The advertising industry burns through buzzwords faster than Apple updates iPhones. “Democratization” is the catchphrase du jour. Having spent my career using data to make mission-critical decisions, I am a big fan of what it represents. However, when companies talk about how they are “democratizing measurement and insights,” I find that particularly cringeworthy.

Democratizing measurement refers to data from siloes and black boxes being made available to and actionable for healthcare marketers. That is something I have been pushing for all of my career. More data to glean insights is fantastic — but only if the data is accurate and complete.

As we talk about democratizing measurement, we have to remind ourselves that democracy can’t exist without adequate representation. That’s as true for measuring healthcare marketing campaigns as it is for politics.

Many companies like to act like cowboys when it comes to data, implying they can lasso whatever results you’re looking for. But without enough rope, they just can’t reach the cattle.

Read on to learn how quality data and analysis are equally important, why transparency is of the utmost importance, and six questions you should always ask every potential measurement partner.

Understanding Quality of Data and Analysis

Measurement companies are often pushed to be faster and more granular. However, that only works if the data supports the ask. Some companies advertise speed and ease of use, but they go to great lengths to obfuscate the depth of their measurement.

The value of data ultimately hinges on two equal parts: quality of data and quality of analysis.

These two work in tandem when it comes to democratizing measurement. Poor analysis can cancel out quality data. On the flip side, data-driven decisions are equally doomed when the data is of poor quality or not representative of the population as a whole and doesn’t support the decisions being made.

Let’s use real estate as an example. If Zillow only reported 30% of homes sold, potential buyers will make decisions based on that incomplete dataset. People may not even know they’re not looking at a complete picture of the housing market.

How This Translates to Health Data

Healthcare marketing is the same. Data is only valuable if the data is representative of the population.

More than 1.3 million Americans suffer from Rheumatoid Arthritis (RA), and while that number sounds large, it only represents 0.39% of the population. Many campaigns for pharmaceutical brands that treat RA are large enough to reach 20% of the population but fall victim to measurement solutions leveraging datasets that are too small.

Measuring an RA campaign with an incomplete health data set will quickly erode the analytics team’s ability to provide meaningful insights with which to optimize a campaign. The following table demonstrates how limited data coverage — in this case, 30% — threatens the utility of measurement solutions:

Keep in mind that the numbers above are cumulative. If you wanted to optimize the campaign weekly, the sample would represent just one of every 52 of the people listed above, or 750 condition sufferers out of 39,000. As the sample dwindles, so does the ability to make impactful decisions.

Why Democratizing Measurement Requires Transparency

As a marketing scientist, I know that before I can draw conclusions from an analysis, I need to know the dataset’s strengths and weaknesses. I need to understand how deep I can go before the flaws in the dataset are impacting the results.

Say a dataset has a weakness in representing retail activity in Florida and California. If I were looking at the national average retail spend for mobile phone use, the data would be effectively useless for understanding the average revenue spent at theme parks. Understanding this helps me understand when to use the data and when not to. If I didn’t know the lack of representation, my conclusions from that data would be nonsensical.

A dataset representing only 30% of the population presents an incomplete picture. Think of rare diseases. Hereditary angioedema (HAE) can cause swelling in any part of the body, including the patient’s face. It’s a little-known condition from which only 1 in 66,000 people suffers. A dataset may exclude HAE patients for no other reason than the disease’s rarity.

That doesn’t make the dataset useless, but it does change how the data should be used. At best, this results in misinformed campaigns with unrealistic insights. At worst, it inhibits the ability of patients to receive important, potentially life-saving, information about treatment options and clinical trials.

The problem is companies that claim to democratize measurement aren’t always transparent about their data’s strengths and weaknesses.

6 Questions for Your Potential Measurement Partners

To understand whether a potential partner is truly “democratizing measurement,” there are six questions they should be willing to answer without hesitation.

• What is the coverage of your claims data in terms of the U.S. population? Specifically, how many people do you have health data for?
• How much of the total available health data do you have? Do not settle for the number of records. There are billions. This should be quantified in terms of percent of all data.
• Thinking of a condition you will measure toward: How many unique condition sufferers are represented in your claims data for the past year?
• What is the total number of claims in your dataset, and what is the time period represented?
• Looking at a specific brand being measured, how many new-to-brand scripts (for pharma) and procedures (for devices) are seen annually?
• What is the data time lag for the results?

The answers to these six questions will help any skilled healthcare marketer make the right decisions. If a potential partner can’t answer these questions, ask yourself one: Why? More likely than not, their results aren’t indicative of what’s happening in the market or their datasets are simply too small.

Democratizing measurement offers so much opportunity but requires transparency.

Making decisions based on insufficient data is worse than no decision at all. If the decision-maker doesn’t know the data is incomplete, it presents a great risk.

How We Democratize Measurement

Our solutions leverage the most comprehensive, timely clinical dataset that covers 85% of the U.S. population. Our DSP only targets healthcare provider (HCP) and patient audiences for whom we can measure. That means that 100% of our impressions reach an HCP or a patient, which makes measurement as reliable as possible.

The idea of democratizing measurement has introduced a slew of providers who make similar claims undercut by the quality of their data. Healthcare advertising campaigns provide relevant—and potentially life-saving—information to patients about conditions and treatment options. With that much at stake, data quality is of the utmost importance.

We’re proud of our data and its breadth, and we know what we can and cannot measure. Committed to transparency, we gladly and willingly share that information so you can determine if it meets your criteria for being valuable, both in terms of campaign performance and improving patient outcomes.