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Data-driven vs. data-informed: Which approach is better for your company?

Does data make the decision, or do you use data to help you make a decision? Your choice will change your company’s relationship with data.

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As you develop your data strategy, you’ll run into a dilemma: should data drive or guide your business decisions?

That subtle distinction makes a big difference. Does data make the decision, or do you use data to help you make a decision?

Your choice will change your company’s relationship with data. It might seem obvious to be data-driven and have data drive everything that you do but that approach can cause you to miss the bigger picture.

On the other hand, if you choose to be data-informed and let your data guide your business decisions, it’s possible to allow too much flexibility into your data. If that happens, you might cherry-pick data to agree with your preconceived notions.

So which approach is best?

Data-driven vs. data-informed: Explaining each approach

It’s more than just a change of words. Data-driven and data-informed determine how much say your data has in your decisions.

What does it mean to be data-driven?

Data-driven means that you always rely on data to make your decisions. If you’re trying to improve the UX of your product, you’ll run tests to get data to find the best approach.

Data-driven doesn’t take your unique experience or insight into account. It’s simply about the cold, hard facts. In this approach, data has the final say.

Here’s an example of data-driven decision-making in action: say you’re running a digital advertising campaign. You have two versions of an ad, but you’re not sure which one to run.

Ad A was created by your co-worker, and even though it’s good, you don’t like it as much as Ad B, which you created.

If you weren’t a data-driven marketer, you might use your gut instinct and only run Ad B since you created it. But since you are a data-driven marketer, you run both ads at the same time and evaluate their performance based on which ad gives you a lower Customer Acquisition Cost (CAC).

After running Ads A and B for seven days, you find that Ad A gives you a lower CAC. Despite your feelings and preferences, you decide to turn off Ad B and keep Ad A running since the data told you it was the winner.

What does it mean to be data-informed?

Data-informed means that you use data alongside your unique experience, user research, and other inputs to make decisions. With a data-informed approach, data is just one part of your decision-making process.

Let’s take the same example we just used above. Ads A and B have been running for a week, but since your sales cycle is 90-days long, you can’t use CAC to evaluate your ad performance. It would simply take too long.

In that case, you decide to use the click-through rate (CTR) to evaluate performance. Ad A has a CTR of 4.35%, and Ad B has a CTR of 3.95%. If you were using a data-driven approach, you’d turn off Ad B because of the lower CTR. But since you’re now using a data-informed approach, you look at the bigger picture.

As it turns out, even though Ad A has a higher CTR, it also has a higher cost-per-click (CPC). If you continue to run that ad, you’ll burn through your budget much more quickly. You also know from your past experience that the CTRs of both ads are higher than your average performance, which means you’ll have better than average performance regardless of which ad you choose.

In your data-informed approach, you turn off Ad A because you took other components into consideration and want to use your budget as effectively as possible.

Pros and cons of data-driven and data-informed

With that one example, it might seem that data-informed is a better approach, but let’s review the pros and cons of each data approach.

Pros of a data-driven approach

  • Decision-making is essentially out of your hands. Data tells you what to do. This helps prevent personal biases and gut instincts from taking over.

  • Since decision-making is out of your hands, it’s easier to push back against company stakeholders who may have their own agendas.

  • According to Harvard Business School, data driven decision making can actually help you be proactive because you’ll identify trends that might signal a future problem.

Cons of a data-driven approach

  • Focusing on data to make decisions can cause you to miss the bigger picture. Remember our digital ad campaign example from earlier? If we had focused only on CTR, we would’ve ended up burning through our budget too quickly.

  • Not having statistically significant data will hold you back. If you’re operating in a true data-driven environment, you need to make sure you’re collecting enough data over a specific period to reach statistical significance.

  • Being a true data-driven company is very difficult. Only 29% of companies are good at connecting their analytics to action.

Pros of a data-informed approach

  • It’s easier to take the big picture into account. You can use other inputs to understand what’s really going on and to help you make a decision based on the whole landscape, not just one piece of data.

  • You solve problems with out-of-the-box solutions. With data being only one input, you can use your experience and creativity to come up with solutions that might not be obvious through data.

  • You’ll be able to stay on top of trends. Early changes in user preferences rarely show up in data. If you’re solely focused on the data, you might miss a competitor or industry change that’s causing people to question the value of your product.

Cons of a data-informed approach

  • You can be more easily swayed by outside decision-makers. Since you’re using inputs other than just data to make decisions, opinionated stakeholders might have more clout than you’d like in the decision-making process.

  • Data can be cherry-picked to support the outcome you want. In a data-informed environment, your personal biases might come into play, whether or not you realize it.

  • You might get choice paralysis because of too many inputs. When you’re allowed to use a number of sources to help make your decision, you’re going to end up with conflicting information. That can be hard to reconcile.

When to be data-driven vs. data-informed

Ideally, you’ll use a blend of both data-driven and data-informed in your company. But your choice depends on the circumstance.

Data-driven is best when decisions can be based on an either/or outcome that uses data. If you’re working on conversion rate optimization, A/B testing, pricing, or other data-based projects, data-driven is the way to go.

With A/B testing, for example, you’re trying to find an either/or outcome. Your answer is either A or B. You can use data to make the decision. In that case, there’s no need for your personal experiences. Data can drive the decision.

The key to data-driven decisions is to choose your determining factors ahead of time. If you’re A/B testing the color of a button on your website, you need to clearly define what success is — in that case, it might be click-through rate.

Data-informed, on the other hand, is best when you’re working on a complex project that requires a number of inputs. For example, a new product feature isn’t something you can build solely with data. You need feedback from your users, your personal experience, stakeholder input, competitive data, and more.

You’ll also use a data-informed approach when creativity is an important component of your work. Designers are a perfect example of this. Their work is based on creativity, but data can help inform how they should execute their work.

For example, a website designer would probably love to create a unique, beautiful website that’s unlike anything else. But experienced designers use data to understand what kind of structure works best to keep website visitors engaged. That’s one of the reasons many SaaS websites have a lot of similarities.

You need to be both data-driven and data-informed

In the battle of data-driven vs. data-informed, there isn’t a clear winner. Both approaches are useful in certain settings.

As a general rule, for decisions that can be based on quantitative input, use a data-driven approach. For decisions that need to include qualitative input, use a data-informed approach.