helping you master customer insight leadership

Questionable practices by Data Scientists – where do you stand?

Just 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.. Read More

Beyond the coding, here are some personal Data Science stories

I 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… Read More

Mango share some keys to Data Science readiness

Following 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… Read More

Data Science programming languages: (3) Resources for Julia

As 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.. Read More

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

During 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.. Read More

Data Science programming languages: (2) Resources for Python

As promised in our previous post, for the R programming language, this one will focus on resources for Python. Although R may have a longer heritage within the Statistics and Data Science community, Python could be described as a more complete programming language. In my conversations with clients and Data Science leaders, I’ve also heard a number praise Python as much quicker to learn. So,although both languages are proving popular.. Read More

Data Science programming languages: (1) Resources for R

This month, let’s turn our attention to Data Science programming languages; today, resources for R. Ever since the rise of R as an alternative to traditional statistical packages (like SAS, IBM Analytics etc), there has been a growing focus on coding. In the past I have tended to avoid these programming languages as a topic for this blog, as I have some concerns. Namely that the role of insight analysts,.. Read More