A deep dive into responsible data and analytics


Having done some work in permaculture and sustainability, I was very interested in this paper that I recently came across entitled ‘Responsible Data and Analytics’. We know that data and analytics can do a lot to enrich our lives, increase productivity, even assist in improving health. But if done incorrectly, or deployed irresponsibly, it can also ruin businesses, lead to great failures, expose private details, and cost lives.

In this paper, TDWI describes responsible data and analytics as an approach that “considers the ethical, societal, compliance, legal, and environmental ramifications of using data in a wide variety of applications and processes.” More formally, the authors describe it as “a strategic framework for proactively addressing broader planetary, societal, and business concerns, or a set of tactics for mitigating the risks and downsides from accidental or ill-advised uses of data and analytics.”

The TDWI report features an insightful framework for responsible data and analytics. I’ve included it here as it provides a very useful description:

*Image sourced from https://resources.snowflake.com/report/tdwi-bpreport-q422-responsible-data-and-analytics

As per other TDWI best practice reports, they requested responses from their readership regarding this particular topic. As such, those surveyed highlighted the following as key drivers for implementing a responsible data and analytics programme:

  • Maintain trustworthy data (69%)
  • Ensure privacy and security of data (59%)
  • Regulatory compliance (53%)
  • Increased profit (42%)
  • Ensure ethical use of data and analytics (41%)
  • Sustainability (27%)

It’s apparent that there’s an overlap with more ‘conventional’ drivers of data governance. For instance, the first three points are covered by a data governance programme. I’d like to see the case studies where the responsible use of data and analytics led to an increase in profit. I’m not suggesting they don’t exist. Rather, these would be the best examples to use especially when it comes to training and education programmes.

Data privacy and security were rated by over 80% of the TDWI respondents to be an important aspect of responsible data and analytics. The report has an interesting section on ‘privacy by design’ – a concept developed in the late 90s which has seven core principles. It’s well worth your time to read up more about it.

It’s also interesting to note that data ethics (covering the right and wrong use of data) was rated far lower than data trustworthiness. The respondents rated that data that is complete, accurate, timely, reliable, valid, relevant, and compliant is more important than the “moral issues and practices related to data topics including data curation and data sharing as well as the ethics of AI/ML algorithms and the design of applications that use data.”

Hopefully, our responsibility and respect of our fellow human beings will change this view over time. Equally, I just hope it doesn’t take a few catastrophic data breaches to cause this shift in mindset.

I was disappointed to see that sustainability of data and analytics activities ranked low on the list. I guess sustainability, and the various areas it touches on – like clean power, reduction of waste, product miles, carbon footprint, and so on – is more important in sectors such as logistics, manufacturing, agriculture and even tourism, than the footprint of the data and analytics function. However, we have to keep in mind that in some industries, like banking and insurance, there is less ‘bricks and mortar’ to the business. It’s all based on data and the use of analytics and insights. In those organisations, the key business processes contribute considerably to their level of sustainability.

This is a really interesting topic for me, and I enjoyed reading and dissecting this TDWI paper. In fact, I may revisit it and delve a bit deeper in future posts.

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