helping you master the people side of data & analytics

Too big for Agile? Is there a middle ground? (AgilePM 1)

AgilePM

Having recently lectured MSc students on project management, I’ve been pondering the issue of being too big for Agile.

Like everything that comes into fashion, there is an inevitable backlash once the hype passes. What Gartner would call the “Trough of Disillusionment“.

As I work with Data & Analytics teams who are seeking to embed Agile working, it’s clear one size does not fit all. Many business leaders have been trained in Scrum, Kanban or Design Sprints. But they can also face large or complex projects that struggle with this approach.

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Why business games are so much more than just fun

business game

This week I had the pleasure of visiting a business game being run over 2 days for some management trainees.

These graduates came from a variety of functions, including Finance, Marketing, Sales, HR & Operations. It was a really fun day & I was very impressed with how my new associates ran the day.

But beyond the fun, I was struck by what a powerful learning experience this event provided. There were several ways that participants recognised they had learn & grown as a result. So, building on my post about leaders as educators, I thought I’d share my own reflections.

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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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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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The need for analysts to have improved commercial awareness

Commercial Awareness

I have mentioned before that analysts benefit from stronger commercial awareness. But what do I mean by this term?

The easiest way to explain is to set it in context. A lack of commercial awareness is normally shown when an analyst presents their findings. The impression they leave with internal customers is that they are commercial naive & irrelevant.

I’ve written before that this too often happens to otherwise very technically capable analysts. Their work may be based on high quality, well prepared data. Their analysts may be statistically robust & perhaps even presenting using engaging data visualisation.

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The case for more Specialists as your Data Science team grows

specialists

Following on from the potentially controversial topics that I suggested, I will make the case for more specialists.

You may recall that the first topic I offered was based on a post from Vincent Granville. In that post, he proposed that there was a need for more Data Science Generalists & less focus on Specialists.

He makes a good case and I am cautious to disagree with such a thought leader. But I think it is worth making the counter case for why more specialist roles help, especially as your team grows. I began making part of this case when arguing for Business Partner roles.

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