Is your organization ready for AI?

I've worked in data roles in organizations ranging from startups to Fortune 500. No matter the size, each company had to answer the same questions when it came to data architecture.

Everyone wants to implement AI, machine learning, (insert another data buzzword here), etc. However, failing to set up the right data infrastructure beforehand can cause projects to derail or cease completely. Having the 5 characteristics below will ensure that you're able to tackle any data problem in the future.

Now, let's dive in.

What are you tracking?

You can't analyze what you don't have.

Take inventory of all the different types of data your company is tracking or has access to. Examples are payments, customer information, website clicks, etc.

Where are the data coming from?

If you understand the data sources, then you can understand the data outputs.

Think about how each data point is created. What systems or people are involved? How do changes upstream affect data downstream?

Who owns what?

Each data source should have an owner — a person or a team who is accountable for the data.

When there isn't a clear owner, you run the risk of the data becoming incorrect or irrelevant.

How can you connect entities across different data sources?

Cross channel analysis is more important now than ever before.

What are all the systems a user interacts with for your product or service. Can you map a user's billing data to their app data, customer support data, and email activity data? This is what it means to have a full picture of the customer journey.

How do you know when there's a problem?

Analysis is only as good as the amount of trust people have in it.

There will be mistakes in data. That's inevitable. This is why data quality checks and automated data cleaning are essential so that you know you always have valid and ready-to-go data available.

I hope this list helps you take the next step.

Data operations is a journey, not a destination.