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Why your business may be on a data-driven ‘coddiwomple’

(Image credit: Future)

In the endless sea of data conferences, a word that’s heard time and time again is “journey”.  It’s an incredibly popular word in the data-driven transformation world, and it’s common to hear a speaker talking about the “journey” their business is on.  However, as an overarching synopsis of a data-driven endeavour, this description is lacking.

To begin with, the Oxford Dictionary defines a “journey” as “the act of travelling from one place to another”. Thus, to be on a journey, a business would need both a very clear understanding of where it is travelling from and what the destination is. With this information, you would expect a business to be able to chart an established route from point A to point B and have some sort of understanding of how long it will take to get there and the resources involved. Moreover, with such a fixed plan, should there be any difficulties or delays, one might expect there to be an established strategy for working around them in an appropriate and timely way.

If we compare this to the use of the word “journey” in the context of data-driven transformation, the same does not hold true. Often there is a surprisingly lack of clarity over exactly where the initiative is heading, but also what the starting point is. In practical terms the lack of clarity on the starting point can involve a lack of vision into what the specific objectives of the team are, or what human resources and skills are already in house. Meanwhile, the diverse and siloed stakeholders in a “destination” for the data-driven endeavour may all have slightly different ideas on what the result should be, leading to a divergent and fuzzy path to follow. It’s nearly impossible to plan and measure progress in the same way one would expect on a normal journey.

Etimology unknown

This is not to say that the essence of a “journey” is at odds with a data-driven transformation. It conjures accurately the sense of momentum and change, and the desire to move from “where we are now” to “where we want to be” in a relatively structured and logical way. However, there is perhaps a word that describes the reality of most current data-driven transformation better: Coddiwomple.

The origins of the word are unknown, and it is likely a rather recent addition to the English language, but it is defined as follows: “to travel in a purposeful manner towards a vague destination.” This is nicely echoed in the following statistic: according to EY, 81 per cent of senior executives agree that data should be at the heart of all decision-making. It’s a very purposeful statement, but the actual implications, deliverables and impact of this is vague. After all, what does it really mean for data to be at the “heart” of something? In today’s business culture, we hear a lot about SMART goals – specific, measurable, achievable, relevant and time-bound. Making data the “heart” of all decision-making is none of these things, except perhaps relevant for success. It is also impossible to measure the maturity of this endeavour as there is not a clear set of steps to get there that can be marked off along the way.

Most likely as a result of this, the same EY report found that just 31 per cent of companies have significantly restructured their operations to achieve data centricity, and only 23 per cent have an organisation-wide data strategy in place. This means that the vast majority of organisations are off on a coddiwomple, and even some of that 23 per cent may find that they have made very purposeful movement in the direction of a wholly undefined endpoint. Recognising that a business may be on a “data-driven coddiwomple” is only one part of the challenge.  To get onto the oft-prophesised “data-driven journey”, you need to pick a destination – in other words, what will a future “data-driven” version of your current business “look and feel like”?

Taking a critical look

Unfortunately, this is often easier said than done; becoming a data-driven organisation is complex. Thinking back again to making this a SMART goal, businesses need to reframe the conversation on being data-driven to ensure all stakeholders agree on what the end-point should look like, what the measures of success look like, whether the success is achievable with the current data and resources (and if not, how to address this) whether success will deliver long term success for the business, and what the timeline is for each stage of the project. By doing this, you can align your data and analytics strategy back to your business objectives (make it relevant), budget and plan for the right infrastructure and skills (make it achievable) and have the right methodology in place to achieve real value in specific areas of the business rather than vaguely across the board.

Once this is decided, businesses can build data-driven maturity models to measure value against the agreed KPIs and inform downstream investments. This is where data science has a key role to play in turning your coddiwomple into a journey with a purposeful destination. By this, I am referring to the culture and practice of data science, rather than some of the tools and solutions we see marketed as silver bullet data science solutions.  Real data science, lead by a cross-departmental collaboration of experts, blends technology, data and business awareness to extract value -- not just information -- from data.

By taking a critical look at whether a business is on a coddiwomple or a journey, organisations stand the best chance of making data science decisions that result in a smarter, more agile business that delivers better solutions to customers. However, all of this comes down to demystifying the “data-driven” process, and ensuring a clear start point, established goal and cross-company understanding of how to get there. Once that foundation is in place, organisations can build towards putting data at the heart of all decision making and stop coddiwompling around once and for all!

Rich Pugh, Chief Data Scientist, Mango Solutions

Rich Pugh is the co-founder and chief data scientist at Mango Solutions.