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Post: Why is Excel the #1 Analytics Tool?

It’s Cheap and Easy. The Microsoft Excel spreadsheet software is probably already installed on the business user’s computer. And many users today have been exposed to Microsoft Office software since high school or even earlier. This knee-jerk response as to why Excel is the leading analytics tool may not be the right answer. The real answer may surprise you.

To dive deeper into the answer to the question, let’s first look at what we mean by analytics tool.

Analytics and Business Intelligence Platforms

The industry-leading analyst, Gartner, defines Analytics and Business Intelligence Platforms as tools that enable less technical users to “model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). ABI platforms may optionally include the ability to create, modify, or enrich a semantic model, including business rules.” With the recent growth of AI, Gartner recognizes that augmented analytics is shifting the target audience to consumers and decision-makers from the traditional analyst.

For Excel to be considered an analytics tool, it should share the same capabilities.

CapabilityExcelABI Platforms
Less technical usersYesYes
Model dataYesYes
Analyze dataYesYes
Explore dataYesYes
Share dataNoYes
Manage dataNoYes
CollaborateNoYes
Share findingsYesYes
Managed by ITNoYes
Augmented by AIYesYes

So, while Excel has many of the same capabilities as leading ABI platforms, it is missing some key functions. Likely because of this, Gartner does not include Excel in the list of major players in Analytics and BI tools. Furthermore, it also sits in a different space and is positioned differently by Microsoft in its own lineup. Power BI is in Gartner’s lineup and has the features missing by Excel, namely, the ability to share, collaborate, and be managed by IT.

The key value of Excel is its downfall

Interestingly, the real value of ABI tools and why Excel is so ubiquitous is the same: it is not managed by IT. Users like the freedom to explore data and bring it to their desktops without the interference of the IT Department. Excel excels at this. Meanwhile, it is the IT team’s responsibility and mission to bring order to chaos and apply governance, security, and overall maintenance to all software under their supervision. Excel fails this.

This is the conundrum. It is imperative that the organization maintain control over the governance of software that its employees use and the data that they access. We’ve written about the challenge of feral systems before. Excel is the proto-feral IT system with no corporate governance or control. The importance of a single, well-managed version of the truth should be obvious. With spreadsheet farms everyone creates their own business rules and standards. It can’t even really be called a standard if it’s a one-off. There is no single version of the truth.

Without a single agreed-upon version of the truth it makes it difficult to make decisions. Further, it opens the organization to liability and makes it more difficult to defend a potential audit.

Excel’s price-to-value ratio

I initially thought that one of the reasons that Excel was often called the number one analytics tool was because it was so inexpensive. I think I can say that literally every company I’ve worked for has provided me with a license for Microsoft Office, which includes Excel. So, for me, it has often been free. Even when the company didn’t provide a corporate license, I opted to purchase my own Microsoft 365 license. It’s not free, but price had to be a contributing factor.

My starting hypothesis was that Excel must be significantly less expensive than the other ABI platforms. I dug into it and discovered that it wasn’t as cheap as I thought. Some of the ABI platforms which Gartner evaluates may actually be less expensive per seat for large organizations. I selected a few of the softwares and asked ChatGPT to help me compare and rank them in terms of cost for different sized organizations.

What I found was that Excel was not the least expensive option for any sized organization. It comes with a cost. Obviously, it is difficult to obtain exact pricing and there are often substantial discounts offered to migrate to a specific vendor. I do think, however, that the relative rankings will be consistent. What we notice is that Microsoft Office Suite of which Excel is a component is not the cheapest option. Surprise.

Excel is missing key components of an enterprise class ABI and there are more cost-effective alternatives in the world of analytic tools. Big hit to the Excel price-to-value ratio.

Collaboration

Collaboration using software for data Analytics and Business Intelligence within large organizations offers benefits that can significantly enhance decision-making processes, operational efficiency, and strategic planning. Collaboration recognizes that no individual contributor is an island and the wisdom of crowds can provide better insight and decisions. Organizations value collaboration so highly that they are willing to pay a premium over tools like Excel that do not provide the feature.

Tools that promote collaboration among team members provide:

  • Enhanced Decision Making
  • Increased Efficiency
  • Improved Data Quality and Consistency
  • Scalability and Flexibility
  • Knowledge Sharing and Innovation
  • Cost Savings
  • Enhanced Security and Compliance
  • Data integrity
  • Empowered Employees

The value of using software for data analysis and BI that provide collaboration within large organizations lies in the synergy of enhanced decision-making capabilities, operational efficiencies, and a culture of innovation and empowerment. Tools that don’t provide collaboration promote islands of information and silos of data. Excel lacks this key feature.

The business value of Excel is decreasing

Excel may be the most used data tool within organizations but for all the wrong reasons. Besides, the reasons we think we use it — because it’s cheap and easy — are becoming less and less true as enterprise analytics and BI tools become more affordable and integrate AI to assist with more complex tasks.

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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.