helping you master customer insight leadership

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 welldesigned 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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10 all too common VoC program mistakes and how to avoid them

VoC program

Our focus this month is how to achieve success, including with VoC programs.

Previously, Alan Murray shared his advice on keeping analytics team roles fuzzy, especially for smaller teams. In this post, guest blogger Annette Franz returns to talk about Voice of the Customer (VoC) programs.

Like Annette, I have all too often seen such programs lose their way or just be badly designed from the beginning. Whether using NPS, CES or CSat scores, the focus can become on the metric not action from insights.

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Setting up an Analytics team for success = Get Fuzzy!

fuzzy

Building on our month focussed on controversial topics, let’s turn to what will set your team up for success.

Different contexts can require different types of analytics team. A lot of the advice that I offer within the Opinion section of this blog is based on a lifetime leading teams in large corporates. So, I’m pleased to partner with guest bloggers from other settings.

On that basis, I am delighted to welcome a new guest blogger. Alan Murray. After a career in the worlds of consultancy & large corporate, Alan has spent plenty of years in both medium-sized firms & lately startups.

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Unicorn Farming: Building Capabilities Against the Odds (Part 2 of 2)

Unicorn Farming 2

Guest blogger Ryan Den Rooijen returns to complete his two-part series on Unicorn Farming.

It’s been great to see the conversations that part 1 has sparked on social media. It’s always encouraging to hear other experienced analytics leaders, like Martin Squires, agree with many of the key points & share their insights.

Hopefully you find part 2 as interesting and useful. I’m tempted to make a “neigh” joke, but I’ll resist. Here is part two, with a focus on how to assess candidates background & experience.

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Unicorn Farming: Building Capabilities Against the Odds (Part I)

unicorn farming

Continuing our focus on controversial issues, let’s talk about unicorn farming.

I’ve previously used the term recruitment & retention, but Ryan den Rooijen has the more poetic term of Unicorn Farming. How to successfully recruit for your Data Science team.

Ryan is Global Director of Data Services at Dyson and has previously blogged for us on data artists. We also interviewed Ryan on his role, so you can find out why he know what he’s talking about (including his background at Google).

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