The Business Intelligence Pyramid: A Chaotic Introduction

Not All Business Intelligence is the Same

Today, were hitting on a topic that I've spent much of my career working on: Business Intelligence. Sidenote: I actually started in Machine Learning/Data Science but then "step backed" to BI–thats because I wanted more action in my solutions.

But back to Business Intelligence: it's easy to be drawn to the latest and most advanced technologies. They promise real-time insights, boast sleek interfaces, and often come with a hefty price tag.

While these tools might be impressive, they aren't always the best fit for every business need. As experienced leaders understand, the most expensive or newest solution isn't necessarily the most effective.

The reality is–and seasoned executives will back this us–what glitters isn't always gold. Behind the glossy exteriors and impressive demos, there's a deeper story, one that often gets overshadowed by the razzle-dazzle of these "high-end" technologies.

It's the story of the countless hours spent on integration, the steep learning curves, and the potential for analysis paralysis amidst a sea of data points and visualizations.

While these tools promise comprehensive insights, they often cater to a niche audience within an organization–the tech-savvy few. The broader organization, from the frontline salesperson to the mid-level manager, might be alienated–unable to derive actionable insights from these sophisticated platforms.

This isn't to downplay the importance or capabilities of top-tier BI tools. They have their place, especially in complex scenarios requiring deep dives into data. However, it's crucial to understand that they represent just one part of a broader BI ecosystem. An ecosystem that, when visualized, resembles a pyramid. But more on that later.

For now, it's essential to recognize that while the top of the BI pyramid might be alluring, it's the base that often holds the most value. It's where the majority of the organization lives and operates. And as we'll explore, ensuring that this base is strong, accessible, and effective is the key to a truly data-driven organization.

The Appeal of Specialized BI Applications:

In boardrooms across Fortune 500 companies, presentation matters. A well-crafted, interactive dashboard or PowerPoint goes a long way. It's the difference between a nod of approval and a glazed-over look.

Execs are also unaware of the time and effort it takes to cater solutions to their small group in the organization–and while insights are important for the top-level to keep visibility to the success of the organization–you can't have an organization that's only focused on providing insights to a few people (albeit an essential group to have data-driven).

For some technologies its the promise of being able to search and find whatever you want in a few seconds–or real-time insights (that are rarely real-time).

Or it makes AI somehow easier, when we all know its a bunch of Data Scientists and Engineers crunching the data behind the scenes.

While the top-tier BI tools offer many advanced features and capabilities, they come with their own challenges that can sometimes overshadow their benefits, especially when viewed from a broader organizational perspective.

Business Intelligence as a Pyramid

I think the best way to think about the BI category is by visualizing a pyramid, where each tier represents a distinct level of BI maturity and capability. From the foundational tools that form the bedrock of data-driven decision-making to the ornate details of advanced analytics at the apex, this pyramid serves as a roadmap for organizations. It guides them in strategically investing in BI tools, ensuring that each step taken is on solid ground, building towards a cohesive, comprehensive, and effective BI strategy.

Base Layer: Foundational Tools

At the foundation of our BI pyramid, we have tools catering to businesses' immediate and essential needs. These tools are often the first touchpoint for employees interacting with data. They're designed for simplicity and accessibility, ensuring that a large portion of the organization, from interns to senior managers, can utilize them for their daily tasks.

Ad Hoc Analysis: Quick data extraction for immediate questions. It's the agility tool for on-the-spot decisions, impacting immediate tactical decisions.

Spreadsheets: The go-to for raw data exploration. Their universality ensures data is accessible and malleable.

Presentations: Transforming data into decision-making narratives, influencing strategic and operational decisions.

These tools are ubiquitous and often the first point of contact for employees with data. Their widespread use means that any inefficiencies or inaccuracies here can have ripple effects throughout the organization.

I would invest in training programs to ensure that employees use these technologies that can solve their problems. I'd focus on business-unit-specific challenges and scaling each team based on every challenge.

Building: Small-Group Collaboration

The need for collaborative, scalable technologies emerges as organizations mature in their data journey. This layer serves teams or departments that require a collective approach to data. These tools are designed for a smaller subset of the organization, typically specialized teams or departments, ensuring that insights are shared, and strategies are aligned.

Traditional BI: Data becomes a shared asset, driving collective strategies and bridging individual insights to form a collective view.

These are specialized tools that require significant investment. The challenge is ensuring that they are used to their full potential and that they are delivering ROI.

Regularly review and assess the usage of these tools. If certain tools or dashboards are unused, it might be time to retire or revamp them. Feedback loops with end-users can provide insights into how these tools can be improved.

You invested in the technology, so make sure you invest in people on a 60/40 split between technology and people.

At this level, these tools are crucial for team-based initiatives. But you will run into the same issue everyone else does in data silos–where different teams have different versions of the "truth."

So, you’ll also need to implement a centralized data repository or data lake where all data can be stored and accessed. This ensures that the underlying data remains consistent even as teams use their tools.

Moving Up: Tailored Data Products

This layer represents a more refined approach to BI. Tailored to specific business needs, these tools cater to specialized teams or entire departments. They're designed for a more limited audience within the organization but have a profound impact, driving strategic initiatives and providing a deeper dive into data.

Dashboards/Embedded BI: You might spend more time developing dashboards through an SDLC process. This is because these dashboards will be a vehicle for the people who are not fluent in building your business intelligence technology. Honestly, the tech doesn’t matter for the people you are supporting with the dashboards–just good onboarding to the dashboards.

If you don’t train them, they will never take to them.

