Using Data as the Strategy Engine, Not Just as an Instrument of Validation

Rethinking What “Data-Driven Strategy” Means in Pharmacy Benefits

Key Points

• Many leaders use data to validate strategy, but the greatest value emerges when analytics become the starting point for strategy design.
• A data-first approach shifts teams from answering predefined questions to uncovering early signals, hidden risks, and overlooked opportunities.
• Exploratory analytics can reveal meaningful cost, utilization, and contract performance issues that traditional reporting often misses.
• Organizations that embed analytics into daily decision-making build clearer priorities, faster interventions, and more durable results.

Leaders in pharmacy benefits already use data every day. Trend reports, forecasting models, and performance dashboards are built into how decisions are made. But even with this strong foundation, most organizations still think about data in a familiar, linear way: first define the strategy, then measure against it. In that structure, analytics help confirm direction, but they do not always shape it.

Here we explore a different way of working: Instead of treating data as something that supports an existing plan, what happens when data becomes the starting point for building that plan? What happens when leaders begin with open‑ended exploration rather than predefined questions? Early signals appear before they become trend drivers. Unknown opportunities become visible. Strategy shifts from being informed by data to being designed with it.

The remainder of this article will break down what that shift looks like, why it matters, and how organizations can build a strategy model that begins inside the analytics environment instead of ending there.

The Ongoing Importance of Traditional Reporting

Don’t misconstrue our point; traditional reporting methods are still essential. Leaders need to monitor results, validate decisions, and understand how performance changes over time. Retrospective reporting provides structure, accountability, and clarity. It ensures that strategies are evaluated in a consistent way and that teams know whether their efforts are generating impact. None of this goes away.

But there is an abundance of untapped value in looking at data a different way and letting it drive your strategy instead of just validating it.

A strategy that begins with data asks a different starting question. Instead of beginning with last quarter’s goals, it begins with: What is changing in our population, our utilization patterns, or our cost structure that should inform our thinking?

This shift sounds subtle. In practice, it changes everything. When teams spend time exploring the analytics environment without a predetermined agenda, they often spot signals that formal reporting has not yet surfaced.

Where Exploration Adds Something Traditional Reporting Cannot

Even when leaders use data regularly to inform decisions, it is natural to rely most heavily on the information that feels familiar or immediately relevant. Early strategy conversations often begin with expected cost drivers, known therapeutic categories, or population segments believed to be most influential. While these inputs are informed and still valuable, they can also create a narrow lens without anyone realizing it.

If a team believes that a certain therapeutic class will continue to be their primary driver, they often start by examining that area in detail. If leadership expects a particular population segment to be a major contributor to trend, that is where most analysis tends to concentrate. None of this is misguided, but it can unintentionally limit the search for other meaningful patterns.

A data-first approach broadens that lens. Instead of starting with what is already known or expected, leaders begin by looking at what the data may be surfacing that has not yet entered the conversation. This type of exploration often highlights areas that were not previously top of mind and invites teams to consider factors that standard reporting or past experience might not have emphasized.

Example: Uncovering Hidden Specialty Cost Drivers

Unbiased, exploratory analysis can surface early signals that might otherwise go unnoticed, such as an unexpected increase in short-term specialty fills among a small subset of members. While this may initially appear to be a minor issue, it can have a meaningful downstream impact. For example, a client found increased use of rescue or acute medications for hereditary angioedema (HAE), which may indicate a change in members’ medication patterns and progression of disease. Shifts like these can signal a need for care management support, a transition to a different therapy, or a pharmacist-led medication review. Without this insight, the team may have continued focusing primarily on a high-profile therapeutic class they believed was driving overall trend. Instead, they can redirect strategic efforts toward understanding the root causes of these early specialty starts and identify opportunities for prescriber education and targeted member outreach that would not have surfaced through standard reporting alone.

Example: Identifying Lost Value in Contract Performance

Another example can be seen on the pricing and rebate side of the house. Traditional PBM reporting reflects the PBM’s interpretation of contract terms and the pricing and rebates applied under that interpretation. Independent, third-party analytics are needed to objectively assess whether those terms have been applied in full compliance with the contract. For our clients, this deeper, objective analysis has revealed specialty drugs that were not receiving the correct rebate guarantees, incorrect rebate guarantees being applied to claims, and claims being excluded from rebate or discount calculations even though they did not meet contract-defined exclusion criteria. Each of these discrepancies represents lost value, and none would have surfaced without a data-first approach. Data becomes the mechanism for prioritization, negotiation strategy, and accountability. Evidence is used to challenge assumptions and strengthen vendor oversight.

Both of these examples demonstrate using data intentionally to define where the organization should focus, how it should engage partners, and what success should look like going forward.

