On June 30^th, RWS Tridion and its partner, Content Bloom, held a webinar highlighting how their combined solution can deliver content insights and help enterprises optimize and drive max ROI from their respective content. Pankaj Gaur, Director at Content Bloom, shared insights about their solution, Content Intelligence Platform, and the way it solves some of the most pressing content challenges. He also demonstrated the Content Intelligence Platform in action using data from one of their actual past big implementations, of course masking the confidential information.

On June 30^th, RWS Tridion and its partner, Content Bloom, held a webinar highlighting how their combined solution can deliver content insights and help enterprises optimize and drive max ROI from their respective content. Pankaj Gaur, Director at Content Bloom, shared insights about their solution, Content Intelligence Platform, and the way it solves some of the most pressing content challenges. He also demonstrated the Content Intelligence Platform in action using data from one of their actual past big implementations, of course masking the confidential information.

Pankaj emphasized the fact that data by itself is disconnected and doesn’t add much value. To derive maximum value, it needs to go through a transformation process. The process starts with assigning particular attributes to the data to distinguish them from each other and turn them into information. Connecting different attributes and parts of information transforms information into knowledge. Further, going deeper into the knowledge based on experience and expertise leads to insights. And when this process is repeated, the senses turn into wisdom.

Undoubtedly, each enterprise, big or small today, is facing a content challenge and is incurring significant costs in managing it. The real issue lies in deriving maximum benefit from it, despite investing heavily. Pankaj summed this up nicely, “Maintaining content is easy; maintaining content efficiently is much harder”.

Reason being the fact that there are several stakeholders and hence content creators, e.g., CIO, CEO, CMO, customer experience managers, content head, and so on, located across the globe in different locations/countries, using different languages. This leads to simultaneous content creation that may overlap (duplication) and, even worse, contradict each other.

Several reasons lead to such disconnect, including poor internal control, rigid and cumbersome reporting, and no single high-level view of existing content. This leads to human resource inefficiency and a higher cost of IT infrastructure and management. As a result, enterprises keep investing in content. Still, they cannot generate any sizeable return on it due to the ripple effect of content inefficiency on all functions and departments.

Example: Ripple effect of content inefficiency on Localization:

Content Bloom designed a Content Intelligence Platform to overcome such challenges. It is a ‘scalable’ and ‘flexible’ tool that extracts content information from Tridion CMS and derives insights for all types of stakeholders.

 This Content Intelligence Platform comes with a relatively simple and effective logical architecture, which starts with the CMS and an analytics engine (Google analytics or any other renowned engine). The data extracted is then processed, where the Content Intelligence Platform turns the data into insights. The insights are then converted into visuals customized per the needs of various stakeholders. It uses popular visualization tools such as PowerBI and Tableau. Access to the visualization is subject to role-based access control; thus, only authorized users to have access.  The visual reports during the demo were shown utilizing Microsoft Power BI (however, any other visualization tool like Tableau, QlikSense, etc., can be used). The setup with the Microsoft Power BI visualization tool enables the distribution of the reports among various stakeholders either on the web through Power BI Service or can be distributed in the organization’s Microsoft Teams or can be embedded into custom applications/websites – and all accesses are governed through role-based access control.

Data module refresh requirements may vary depending on the specific needs of the organizations and Pankaj clarified that the Content Intelligence Platform is flexible and can perform the refresh in real-time or in standard fixed intervals (daily, weekly, monthly, etc.). Processing the data for the first time would require a few hours or may take a day if there are millions of components – Content Bloom tested with around half a million components, and it took around 4 hours. The data model refresh frequency typically depends on the frequency and the volume of data changes being made and can be customized accordingly.

Pankaj then took everyone through a demo to illustrate how the Content Intelligence Platform derives insights on localization as a content performance indicator. He started with the localization example, which covered a high-level view of localization:

The localization summary view above splits the content into % localized, % native, and % inherited (used as is) for each country. The analytics engine displays the number of page views and average time on page, measured in seconds. The Content Intelligence Platform can also pull out any other attribute of choice from the analytics engine. In the map above, one can click on any particular country to display details of that specific country (the bottom graph splits the content into content types and displays total content within each type and percent localization. Further, the view can also be filtered by year.

The summary report is translated into insights that display the top 5 languages with most and least localization and similarly top 5 content types with most and least localization. It helps the content team pinpoint the location where the localization effort has been more or less.

Similarly, he shares examples of content trend analysis which measures the amount of content created and then split into published, localized, and native. It allows the user/stakeholder to apply filters on this by year, content type, and country (flexible to add more filters if needed) to compare and contrast trends and ultimately derive insights. E.g., if the chart displays that 90% of the content was not published, it could mean that a bulk of that content was linked or dependent content, which is perfectly fine. However, if it was the case that the bulk of it was junk content and hence not published, then it’s an issue that needs to be resolved. Further, the trend also helps in budgeting or planning the number of resources that the content team would need.

Pankaj also demonstrated a few other reports such as content publishing trend, content aging trend and content country comparison views that use similar filters and pinpoint changes over time, whether by country, content type, or year. One of the exciting and key features is that one can right-click on any part of the chart and fetch details. E.g., if the user wants to fetch details on unpublished content to understand why the content was not published, then the user can do that by right-clicking on the unpublished part of the pie chart and clicking on ‘details’ from the list that shows up. That will display all the details such as content ID, country, content type, local copy, localized or not, and age (in days). The user can thus scroll through it and arrive at a decision or probe further with the content team to drive efficiency.

Pankaj clarified that all these demonstrated analytics are current state analyses that are diagnostic and to an extent, descriptive. Pankaj mentioned that the Content Intelligence Platform might develop a more extensive process wherein it can perform predictive or prescriptive analysis.

To sum up, working in tandem with Tridion, Content Bloom helps the decision makers get a complete view of the content and pinpoint the areas they need to focus on to drive efficiency and derive maximum value.