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Comprehensive Solutions for Qlik

Deliver the Power of Qlik Faster

Master Your Qlik Cleanup

Managing Qlik assets is crucial to uncovering the insights necessary for decision-making. Through asset management, teams identify clutter, deliver a thorough inventory of assets, including usage patterns, lifecycle stage, and value. With this insight, teams prevent redundancy, reduce costs, and quickly resolve issues to uncover the insights teams care about.

Better Qlik Development with DevOps

Implementing DevOps for Qlik revamps your analytics development by creating a repeatable, reliable release process that supports consistent updates and minimizes errors. Automating essential tasks—such as testing, deployment, and versioning—frees your team to focus on delivering high quality insights. This approach not only builds quality into every stage of development but supports continuous improvement, system accuracy, and effectiveness, which leads to a more productive Qlik experience.

Multiple Tools, One Location

Enhance the understanding of your analytics by consolidating Qlik and other tools into a single, unified portal. An analytics catalog makes you aware of the assets available within your organization. It addresses the challenge of managing diverse analytics platforms by allowing you to search, bookmark, and receive tailored recommendations for your most frequently used reports. By streamlining navigation with visual cues and tips, this approach helps you make more informed decisions and fosters better collaboration across platforms.

Quick Qlik Cloud

Transition your Qlik analytics to the cloud with solutions that streamline the process and reduce costs. Focusing on customer needs, our approach prioritizes savings in infrastructure and administrative time. Moving to Qlik’s cloud platform means lower infrastructure costs, minimized manual tasks, and seamless access to Qlik’s advanced analytics and AI features. The shift is driven by a focus on efficiency, with a structured process simplifying migration from client-managed environments to a cloud-first model. Our approach ensures a smooth transition, enabling businesses to harness Qlik’s cloud capabilities while staying future-ready entirely.
Deliver the Power of Qlik Faster
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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.