployment: providing the platform and the delivery products that bridge analysis and action. They include IBM SPSS Collaboration and Deployment Services and IBM SPSS Decision Management.

The Optimal Portfolio of Services and Products

Ironside has over a decade of experience working with IBM and a team of predictive analytics consultants with more than 30 years of cumulative experience. We offer modular and scalable solutions adapted to your needs, size and complexity, from SOHO and small business to multinational organizations and from consulting and training to full turnkey systems.
 

IBM Netezza: The Importance of Data Distribution for Optimal Performance

Source:http://www.ironsidegroup.com/

August 7, 2012 by Nirav Valia

 

What is Netezza?

Netezza is a dedicated data warehouse appliance that uses a proprietary architecture called Asymmetric Massively Parallel Processing (AMPP) that combines open blade-based servers and disk storage with a proprietary data filtering process using field-programmable gate arrays (FPGAs). Netezza integrates a database, server, and storage, which are all interconnected by a powerful network fabric into a single, easy to manage system that requires minimal set-up and ongoing administration, leading to shorter deployment cycles and faster time to value for business analytics.
The focus of this article is defining how Netezza works and the importance of data distribution in achieving optimal performance in a Netezza system.
Let’s first look at several of the key benefits associated with Netezza systems, which include:

• Speed

 10-100x faster query performance than traditional systems.

• Simple

   Delivers high performance out of the box, with no indexing or tuning required.

• Scalable

Provides predictable and linear scalability based on user data capacity needs.

• Smart

         Embedded In-Database Analytics platform allows integration of its robust set of built-in analytics with leading analytic tools from such vendors as Revolution Analytics, SAS, IBM SPSS®, Fuzzy Logix, and Zementis, on IBM Netezza’s core data warehouse appliances.

Before we discuss the data distribution mechanism, let us first understand how Netezza stores the data on disk. Each Snippet Processor in the Snippet Processing Unit (SPU) has a dedicated hard drive and the data on this drive is called a data slice. Each disk is divided into three partitions: Primary (user data), Mirror, and Temp (intermediate processing data). All the user data and temp space from each primary partition is copied to the mirror partition in another disk, which is called replication. Tables are split across SPUs and data slices and the data is stored in groups according to rows, while data is compressed according to identical column values (columnar compression).
The actual distribution of data across disks is determined by the distribution key listed as part of the table definition. There are two types of distribution methods, Hash and Random. If the DISTRIBUTE ON clause is not specified, the system defaults to using the first column as the Distribution column, using the hash algorithm.
The maximum number of columns that can participate in the distribution key is four. When the system creates records, it assigns them to a logical data slice based on their distribution key value.


The performance of the system is directly related to uniform distribution of user data across all of the data slices in the system. As depicted in the graphic below, the overall response time is impacted by the slowest performing data slice (S-Blade). In the diagram this would be slice 2.

Processing Skew is caused when the distribution key selected is a Boolean value, e.g. True/False or Y/N values. It will distribute data to any two data slices since it will have only two hash values.

A distribution method that distributes data evenly across all data slices is the single most important factor that can influence overall performance. Bad distribution methods and keys can result in uneven distribution of a table across data slices and SPUs (causing skew), causing data to be redistributed or broadcast, which results in bottlenecks and bad performance. As such, it is extremely important to correctly identify the right distribution key and method to ensure effective data distribution.

IBM Cognos and IBM SPSS with Netezza

The combination of IBM Cognos Business Intelligence and IBM SPSS Modeler with a Netezza enterprise data warehousing appliance ensures the fastest distribution of the best information to your entire business; accelerating decision-making for better business results.
 

What’s New in IBM Cognos 10.2

Source:http://www.ironsidegroup.com/

September 10, 2012 by Greg Bonnette

 

IBM Cognos 10.2 Enterprise was announced today, and with it comes increased usability, performance and a host of new features. Below we’ll take a walk through a few of these new features and highlight how they’ll impact your bottom line business.

