Turning Business Intelligence into Action and Outcomes

Business intelligence (BI) is the unity of data management, department-level strategy, reporting, and visualization. For the end-user, BI most often takes the form of dashboards, reports, and creative data visualizations that allow for at-a-glance monitoring of the metrics that chart a company’s business and operational health. The right BI tools — when implemented correctly and built on top of a stable data warehouse — are the link between past performance and future success. 

One level down, BI is the backbone of a well-built data governance system, integrating data from marketing platforms, CRMs, product, and sales and applying a uniform level of aggregation and pre-processing. BI analysts working on the data team or embedded in other departments can use that uniformity to configure a set of visualizations that build trust, increase efficiency, and improve both employee and customer experience across the organization. 

These two elements define the core function of BI: not just to present metrics or generate visuals, but to activate data. Any one form of data visualization or monitoring — from traditional charts and old-fashioned reports up to interactive three-dimensional graphics and explorable custom displays — is just a technique in the BI analyst’s toolbox. What defines an effective BI strategy is that a broad range of decision makers throughout a company, from team leads up to executive officers, should have tools available that guide their immediate decision making in ways that are seamless, reliable, and effective. And while selecting the right BI tool is an essential element of an effective BI strategy, your plan to activate data must also emphasize collaboration and adaptability. 

In this article we’ll share how the best BI systems are designed, built, and run, exploring why these tools are so vital to the long-term performance of all businesses.

Table of Contents

Why Should You Care About Data Visualization?

Business intelligence (BI) is the unity of data management, department-level strategy, reporting, and visualization. For the end-user, BI most often takes the form of dashboards, reports, and creative data visualizations that allow for at-a-glance monitoring of the metrics that chart a company’s business and operational health. The right BI tools — when implemented correctly and built on top of a stable data warehouse — are the link between past performance and future success. 

One level down, BI is the backbone of a well-built data governance system, integrating data from marketing platforms, CRMs, product, and sales and applying a uniform level of aggregation and pre-processing. BI analysts working on the data team or embedded in other departments can use that uniformity to configure a set of visualizations that build trust, increase efficiency, and improve both employee and customer experience across the organization. 

These two elements define the core function of BI: not just to present metrics or generate visuals, but to activate data. Any one form of data visualization or monitoring — from traditional charts and old-fashioned reports up to interactive three-dimensional graphics and explorable custom displays — is just a technique in the BI analyst’s toolbox. What defines an effective BI strategy is that a broad range of decision makers throughout a company, from team leads up to executive officers, should have tools available that guide their immediate decision making in ways that are seamless, reliable, and effective. And while selecting the right BI tool is an essential element of an effective BI strategy, your plan to activate data must also emphasize collaboration and adaptability. 

In this article we’ll share how the best BI systems are designed, built, and run, exploring why these tools are so vital to the long-term performance of all businesses.

When to Use BI Tools

Despite their essential nature and pedigree as foundational elements of strategic planning, business intelligence systems are not always the right tool for the job. In particular, they are ill-suited to answer certain kinds of one-off, time-sensitive questions, especially those about individual products or particular user experiences. Those answers and insights are more readily sourced from product analytics tools (also called behavioral analytics tools) like Amplitude, Heap, and Mixpanel. It’s worth spending a minute to clarify the difference, as their functions overlap and can interfere with one another if not managed correctly. 

In our experience, BI differs from product analytics in several  key ways, ranging from use cases to data sources and ideal end users. 

Business-level vs. product-level insights - Product analytics tools tend to specialize in answering very specific questions centered on user experience within an app, digital product, or website. While BI tools can also return product level insights, these systems really shine by  letting you track organization-level metrics, including sales performance, inventory, and revenue. BI tools offer almost unlimited possibilities for data analysis, as long as you base your questions on clean, accurate, consistent data.

Multiple sources of data vs. a single data source - Accordingly, the two types of analysis leverage very different datasets. Product analytics solutions typically draw information from a single source, one that tracks activity in the domain of a particular product, platform, or service. When you need to track metrics across multiple sources  — for instance, when comparing various sales channels or matching marketing attribution from your app store data with Google Ads data — it’s time to fire up your BI software. 

