conversation about maximising value from customer insight

Are your interactive data visualisations working for you or your users?

interactive data visualisations workingHow are your interactive data visualisations working for you or your users? I ask as there have been a number of useful posts published by Data Viz experts recently that suggest all is not well in the world of interactivity.

So, building on our recent themes of Data Visualisation and Data Science, I’ll spend this post exploring how well interactive data visualisations work. If hardly anyone is using them, is it worth bothering?

Given the amount of media coverage and interest in interactive data visualisation, it’s important to consider if they are actually working for their intended audience. (more…)

On the Bank Holiday, here’s a variety of topics for Insight Leaders

topics for insight leadersSitting at my desk on a Bank Holiday, before taking a break, I’ve been reflecting on a variety of topics for Insight Leaders.

One of the reasons I found Customer Insight Leadership such a rewarding career and still find it fascinating to help such leaders, is the diversity. One day your focus may need to be on improving specific Analytics skills, or complex Data problems, the other it could be how to change culture as a leader or summarise key insights from qualitative research. It really is a polymaths’ playground.

So, as we near the end of a month focussed on Data Science, I thought I’d share a variety of content including some for Data Scientists. (more…)

Questionable practices by Data Scientists – where do you stand?

Questionable practicesJust because you can, doesn’t mean you should; this is perhaps the simplest way to introduce questionable practices.

Last year we posted on the desire amongst many data scientists to achieve social good through their work. As for all disciplines, there is also a potential “dark side to the capabilities of data scientists. So firms have or still do seek to use data as a weapon or persist with questionable analytical activities.

In this post we will explore 3 examples that should prompt your own reflections on data ethics and any implications for you.

I’ll share on uses of data science for: employee surveillance; winning elections & proliferating fake news. More encouragingly, I will close with how two organisations are working to advance a code of ethics for data scientists, as a positive response to this challenge.

But first, let’s explore the darker side of Data Science use. (more…)

Beyond the coding, here are some personal Data Science stories

personal data science storiesI hope you’ve found this month’s Data Science content to be useful, as I’d also like to share with you some personal Data Science stories.

To complement the technical resources I’ve shared recently, it’s important to also focus on the people side. Customer Insight Leader exists to encourage and inform more holistic customer insight leaders, covering the disciplines of data, analytics & research, as well as the requisite leadership skills.

In this post I’ll share 3 different perspectives on Data Science careers: from those starting out in tech companies; to an ex-freelancer; plus an experienced data scientist sharing his experience & resources with us.

So, if you are an analyst considering a move into Data Science, or a leader wanting to better understand your Data Scientists or options for your people – I hope this helps. (more…)

Mango share some keys to Data Science readiness

Data Science readinessFollowing on from our series on programming languages, I’m delighted to introduce a guest post on Data Science readiness.

Having the strategy or aspiration to make use of Data Science is one thing, executing it effectively is another. Too many businesses I know have hired data scientists without really knowing what to do with them or what they need to succeed.

So, I’m pleased to introduce a new guest blogger. Mark Sellors is Head of Data Engineering for Mango Solutions.

It helps to hear a Data Engineer’s perspective, as that can be such a foundational role to a successful Data Science team.

(more…)

Data Science programming languages: (3) Resources for Julia

Resources for juliaAs promised, I’m returning to our series covering data science programming languages, this time-sharing resources for Julia.

My first introduction to the Julia language, was mentions at R or Python events, that “the cool kids are writing in Julia these days“. Now, bloggers are always in danger of being on the look out for something topical or trendy, but further investigation revealed that Julia is indeed a useful language with growing usage amongst data scientists.

So, to ensure we are not limited to the more familiar R and Python languages, I’m delighted to extend our series to also look at resources for Julia programmers, or those wanting to consider this language. As before, I’ll share a book recommendation for learning Julia, as well as some online resources, cheat sheets and an event to attend.

I hope this proves useful, for Data Scientists and Insight Leaders, who are seeking to expand their repertoire or achieve better performing code.

(more…)

Getting the most out of your Data Science team, pitfalls to avoid

your data science teamDuring our month focussed on Data Science programming languages, my thoughts have turned to getting the most out of your Data Science team.

This topic has arisen, because of what I’ve observed in a number of organisations seeking to implement Data Science.

As I’ve worked with more clients and talked to other leaders at Data Science events, it has struck me how many fall into common pitfalls. These limit the impact made by their Data Science teams and so may limit the lifespan of business willingness to invest.

Like many such business problems, the challenge is not with the technology or innovation itself, but rather how it is managed. Once again, it’s what you do with it that counts. (more…)