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BI Fabric: how to rationalize your BI tools while doubling adoption

Gartner claims 40% of their customers have more than 1 BI tool in their organization, and Forrester claims there is an average of 5 BI tools in enterprise-sized companies.

In their BI Fabric research, Forrester advises to rationalize and consolidate analytical tools to between 3 and 5 before integrating them in a new type of BI technology to simplify the management of these BI platforms. This new type of vendor, called BI Fabric or Analytics Catalog vendors, promises to provide visibility onto Tool Usage vs Cost for BI cost optimization, centralized access for business users who aren’t able to find insights easily, stronger governance, and the ability to use existing pre-approved assets in an effort to reduce duplication.

Executive Summary

Although the promise of BI Fabric tools is attractive as it is, Data Leaders should be particularly aware of one of the principal problems of why there are so many BI tools in an organization in the first place: a lack of differentiation amongst BI platforms. Data Leaders should use BI Fabric tools that also incorporate a component of Data Communication to not only rationalize their BI tools but also reap longer-term benefits.



  1. The presence of numerous BI tools in an organization is explained by undifferentiated offerings, a weak positioning, a lack of focus on whom the customer is, and a shared result of low analytics adoption amongst untrained business users.
  2. For longer-term rationalization and adoption results, Data Leaders should look towards BI Fabric vendors that better their data communication strategy by drastically improving the User Experience.
  3. Simplifying the way data is communicated will impact adoption rates, rationalization, consolidation, and ultimately, value.



Weak Positioning in BI

When BI became mainstream amongst data analysts, the messaging of the platforms evolved to address a significantly larger market segment: untrained business users. The platforms’ products, however, didn’t evolve in that direction. If you were to put a modern BI tool in the hands of a non-technical user, They probably won’t understand much and will drop out very quickly because they will be overwhelmed by the amount of information, technicalities, and the sheer amount of choice. Consequently, they would need to find the time to get trained (and we know how much time is precious nowadays) and guided to deploy dashboards that will serve them well. OMG 🤯 it seems already quite draining.


The need to gain data literacy skills in order to use these platforms is not only a strong sign that these products are still too complex, but it’s also a major obstacle to the wide adoption of analytics. And even if untrained users were only served ready-to-be-consumed dashboards, the user experience is still far from the flawless, easy-to-use, intuitive, guided user experience they’re used to from the apps they use on a regular basis to manage their private lives.


For example, mobility in BI is still a matter of discussion and needs deployment consideration and time. Also, the loading time of visualizations often takes 10 seconds because of the large volumes of data ingested; thereafter degrading the user experience and leading to abandoning rates of BI tools.


It is also not uncommon to hear about customers limiting their dashboards to 3 to 4 KPIs for their business users in order to limit complexity. However, if attaining a data culture at scale is the customer’s goal, their workforce will need to be comfortable looking at more metrics to be better informed, independently of their data literacy level and without the issue of complexity taking place.




Figure 1: Building a dashboard may have been facilitated, but the resulting customer-facing product hasn’t evolved at all.


When we look at the picture this is quite obvious, that to meet the desire for aggressive growth, modern and traditional BI tools must adapt their products (As expected, many has led to a halt in the adoption of analytics at 32% (Gartner)and/or create new ones with a more tailored front-end user experience to match this untrained audience.

  • The market talks about Augmented Analytics* and the automated delivery of insights through Natural Language Generation/Processing. However, a picture is worth a thousand words, this is why NLG (Text & Voice) won’t replace the delivery of insights on a graphical visual format. If the graphical delivery of insights as we know it today and called “dashboards” is not working for business users.
  • Vendors also know how much progress and work is required to get a flawless AI and human/machine interaction for NLP/G to work.


Hence, if BI solutions are not being widely adopted effortlessly, why haven’t vendors reviewed the way data has been delivered graphically in these last decades?


Augmented analytics* is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.  





BI vendors have been on a constant quest to answer every need, tick every box in an RFP (Request for proposal) and do the necessary to win deals. This has pushed vendors to deploy new capabilities, front, and back-end, every month or quarter, to satisfy everyone with a single product, rather than a specific segment in the market.


Whereas today, the only question you’ll ever need to ask is “does your tool do” to which you’ll get an instant “Yes”.


What are the consequences of “talking to everyone vendor strategy?

  • This has led to similar capabilities, generally undifferentiated offerings, and a lack of focus on who their product is made for.
  • Plus as these tools were made with data analysts in mind, they’re highly adopted amongst them which explains why there so many passionate communities built around these vendors.

Results? These products share the result of low adoption amongst untrained business users.



Today the only question you’ll ever need to ask is “does your tool do” to which you’ll get an instant “Yes”, and on another hand is my BI tool stands out in a compelling and significant way as being for untrained business users?



Of course, we’re many to hear about how an organization was able to win its bet against change management and put analytics in the hands of everyone. When I worked in sales, I would sometimes face a team that would showcase a lot of pride telling me about their award-winning BI project. And they should be proud, after all the investment in time, effort, experts, resources workshops, and professional services they invested in the tool.


But this is exactly the problem.




Analytics for untrained business users are considered a technology-agnostic mission, since all vendors generally do the same thing, notably when it comes to the front-end experience. So aside from prospects wanting the latest capabilities, wanting to AI-everything, and having a long list of “can you” questions, if being a do-everything solution is seen as the more attractive offering, vendors will not invest in the effort in adopting a different positioning or deliver information in a drastically different way, compared to their competition. Furthermore, speculation related to vendors opening up their ecosystem to other vendors has been circulating, however, we don’t think it will happen because of the vendors’ wish to maintain a homogenous environment for their users, and ultimately, higher revenue.


