As demonstrated by the global demand for talent, most organisations have now woken up to the importance and potential impact of data, analytics, and AI. Yet discussions with employees across for-profit, non-profit, and governmental organisations have exposed an interesting paradox. Even in those cases where their executive teams have decided to invest in data and analytics, many employees still feel like their managers are not engaging with the topic. As a result, they struggle to get the necessary buy-in to drive analytics change.

This raises two important questions for us to address. Firstly, why is there so much resistance at a management level? Secondly, how can employees work around this to ensure they are able to deliver the type of impact they know is possible?

While the reasoning will vary by individual, there are a number of themes that tend to surface time and time again: Artificial Intelligence is not relevant to our business unit. Analysts do not drive any revenue. Our team does not have any suitable data. We have more important things to focus on right now. Perhaps most disappointing: We have always done things this way, so why change this now? They all indicate either a lack of understanding, or an unwillingness to embark on the necessary change journey.

Did you hear that starting gun? No? Seriously? Then why is everyone else running! Photo by Adi Goldstein.

To positively influence this unproductive mindset, there are a number of levers at individuals’ disposal. Firstly, in cases where their organisation has defined a clear strategy for data and AI, they can set objectives that bridge both the team’s strategy and the data strategy. For example, on a planning team where forecasting accuracy is critical, an analyst could propose machine learning to improve inconsistent human forecasts. Bringing in examples from outside the organisation can be a powerful tool.

Secondly, in cases where a manager is unwilling to engage because they are worried about the risks – of faulty conclusions, of data loss, of wasted investment etc. – it can help to quantify those. For example, while an analysis could turn out to be a dead end, how much time would really be lost? On cloud platforms, how big is the risk when cybersecurity best practices are implemented? Perhaps most importantly, what is the risk of inaction? What if the executive were to ask for an update on the data strategy?

I'm telling you Bob, we are never going to get ahead at this rate! Photo by Bruno Aguirre.

Thirdly, if clearly articulating the benefits and assessing the risks turns out to not be enough, there are various ways in which team members can apply pressure to their managers or department heads. Showcasing work done by competitors can be incredibly powerful, particularly if they have published case studies on the investment required and the outcomes realised. This can also work with internal examples, particularly when managers are jostling for promotion and are trying to distinguish themselves in the eyes of the executive. “Have you heard about team X? They increased throughput by 15% in a week! If only we had a means of doing so as well…”

To achieve the desired results, you might need to try these approaches multiple times over a period of weeks or months. After all, analytics and AI is a complex field that can take time to understand. However, if you are a data analyst, data engineer, data scientist, etc. and despite your best efforts your management still does not engage, then it is time to move on. There are so many exciting things happening in our field that while some patience is warranted, you would not want to find yourself years from now having missed out on most of the excitement. Life is simply too short.

– Ryan