Why You Are Failing with Your Data Strategy

You work in an org where someone else is making strides in their work, and you not so much. They're sociable, confident, but not much of an expert.

You're struggling, you want to exceed with your value you provide in the org. But it's like you're on a hamster wheel.

You don't have the correct data to succeed. Others with relationships are making strides, when your talent is hindered by poor intel.

I'm here for you. Here's the common failures of data strategy I've see most often.

No Defined Data Strategy

No one makes any decisions with the data, they just make decisions that they're told from above or just pull some numbers from GA and wing it, there might be some obscure reporting structures but it's pretty rogue.

No Tools That Fit

You make decisions off the types of customers that come in, but you only have visibility of the behaviours of which everyone does.

When you choose providers you want visibility of the customer, not just the behaviours. I see this in segmentation strategies based off behaviour, but it's a lie.

No Clear Budgets

Having a budget let's you improve your data literacy across the majority of the org.

No clear budgets means the data team are usually just taking insight from Google Analytics or the cheapest possible tool. This leaves the door wide open for massive holes in the data strategy.

No Meaningful Reports

Reporting should give parts of the org operational success.

Re-usable insight gives great learning across the org. This should prompt communication when anomalies are seen in the charts.

No Training

In an ideal world would see everyone be data literate and have access and go about their business. But training is fundamental in our tooling.

We want to teach the correct staff members to be able to fish. Not all of them perhaps, but the right ones to push a data-supported culture.

No Analysis or Insights

If you don't have the accessible insight you need, you leave a lot of value on the table. This usually happens when there is a rigid backlog and you're not iterating delivery based on data.

You could even be too focused on implementing your data tools and overlooking insight for implementation. Be lean and mean out of the gates. And if you get too much overwelm feel free to subtract from your data stack.

No Decisions from Data

If you aren't mapping decisions to value then you're probably not using your data correctly.

As stated above, rigid backlogs can hurt decision-making. My concern is generally always around speed to make a decision.

No Experimentation

When we aren't making decisions with the data, we aren't close enough to ideas to improve.

When you're not close to that you don't understand how to prioritise experiments well, so you're probably just pissing into the wind here. Go back to mapping decisions to value and get really close to the data.

No Advanced Data Use

So far I've only spoken about getting the data in place to be used. Now if thats not in place, we can't forecast for the future.

It's good to point out that it would shock me if you went to machine learning when you can't even make experimentation lean and mean in your organisation.

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