Cognos Insight 10.2

Cognos Insight, released in Early 2012, is the Personal Analytics solution that has taken the marketplace and user’s desktops by storm, garnering a #1 ranking among all Self Service Business Intelligence Platforms in The Forrester Wave (Q2 2012). Cognos 10.2 continues to improve ease of use, and integration with IBM Cognos Enterprise to encourage collaboration and enterprise distribution while avoiding data silos that other competitive solutions can create. What’s the use of personal insight if the entire organization cannot stand to benefit?

Tree Maps
Customers and Partners alike have clamored for improved data visualizations and IBM has responded in kind. The addition of Tree Maps to Cognos Insight pays homage to the applied data visualization theories of luminaries such as Edward Tufte and Stephen Few and it is a theme that is quite prevalent in Cognos 10.2. Unfortunately, we’ll have to wait for Tree Maps in the enterprise studios as this is a Cognos Insight-Only feature for now.
Smart Metadata
A new data discovery engine in Cognos Insight will automatically detect dimensional levels in hierarchies and even differentiate between numeric attributes and measures. This means users will spend even less time importing data, further shortening the learning curve of this already intuitive platform.

Drill-able, Drag and Drop Charting and Top/Bottom Filtering
Users can now drill up and down directly on charts, just as is possible in Cognos Enterprise, and they can also drag and drop dimensions and measures directly onto charts as opposed to having to retain a crosstab in prior versions. Also included is rapid top/bottom filtering in Crosstabs that is always a simple right click away.

Package Import
Prior to this latest, release users could import data into Cognos Insight from existing IBM Cognos Reports. IBM takes the story of unified metadata one step further allowing users to source data directly from the same ad-hoc reporting packages that are published in the Cognos Enterprise environment. For IT and business alike this means master data management and data governance from the enterprise to the desktop: unified business logic and terminology at every step of the way.
Time Rollups
Cognos Insight now has out-of-box functionality to build custom time dimensions or roll-ups. Populate entire years regardless of how sparse your data may be or customize it to match your organizations fiscal calendar.
High Fidelity Publish
Customers who also have IBM Cognos TM1 Enterprise can publish their Cognos Insight dashboards to Cognos 10.2 Enterprise as full-fledged, tabbed Cognos Workspaces. This means that Cognos Insight users can share their analyses with other Cognos Enterprise users who do not have Cognos Insight capabilities. This is huge from both a customer licensing and a usability perspective as prior releases required that any users who wanted to view an Insight dashboard must have Cognos Insight capabilities, which came at the higher Advanced Business Author licensing level. To view a Cognos Workspace a user need only be at popular Enhanced Consumer license tier. What’s a Cognos Workspace you ask? Read on…

Cognos Workspace (Formerly Business Insight)

It seems a little strange to say we’re glad that IBM Cognos 10.2 delivers fewer “insights”, but the renaming of Business Insight and Business Insight Advanced to Cognos Workspace and Cognos Workspace Advanced respectively, is a welcome change. Hopefully this will help to ease some of the confusion that’s arisen in recent years among a myriad of “insight” labeled components in within the IBM Business Analytics solution portfolio. Along with this re-branding, come a number of useful new features.

Multi Tabbed Workspaces
A major enhancement to Cognos Workspace is the ability to easily create multi-tab workspaces enabling you to expand your workspace’s visual footprint without the need to scroll. With tabbed workspaces also comes a new control known as an Action Button which can be programed to trigger tab changes.
Freeze and Unfreeze Column and Row Headers
Excel users rejoice, they’ve added the functionality to freeze and unfreeze crosstab column and row headers while scrolling.
Data Visualization Guide
As mentioned previously, the theme of applied data visualization theory is a recurring one in Cognos 10.2. The new Visual Recommender in Cognos Workspace will help you to select the appropriate chart type based on the data values in your workspace, along with some insightful rationale for that decision.
Graduated Capabilities
Administrators can now assign graduated capabilities to the Cognos Workspace tool. Users can have the option of Authoring, Interacting or just Consuming any Cognos Workspace. This is useful in controlling the available Cognos Workspace feature set for governance purposes, or to even further simplify the experience to drive adoption in a user population of diverse skill sets.
Other Features
Cognos workspace 10.2 also brings support for printing and use in Google Chrome and Safari.

