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Enhance development with guided governance, automated version control, and robust deployment options. Boost productivity by tracking changes, reducing errors, and adhering to company standards.
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Optimize DevOps processes for accurate analytics with zero-touch control, automated testing, and flexible deployments. Streamline workflows and boost traceability, all within the Qlik UI.
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Accelerate Qlik development by automating quality checks and early issue detection. Reduce manual testing, maintain accuracy, address performance issues, and support design best practices.
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Ease development by integrating powerful tools directly into the Qlik UI. Capabilities like visual comparisons, change management, and dynamic QVD links make workflows more productive.
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Qlik users and developers often struggle with onboarding and mastering complex features. Guided Tour removes these barriers by delivering interactive, step-by-step explanations directly within Qlik, making data exploration intuitive and seamless.
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Simplify Cognos projects and upgrades by reducing risks with change tracking, daily testing, and scalable deployments. Zero-touch versioning and easy rollback streamline analytics delivery.
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Enhance Cognos management with quick access to system content and mass updates. Support tasks like searching, replacing, and updating information, saving time on administrative work.
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Optimize Cognos by tracking user interactions and system performance to pinpoint issues, prevent problems during upgrades, and receive real-time alerts for quick resolution using actual system data.
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Integrate Cognos with your security provider to eliminate in-house coding, ensure up-to-date security, support SSO with SAML tokens, and manage updates seamlessly.
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Facilitate smooth Cognos authentication transitions by mapping users and consolidating sources. Preserve security IDs, minimize disruptions, and reduce downtime during namespace changes.
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Stay on top of the latest reports by managing and organizing all your analytics in one portal. Get personalized updates, track usage, avoid duplicates, and boost engagement effortlessly.
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Understand asset usage and complexity to sharpen BI decision-making. Evaluate performance, remove clutter, and revitalize resources, alleviating wasted time and costs.
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As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
Not all reports and dashboards fail the same; some reports may lag, definitions might change, or data accuracy and relevance could wane. Understanding these variations aids in better risk anticipation.

Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

It’s time for the company’s monthly business review. The marketing department proceeds to report on leads acquired per salesperson. Unfortunately, half the team has left the organization, and the data fails to load accurately. While this is an inconvenience for the marketing group, it isn’t detrimental to the business. However, a failure in financial reporting for a human resource consulting firm with 1000s contractors that contains critical and complex calculations about sickness, fees, hours, etc, has major implications and needs to be managed differently.

Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
Reports and dashboards are crafted to deliver valuable insights for stakeholders. Over time, though, the worth of assets changes.
When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.