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Upgrade Factory

Upgrade Cognos Analytics

With over 20 years of experience, our global team can help you design your Cognos Analytics upgrade project and draw up the plan. 

During execution, we:

  • Analyze your current Cognos environment
  • Remediations
  • Narrow the scope
  • Migrate
  • Test and validate
  • Go live
Our software technology enables us to reduce costs and time up to 50% compared to manual process. Looking for other professional services?
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Benefits

A revolutionary way to keep up with each and every new Cognos version and align your business with IBM’s common release strategy.

Say goodbye to that unsupported version of Cognos, stop avoiding that upgrade, and say hello to The Upgrade Factory!

  • Compete on analytics
  • Upgrade quickly & efficiently
  • Utilize latest capabilities & features
  • Minimize lagging & legacy systems
  • Eliminate stressful tasks & projects
  • Perform day-to-day tasks

Customer Success Stories

Case Studies

Don’t just take our word for it. Read about our clients and how Motio has helped them improve their analytics platforms and save valuable time and money.

See All Case Studies
Financial ServicesMotioCI

MotioCI

The team at CoBank relies on Cognos for its operational reporting and main...

HealthcarePersona IQ

Persona IQ Securely Migrates HealthPort’s Cognos Authentication

Since 2006, HealthPort has made heavy...

Financial ServicesSoterre

Baker Tilly Trusts Soterre with Audits

Financial Firm Built on Trust Baker Tilly is a leading advisory, tax, and assurance...

MotioCIMedia Entertainment

Telus Increases Speed and Efficiency of Cognos Releases

TELUS, a leading national telecommunications company in Canada...

Testimonials

Automating Our Manual Testing Sped Up Our Project Time By 91%

“We’re way more confident in keeping up with Cognos upgrades with Motio. Going to (new CA versions) will take us weeks instead of months.”
Missy Price
BI Analyst, State of North Dakota

Using Motio Dropped Our Upgrade Time By 60%

“Upgrading Cognos used to take six months and was a painful process but implementing Motio’s outlined agile approach made the turnaround time 2 months, and kept us within budget.
Ashish Smart
Enterprise Architect, Orlando Utilities Commission

Inventory Dashboard Info Sheet

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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.