Data quality and Customer Knowledge

Lately, I’ve been focusing on the impact of sharing customer knowledge in your organization.

That all implies that the customer knowledge is not misleading.

Customer knowledge has to be based on accurate data to have real worth.

Usually, this is where organizations falter.

I’ve mentioned Conversion Tagging and Whim tagging as examples of getting data that paint an incomplete picture.

You need to have good data quality, anything else may lead you astray.

Good data quality is data that fulfill three criteria:

1. Understandable: When you read the report of an interaction, you can tell what has happened and where.
2. Consistent: Every interaction is reported every time. Nothing gives a more incomplete picture than being unsure if there are any unreported interactions.
3. Structured: Every interaction reported has the same data structure, independent of which interaction.

If you have data that don’t fulfill the criteria above, you’re hindering the work of the analysts.

Best case, you make them spend a lot of time to fulfill the criteria above manually, for example restructuring badly structured data.

Worst case, they can’t do the analysis at all.

Keeping good data quality at scale is a real challenge for any large-scale website.

That’s where Component Analytics comes in.

By tracking every interaction in a structured manner, it eliminates all issues of inconsistency.

And it’s built to scale.

– Samy