Code-based and technical vs. GUI-based and non-technical - Although the market for BI applications is actively shifting to allow for greater accessibility by end-users without data science experience, this remains a core difference. BI dashboards, visualizations, reports, and other displays are almost always built and configured by BI developers or data analysts. By contrast, product analytics solutions are typically usable by anyone at the company right out of the box, especially members of product teams who are responsible for increasing customer acquisition, conversion and retention.

Overviews and monitoring vs. time-stamped user actions - A fourth difference between BI and product analytics combines scale and subject. When a company sets up Tableau, Sigma, or Looker, common goals include tracking metrics over time, monitoring past performance, generating comparisons YoY or quarter-to-quarter, and contextualizing the most recent data against previous benchmarks. Product analytics tools, meanwhile, tend to answer granular questions about UX, user behavior, and product functionality using fine-grained time-stamped behavioral data. 

Historical analysis vs. projections and predictions - A closely related difference is that where BI tends to compare past and present indicators of performance, many applications of product analytics are focused on prediction and projections of future user behavior. Another way to say this is that BI often deals with outcomes, whereas product analytics zooms in on individual steps of the customer journey. 

Flexible data surfacing vs. topic-specific questions - Ultimately, a strong BI system can address any question one might ask of a product analytics solution. The difference is in speed and use-case. Business intelligence incorporates data from a range of different tools to surface insights through display, reporting, monitoring, and visualization. Product analytics tools tend to offer just one kind of answer dealing with narrow topics, and often have restricted options for reporting or visualization. A tool designed to track customers’ engagement with your app-based sales funnel can answer extremely fine-grained questions about those particular behaviors, but can’t contextualize them by referencing sales, marketing data, lead generation, or other key pieces of information. 

Perennial questions for longer-term, consistent decision-making vs. ad hoc questions - Finally, the most fundamental difference is that BI deals with perennial, repeating questions. If a query or investigation can be automated and given flexible parameters, BI is the right analytical approach. If questions arise in the moment and need to be answered only once, standard product analytics tools are better-suited to your needs (though even then, be sure to monitor the results of these ad hoc requests).

A diagram contrasting ideal use cases for Behavioral / Product Analytics tools vs BI (Business Intelligence) tools. Behavioral / Product Analytics tools are well-suited for exploring data from a single source, such as user event data aggregated within a CDP. Business Intelligence platforms are better for evaluating well-defined business questions that draw from multiple data sources, such as app store downloads, ad networks, lifecycle marketing data, CRM data, and more.
When to use a Product Analytics tool vs a Business Intelligence tool. Behavioral / Product Analytics tools are well-suited for exploring data from a single source, such as user event data aggregated within a CDP. Business Intelligence platforms are better for evaluating well-defined business questions that draw from multiple data sources.

Ultimately, many companies would benefit from investing in both BI and product analytics tools. As we’ve shown above, different types of questions require different approaches. By combining BI and product analytics tools, organizations can unite a top down perspective with a bottom up approach. See this article for more details.

Elements of an Effective BI Strategy

The technical elements of effective BI are varied, with each solution ideally being tailored to the size, nature, organizational culture, and IT capabilities of each company. A set of core principles and best practices nonetheless underpin every truly effective BI system or strategy. 

Data Governance 

Data governance, management, and democratization are the beating heart of good business intelligence systems. Many data governance practices become mandatory for any company expanding its BI capabilities, starting with the creation of a centralized data warehouse for all relevant data sources, creating a single source of truth on which individual displays and platforms can then be built. 

A second concern has to do with data granularity and aggregation. Much of the power of BI tools is derived from their ability to produce insights across data sources, and that integration requires thoughtful design choices. The most important of these is where calculations are actually carried out. If all data processing precedes delivery to the BI system, end users have no flexibility. If data is passed on to the BI tools fully disaggregated, it will take too long to process user queries. 

This aspect of data governance has its own name: the “semantic layer” of pre-processing and standardization that should run underneath all BI tools used by an organization. If two departments need to visualize distinct but overlapping metrics, a system is needed that allows them to reference the same underlying data.