Translating Exploration Into Strategy

A data-forward strategy does not stop at discovery. The next step is to translate insights into clear priorities. When teams see a pattern emerging, they focus on what can be influenced within the next 60 to 90 days. They choose interventions that are measurable and actionable. They create alignment across pharmacy, clinical, and financial teams because everyone can see the same information and the rationale for each decision.

Example: Identifying Off-Label Utilization Driving Cost Trend

A team may determine that a rising cost trend is not being driven by new high-cost drugs, but by increased off-label use of a commonly prescribed therapy. From there, next steps could include analyzing prescribing patterns by provider, updating clinical criteria, or working with PBM partners to implement appropriate utilization guardrails. For our clients, Dupixent is one such medication where expanded indications have led to increased utilization, including growth in new diagnoses, higher-than-expected dosing, and prescribing across a broader range of specialties. Thoughtful analysis of these usage patterns can inform targeted provider engagement, particularly around appropriate use in conditions such as asthma or COPD.

Example: Closing Adherence Gaps to Improve Outcomes

Another team may discover that its greatest opportunity is not related to cost trend at all, but to an overlooked adherence gap within a specific member population. This was the case for one of our clients regarding HIV treatment and HIV PrEP, two areas where adherence is especially critical to achieving positive member outcomes and preventing disease progression. Improper management can lead to a need for more complex and costly therapies. Upon identifying members with adherence gaps, we suggested our client design targeted member outreach, analyze pharmacy fill behaviors, and deploy focused care management support for the affected segment.

Building a Team Rhythm Around Analytics

Organizations that succeed with data-led strategies build repeatable rhythms around the work. Some use daily logins to review opportunities. Others run weekly or biweekly clinical exploration sessions. Many establish dedicated roles that focus on identifying intervention opportunities, tracking outcomes, and experimenting with new ways to extract value from the analytics platform.

This operating structure reinforces the idea that data is not something viewed once a month in a meeting. It should be part of how the organization works every day.

Example: Monthly Outlier Reviews to Reduce Cost Leakage

For example, one client established a monthly process to review claim outliers and decide which ones need follow-up in their upcoming strategy. By regularly catching quantity variances, they are able to pursue reimbursement rather than letting those costs slip through. This work was supported through analysis in PSG’s proprietary data analytics platform, Artemetrx, bringing the most meaningful outlier claims into a single view to help teams focus their time on issues with real financial impact.

Continuous monitoring ensures issues are not just identified once but prevented from reoccurring. By routinely analyzing data, payers can maintain ongoing visibility into contract performance, quickly detect emerging discrepancies, and safeguard their financial position over time.

What It Looks Like When Organizations Treat Analytics as the Strategy Engine

Teams that use their analytics tool this way operate differently. Their strategies evolve more quickly because the information driving them is timely. Their priorities are clearer because their decisions tie directly to observable patterns. Their results improve because their interventions are rooted in unbiased evidence.

Across clients, we see that the most effective organizations are the ones who spend the most time in the data environment, not after strategy is written, but before and during it.

Example: Gaining Data Visibility to Inform Strategy

For example, one client didn’t have visibility into their overall drug spend and was concerned about pharmacy costs being a top contributor to overall benefit spending. Prioritizing a strategy grounded in data, they began using Artemetrx to better identify drivers of spend, uncover emerging cost issues faster, and focus interventions on the right members and therapies. This initiative resulted in a 2.5% decrease in PMPM gross cost within the first year.

Example: Flagging Hidden Anomalies Through Proactive Data Monitoring

In another real-world example tied to eye disorder management, proactive monitoring surfaced a major utilization anomaly: a 400% spike in claims per member and a 250% increase in users for Miebo (a dry eye disease medication) across the plan. Active identification of this outlier prevented ongoing cost leakage and influenced the strategic decision to implement tighter utilization management controls.

Enhancing Insights and Driving Strategy Through Artemetrx

At PSG, we also live by this mindset and are uniquely positioned to support our clients by using data to drive strategy. Our integrated data analytics platform, called Artemetrx, helps our clients gain a transparent view of their pharmacy benefit data. Over 1 billion claims are actively managed on the platform by our team of clinical pharmacists and benefit experts. And in the last three years, we have identified over $10 billion in savings opportunities for our clients through Artemetrx.

We have seen firsthand how a more intentional use of data can create meaningful impact for businesses. This is why we believe in the power of data as a lever not only to inform your plans, but to drive them forward.

Shifting Your Mindset

Turning analytics into the starting point of strategy requires a shift in mindset. Instead of viewing data as a validator, leaders must see it as a designer. Instead of using analytics to confirm what they believe, they use it to discover what they should believe.

Data will always support decision-making, but its greatest value appears when it shapes decisions from the beginning.

If your analytics are not influencing what you choose to focus on next, you have untapped value waiting to be captured. When you begin with data instead of ending with it, strategy becomes clearer, decisions become easier, and your organization becomes more aligned around the work that truly matters.

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