MeasureCamp’s data heroes

Colin Smith from TestBoard shares why MeasureCamp is a breeding ground for data heroes.

At TestBoard, we’re sort of a bit obsessive about making heroes of data analysts, which is why we’re so chuffed to be supporting MeasureCamp for the first time this year!

Everywhere you turn, everyone wants to be data-driven nowadays, and we’re told that we should all be statisticians… making decisions off the back of data… incremental gains, test & learn, always-on versus campaign, etc…

It’s like suddenly everyone’s realised the importance of all this stuff that we’ve known for ages, and suddenly they want a piece of it!

That’s driven up the expectations on analytics teams; in some cases data & analytics folk are getting a seat at the top table in businesses. It’s increased the sheer volume of analytics work being generated, and the teams whose job it is to respond, are ballooning in size.

With great power comes great responsibility, and to take our rightful place as the heroes of today’s businesses, we need to be ensuring that we are fighting the good fight – earning the trust of our colleagues – that we don’t become a bottleneck (as ‘digital’ teams did 10 years ago).

So we’ve been investigating (with some of our friends) what are the core characteristics of data heroes. It’s a work in progress, but it’s also had a decent amount of input (and a few pub debates) from analysts and their main ‘customers’. Can it be codified? And how could it help?

Here are some of our findings. We’d be really interested to hear what you think.

1. Time tracking

Some say it really helps to quantify the resource being deployed on each analysis, actual and estimated, to inform decisions about whether to proceed, whether the ROI was/could be there.

2. Structured briefs

Most analysts wish for more detail in the brief they receive; this is thought to assist with time wasting in the early stages of triage. Give a co-worker a form and they’ll fill it out.

3. Structured feedback

Everyone wants feedback, the more specific, the better. Since it often requires little bit of reflection upon the insight offered, a routine of structuring feedback would be welcome.

4. Perspectives

Feedback from different roles, inside and outside the organisation, where appropriate, brings different and additional domain expertise to an enquiry.

5. Value track

It helps at review time, to know how much value (revenue increase or costs saved) your insight has contributed.

6. Model archive/library

There’s no point in your team learning things twice; if someone’s already done the work, it would be great to access a library of stored enquiries.

7. Iterative or continuous improvement

More like an attitude you want the organisation to acknowledge and embrace, to provide an atmosphere in which to hack together insights

8. Testing hypotheses

Prevent your insights never seeing the light of day – demonstrate a backlog of insights and recommended tests.

9. Inquisitive/lateral-thinking

We all like to learn, to improve and sometimes, for a bit of fun, to be confident taking a contrary position to stimulate discussion

10. Learning new skills

We all want to know how to better deploy ML technologies to do execute our function to a higher standard.

…that’s why MeasureCamp is such a cool place to be; it’s like the Marvel Universe of data heroes (cue jokes about Peter O’Neill being the Doctor Xavier of MeasureCamp). The spirit or essence of the combined list above is embodied in the atmosphere at MeasureCamp. We see that learning/perspective sharing/ collaboration/empowerment spirit in spades, and we can’t wait to get stuck in again!