Defining Metrics, Terms, and Measurements

An effective semantic layer has a second, follow-on benefit in addition to data accuracy: it prevents sprawl and redundancy in the BI tools used by different departments. When teams have too much control over how they answer questions or track KPIs, they can frequently arrive at contradictory answers by referencing different subsets of the relevant data or defining the KPI itself in different ways. 

Setting up data governance is therefore part of the broader process of defining key terms. One of the most important actions a business can take before any BI project, is to perform a detailed walkthrough of important concepts, fields, metrics, and actions. For example:

  • How are registrations calculated? 
  • When and how do we check for paid purchases? 
  • What time period will we reference for benchmark calculation?

Addressing these questions in advance — and getting client buy-in for important decisions — means that the project leader can be sure the tools under development will meet immediate business needs. 

Managing Roles and Responsibilities

A final set of considerations has to do with ownership of the BI tech stack. For some companies, this is best treated as a centralized responsibility, with all business intelligence functions handled by a separate IT department. In most of these cases, the IT department will then establish a center for excellence or reporting team to handle ongoing development, maintenance, updates, and other BI-related demands. 

Other companies need a decentralized approach that spreads expertise across departments and key teams. In these cases, a BI analyst or small BI team will work within each of several groups—digital marketing, sales, operations, etc. 

It’s best to have a clear sense of how your BI system will operate at the personnel level before you start putting technologies in place. As the process moves from conceptualization toward hands-on-keyboards coding and eventually rollout, there are three key personnel roles to fill if you hope for the process to go smoothly. 

The first is a project leader, typically either the company’s lead analyst or a senior consultant. Regardless of title, they are an indispensable source of big-picture thinking; it is their responsibility to identify the business requirements and informational demands for the new BI system. 

From the project leader’s goals and broad outlining, implementation then progresses to the planning of concrete elements, typically including iteratively developed wireframes and building out a model dataset. These processes are handled by an internal BI engineer or a BI consultant working closely with your team. 

The final team member is a BI developer who will work alongside the data team to create the individual BI interfaces and elements that the new system will rely on. For each new dashboard or visualization, they identify a source table or other component of the data warehouse and build a suitable display on top.

What Companies Get Wrong About BI

In the vast majority of cases, the most common challenges companies face with BI involve asking the wrong questions. 

Most businesses have BI systems in place already, or at least BI-adjacent technologies and strategies. Frequently, however, they are set up to track only outcome metrics, sometimes also called “vanity metrics.” These numbers look good up on a screen, but they’re disconnected from the company’s underlying health and future performance. Even traditional baseline measures, such as gross revenue, can sometimes be vanity metrics. 

This point is vital. Above, we mentioned that BI tends to look back and to assess outcomes whereas behavioral or product analytics is often concerned with forecasting future results. It’s important to acknowledge this contrast, because not all outcomes are created equal. The hallmark of a poorly thought-out BI strategy is reliance on surfacing the same metrics and statistics that a company previously used to measure their success. Good BI needs to zero in on insights that could impact a company’s overarching business goals, which demands customization and updating as markets shift and the organization pivots in response.

A Tableau dashboard of sales performance created using fake data for demonstration purposes only. Within the dashboard linked from this image, one can explore sales performance by region, state, category, segment, and more.
An example of the types of dashboards you can make with the right BI tool. This dashboard of sales performance was created using fake data in Tableau for demonstration purposes only. Explore this interactive dashboard in greater detail here.

Using business intelligence well means moving away from tracking these kinds of vanity outputs and toward a BI system that lets them stay deeply connected to their input metrics, the numbers that index the company’s most important levers for growth. The data displayed by effective BI tools must be actionable to the person viewing it; new dashboards, reports, or visualizations should be created to allow for decision-making in particular contexts rather than for the sake of the display.

A related issue has to do with context: keeping tabs on total sales or available inventory is useful only when the relevant benchmarks or comparisons are available on the same screen. This mistake has more to do with design than data governance or BI strategy, and is grounded in the same core idea: that data-based insights are only as valuable as they are actionable. 

