MotioCI for Cognos Analytics

Post: MotioCI 3.2.8 – The Latest Release

MotioCI 3.2.8 is live, and we will be giving you a run down of the newest benefits to you- the end user!

Multi-page HTML has been added as an output type for testing. With this, MotioCI can better approximate how users consume reports — one page at a time. Reports can now be tested more accurately and at greater scale using HTML output. Just like you were able to compare PDF outputs side-by-side, you now can also compare the multi-page HTML format side by side to spot differences more quickly. In classic MotioCI fashion, errors will be flagged and highlighted, and you can see on what page they occur so you don’t have to dig for discrepancies.

The Assertion Studio now provides even more tools in your testing tool belt. We’ve added several string manipulation steps as well as numerous quality of life enhancements to make Assertion Studio more flexible and more productive than ever.

With your security in mind, SSL encryption is now enabled by default, and configuration is easier than ever.

The ability to assign users as project contacts has been added. It was designed to help improve workflows and communication throughout the team. Now for all of your projects, you can select an employee as the designated project manager so all questions go through them. All users working on the report will see who to direct their questions to.

Another new capability in MotioCI 3.2.8 is that uploaded files and data sets can now be promoted.

Sometimes a data modeler will need to verify the impact of changes in the model to existing reports.  Now, you can look across all packages for a data item name to see what reports use it and what its definition is. Ex. if the spelling changes, or if you remove an underscore, you can see the reports affected. This can also help ensure consistency in naming standards across all objects by allowing authors to do QA and scan published packages for inconsistencies of spelling.

We hosted a webinar all around compliance data testing. It shared a modern approach to data compliance testing to ensure sensitive data stays secure. The examples used in the webinar were PII and PHI. You can replay it at any time by clicking the button below:

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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.