If you are reading this article, then you probably care about data-driven thinking and like to make decisions backed by data. Bad news!

Not everybody at your company is like you!

I’ve worked with a lot of startup/e-commerce companies as a consultant (or as a dedicated data analyst) in the last few years and I found that one tricky question came up time to time at many, many companies:

How can you evangelize data-driven solutions in an environment where there is no established culture of using data?

Sounds like a foolish question, right? Data analysis is designed to support the work of everyone at the company. It actually helps them! Learning about your users and reacting to their needs is the one most obvious thing you can do.

So how come there are always people who are opposed to the data analyst’s findings? How come when you present your first results, there will be always someone who “knows better” in her guts?

There could be many reasons for data resistance. First of all: personal interest.

You say: “Data shows this, so let’s change that.”

They say: “I don’t get it, but let’s do it like we did before. It worked.”

If you are a great data analyst, you will bring some game-changing ideas to the table. But change is scary. Especially for those stakeholders who are really confident about the things they are already doing. When you say there is room for improvement, they take it as a personal attack against them and their work.

Don’t worry! At the end of this article I am going to share my best practices on how to deal with this kind of issue when you start transforming your organization into a data-driven one.

The other reasons for data resistance

It’s also worth mentioning that a company’s data resistance is not necessarily a specific person’s fault. It can come from the size of the company and lack of communication, too.

I’ll give you a very specific example. In 2015 I worked with an e-commerce company who primarily sold computer accessories. Their business was very simple: they got products from the manufacturer and sold them in their online shop. The producer sent pictures (usually low quality pictures) and descriptions (extremely boring descriptions) for the products.

The e-commerce company wanted to have nice things on their website, so they decided to take it to the next level. They just kicked-off the “HQ project,”  replacing all the pictures with high quality and unique pictures and all the boring text with very enjoyable and readable descriptions. So they hired two copywriters to write content and two photographers to make professional photos. After three months they hired me to find out how the project was going.

data resistance AB test

Unfortunately all my analyses and AB-tests showed one thing very clearly:

Though the new high-quality pictures were working well, none of the customers read the new “creative” descriptions… It turned out that the visitors didn’t care about the creative content.

They cared about two things: price and specifics. (Got it, right? The original boring description by the producer!) The new wording wasn’t really useful from an SEO standpoint, either.

Don’t get me wrong – the copywriters really gave their best, worked hard and produced exceptionally great stuff. The bad decision happened on a strategic level 3 months before.

Hmm… So now what?

My point is: as a data analyst, you can’t go to a company and change everything at once. You can’t say to people that their job is useless or the things they were doing were all wrong. (After all: conversion rate and money is not everything, right?)

If you do so there are many scenarios you won’t like:

  1. They leave the company and the company loses good people. Not because they didn’t do their job right, but because they didn’t have the data.
  2. You leave the company: either you are fired or you quit (because everybody hates you.)
  3. Everybody stays at the company, but the data resistance will increase because of personal issues.

None of these sounds too good, right?

How to break down data resistance?

I promised to share my best practices, so here they are. These will help you to make sure that you can do your job as a data scientist/analyst… and at the same time everyone’s happy at the company.

  1. If you are a small company (startup, e-commerce, etc), hire a data scientist (or a data analyst) in early stages to try to prevent this kind of bad decision.
  2. On the other hand, if you are a data analyst and you are about to join a company as their first data analyst, try to join a smaller one, where you can evangelize your methods at an early stage. If you like challenges, you can work with bigger companies as well (like I did several times). More friction, but more impact eventually, if you do things successfully.
  3. Start with a small side project. On less significant projects there is lower data resistance. You can use this data-driven project as an internal reference in the future.
  4. Find multiple channels for communication. Slack, email reports, presentations, workshops, etc. You have to build up the “internal marketing” of data-driven decisions in the company. As a good evangelist, try to attend every important meeting and ask before every decision: Why? Why do you think this is a good project? Do you have any research behind that? Have you tested it before?
  5. Educate. Set up 1-hour workshops, where you show people what AB-testing is, what user testing is, what heatmapping is, etc… People love to learn. Especially if it’s free and during work time. 🙂
  6. Repeat things. I had cases in which I presented the same presentation about one analysis almost 20 times in 2 weeks.
  7. Do one-on-one-s. Sit down with the people who don’t understand or don’t agree on things. Explain everything in detail! If they don’t have time to sit down with you, don’t get mad, just make it clear to them that they have the opportunity to have a one-on-one with you anytime.

Conclusion

As you can see, breaking down data resistance and evangelizing data driven thinking is not an easy thing. Neither is it friction-free or fast. It could take weeks or months. But the more successful data-driven projects go through, and the more great results you deliver, the lower the data resistance will be. I can guarantee that.

Cheers,
Tomi Mester