Two other common pitfalls arise from failures to plan ahead. Too many companies invest in shallow BI solutions without a plan for the shared data models that underlie medium- and long-term scalability. Putting in that work ahead of time ultimately saves both time and resources. 

Much the same can be said of adequately preparing staff, especially non-technical users. Many companies make the mistake of expecting employees working across a range of departments to rapidly onboard and start utilizing new BI tools to their fullest possible extent. Expectations like these are rarely met, and often lead to wasted time, frustration, and employee dissatisfaction. Rolling out a BI system successfully requires thoughtfulness about the distinct end-user profiles involved, and catering your onboarding and training to diverse sets of skills. 

Ultimately, every BI should focus on the specific business question that needs to be answered, and how a new data visualization will change peoples’ behavior to address that question. The key here is to strike the right balance between building a dashboard that doesn’t go deep enough, and trying to build multiple dashboards with the intention of covering every possible situation. We can help you find the right balance. 

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Implementing Actionable BI Solutions

For companies moving into the BI technology space, the implementation and adoption process has its own set of hurdles. For companies seeking to revise, update, or improve existing BI systems, many of those same hurdles apply, and a ground-up rework can often be the best approach. Yet even if you’re confident with your data governance policies, you’re sensitive to all the different roles that will be impacted by a new BI solution, and you have a deep understanding of the key business questions you want to answer, how do you get started?

The complexity and scale of the factors involved in big-picture BI strategy mean that it is typically most efficient to undertake a systematic assessment of your priorities, data sources and infrastructure, and the potential impact of various BI solutions. To best address companies’ goals for business intelligence and data visualization, Mammoth Growth developed a unique Analytics Roadmap program. The objectives of this 6-week project are a complete audit of a company’s tech stack and data infrastructure leading to a roadmap for improvements to their BI strategies. Our Analytics Roadmap program allows Mammoth Growth to develop a deeper understanding of a company’s existing data governance, reporting, and visualization methods and pain points.

When approaching Business Intelligence projects, Mammoth Growth follows these steps within our Analytics Roadmap project:

  • Good BI begins with strong data governance. Verify the company's data governance plan, and how they manage customer data throughout their tech stack.
  • Clarify how they currently manage their customer data, analyze audience insights, and generate automated messaging strategies.
  • Combine these findings with insights into how they use other tools in their tech stack.
  • Understand how customer data flows through their systems to improve data integrity, as well as the timeliness and relevance of their messaging.
  • Prioritize reporting, visualization, monitoring, and exploration gaps that could deliver major results with improvements. 

The outcome of this process is a roadmap for the client’s business intelligence strategy: how to execute it, what results they’re aiming for, and what benchmarks define success. 

Armed with that roadmap, companies can move into the implementation process itself, working within the guardrails of best practices for change management:

  • In the initial stages, be transparent about your goals for the program, and get buy-in from stakeholders in leadership roles.
  • Engage all end-users in the conversation. Show them what new Business Intelligence tools can do to alter and improve their workflows and day-to-day experience on the job, and aim to encourage their adoption of whichever tool you select.
  • Provide training resources to everyone expected to interact with the new system, from executives to individual contributors. Every end user should feel comfortable experimenting with the tool and analyzing company data.
  • Employ good data governance practices so your data is easy to use and accessible to all.

This final step, as discussed above, is absolutely vital to the long-term stability and efficacy of your new BI infrastructure and tools. 

For every company, there’s a unique combination of business intelligence strategy, technologies, and process improvements that can increase data trust and coordination while allowing for more consistent, targeted decision making. Here at Mammoth Growth, as we move through the steps of mapping your Analytics Roadmap and defining a new BI strategy, we adopt an agile approach to deliver business value as quickly as possible. Contact one of our experts today, and let’s talk about your business intelligence goals.

“When it comes to data infrastructure, clarity is everything. Nutrafol was collecting all kinds of customer data from our website and different ad campaigns, but it was difficult to use. Mammoth Growth helped us through a data infrastructure reset, setting up Segment as our CDP and pushing those personas to Mixpanel for detailed insights."

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Dan D'Orazio

Senior Data Architect

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