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Measurement Frameworks: The journey to data nirvana

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Author: Sean Burton
Updated: 07 Apr 2015
Measurement Frameworks: The journey to data nirvana

For many years I've been talking about the benefits of integrated and automated systems which allow businesses to free up their resources to focus on actual data analysis and insight, such as actually investigating the available data, making changes based on the resulting insight, and then measuring the impact. Crazy, I know!

For many organisations, this is an ideal, a Nirvana if you will, rather than a practical reality. They know they should be doing this exciting and valuable stuff, but they have too many obstacles in the way: not enough resource; no senior buy-in; not enough budget, etc.

What can start off as a conversation about funky stuff such as predictive or econometric modelling, quickly turns into a more operational and logistical discussion about practical realities.

I must confess that I find this all very frustrating, as using data effectively will actually save them time and money in the longer run. The issues are usually the same - current resource is overwhelmed with incoming requests and so they cannot possibly do any more. This is often reflected in low morale in the analytics/insight team and frustration across the wider businesses.

The good news? There is an answer!

A measurement framework will help to focus the business on what it actually needs to measure to be effective. It also helps to operationalise the use of data within an organisation by defining 'what', 'why', and 'how' to measure across its people, process, and technology.

Just before my departure from Seren, I had just reached the end of an 18-month engagement with a major telco in the UK. We had been brought into the implement a measurement framework centred on customer experience and when we started we found that the web analyst was struggling with the number of incoming requests and the business was frustrated at the lack of data, with many people simply no longer bothering to ask. We conducted a review across the business to establish what they wanted to achieve, and then worked with the technical teams to see what was practically possible in both the short- and long- terms.
We then established a map of all requied metrics which detailed the Key Performance Indicators (KPIs) and additional contextual measures along with information on how to measure them and what action should be taken. Additionally, we established an insight support process allowing a more structured response for incoming queries - it also allowed us to spot patterns to the incoming requests and to respond accordingly. For example, we soon found that business owners from the marketing teams needed certain data at certain times. By giving them an automated Excel dashboard and providing training on the web analytics platform, we quickly reduced some of the more common queries. This then gave us more time to focus on the strategic and longer term projects.

So, a measurement framework isn't simply a list of things you should measure, but rather an end-to-end process that helps to establish what you need to measure and then actually how you go about achieving that in reality. Ultimately, it can be scaled to fit budgets as it focuses on the practical and achievable improvements that means it actually justifies itself on a rolling basis. Almost by definition - if it isn't providing value to the business then it's not working.

It's important to get the basics right, but you also need to know where you're heading. If not, then you'll be stuck in an endless loop of changing analytics platform and trying to get things implemented - I've seen this happen with organisations large and small. Pretty much all data are 'good data' if used appropriately. Try not to obsess about accuracy, but rather whether action can be taken and whether the impact of the action can be measured.

The Data Nirvana is all about the on-going journey: understand, measure, act, repeat.

So, do you want to head to Nirvana? If so, give me a shout.


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