Toxic positivity? That seems a bit harsh when talking about data, especially given that most modern businesses aspire to be data-driven in both strategy and execution. Indeed, over the last decade or two, data has become almost universally regarded as a key corporate asset and an essential input to quality decision-making.
That said, the rising importance of data as an asset has resulted in significant overheads being required in its protection. Whether we’re talking about security, regulatory obligations, or simply data integrity, it’s clear that there are plenty of risks and concern associated with data.
In recent times the concept of data as a liability has also raised its head, albeit usually in terms of its strategic value, and what might happen if it was compromised in some way. The prevailing analogy here changes from “data is the new oil” to “data is like uranium,” both powerful and dangerous. Savvy data practitioners now realise that governance, while never sexy, has taken on a new and heightened importance.
What effective data practitioners know: context matters.
Yet that’s not quite what we’re talking about here. For me, the idea of toxic positivity being applied to data takes two forms: context and presentation. Likewise, the broader concept of toxic positivity is a social construct that appeals to popular culture and the zeitgeist of today—why wouldn’t it pertain to data?
Thinking firstly in terms of context, it’s easy to see how many data practitioners become enamoured with their analyses and reports, and are blinded to more mundane considerations like relevance and impact. This type of toxic positivity stems from the idea that data is the sole (objective) truth and is therefore unassailable. Overconfidence in your data and algorithms breeds an unwarranted certainty around the insights and can yield fatally flawed decisions.
The solution to this problem is to maintain a healthy scepticism towards prima facie answers and to apply common sense and experience in equal measure. In a flashback to my management consulting days, data is used to prove or disprove the hypothesis, not the other way around.
An AI wake-up call: always question the path of least resistance.
In recent months though, a more insidious threat to decision-making integrity has emerged, in the form of Generative AI solutions, and more specifically their user interfaces. The challenges with AI are both many and well-identified, and include a lack explainability, poor transparency, and variable data quality to name a few. Less obviously, a “positivity” problem now presents itself when we consider the form (or presentation) of AI’s responses—they are delivered in such a prescriptive and authoritative manner, as to silence any debate on their value or correctness.
Here is where the foibles of the technology tend towards positive toxicity: attractive, easy answers that are presented as compelling and “right” answers are the easy option for time-poor analysts or passive insight consumers. This problem is much harder to solve, primarily because Generative AI has such broad applicability, with no clear signature of its usage.
Likewise, without any way of knowing if answers are right or wrong, users will naturally lean towards the path of least resistance. Unfortunately, once headed down this path, it is very hard for them to turn back.
To get the most value from your data, never forget the fundamentals.
The assertions above aren’t intended to question the value of data, or data-driven decision-making for that matter. The right knowledge, thoughtfully applied, can illuminate a decision with new possibilities. Rather, it’s to highlight one of the fundamentals of analytical practice which has always existed: understand your business first, and only then seek relevant and considered insights.
Your business doesn’t exist to “consume insights”; it exists to satisfy customer needs while generating profits. The task of stewardship falls to the thoughtful data practitioner who understands how insights support the creativity, productivity, and tenacity required for business success.
To paraphrase Pablo Picasso’s famous quote from 1964, “Computers are useless, they can only give you answers.” The enlightened leader (and analyst) spends time asking “why” the analyses matter—they don’t stop at “what” the data says. Most often, this behaviour manifests itself in confirmation bias, where leaders latch onto insights that simply reinforce what they want to believe. The ongoing public debate surrounding climate change is a classic example of this behaviour.
Toxic positivity comes in the form of the attractive soapbox spruiker standing on the corner, telling you they have all the beautiful answers (whatever the question may be). At Domo, we want to inspire “data curiosity” within our customers—and it’s never been more important.
