helping you master customer insight leadership

The power of pressing pause, what are you missing by going fast?

pause

I recently had an opportunity to experience one of the benefits of pressing pause.

While presenting to over 30 Chinese non-English speaking executives, I learnt when to pause for my translator. She was excellent and taught me how long to speak before pausing for her translation. (Side note, it’s longer than you might think, as she needed context in order to translate meaning not just words).

The context of my talk was a visit by a leading Chinese insurer, to Cass Business School. To aid their executive education, that prestigious business school invited a number of leading thinkers (plus me) to present to them.

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How to help a non-technical audience understand their readiness for Data Science

non technical

Last week I had the pleasure of working with the University of South Wales (USW) to help non-technical people understand Data Science.

More specifically, I was running a Masterclass entitled “Everyday Data Science“. The brief was to demystify this topic for a diverse audience. It was clearly popular as it drew a good crowd.

The delegates who attended my Masterclass varied from data analysts to directors. However, most worked in some branch of public sector services.

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The Hidden Blocker in Delivering Great Analytics Projects: Empathy

empathy

Continuing our posts on how to achieve success at work, let’s turn to the power of empathy.

This might sound far removed from the worlds of Analytics & Data Science, but as we saw with Hackathons, it can be a vital step.

To help bring to life how greater empathy could breakthrough barriers for your Data Science projects, I am delighted to welcome back Ryan. Guest blogger Ryan Den Rooijen has tremendous experience from his work leading Data Science teams in Google & Dyson.

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Are you a Superhero at work? Are you a cultural fit?

cultural fit

Continuing our series on helping you succeed at work, let’s turn to the question of cultural fit.

So far this month we have covered topics including the benefits of fuzzy Analytics team roles, avoiding VoC program pitfalls and Data Science methodologies. Now let’s take a step back from role specific advice.

In her latest guest blog post, Hanne Sorteberg returns to share her advice on the importance of cultural fit. I’ll leave her to explain how this relates to being a superhero (whether or not you wear a costume).

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More Data Science methodology options – has much changed? (step 2 of 2)

methodology

Let’s continue our focus on Data Science methodologies. The reason for this focus is the need for more methodical delivery by many Data Science teams.

In the first post of this series, I made the case for having a Data Science methodology and shared 3 popular options. I hope you found those useful, but I’m also conscious that they are all old methodologies.

In that first post, I reviewed CRISP-DM, KDD & SEMMA methodologies. All of which were created during the heyday of Data Mining. Before the “AI winter” when exciting things were happening, but largely through using large stats packages or bespoke applications.

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How Data Science teams can be more methodical (step 1 of 2)

methodical

Another tip for success with your Data Science team is to be more methodical.

By this, I mean to establish and use a consistent methodology, process or workflow. This will enable repeatable results, simpler collaboration & knowledge transfer. If it is a well–designed methodology, it should also ensure appropriate QA stages and reduce the cost of rework.

A few different influences have had me thinking about this topic recently.

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10 more all too common mistakes and how to improve your VoC program

improve your VoC program

Continuing our series to help improve your Voc program, here are 10 more mistakes to avoid.

After the popularity of her first post in this series, I’m delighted to welcome guest blogger Annette Franz to share 10 more tips. As I mentioned before, Annette has many years of client-side experience in getting Voice of the Customer (VoC) programs to work.

As is the case for Customer Insight leaders, most of the best learning comes from getting things wrong. So, here is another list of 10 pitfalls which Annette has discovered you need to avoid.

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