If you are reading this article, then most probably you care about data driven thinking and you 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 one tricky question, that came up time to time at many-many companies:

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

Sounds like a dummy question, right? Data analysis is to support the work of everyone at the company. It actually helps you! Learning about your users and reacting to their needs is just the one most obvious thing, you can do.
So how come, there are always people, who are protesting against the data analyst’s findings? How come that when you present your first research, there will be always someone, who “knows better” in her guts.

There could be many reasons of 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 the way, how 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 doing. To whom you say that there are some areas of improvement. When you say they should change, they take it as a personal offense against them and against their work.

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

The other reasons of data-resistence

It’s also worth to mention, that the data-resistance of the company is not necessarily someone’s fault. It can also come from the size of the company and from the lack of communication, too.

I give you a very specific example. In 2015 I worked with an e-commerce company, that’s main profile was selling computer accessories. Their business was very simple, they got products from the manufacturer and they sold these in their online shop. The producer sent pictures (low quality pictures as usually) and descriptions (extremely boring descriptions) to 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”, which goal was to change all the pictures with high quality and unique pictures and all the boring texts with very enjoyable and readable descriptions. So they hired 2 copy-writers to write content and 2 photographers to make professional photos. After 3 months they hired me to find out, how the project goes.

data-resistance AB testUnfortunately all my researches 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 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 what’s next?

My point is: as a data analyst, you can’t go to a company and change everything at once. You can’t say people, that their job is useless or the things they were doing was s**tty. (After all: conversion rate and money is not everything, right?) Because 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. Neither if you are fired nor if you quit (because everybody hates you) is a good thing.
  3. Everybody stays at the company, but the data-resistance will increase by personal issues.

How to break down data-resistance?

I promised to share my best practices, so here they are:

  1. If you are a small company (startup, e-commerce, etc), hire a data analyst in early stage and try to prevent these kind of bad decisions.
  2. On the other hand, if you are a data analyst and you are about to join to a company as their first data analyst, try to join to a smaller one, where you can evangelize your methods in 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 on 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, e-mail 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 on 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? Did you test it before?
  5. Educate. Setup 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 if it’s in work-time. 😉
  6. Repeat things. I had cases, where I presented the same presentation about one research almost 20 times in 2 weeks.
  7. Do 1-on-1-s. Sit down with the people, who don’t understand or don’t agree on things. Explain them everything in details! If they don’t have time to sit down with you, don’t get mad, just make it clear for them, that they have the opportunity to have 1-on-1 with you anytime.

As you can see, breaking down data-resistance and evangelizing data driven thinking is not an easy thing. Neither it is friction-free or fast. It could take weeks and 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.

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Tomi Mester

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