How to Successfully Implement Cognos Self-Service

Editor's Notes

• #3: The first question we usually get is “Can I get a copy of the presentation?” Absolutely! It’s available on Senturus.com. Select the Resources tab and then Resources Library. Or you can click the link that was just posted in the GoToWebinar Control panel. Be sure to bookmark the resource library. It has tons of valuable content addressing a wide variety of business analytics topics.
• #4: Joining us today is…..Pedro Ining Pedro joined Senturus in 2010 and brings over 20 years of BI and data warehousing experience to his role. He has been instrumental in implementing data warehousing systems from scratch and has experienced the evolution of the BI industry through several iterations of BI products including Cognos, MicroStrategy and Tableau.
• #5: Today’s agenda. After a brief introduction of the speakers, we’ll get right into the topic. Stay on after the presentation when we will We’ll then share useful links, provide a quick overview of Senturus and let you know of upcoming events. We’ll wrap with a live Q&A.
• #10: Install and build it; we’re done Make the data available Install the BI Tool Eliminates the need for IT Complex data still needs to be modeled The BI tool is the doorway to access IT controlled data A modern self-service BI tool will be a successful project Your foundational data components need to be strong User’s will automatically understand how to use the tool Change Management and user training are critical
• #12: Continued data governance of existing data warehouses Data warehouses will not go away simply because users can now do more with other sources of data. They will rely on the ‘curation’ of key enterprise data and IT governance will be a critical component of continue data quality. In fact self-service creates other governance/MDM Issues. Senturus has done another webinar on this topic – “Why bother with Data Governance suggest you look at this Requirements gathering techniques that go beyond the typical ‘what data do you need’ type of questions Requirements gathering will need to transform to more of a discovery process. Being able to get more insights from your meetings with users will allow you to create better self-service environments. Semantic Metadata layer that does not overburden users Efforts should be made to make semantic layer easier for users to use. User should not be overburdened with the responsibility of creating commonly used metrics, views, hierarchies etc. Simply serving up the tables and raw metrics to users will eventually cause users to declare that the tool is too hard to use and give up. Semantic Layer that can be modified and augmented with user local data Users will always have a need to integrate other data sources into their analytics. Exposing and training users to these new BI capabilities will allow them to exploit the BI tool to its maximum potential. BI Capability training Training that satisfy the needs of the different types of users is critical. A blanket training approach may not satisfy the needs of certain groups. Collaborating with your users and determining what type of training will allow users to become more comfortable with the BI system. Eventually, consumers may graduate to become producers as their capabilities increase. Continual Performance tuning and Usage Analytics Proactive IT system administration of the BI environment is critical to ensure a stable high performing system for the users. IT needs to have the ability to monitor utilization of all BI components and artifacts. Who is using the system and who is not using the system and why. It should have the ability to determine costly long-running queries so that they do not interrupt the work from other users. All Areas are not suited to self-service Finally, we must realize that there is a place for good old IT developed reports and dashboards. Many times professionally produced and data quality monitored report development is required. Reporting for regulatory and compliance purposes is one example.
• #13: Gathering user requirements are an inherently difficult task. As data and BI professionals, we typically start requirement sessions by asking some questions like: What kind of reports do you need? Do you have samples of the reports? What data do you need, from where? What are the measures you will need? What are the hierarchies you will need? Do you need dashboards? These are all important questions that usually leads a BI professional into a more comfortable discussion of tables, fields, and reports. These types of questions basically stem from the base question of ‘What do you want or what do you want me to do for you?” and as we may have all discovered the following principle ‘Users don’t know or realize what they want from an analytics system’ which in turn generates several iterations of report building where the version 1 MVP product meets 50% of their needs and requires additional fine tuning. So how do we move beyond this basic rut of requirements gathering. We can start by investigating a bit more of their key goals, workflow and issues they are currently experiencing. This will influence our design of a self-service environment.
• #14: This area should be discussed independent of a BI tool’s features. Ultimately, we need to determine what goals our user needs to accomplish. Often times an end user’s goals are in lock step with their job and job responsibilities. For example, within HR different levels of the organization would like to see analytics of AEP FTE Headcount. A discussion that is independent of the BI tool’s Features A user’s goal is often in lock step with their job responsibilities C-Level executive want to see a current snapshot of FTE headcount and how it compares to last year. What areas are growing the most and what areas are contracting. Director level management would like to analyze their current workforce and see existing exempt vs. non-exempt mix. Maybe discover what parts of their organization are experiencing the most attrition. Staffing managers would like to see the company’s location or organization which is experiencing the most growth. They need to plan and forecast future hiring plans and need data to help with this exercise.
• #15: The user’s workflow usually starts with some sort of triggering event, an event that requires them to seek out more information and ends with an accomplished task. We must analyze this workflow and find out the details of the user’s decision-making process. Interacting with a predefined report or a dashboard can be part of that flow. What is the ‘trigger’ that causes the user to seek out more information? Is it a scheduled regulatory filing, a metric on a dashboard or report data that suddenly goes beyond a threshold? Does the user review a report or dashboard on a daily, weekly, or monthly schedule? What are the steps in the current workflow that contribute to a user’s decision making? Which steps cause the most friction and could maybe be consolidated or eliminated. Is the current process too reliant on canned reports, could self-service analytics help with answering follow-up questions?