Cognos Mobile

Cognos Mobile was a major focus in the 10.1 (General) and 10.1.1 (Refresh Pack) releases, with outstanding functionality and support across a broad set of devices. In 10.2 Mobile sees a few minor, but noteworthy updates.
Push Notifications
Cognos Mobile now supports push notifications to the status IOS status bar in the iPad app – users can now be notified when a new version of their report is available, further reducing the latency in decision making on the go.
Improved Performance of Multi-page Reports
Multi-page reports can now be streamed to devices, reducing the loading time. Users no longer have to wait for the entire report to download before viewing.
Administrators Can Now Secure Mobile Access
A new mobile capability in Cognos Administration allows administrators to govern access to Cognos via Mobile devices. This has benefits from a governance perspective for all organizations that deploy Cognos Mobile, especially those that employ a Bring Your Own Device (BYOD) policy.

Report Studio

Report Studio, the work-horse of the IBM Cognos solutions suite, receives a few minor, but high impact updates in the IBM Cognos 10.2 Enterprise release.

Prompt API
The new prompt API is a long overdue and welcome update to Professional Authors and Report Studio hackers everywhere. The API is 100% documented and supported, and like any good API can be expected to persist across future product renditions. The API provides a documented method for setting, reading, deleting and validating prompt values using JavaScript. The API is supported in Report Studio, Cognos Viewer, Cognos Workspace and Cognos Workspace Advanced.
Excel Improvements
Expect improved Excel compatibility of Cognos report outputs with increased spreadsheet maximums to 16,384 columns by 1,048,576 rows. Cognos 10.2 also brings a brand new Excel output format called Excel 2007 Data which is perfect for lightweight data transfer to Excel without any report formatting.

Dynamic Cubes

By far the most exciting new feature in Cognos 10.2 is the addition of a new OLAP technology to the IBM Cognos solution suite which in conjunction with pre-existing OLAP options provides developers with the option of the right tool for the job at hand. The Dynamic Cube solution builds upon existing Dynamic Query Mode (DQM) functionality to close a major gap that exists in many vendor OLAP offerings: the organization with a large or mature star or snowflake schema data warehouse that wants to provide an OLAP experience to their users without sacrificing data details or high performance.

Here are a few highlights of this new feature:

• 100% In-Memory OLAP Engine for a high-performance, low-latency user experience.
• Minimized access to the underlying data warehouse.
• Security aware caching features.
• Aggregate awareness to allow query routing to in-memory or physical aggregate tables in the data warehouse.
• Aggregate Advisor feature will monitor, recommend new physical aggregate tables, and even generate SQL/DDL.
• Uses a new OLAP modeling tool called Cube Designer that should be intuitive to developers with previous OLAP experience.
• Multiple cubes can share conformed dimensions.
• Use of Multi-Pass SQL is easy on data warehouse server resources and DBA’s alike.
• 80 times faster than similarly modeled dimensionally modeled relational (DMR) data sources.

Transformer and PowerCube loyalists (ourselves included) can breathe easy as this new technology is not a signal of their decline. Dynamic cubes are meant to complement existing OLAP technology and provide developers with the most appropriate option for every scenario, rather than trying to force a one-size-fits-all solution. Expect a more in-depth exploration into the Dynamic Cube technology in a future newsletter, as well as the announcement of a new training offering from the Ironside Group.

Architecture and Environment Support

New Data Sources
With every release we see exciting additions to the list of supported environments, and Cognos Enterprise 10.2 is no disappointment. You can expect full support for a number of new data sources, a few of which are listed below:

• Big Data Support: Hadoop / Hive via JDBC Connector
• SQL Server and Analysis Services 2012
• Salesforce.com (Native DQM)
• SAP ECC (R3) (Native DQM)
• Siebel (Native DQM)

