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How Big is Big Data: Part 2

When I started writing How Big is Big Data: Part 1 last week, my intention was to write an article that was completely different to the one I ended up submitting for the blog!

And now with Part 2, I’m about to do exactly the same thing.

So my original idea for the article will have to wait for Part 3 – unless I come up with a different slant in the meantime.

The subject matter for this piece takes me back to something I wrote a while ago about ‘the shape of the sausage’.

As you might expect from me, it’s actually a cycling reference. It refers to one of the elements on the Wattbike display screen.

It was only after seeing Stu Wannop speaking at our Futuretech events over the last couple of weeks that I really appreciated the value of this ‘data visualisation’.

Without getting into the technical detail (which in honesty is actually beyond my level of understanding) the display shows 4 quadrants and captures data based on the forces generated by the left and right leg during the pedalling stroke.

It’s all about efficiency and generating even and constant power throughout the pedal stroke rather than what is effectively a stop/start action at its worst.

In simple terms, if the display looks like a figure of 8 then you’re not doing very well – although that’s almost inevitably going to be the shape if you’ve cranked the resistance up and are effectively grinding your way up a very steep virtual incline.

The optimum is indeed a sausage shape.

But what’s this got to do with data?

It’s actually a great example of how you can use data to make decisions.

The Wattbike is actually capturing huge amounts of data – and if you’re pedalling at around 95 revs per minute that’s a minimum of 190 data points (i.e. 95 x 2 legs) but in reality, it’s considerably more given its ability to pinpoint power and force at almost any point of the pedal stroke.

If that was presented as multiple lines of information it would be pretty difficult to interpret, especially when your heart rate is through the roof and the sweat is dripping into your eyes.

The data visualisation allows me to make subtle but instant adjustments throughout the session that will, hopefully, translate into long term improvements over time – which can, of course, be measured and monitored using the vast amount of other data captured by the Wattbike.

What it demonstrates is that having large quantities of data is only really of any value if you have the tools to help you interpret and analyse that data effectively.

If you have the right data visualisations to offer clear insights, then improved decision making should follow.

Whether that’s about small adjustments or earth-shattering revelations, the impact could be huge.

For me, using the Wattbike to improve my pedalling technique will undoubtedly have contributed to knocking more than 30 minutes off my time for an iron distance triathlon.

The question is, how will you use data to achieve your aims? 

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