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Learning fast: Starting small and iterating

Justin Langford

As great as it would be, creating a data culture can’t happen in an instant. The greatest positive difference to your business will be achieved by building guardrails for data programmes, creating strategy and supporting this journey through data literacy initiatives. This can appear overwhelming, and the most effective way we’ve achieved this with our clients is by starting small and iterate. This enables us to refine our approach and improve impact as you go along.

In this article (the third part of our series), I will focus on the idea of iteration and how it can help you make the most of data. If you haven’t read them yet, make sure to check out part one and two of this series.


Building your data culture bit by bit 

Imagine for a moment that you’re in a sushi restaurant with a conveyor belt. As the belt comes around, it brings you a variety of different types of sushi, each one more delicious than the last.

You take a piece and eat it. Then another. And another. Although tiny, each piece is like a meal in itself, packed with protein, carbs and vegetables. By the time you get up to pay your bill, you’ve had a satisfying and, hopefully, memorable meal.

Similarly, your business can benefit from breaking up data projects into smaller parts. Each of those parts is a bite-sized but complete morsel, and they add up to something more substantial.  


What does this look like in practice?  

Rather than taking a ‘big bang’ or linear approach to project delivery, you should aim to deliver value incrementally and early. Yes, you should have a roadmap with milestones, so you know where you’re headed, but you don’t have to wait until the end of your project to deliver output to users. It’s much better to create business value checkpoints and regular releases, laying a foundation you can continue to build on and improve.

Using this approach, each delivery package is essentially a mini project, an end-to-end process with ingestion, modelling, visualisation and release to your business users every two weeks.

Returning to our sushi analogy for a moment, these work packages are the individual pieces, delivered at regular intervals by a conveyor belt. Of course, you could decide to fill your plate before you tuck in, but why wait? By trying different things as they come to you, you can decide what works and what doesn’t.  


A better way to bring about change 

Having an agile data strategy like this leads to quick wins. They’re short-term benefits for your business, but they’re still important, and they should be part of your overall long-term plan.

Taking this kind of iterative approach has delivered real benefits to our clients. Data quality and governance is a challenge for every business.  It can be difficult for users to validate and get behind the solutions we build, and we’ve found that integrating data quality and data governance into programmes from the outset yields the best outcomes.

For the best outcomes and engagement, we encourage our clients to tackle data quality issues inline with project delivery phases. We see this as an integral part of creating successful outcomes and winning user confidence in projects.  


Final thoughts 

Starting small, engaging users early and regularly creates a meaningful data culture within your business. When the whole choir are involved in the early stages of your data projects, realising benefits sooner and tackling data quality issues – this provides real results. It’s important to keep in mind a view of your destination in long term goals, this enables building a data foundation that makes a real difference to your success.

And, should you ever doubt the value of starting small, just think of sushi.  


Next time around 

In part four of this series, I’m going to be looking at ‘Working at pace: Avoiding fear of failure’. Be sure to join me!

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