Republishing here the latest edition of my monthly newsletter on LinkedIn (The Punk CX Dispatch – Insights from the frontlines for those ready to smash the status quo and create experiences people actually love.)

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Data readiness, AI, CX and revisiting the idea of Lean Data

A recent article on CIO.com highlighted 4 critical misconceptions that are derailing enterprise AI adoption:

1. The organisational readiness illusion, which suggests that many organisations fall into the trap of thinking that their organisational capability increases when they buy new technology.
2. AI expectation myths, where leaders often overestimate the impact and the applicability of AI, thinking that it’s a panacea for all ills.
3. Data readiness bias, where many executives think their data is clean and ready to be used, as well as thinking that more and more data is always better and paves the way to AI success.
4. The deployment fallacy occurs when many leaders believe that implementing AI-powered software is the same as deploying traditional software. As the article highlights, too many think they can adopt “a set-and-forget approach”, but “that’s incompatible with AI’s operational requirements.”

While all four pose significant challenges that organisations must overcome if they are to harness AI’s potential, I wanted to highlight the third one, which concerns data and data readiness, in this newsletter.

Why?

Well, the idea that more data is better reminded me of a conversation I had on the Punk CX podcast way back in May 2017, with Jascha Kaykas-Wolff, who, at the time, was Chief Marketing Officer at Mozilla and is now CEO of Visiting Media.

During our conversation, Kaykas-Wolff opined that marketers were getting lazy and were collecting more data than they knew what to do with.

As a side note, it seems that nothing much has changed since then, with more recent research from Braze indicating that CX and marketing leaders still have more data than they know what to do with.

However, the idea that marketers were collecting more data than they knew what to do with led the team at Mozilla to develop the concept of Lean Data practices. This framework helps them (and other interested companies) make better decisions about data, focus only on the data they need, build appropriate security around that data and engage users to help them understand how their data is being used.

Central to Mozilla’s approach is the following question:

“Do I need this data to provide the value I’m trying to deliver?”

That’s a great question and one that every leader should be asking themselves and their teams.

But to answer that question, organisations need a clear understanding of the experience they would like to deliver, as well as the technology and data required to achieve it.

However, in reality, they often lack the clarity of understanding to do that.

Too many organisations end up either reacting to technology trends or purchasing technology only to figure out what to do with it afterwards.

That’s the wrong way round.

I’m a big believer that we should start with the end in mind, particularly when it comes to service and experience.

I think we should always envision and design the experience we want to deliver to our customers, and then determine what data and technology are necessary to bring our vision to life or help us deliver it.

So, considering the scale of the data problem organisations face, the potential of AI, and the opportunity to use great customer experience as a marketplace differentiator, is it time to revisit and take a closer look at Lean Data?

I believe so.

This approach could help brands address not only data cleanliness and readiness issues but also support overcoming challenges related to building trust with customers on data use, security, and privacy, while at the same time reducing their exposure and risk.

What’s not to like?

Lean data, anyone?

Image credit: Photo by Claudio Schwarz on Unsplash