Posts in June
It Does You Good
The flavour of the month may be data, especially in its ‘big’ form. But are we deluding ourselves into believing that big is always beautiful? Sure, big data identifies trends; helps to better understand and target customers; recognise and optimise business processes; and improve mechanical performance. It also has a role in public health, scientific research, and financial trading.
But, should we show caution when it extends unchallenged into security and law enforcement, or the ‘optimisation’ of cities and countries? It cannot be assumed that all data will ultimately be used for social good. Sometimes projects based on mass data increase inequality, and consequently harm those they were designed to help.
Bigger the Better
In 1907, Charles Darwin’s cousin Sir Francis Galton asked 787 villagers to guess the weight of an ox at a country fair. None of them got the right answer, but when Galton averaged their guesses, he arrived at a near perfect estimate. Beating not only most of the individual guesses but also those of alleged cattle experts. Thus the ‘wisdom of the crowds’ was born.
Groups of people pooling their abilities to demonstrate collective intelligence and average judgement converging on the right solution. It’s a pleasing theory and tempting to apply to all sorts of decision-making processes. Until, that is you realise that the crowd is far from infallible. Good crowd judgement only arises when people’s decisions are independent of one another. Influenced by other’s guesses, there’s more chance that they will drift towards a misplaced bias. In other words groups, when fed with information, tend towards a consensus to the detriment of accuracy. Witness the recent election polling predictions.
Analysing the Detail
Nothing in doing data analysis is neutral. How data is collected, cleaned, stored. What models are constructed, and what questions are asked. All tend towards discrimination.
As Dana Boyd, in her excellent article, ‘Toward Accountability’ asks, “How do we define discrimination? Most people think about unjust and prejudicial treatment based on protected categories. But discrimination as a concept has mathematical and economic roots that are core to data analysis. The practices of data cleaning, clustering data, running statistical correlations, etc. are practices of using information to discern between one set of information and another. They are a form of mathematical discrimination. The big question presented by data practices is: Who gets to choose what is acceptable discrimination? Who gets to choose what values and trade-offs are given priority?”
Even so, making data available to the public must be a good thing – it’s democratizing – right? But what if it’s not? For instance, what happens when big data is used in conjunction with a computer algorithm to predict crime? In theory analysing large amounts of crime data should spot patterns in the way criminals behave. Resources could then be deployed more effectively in the areas of predicted criminal activity. Result!
Or, what happens when parents are encouraged to select their children’s school places on the basis of an education data ‘dashboard’. Benchmarking every aspect of a school’s performance against the mean should tell you everything you need to know to make a rational decision about your child’s future. Simple!
Lastly, how good would it be if, when you applied for a job online, you were swiftly shortlisted for interview on the basis of your merits? Your CV having been analysed against the qualities of those who had previously succeeded in that role. Brilliant!
But wait! Critics of this kind of data analysis raise a number of ethical concerns. They claim predictive policing, for instance, leads to victimisation, and unnecessary stop and searches in areas with high crime rates; displacement of crime elsewhere; gathering of sensitive data, leading to invasions of privacy; and lastly, that it ignores the social, economic and cultural factors that cause crime. Advocates, on the other hand, argue that a variety of policing approaches are necessary; that research has found no evidence of victimisation; and that it makes police decision-making less biased.
Surely no one can argue that giving parents access to school data is a bad thing? But what data? What constitutes a good school? Is it test scores, student makeup, parent ratings, or facilities? Presented with the data, does every parent have the time, language skills, and ability to interrogate the statistics? And, if they do, is everyone equally able to act upon their findings by dint of wealth or mobility?
Oh yes, that job you applied for! Being filtered for interview on the basis your abilities is one thing. But what about your gender, ethnicity, or sickness record? You’ll never know, because you won’t get the chance to explain. Not that anyone would be so crass as to filter on that basis. But subtle clues, like blips in your career timeline or post-code may result in unwarranted inferences. Combine these factors with feed-back loops and machine learning and before you know it you may never work for a large company again.
“Data scientists”, said Mike Loukides, VP of O’Reilly Media, “are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” So, I remain conflicted on the benefits of big data. It has its uses. But, rather than thoughtlessly surrender ourselves to its machinations – in the belief that the outcome will always serve the interests of humanity – we should remain sceptical, questioning, and downright belligerent. Especially when told that it’s for ‘our own good’. I plan to keep in mind a quote from Ronald Coase, winner of the Nobel Prize in Economics, when he said, “Torture the data, and it will confess to anything.”
Sources / Further Reading:
Hang on a minute! What is all this stuff about ‘engagement’? Everywhere I look these days membership organisations are talking about engagement as though it was the be all and end all of their existence. But why? And what do they mean by the term? Look up ‘engage’ in any shorter dictionary and – apart from a ‘promise to marry’ – to engage somebody means to attract or hold their attention of sympathy, or to cause them to participate. But that isn’t what the tech wizards appear to mean when they come knocking at your door with a ‘solution’ to your dwindling membership. What they have in mind seems much more superficial. Just clicking in some cases!
Now, if I click an online petition, that no more makes me an activist, than liking a post or a tweet makes me engaged with the author or their organisation. The truth is, there is no definition! We may have been bandying the word around since the mid-2000s, but in reality you can make engagement mean anything you like. It could be defined as consumers’ behaviour online, or the strategies brands use to attract attention, or the things you can count. Context is everything!
If you’re a writer looking for blog readers, or you’re an ecommerce site looking for shares, it will alter the type of engagement you’re looking for. If you want people to purchase, then it’s all about the first meeting and activity leading up to the sale. But if you’re a blogger, then engagement may be a comment or a share by an influencer.
When it comes to associations, I contend that engagement is the result of a member investing time and money with them in exchange for value. That value may me financial, practical, emotional, or a sense of belonging. The more resources they invest, the more engaged they are. And that happy state can’t be brought about by clicks alone!
Engagement is also about value. The value for the person doing the engaging as well as the value of that engagement for the association. It’s not the ‘output’ of a programme, but the strategies and actions that go into establishing relationships. It’s a discipline not a goal.
So, I reckon that any system that offers to analyse your engagement by counting clicks is leading you into a fool’s paradise. Vacuous statistics are just vanity metrics. Handy for keeping critics off your back, but essentially worthless when it comes to predicting outcomes or measuring success!
The twin goals of most associations are member acquisition and retention. When it comes to acquisition, the numbers that view your website, blog, or twitter account; share content from your publications; or even read your press coverage, are superficial. They’re not a signal that you have held attention or triggered participation. And transactional interactions, like buying a product, or paying for a course, are unreliable as an indicator of likely member retention. Attention gained through financial incentive tends to be transient!
It’s only when you put issues of empathy into the mix that you can really start to measure engagement; when participants align with your ethos, and the significance of the relationship outweighs the financial cost of membership! Indicators of that state of mind are a willingness to write or speak on your behalf; volunteer for a committee or task force; serve in a leadership role; achieve status; invest in sponsorship or similar. Of course, not every member can achieve this, but at least they should have the feeling that they could!
Healthy associations create more engagement opportunities in areas that create value for both organisation and member. Strategically, it’s also worthwhile for associations to plot the members likely progress from pre to post engagement, and consider what the first steps on the commitment escalator might be. As an efficient flow from low to high value engagement will tend to be healthier from both revenue and mission fulfilment perspectives.
Edition 259, Association News, 9th June 2017