• #16: Discussing a user’s current workflow will naturally lead into a user’s analytics problem with that workflow. Document these issues as they will lead to design requirements in a self-service environment. Some typical issues that you may find include: User’s don’t trust the data – Find out what the data quality issues are. Are they actual data issues or maybe simple report logic errors? It takes a long time to get the answer – This could be a simple performance issue that can easily be resolved through some database tuning. It could also be the result of a more complicated end-user ‘data prep’ activity that could maybe be moved upstream into the BI tool. I can’t do a simple YTD in Cognos – Although this example is very specific, it points to a general problem in the semantic layer. Most likely the semantic metadata layer was only built to just simply present data. Calculations like YTD were left to the end-user to create. Embedding commonly used calculations in the semantic layer will allow users to concentrate on their analytics and not on creating commonly used calcs. Addressing user issues with the current analytic environment will get users excited about the benefits of self-service BI in Cognos. Many times, users will blame the entire BI tool as being too hard to use when the actual problem is the design of the semantic layer.
• #17: It is time now to consolidate your learnings in a type of user story. The story describes the journey a user needs to go through to accomplish their goals. The story would describe as many possible scenarios that they may encounter.
• #20: Dashboards The fact that Cognos still can’t compete with some of the visualization tools like Tableau/Power BI because of the portability and graphics. That is why we developed our Analytics Connector and Report Insights products Reporting Cognos’s key strengths. Tableau is great visualizations but lousy report writing tool
• #21: IT gathers report requirements IT Develops the Framework manager model IT publishes Packages from the Framework Model IT creates standard reports from the package Users may create their own ad-hoc analysis reports from the package This model could still be an option especially for use cases where the concept of self-service is limited to just running IT developed reports. Usually, the semantic FM Model layer was developed for use by report developers. Semantics layers that are built using this paradigm are typically not well-suited for use by self-service users. The is approach could, however, still enhance a standard FM Package with self-service enhancements like pre-built calculations, better organization, or more specific package subsets of the original FM Model.
• #22: A Cognos 11.1 twist to the IT Driven FM Package model, is a model that leverages the use of Cognos 11 Data Modules as a replacement for FM Packages. IT would still control the development of such a model, however, IT developers would now be able to leverage to new functionality enhancements that are part Cognos 11 Data Modules. Some key Data Module benefits include: Use of Relative Time modeling (YTD, MTD, PY MTD, etc.) Measure Binning & Data Grouping Built-In Data Cleansing Built-In Hierarchical Navigations paths Column Dependencies Integration of User Excel and CSV Files
• #23: This model allows end-users to develop their own semantic layers. Many newer generation BI tools – Tableau, MS PowerBI, Qlik – promote this model as a way to provide high flexibility and speed. This model does not guarantee data consistency as end-users may model common database tables differently. It may eventually drive ‘multiple versions of the truth’ and is harder to maintain. But it does allow end-users to utilize the spreadsheet data that they have carefully curated through the years in an enterprise BI tool. The Cognos Analytics 11.1 platform allows for end-users to build their own semantics layer through the use of Data Modules. IT maintains the source databases or data warehouses Users utilize Cognos self-service modeling components to model the database for their specific requirements. Users may extract subsets of data from source databases via Cognos ‘Data Sets’ technology. These Data Sets are then integrated into Data Modules Users may upload their own privately maintained curated data that could be in the form of spreadsheets or text files This model gives the end-users the most flexibility. It generally would be used by ‘Power’ users that have gone through specific training and have done enough work with the tool understand the benefits. If the user will be modeling directly against a database, these users typically are already familiar with the tables and relationships. At AEP there are many users who already perform direct queries against OLTP database like STORMS. They could be ideal candidates for such a model. The use of user uploaded files can provide users instant ability to analyze their own curated data in Cognos. These uploaded files could – in some examples – be the only data that the user may need to model. The one drawback of this model could be issues with data consistency
• #24: This model leverages the IT centrally maintained semantic layer but allows users to integrate their own data and enhance the existing IT metadata. In this scenario, only part of the semantic layer is built by IT and the rest could be built by end users. Professional Cognos developers can also build Hybrid models to satisfy specific requirements by end-users such as the loading of their own spreadsheet data. For large legacy Cognos organizations this model is a very appealing choice. Over the years many FM packages have been developed and they typically are very stable and have the data integrity blessing of the central IT organization. Because of the flexibility that Data Modules provide there can be very many permutations of this model. The diagram below shows some common use cases that could be used In the Hybrid Model, we integrate IT built FM Packages into the mix FM Packages can be used to create Data Sets which can then be used inside a Data Module You can directly access FM Package tables inside a Data Module but you will need to monitor any performance issues with this technique. This is why subject specific Data Sets from packages may be a better choice.
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