Other controversial topics for leaders of Analytics & Data Science teams

Following the popularity of our open debate on Business Partners, I have been wondering about other controversial topics. Which would be worth exploring further on this blog?

In the past, I have shared research for the Data Leaders Summit that highlighted some polarised opinions amongst leaders. These included the perennial debates around challenges including:

• Should Analytics teams be centralised or decentralised?
• How should you resource your Analytics team (in-house, outsource, other)?
• Which specialisms should sit together as a Customer Insight function?
• How can you make the business case for investing in Analytics/Data Science?

We have also previously included posts on the ethical issues posed by use of AI.

However, it struck me that the above are frequently discussed, whereas there are a wider number of concerns raised by today’s analytics leaders. I have previously shared some of these, both as a debrief of a public debate on Real Time Analytics & reflecting on questions posed to me. But, what else might you want  to discuss?

In this post, I’d like to share 5 other useful articles published by others. Each highlights a different controversial worth considering. I hope you find them interesting. I have a question for you at the bottom of this post.

Until then, here is my initial offering of other controversial topics to discuss…

Do we need more generalists or more specialists in Data Science?

As Vincent Granville makes clear at the start of this post, it has become fashionable to make the case for more specialist roles. From Data Engineers to Data Artists, the advantages with scale appear obvious & even emerged as we discussed Business Partners.

However, there are also strong benefits to have generalists. Both in more traditional analyst roles (as previously argued by Martin Squires) and even in Data Science. Here is Vincent’s case for more generalist Data Scientists:

What are the societal implications of use of Predictive Analytics within Education?

Is Education, like politics, getting tarnished through greater use of data and predictive analytics? Are the potentially exciting opportunities for greater automation, personalisation and use of robotics actually a smoke screen for dubious soceital benefits?

This post caused me to discover the excellent blog being published by Ben Williamson. In this post he usefully summarises some of the issues posed by EdTech, data leaks and algorithmic mistakes. A really useful of ethical issues that must not be overlooked:

Do we need greater controls to prevent experimentation on social media users without consent?

Despite the regular media coverage on privacy concerns and damaging content, there has been much less discussing online experiments. Yet, there have been a spate in recent years of companies experimenting on their customers & social media networks on their users.

A number have been secret and all have lacked informed consent. Does GDPR provide sufficient protection? Does the ICO need greater teeth or backbone? This concerning post from Sabrina at Data Science DoJo lists 10 experiments that should have been stopped:

Are governments using Big Data to control their populations or is it a much needed improvement to public statistics?

A number of the developments at the Office of National Statistics in the UK have been very welcome, including their Data Science Campus. However, increased use of Big Data by goverments should give us cause for concern.

This post from The Conversation blog, based on Australian perspective, makes the important point that enthusiasm for use of such data must not outrun public engagement. Worth reading for its relevance to other countries too:

Do we need to be more pedantic about Data Scientist job titles and the explosion of such roles?

Finally, let’s return to our original agent provocateur, Vincent Granville. In this post for Data Science Central, he argues for greater clarity in identifying real Data Scientists.

My own experience working with a range of businesses confirms that there is widespread confusion. In fact I still come across a lot of muddying of BI, Analytics & Data Science roles. Too many analyst roles have just been relabelled Data Scientist wihout changing anything else.

If that might be someone you know, try testing their role against these 10 telling characteristics…

Which controversial topics do you want this blog to debate?

All that brings me to the question that I promised earlier. I hope this post has helped get you thinking & fired up some enthusiasm for your favourite talking points. So, here is my question for you, dear reader:

Which controversial topic should I debate next on this blog?

Please let me know either in the comments box below or via my social media posts. I look forward to hearing from you.