#BDW14 - Quantified Self

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This theme was kicked off by an interesting session from Ruth Thomson at Cambridge Consultants.

The quantified self is about measuring yourself, and incorporating measures into your daily life in order to gain actionable information (not just data) to help make you fitter, better, whatever it is that you’re interested in.

So in the fitness area, systems can help you improve your performance or technique – measuring your steps or checking your pulse rate and whether you’re in the right exercise zone – plus the community aspects allowing you to compare your performance with someone else’s, or providing data to a coach etc.

And in wellbeing, systems can monitor your posture, sleep, chronic conditions etc.  These are supported through a range of technologies ranging from smart socks helping with posture to an intelligent pill that knows when you’ve taken it!

Technology availability is improving rapidly – there are many small, lightweight, cheap and high performing systems – wearable or around us in the environment, often on our phones – though this isn’t always ideal – eg for the bathroom or swimming pool (I wear a FitBit Flex, and link this to my digital weighing scales and iphone / ipad app). 

Therefore most consuumers already have part of the system in their pocket and quantified self systems can piggy back on this existing infrastructure (eg Nike Fuel Bands potentially being replaced by Apple’s iwatch).

Cloud storage also means consumers have somewhere to store all of this data and provide secure access to the right people at the right time.

But most often today is about single devices communicating in silos to the cloud, not really connected devices communicating intuitively with each other, gathering data about what you need and the environment and allowing smart inferences on what you want or need.

Again, we’re at the cusp of this – and we’re getting mass attention, not mass adoption.  But I do think this is something HR should be paying attention to.

In later sessions, Pravin Paratey at Affectv talked about the role that sensors and devices are playing in the growth of big data as these become incorporated into our lives.    The internet is becoming an extension of our lives, a medium for creating and interacting rather than just somewhere to find information.  And a consequence of this is that our every action is logged.  Businesses need to:

• Ask the right questions (what business challenges are you trying to solve?)
• Look at how they can augment their existing data – via internal and external sources
• Move to statistical vs rules based approaches to cut through big noise
• Accept approximate vs exact data
• Understand the difference between big and fast
• Manage data security and build trust – and consumers need to understand that our data is out there.

Splunk provided some examples of developing insights from unstructured data for e-commerce.

• Tesco – understanding what you’re doing on their website – and linking to other information eg on the weather - so they can push other products to you
• Dominoes selecting the email offers they send you
• Self service car analysis.

Big Step discussed using Splunk to extract data from social media feeds.

These are good examples but I didn’t like the suggestion that we need to change the process of collect – prepare – ask to put more focus onto ask.  In my view, we really need to move ‘ask’ right to the front of this.  The power of correlation may be replacing the value of causality but at least in strategic vs operational aspects of this we still need a level of intelligence in our analysis.

Path Intelligence talked about some of their successes in retail eg in shopping habits which is difficult to get otherwise as other data sources face difficulties in understanding whether there are multiple people or just one person coming back multiple times.  They’ve also linked these to other data sources such as labour planning to ensure workforce scheduling better meets customer demands.

It was good see an HR application of big data coming out finally.  But actually there was a big lack of business functionality throughout much of the day.  I  personally think this focus on the technology might be one reason big data is failing to take off in the way many people have predicted.

The other problem is peoples' push back against providing their data to firms - which Ernst & Young have been articulating too.

But I still think the HR version of this, the quantified organisation, using wearable technologies and other devices, will happen.  And of course, it's already happening in places, eg I think this case study on Bank of America and Sociometric Solutions is quite compelling.

But as always with technology, it's organisational culture rather than the technology that's key.   And it'll be interesting to see whether we end up with QS being used in a controlling sort of way, a bit like existing work in Amazon's warehouses, or in a more empowering style, using the information to help employees make better choices about their actions.

So once again, QS needs to be an HR strategy rather than an IT one.

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