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Analytics Asset Management

Corporations invest heavily in their analytics, from software licenses and platforms to hardware, personnel, and data. The process is not easy, and costs are high. Data lies in several locations and formats and has quality issues. Security is key, and data needs to be protected.

The result is worthwhile: dashboards, analysis, and reports (DAR) deliver great value after adoption, but over time, key aspects change. Organizations have processes in place to create and maintain these assets but do not apply key principles of asset management that are common for financial and other assets. Analytics teams have much to gain from managing their analytics assets.

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Motio’s Holistic Approach
Successful business intelligence outcomes rely on having the correct assets when they are needed. Motio’s Analytics Asset Management is the “secret” that keeps the necessary reports, dashboards, and analysis at your fingertips to spur your data-driven endeavors. Use of Motio’s Analytics Asset Management provides:
Comprehensive Asset Inventory
  • Gain a complete understanding of your existing assets
  • Identify, organize, and track your assets, ensuring nothing gets overlooked
Detailed Assessments
  • Understand the complexity and usage of objects, reports, and dashboards
  • Provides insight into assets that are strategic or critical
  • Mitigate the risk of BI projects
  • Starting point for scoping your project
Identified Design & Maintenance Challenges
  • Uncover underlying design or maintenance challenges that may hinder your analytics assets’ performance
  • Address challenges that lead to improved efficiency and accuracy in your BI processes
Valuable Insights For Projects
  • Discover change impacts and evaluate risk to resource estimations and testing strategies
  • Equip your team with the knowledge necessary to execute successful projects
Integrated Analytics Asset Management Dashboard
  • Centralized view of your analytics assets, giving you full control and visibility.
  • Stay organized, monitor performance, and make informed decisions effortlessly
Ready to streamline and automate your BI & analytics platforms?
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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.