Blog Post

Business Intelligence Initiatives Require More Than Just Technology to Succeed: Metric Design and Development

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This blog post is the first in a series focusing on the factors that drive a successful business intelligence1 (BI) strategy. This series will discuss thoughtful metric design and selection, BI processes that allow your organization to continuously adapt to business needs, and targeted BI talent strategies as key elements for all organizational initiatives.

In the current fast-paced, risk-based, and value-driven environment, every healthcare organization must have a data-driven culture. Its presence allows organizations to make better business decisions by getting the right information to the right people at the right time. By contrast, the absence of a data-driven culture results in decisions that do little to nothing to advance an organization’s goals.

The core of any data-driven culture is the alignment of business goals with performance metrics. While technology infrastructure is critically important to any organization, goal-metric alignment is its lifeblood: it is the only element that allows an organization to state whether it has truly been successful. To achieve this alignment, one must first recognize how elusive it is in practice and then accurately diagnose your organization’s current state.

Where performance is measured, performance improves. Where performance is measured and reported, the rate of improvement accelerates.

Pearson’s Law

Defining Goal-Metric Alignment

While it sounds simple enough, a lack of alignment between an organization’s goals and the metrics it uses to articulate performance is a core reason many organizations fail to achieve their goals. This holds true regardless of the number of BI-focused technology initiatives.

For example, an organization developing a new service line may (appropriately) select patient appointment counts as a key growth metric. However, if the true limitation of growth is provider capacity, and no associated metric is reported, members of this organization will quickly feel disillusioned because of the effort spent on growing patient volume with no actual growth—and will not be able to diagnose the reason why. Unfortunately, given the complexity of the swiftly changing healthcare environment, this goal-metric misalignment scenario is all too common in many organizations’ critical performance areas.

Consider the matrix below, which illustrates the goal-metric alignment spectrum. To get a sense of where your organization lies on the grid, ask yourself: if I asked the top leaders of my organization to define our three highest-priority goals, and to quantify our performance for each, would they give the same answers?

The Goal-Metric Alignment Quadrants

Quadrant Name Quadrant Description Best Path Forward
Dysfunction With no goals and no metrics, an organization will be unable to manage the dysfunction and low morale it perpetuates. Many organizations have specific departments or initiatives that fall into this quadrant, even if the entire organization does not.
  • Research industry trends and best practices.
  • Formulate organizational goals.
  • Select and define performance metrics for each goal.
Lemming These organizations are unaware of what they want to achieve but measure performance based on metrics identified by other organizations or simply because they have the data available. They may sometimes find success, but only in short spurts.
  • Formulate organizational goals.
  • Select and define performance metrics for each goal.
Wishful Thinking These organizations have focused goals but no reliable method of measuring performance and thus no way of knowing if they’ve reached their goals.
  • Select and define performance metrics for each goal.
  • Ensure that reliable data sources exist for each metric.
High Alignment Organizations with clear goals and metrics know what they want and if they’ve achieved it. These organizations are well positioned for high performance. Focus on high-priority areas for improvement.

Achieving Goal-Metric Alignment

ECG recently facilitated a senior leadership retreat for a client where business goals and their associated performance metrics were discussed. After reviewing industry trends and best practices, the executives identified and prioritized five key themes for their organization and selected metrics to measure performance in each. However, once these metrics were identified, it turned out that:

  • Only 20% of the metrics on their existing board-level dashboard were prioritized as key metrics during this exercise.
  • Data was not measured or did not even exist for nearly 40% of the prioritized metrics.

This was an eye-opening exercise for these leaders because it showed a clear misalignment between their performance metrics and organizational goals. This is not to say that this organization is dysfunctional; in fact, they are considered a leader in their industry segment. What it did reveal, however, was a significant opportunity for improvement. In particular, leadership expressed that this lack of alignment was:

  • Limiting their ability to confidently articulate organizational performance.
  • Preventing discussions about the most important improvements that needed to be made.
  • Perpetuating the use of inadequate proxies for the data they really needed.

This exercise was a catalyst for launching several key organizational initiatives to capture better data and improve performance, and the BI team was a key stakeholder in each. These leaders now feel much more confident in their direction and in their ability to meet the commitments they’ve made to their patients, employees, and partners.


Goal-metric alignment is the core of any data-driven culture, but it does not occur without significant effort. If you want to control your organization’s success, act now by performing a detailed review of your goals and performance metrics to ensure the outcomes you seek.


  • 1.

    While ECG uses the term “business intelligence” in this article, the principles apply to similar terms, such as “big data” and “advanced analytics.”