The big news here is native support for Big Data and seamless integration with IBM InfoSphere BigInsights (Hadoop). Additionally, native support for a number of ERP vendors has been added to Cognos via Dynamic Query Mode for increased performance and reduced implementation complexity. Prior to 10.2, support for data sources such as Saleforce.com, SAP ECC and Siebel required a connector via Virtual View Manager (VVM).  We should also note that VVM 10.2 will be the final release of this solution, and while it will continue to be supported, developers should take note that it is now deprecated in favor of DQM.
100% 64 Bit
These remaining 32 bit components of IBM Cognos 10 get an update for full 64 bit compatibility: BI Gateway, Metric Studio and Data Manager

Multi-Tenant Support
Cognos 10.2 now supports native multi-tenancy. While this feature may not have mass appeal, it will be incredibly valuable to certain IBM Cognos OEM partners as well as organizations that have deployed Cognos in a federated manner and are looking to more easily segment different business units or sub-organizations within their shared IBM Cognos platform architecture.
Content Archiving
Last but not least, Cognos 10.2 now natively supports fully integrated content archiving to a file system. This feature will be coveted by organizations that have content retention and audit requirements that are looking to keep their Cognos content store free from large report outputs to maximize their environmental performance. This new feature has much of the same archival functionality that IBM FileNet customers have enjoyed since the 10.1.1 release.
 

Cognos Insight, Rapid Prototyping on Your Desktop

Source:http://www.ironsidegroup.com/

April 3, 2012 by John Fehlner

 

Cognos Insight has many potential business uses.  One of these is the ability to rapidly prototype a report or planning scenario by comparing a report from Cognos Connection with an external Excel or CSV file.  This particular example will walk us through the steps to import and compare an Excel budget scenario with Sales data from a Report Studio list report.
The first step is to begin with a new ‘workspace’ in Insight.

Import Cognos Report

Let’s get started with importing data from Cognos Connection.

Note: A simple Report Studio list report works best for the import data feature.

 
For source type, we’ll choose “IBM Cognos Report Data” and navigate to our report location in IBM Cognos Connection.

Preview the data:

Define Mapping: This is perhaps the most important step since we’re essentially setting up the foundation of our multi-dimensional structure which is a TM1 OLAP cube.   At a minimum, we will map and define dimensions, levels and measures.  For this example, our first three columns will become our ‘Dates’ dimension (Year, Quarter, Month as our levels).  The next two columns, ‘Order Method Type’ and ‘City’, will become two single-level dimensions and our Revenue column will be the only measure. Notice that Insight assumes that Year, Quarter and Month will be their own dimensions.  We need to map these as their own levels within a single dimension.  We do this by selecting each column and mapping them accordingly:In this case, we’ll rename ‘Year’ to ‘Dates’ as our dimension name and will set year as level 1, quarter as level 2 and Month as level 3.  The other two dimensions look OK and Revenue has been correctly identified as a measure with a data type of currency. Next, we confirm the import.  Now the TM1 cube will be built based on our mappings.
Next, we’ll re-order our workspace to put Order Method types in the rows and 2007 as our columns of our crosstab.  We’ll also filter on just Revenue as our measure.

Import Excel Budget Scenario

Let’s import our Excel budget file and merge with the current Sales Data cube.  An important point here is that we do not want to simply go to the ‘Get Data’ menu to import the file.  If we do this, a 2^nd cube will be created.  In this case we want to import the Excel budget data into the same cube so we can compare budget to revenue in the same crosstabs and charts.  In this very simple example, we were careful to make sure that our budget file contains the same dimension and level structure as our Sales source report does.
Right click on the cube in the Data explorer and select ‘Import into cube’ (shown below):


The import exercise is similar to before, except this time we will choose a file as our source (Excel in this case).

On the next screen, we will apply an advanced mapping to map our columns to existing dimensions and levels, and bring in a new measure called Budget:


Now we can see our new measure, ‘Budget’ alongside ‘Revenue’ in our crosstab:

Next, we can create a comparison calculation in Cognos Insight.
The comparison calculation uses an expression understood by a TM1 cube which uses a scorecard-style red/yellow/green indicator based on a <90%, 90%-110% and >110% expression.  If we chose to, we could edit this calculation to change the thresholds and see different results.  The expression looks like the one below.