“… The beauty of me is that I’m very rich”

 “…The beauty of me is that I’m very rich”: While I’m not sure this statement is true when referring to people, I really believe it is true when talking about data. I have absolute belief in the richness of data and its ability to enable us to make evidence based decisions. When we ask ourselves When?  What? Where? Why? or Who?, data can provide the answer.

In my early days, post-uni, I held an academic position where I was also the Chair of the Centre for Data Mining. This was in the early noughties and it seemed that data, data everywhere was beginning to take off. I met many business clients who felt they had lots of data and they should be “using it”. For what? Who knew!

Major advancements in all things analytics have been recognised since then and most forward thinking organisations are using their data to inform decision making. In fact, many of the bigger, hugely successful think-tank houses like Google, Facebook and Amazon (who I believe are bracing themselves to take on the traditional form of grocery retailing with their recent acquisition of Whole Foods in the US – how retailers respond to this is another discussion in itself) were founded, and are future proofing themselves, by using and acquiring data.

Back to those early days! The idea of data mining was that never before seen patterns in data would be uncovered. That somehow, once you ran your data through some software package, these patterns would pop out! The old ‘nappies and beer’ purchasing habit did the rounds for a while.  If this was the case, how could we explain why students would obtain different outcomes when using the same dataset? When we think of organisations, some get really insightful actionable outcomes from their data and others, not so much. Why is this?

In my experience there are many reasons for this, not least, ill-defined business objectives or lack of buy-in from senior managers, maybe it’s the data itself. However, I believe one of the main reasons, and one I would like to focus on here, is lack of a good analyst. So what makes a good analyst?

• Curious about Data

To be a good analyst you need to be naturally curious about data – wonder there the gaps are coming from, what are the patterns of behaviour, how have these patterns changed over time, what has caused these changes. Having an innate curiosity about what the data might be telling us is a must-have in a good analyst.

• Challenging the Data

While being curious about data is very important, it is also imperative that an analyst has the ability to challenge the data. They cannot be afraid to question outcomes to ensure the veracity, quality and reliability of the data they are working with.

• Ability to understand the nature of the business at hand

Very often we hear about the ‘nerd in the corner’ beavering away, number crunching to beat the band. This person has a great ability to interrogate data in an appropriate manner but may lack the skills necessary to connect the outputs with real action from a business perspective. Having a natural ability to connect with the business and convert sometimes complex output to action items at the delivery point is imperative for successful deployment.

• Story Telling – Communication is key

A large part of the analyst’s job is to communicate findings top up and bottom down. Tailoring the message to the audience at hand is vital. Not all parts of the business will require the same level of detail in understanding and often, you only get one chance to engage the relevant stakeholders. When this is done correctly, you inspire them to join you on the analytical journey and become a sponsor and supporter of your work as an analyst. Telling the data story with an appropriate start, middle and end is an often unrecognised skill of a good analyst.

• Technical ability

A good analyst will also have the technical ability to use the appropriate methodology to answer each specific business question. They will have the skillset to perform an exploratory data analysis, build a predictive model, and segment a customer base, among others. They will know which assumptions can be ignored from a business perspective and they will have the know-how to explain the technical data requirements.

The beauty of data is that it is very rich. However, only a skilled analyst will realise the richness within any dataset using their unique skillset to maximise the insights. Data only becomes rich and has value when the analytics team interrogating it extracts quality actionable insights. Individuals with these combined skills are a rare breed and can be difficult to find.

So if you are at the beginning stages, wondering how to set your organisation up to realise the true richness of your data, or you have been working on your data for quite some time without necessarily realising the success you originally envisioned, ask yourself the following question:

Do I have a good analyst working on my data?

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