Formal Data Products: Custom solutions–including ML interfaces and scenario plans–that turn data into actionable insights, driving strategic initiatives. I love these technologies–they are where I started, but you shouldn’t be investing in ML-specific BI. Leverage your existing ecosystem–or at least select a technology that can do more than just sit on top of a model.

Top: Advanced Technologies

At the top of the BI Pyramid is a host of technologies that support small use cases–it’s not to say they aren’t important, but the reality is they are likely serving a small portion of your audience.

Applications: I’ve seen a wave of effort shift to application-centric solutions. The idea is to build an application for your customer (internal or external), which is the product. I struggle with this as an option for most businesses. Why are you going to invest in building an application when many of the needs of the business are going to be agile. Building with BI tech should always be faster and cheaper than building full software.

Chat BI: First, Thoughtspot was first to be somewhat successful in this space, but the advent of LLMs like ChatGPT are now helping all organizations move to this natural language search for insights. But once again, getting this right requires significant data, which requires significant engineering to tailor the data appropriately, which after the data is tailored requires more engineering.

While all that effort is being had, you could invest in more collaborative solutions down the pyramid.

Self-service Solutions: I’ve already chatted about my thoughts on self-service–its almost impossible. But if you do it, you have to be all-in as an organization.

These are the most advanced tools, and while they offer a lot of power, they also come with complexity. The risk is that they become white elephants: expensive tools that are underutilized.

Before investing in these tools, I'd conduct pilot programs to assess their real-world utility. Additionally, I'd work closely with IT to ensure that the infrastructure supports these tools, especially in terms of data processing speeds and storage.

Challenges with the Top

If you are ever interested in the top of the pyramid, just remember these challenges:

1. Complexity Over Clarity: These tools can sometimes introduce unnecessary complexity with their myriad of features. Sifting through layers of data visualizations and analytics can be time-consuming for a data executive, potentially delaying crucial decisions. The very depth that makes these tools appealing can sometimes become a hindrance, especially when quick, clear insights are needed.

2. Integration Challenges: Top-tier tools often promise seamless integration with various data sources. However, the reality can be quite different. Integration challenges can lead to data silos, where valuable information is trapped in one part of the business, inaccessible to other departments that might benefit from it.

3. Scalability Concerns: As businesses grow and evolve, so do their data needs. A tool that works perfectly for a business at one stage might become a bottleneck as data volumes increase. Scalability is a concern, especially for Fortune 500 companies that deal with vast amounts of data.

4. Over-reliance: There's a risk of becoming overly reliant on these advanced tools, sidelining simpler, more accessible BI solutions that might be more suitable for certain tasks. This over-reliance can lead to inefficiencies and missed opportunities.

5. Invest in people and processes, too: You can’t just build something and expect adoption, you’ve got to build around existing ways of working and make sure people are trained and leaders are emulating the behaviors you want to see.

Challenges at the Bottom

1. Integration with Existing Systems: One of the primary challenges with foundational BI tools, especially in a large organization like ours with legacy systems, is ensuring they integrate seamlessly. This requires robust middleware solutions and APIs that can pull data from diverse sources and present it cohesively. In our experience, ensuring new BI tools integrate without causing disruptions has been crucial. Middleware solutions act as the bridge, ensuring data flows seamlessly across systems.

2. Cost-Effectiveness: While foundational BI tools might seem basic, they can come with significant costs, especially when implemented at scale. Every dollar spent needs to bring value. It's essential to conduct a thorough ROI analysis before investing. We've also found value in considering open-source or cloud-based solutions, which offer more flexibility at a reduced cost.

3. Ensuring Maximum Value from Investments: It's not enough to just have the tools; they need to drive action. Regularly assessing the effectiveness of BI tools, collecting feedback from users, and monitoring usage patterns ensures that the insights derived are actionable and driving decision-making. In our operations, focusing on actionable insights has been pivotal in deriving maximum value from our investments.

4. Customization and Flexibility: A one-size-fits-all approach doesn't work in a diverse operation like ours. Foundational BI tools need to cater to the varied needs of a large organization. This means they should be customizable, allowing for specific modules or features to be added based on departmental needs. Our BI tools have been tailored to ensure every department, from marketing to supply chain, gets the insights they need.

5. Data Security and Compliance: With increased access to data, ensuring its security is paramount. This involves not just robust access controls but also ensuring compliance with global data protection regulations. Our stakeholders trust us with their data, and ensuring its security while staying compliant with global regulations is not just a priority; it's a commitment.

Regardless of where you put effort: THERE WILL BE CHALLENGES

Key Takeaways

If You're Leading the Charge:

• Embrace the Entire Pyramid: Don't just focus on the top-tier tools. Recognize the value of foundational and intermediate BI tools. They're the bedrock of your data-driven culture.

• Prioritize Training: Ensure your team is well-versed with tools at every pyramid level. A well-trained team can extract maximum value from BI investments.

• Promote Cross-Functional Collaboration: Encourage departments to share insights and data. This breaks down silos and ensures a cohesive data-driven approach across the organization.

If You're Trying to Influence Leadership:

• Highlight ROI: When discussing BI tools, especially those at the pyramid's base, emphasize their cost-effectiveness and broad impact on the organization.

• Integrate Data Insights: Weave data-driven insights from various BI tools into your presentations and reports. This subtly underscores their value.

• Engage in Constructive Dialogue: After presenting data insights or proposing a new BI tool, seek feedback. Understand leadership's concerns and priorities, and tailor your approach accordingly.