In short, the proposal of BI Fabric vendors to centralize BI tools, decommission unused licenses, and offer easier access to users is compelling and creates value. However, they must also further address the outdated methods of graphical data delivery by offering an enhanced User Experience to untrained end-users. Despite BI Fabric vendors highlighting an enhanced UX, progress is still required:




Figure 2: Despite rationalizing BI tools, simplicity is key for the adoption of BI Fabric Dashboards


To sum up, today’s BI tools are not that different from an untrained business user’s point of view. By wanting to do everything, BI vendors failed to gain a strong positioning, meaning that the choice between vendors will most likely depend on how an analyst was brought up into analytics or whether the client already uses a specific type of environment or not.


This is different from choosing a platform based on how well it delivers value to untrained business users. The weakness of positioning, the desire to be do-everything products, and the lack of consistent results amongst untrained business users that helps them stand out in a compelling way is one of the strongest reasons why there are so many BI tools in a single company.

A focus on Data Communication

The complexity and user-unfriendliness of the UX in BI tools is common feedback: dashboards don’t deliver a clear message, seeing several KPIs at the same time is overwhelming, and the user experience needs greater simplicity.

The business users don’t plan on exploring their own data on a regular basis but do require access to the everyday metrics they need to perform well.


A typical usage would be a daily check-up, lasting a few minutes, to understand what actions should be taken in the day. Then, if they should have questions requiring more in-depth analysis, they would rather ask a specialized resource to look into it.




Figure 3: A stronger positioning.


By focusing solely on the communication aspect of data to untrained business users, strong design and technological choices can be made in a product from the very beginning. Similarly to popular consumer sites such as and – which offer business owners the ability to set up their own pixel-perfect website by leveraging a built-in UX without the need to code or design – a BI Fabric Vendor that integrates an aspect of Data Communication in their product would also integrate a built-in User Experience that packages the best practices of Analytics Design and Data Storytelling for the sake of expert-free deployment and high adoption.


But what is storytelling? It’s a relatively new variant of delivering data visually that involves adding context to insights in order to deliver a clear message to wide audiences. However, this capability hasn’t entered the everyday life of business users yet and it’s used on exceptional occasions; when a discovery in the data’s been made and an analyst wishes to present a Show-then-Throw PowerPoint-like presentation to their audience.


In a nutshell,

  • By taking the contextualization aspect of storytelling and integrating it in a built-in narrative environment, data communication allows the industrialization and standardization of a new way of delivering information with graphics, and more user-friendly experience for business users.
  • By building-in a guided framework, data translators are also able to benefit from a stronger tool positioning by deploying faster thanks to built-in design best practices.


For example, pie charts should not be allowed in a communication context since such a chart type doesn’t communicate a message clearly. Similarly, more than 2 KPIs should not be allowed on a single screen, because it’s been measured as being overwhelming for untrained users.




Figure 4: Industrialized Storytelling vs Show-Then-Throw Storytelling


The built-in environment used to communicate data is positioned differently than the unlimited flexibility provided by BI vendors. As previously mentioned, Modern BI tools were built to allow the exploration of unknown datasets to find insights.


To use a data communication platform, it must be acknowledged that it will be used to communicate validated and regularly needed metrics to a large audience asking for simplicity, guidance, and autonomy, whilst data experts are liberated of the manual tasks of designing a user-friendly dashboard, now taken care of by the platform.




In short, BI Fabric vendors can offer more value to their customers by providing a data communication aspect in their product – designed to be clear and simple.


The decision to incorporate a built-in narrative experience, a contextualized environment, a built-in collaboration environment, and select charts in a dynamic and interoperable platform helps replace the everyday complex dashboards with a more modern and fluid tool that maximizes existing data investments.


But [Disclaimer🔥] it doesn’t overlap with existing platforms at all, it just delivers clear insights graphically at scale with no training required for end-users, so no reason to worry.


By choosing to focus on business users, BI Fabric vendors become a reference point for all customer-facing analytics without replacing exploration tools, but to rationalize BI tools and the licenses to only data experts; improving governance, centralization, cost reduction, ROI, and adoption.


They will be able to position themselves as the platform for untrained business users and fix the underlying issue behind why there are so many BI tools in a single company. So it’s a win-win situation.



Figure 5: BI Fabric with Data Communication offers a clearer user experience for business users

The advantages of BI Fabric vendors who focus on data communication

BI Fabric tools, as previously mentioned, is meant to consolidate a group of tools that are unused …. However, they fail to provide a drastic new way of consuming analytics for the untrained business user through a more guided, clearer format than traditional dashboards.


The issue with untrained users is not only a lack of accessibility only, but a lack of team alignment due to numerous tools, consistent comprehension, and a clear user experience. And although BI Fabric vendors may market a new and easier UX, there is still room for progress.



Figure 6: The IA vs Power BI, the difference is not compelling.



Figure 7: Toucan vs Power BI: single vs multiple charts, contextual environment vs non-existent, structured vs unstructured user experience





Figure 8: BI Fabric vendors focusing on Exploration or Communication


BI Fabric vendors offer a compelling and valuable offering to AD&D professionals looking to better manage the portfolio of BI tools and provide their business users with easier access to analytics.


However, by not revisiting the way data has been communicated for years towards untrained users, the value delivered by BI Fabric vendors will limit itself to a short term financial ROI related to cost optimization, decommission of unused licenses, and rationalization of tools.


By adding a data communication component to their platform with standardized practices, BI Fabric can occupy an even stronger positioning and deliver bigger value by easing the deployment and adoption of more user-friendly analytics.


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