Post: Analytics Catalogs – A Rising Star in the Analytics Ecosystem


As a Chief Technology Officer (CTO), I am always on the lookout for emerging technologies that transform the way we approach analytics. One such technology that caught my attention over the last few years and holds immense promise is the Analytics Catalog. This cutting-edge tool might not directly touch or manage data sources, but its potential impact on the analytics ecosystem cannot be underestimated. In this blog post, I will explore why Analytics Catalogs are becoming increasingly important in the realm of data analytics and how they can revolutionize our organization’s approach to data-driven decision-making.

The Rise of Analytics Catalogs

The proliferation of data in today’s digital landscape is staggering. Organizations are collecting vast amounts of data from various sources, leading to an explosion in data complexity and diversity. This deluge of data presents both an opportunity and a challenge for data-driven organizations. To extract valuable insights efficiently, it is crucial to have a seamless analytics workflow that enables data professionals to discover, access, and collaborate on analytics assets with ease. This is where the Analytics Catalog comes into play.

Understanding Analytics Catalogs

An Analytics Catalog is a specialized platform designed explicitly for managing and organizing analytics-related assets, such as reports, dashboards, stories…e.g. think about anything with pretty visualizations to paginated reports. Unlike traditional data catalogs that focus on managing raw data assets, the Analytics Catalog centers on the analytical layer of the Business Intelligence stack. It acts as a centralized repository of insights, making it a powerful knowledge hub for the entire analytics team and end consumers. One such player in this space is Digital Hive which Motio helped shape in its early days.

The Importance of Analytics Catalogs

1. **Enhanced Collaboration and Knowledge Sharing**: In a data-driven organization, insights gained from analytics are only valuable when shared and acted upon. Analytics Catalogs enable better collaboration among data analysts, data scientists, and business users. By providing a shared platform to discover, document, and discuss analytical assets, the Catalog encourages knowledge sharing and cross-functional teamwork.

2. **Accelerated Analytics Asset Discovery**: As the volume of analytical assets grows, the ability to find relevant resources quickly becomes paramount. Analytics Catalogs empower users with advanced search capabilities, intelligent tagging, raking, AI, and categorization, significantly reducing the time and effort spent on asset discovery. Analysts can now focus on deriving insights rather than hunting for the right data.

3. **Improved Governance and Compliance**: With the increasing focus on governance and compliance, an Analytics Catalog plays a pivotal role in ensuring the security and privacy of sensitive data via visualizations. Too often the focus is placed on Data Governance without thoughts of Analytics Governance (could reference By maintaining and creating asset metadata, permissions, and leveraging the user community the Catalog helps in adhering to governance policies and regulatory requirements.

4. **Optimized Resource Utilization**: Organizations have multiple analytics tools and platforms in their tech stack (25% of organizations use 10 or more BI platforms, 61% of organizations use four or more, and 86% of organizations use two or more – according to Forrester). An Analytics Catalog can integrate with these tools, allowing users to discover and access analytics assets across various BI / analytics platforms seamlessly including SharePoint, Box, OneDrive, Google Drive and more. This integration reduces duplication and optimizes resource utilization, leading to cost savings and improved efficiency.

5. **Holistic View of the Analytics Ecosystem**: By serving as a centralized hub of analytical insights, the Analytics Catalog provides a comprehensive view of the organization’s analytics ecosystem. This visibility aids in identifying analytical redundancies, gaps in analytics coverage, and opportunities for process improvement and resource utilization.


As the analytics landscape continues to evolve, the role of Analytics Catalogs as an emerging technology is set to become increasingly important. By facilitating collaboration, streamlining asset discovery, helping to ensure governance, and providing a holistic view of the analytics ecosystem, an Analytics Catalog acts as a catalyst for data-driven decision-making. Digital Hive is at the leading edge as a pure Analytics Catalog. I call out “pure” as its differentiators are:

  1. Not touching, storing or replicating data
  2. Not replicating or redefining security
  3. Providing a Unified Dashboard with Unified filtering allowing pieces of analytics assets to be assembled into a single asset vs recreation.

These are key points for easy adoption, lower cost of ownership and simply not ending up with yet another BI Platform to manage.

As the CTO and a long-time member of the Analytics community I am excited about the transformative potential of Analytics Catalogs, and I believe that embracing this technology will enable companies to stay ahead of the curve in the fast-paced world of analytics that we all love.

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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.