The Dream of a Single Analytics Tool Is Dead

Post: The Dream of a Single Analytics Tool is Dead!

The Dream of a Single Analytics Tool is Dead!


There’s a persistent belief among business owners that an entire firm needs to operate on a single business intelligence tool, be it Cognos Analytics, Tableau, Power BI, Qlik, or anything else. This belief has resulted in billions of dollars lost as firms scramble to force their various departments to move software. The business world is just now waking up to a better solution – combining multiple BI tools into one single space. 


How Many BI tools are in Concurrent Use?


If you were to investigate what the most common and widespread BI tools were across all industries, the answer would almost certainly not be the biggest names in the space. That’s because of one central fact:


Analytics are everywhere. 


Point of Sale systems occupy every retail space in the country. Any firm that has employees has some software that manages payroll. Sales reports are almost universal. All of these constitute examples of BI software, and are far more omnipresent than any relatively sophisticated tool.


With this in mind, it’s easy to see how it’s already the case that multiple BI tools are being used within a single company in every firm in the world. 


While this fact has been recognized for decades, it’s often been seen as a hurdle to be overcome. We raise the question – is this the best framing? 


The Myth


Contrary to the popular belief that the coexistence of multiple BI tools poses some great hurdle to the progress of high quality analytical output, it is in fact the case that there are many ways in which multiple tools being allowed concurrent use comes with numerous serious benefits. 

If you give your disparate departments the liberty to select the best software for their needs, then they can independently home in on the more precise tool for their highly specific needs. For instance, the software that best manages and processes payrolls is unlikely a great tool for managing mass amounts of POS data. While both of these things fall under the umbrella of BI, they’re fundamentally different tasks.



This is a simple example, but you can find many other cases across departments and industries. Analytics is a highly complex undertaking, and different types of data demand different types of treatment. Allowing your employees to find the best fit for their needs is likely to result in a better outcome, both in terms of quality and efficiency of analysis.


In other words, you’re never going to find a single piece of software that can handle all of the idiosyncratic, multifaceted needs your company has. 


If it Ain’t Broke…


For many businesses, the status quo (using multiple different analytical platforms) is already working great. Trying to push everybody onto one service is a misguided attempt to streamline analytics and bring greater efficiency.


For an analogy, let’s imagine a company operating in an office that has some unfortunate quirks. The floor plan is a little awkward, the air conditioner is sometimes overzealous, and there’s no pedestrian covering between parking and the building’s entrance, meaning sometimes you have to walk in the rain.


In an effort to make things easier for all the employees, the leadership decides to move spaces to somewhere nearby. The new office is the same size, and it isn’t cheaper. The only impetus to move is to remedy some of the annoyances that the employees have, annoyances that may present a legitimate drain on productivity.


This move will cost tens of thousands of dollars and weeks to months of time, not to mention the more immediate loss in output during and immediately after the move. Additionally, the new space will almost certainly come with its own quirks and annoyances that over the years will start to seem more and more annoying, especially considering the cost of having moved. 


If the company had just employed some measures to make their old space work a little better, then all this wasted time and money could have been avoided. 


That’s essentially the case here. Various actors in the BI space are working to make the current, slightly awkward situation better, rather than continuing to inflict costly and questionably worthwhile attempts to move onto one single analytics tool. 

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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.