Whether we like it or not, bias exists ― in humans and the systems we create. In data collection and analysis, bias can cause errors that skew information and omit important data.
Acknowledging its existence is the first step to removing its claws so it can’t cause damage to communities or your business goals.
Low quality or poorly analyzed data can reinforce social disparities, cost you revenue, and put your organization at risk. Even with rapidly-evolving AI models simplifying complex analysis, your results will only be as reliable as the data their algorithms are trained on. They may use data from small sample sizes to provide estimates on a much larger group, resulting in an inaccurate view of the population.
In 2019, Forrester Consulting conducted a survey of 409 companies. Their report revealed that firms had wasted 21% of their annual media budgets because of poor data. That cost midsize and enterprise firms an average $1.2 million and $16.5 million per year, respectively.
Going with the biggest names in the industry is understandably attractive, with brand recognition, accessible documentation, and lower costs up front. But even “free” tools come at a cost, and often that cost is information.
As regulatory compliance becomes more complex, the burden of data protection is resting more heavily on business owners like you. To keep your business’ and consumer data safe, it’s worth considering privacy-forward alternatives like Plausible, Matomo, or Fathom.
There are many ways bias in data collection and analysis can be introduced, so reviewing your data sources is a good place to start. The closer you are to your data sources, the easier it is to audit for potential bias. It can help you understand if you're using a misleading audience sample in the data collection process that may affect the overall results.
Where possible, leverage information provided directly by your users with their consent. You’ll need to be up front with them about what you’re collecting and how you’ll use it, but that also enhances your overall user experience and bolsters your cybersecurity plan.
You can’t expect perfect data or flawless analysis, but according to Forrester Consulting’s report, companies saw the best results when their data met criteria in these seven areas:
Timeliness ― your data comes from sources that are up to date.
Completeness ― your marketing data set contains all expected attributes (such as behaviors, intentions, permissions, and sentiments).
Consistency ― your data references all have consistent naming conventions across platforms, channels, and campaigns. This way you can more accurately collect and analyze patterns over time.
Relevance ― your data should help answer specific questions about customer behavior and inform basic day-to-day decisions. Think quality over quantity.
Transparency ― your data comes from sources that are easy to trace and identify.
Accuracy ― your data reflects actions actually taken by you and your customers.
Representativeness ― your data used for insights accurately represents the market and/or your target audience.
Once you’ve started refining how you collect data, it’s important that decision makers and those who interpret data are prepared to spot bias and avoid flawed judgement. Experfy’s summary of Five Common Biases in Big Data is a good starting point for parsing complex information with a more critical eye.
Surveys are a great way to collect data directly from people who interact with your business. The way you frame your questions is critical, and it’s very easy to let bias slip in here. A leading question is one that presupposes an idea or influences the user’s answer.
For example, we wouldn’t want to ask, “are you more likely to recommend Sol Minion Development because of your experience?” That would be a leading question with a yes or no response.
Instead, we’d ask “How likely are you to recommend Sol Minion Development based on your experience?” and include a Likert scale with options ranging from “Extremely Unlikely” to “Extremely Likely.”