Data can be a powerful enabler for innovation, but when used incorrectly, it can be a paralyzing force. It’s a critical component in measuring and understanding the need for innovation—if you cannot measure something, you cannot improve it. Whether you collect data based on historical perspectives or through experiments, it can bring the insights needed for truly great innovations.

Where many get off track is in approaching innovation like the wild, wild west. Teams and companies can often jump from idea to idea, from “let’s try this” to “let’s try this,” without analyzing and learning from their efforts. Others are so busy gathering data that creativity, productivity, and excitement go out the door and soon innovation stalls in the data collection stage.

While impactful innovation is both intentional and data-driven, not all data is created equal or useful. These five data missteps will help you understand exactly when data is helping you, and when it is paralyzing your innovation efforts.

An Inadequate Amount of Data

You cannot innovate ghost problems. Nor does one data point mark a trend. If you cannot access enough information—either qualitative or quantitative—around a problem, audience, or topic, you will only be guessing or making assumptions. And your guess may or may not be right. Assumptions bring ambiguity, and water the seed of confusion and wasted opportunities.

This is why you need both a multitude and a high veracity of data to be confident that your interpretation of your innovation needs is accurate. Challenge yourself to collect as much data as possible from multiple sources and use that as the basis of defining potential solutions. If you want to innovate, you need to have a lot of data that is also easily accessible to your team.

The Wrong Data for Your Problem

People often use the wrong data to justify a need for innovation. It might be incorrect data to indirectly associate a cost-effect relationship or the wrong data to think through a specific problem. As an innovator, you should make sure that you have the right data for your specific context.

For example, if your organization wants to improve customer service you must ask: “What specific aspect of customer service? What data directly measures that aspect?” If the goal is to improve response time to customer queries, response time is your metric. You cannot use other data such as perception or satisfaction. While that information is important, it’s not a direct measure of response time. You’d not only be using the wrong data, but also making assumptions about customer satisfaction relating to response time that may not necessarily be accurate. Be sure to have correct and specific data for your innovations.

Inaccurately Measured Data

You can also end up with the wrong data by using inaccurate data, which means that you are not measuring your data in the right way. To ensure accurate data, you need reliable and consistent processes for measuring your data. It ensures you’re not only identifying the right metrics but that you are measuring the data reliably every time.

In determining response time to customer queries, for example, you should understand when the measurement begins and ends. If the query is via email, does it begin when somebody opens the email or when the organization receives the email? Does it end when the email is responded to or when the customer says, “Thanks, you solved my problem”?

Failure to Analyze Your Data

One of the biggest problems organizations face is not a lack of data, it’s the lack of understanding the data they have and how to use it the right way. Companies sit on mountains of data, but most don’t take the time to analyze it.

Consider having a turnover problem in your organization. The easy way to deal with this is to work on new hire and retention strategies. But turnover is a result of other aspects that lead to people leaving the company—and there’s a lot that could be behind it. Think about how well these different metrics and components are mapped out. You may find that you don’t need to change your hiring practices at all, but rather that you need to incorporate an intervention program.

This can only be informed by looking at the data you already have, both qualitative and quantitative. It could be things such as environment, flexibility, employee perception of managers, or even employee psychological safety, that inform what is contributing to turnover. Unless you are measuring and learning from those elements, you will not be able to see the cause effect relationships of your true problem.

Reacting too Quickly to Data

Another common data mistake is making decisions using only a small amount of data. For example, organizations often want to develop a new product in response to competitors’ new products. The tendency is to look at what the competition did and then do something slightly different to protect your market share. Here’s the challenge: what your competition did is only one data point. You need to understand why your competition made the decision to come up with a new product, and the impact it’s having on their customers, before coming up with your own product.

You may find that instead of mimicking the competition, you could innovate on an existing product or process that allows you to address the market need in the right way. You shouldn’t always respond to market pressure or market needs the same way everyone else does. Analyze enough qualitative and quantitative data points from multiple sources, then make a high-confidence decision. That confidence comes from the variety and volume of the right data and allows you to refrain from hasty decisions.

When organizations blame their innovation failures on a lack of data or a lack of access to data, the flaws in their innovation strategy show. Meaning, the organization doesn’t understand how their current world affects their future world, and how their future world can be formed by data they already have. Looking into the past helps inform who you are and where you have been. Looking into the present informs what your current strengths are. Looking into the future informs you what you could be.

Don’t make data a hindrance to your innovation. Address these five elements to ensure you are innovating with the right strategy—and use data to help you get there.

About the Author

Dr. Evans Baiya is an internationally recognized and trusted guide to business leaders and innovators. Using his 6-stage process, he helps the businesses identify, define, develop, verify, commercialize, and scale ideas so the businesses and individuals can learn, grow, and thrive.  He is the co-author of the award-winning book, The Innovator’s Advantage and co-creator of The Innovator’s Advantage Academy, a detailed step-by-step innovation training. Learn more at

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