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Rise up: What the chemical reactions in bread can teach us about data strategy

When you make a loaf of bread, understanding the steps you need to take is vital to getting the right chemical reaction. It’s no different when it comes to your data strategy (just with less flour and yeast). Here’s a lesson from the Amplifi lab…

You can’t get a simpler and more satisfying chemical reaction than bread.

A handful of store cupboard ingredients, a bit of manual effort (or a bread maker if you’re lucky) and hey presto – you’ve got a fresh loaf.

But anyone who has taken a disappointingly flat, stodgy sourdough out of the oven will know that it’s not actually that simple at all. Creating the chemical reaction that makes bread rise is a delicate balancing act. To get it to work, you need to pay close attention to the steps you take, the ingredients you put in, the apparatus you use and myriad other factors that might affect your end result.

The same principles apply to your data strategy. The steps you take are, inevitably, going to influence the results you get out. To get the reaction you want, you need to look carefully at your data’s processes, the people who handle it and the technology you use to manage it.

In our 6 expert tips for building your Data Strategy, we show you how to create a strategy that enables data to deliver your business’ commercial goals – from tackling data quality to migrating your data without a hitch.

In the meantime, we look at four key aspects of a successful data strategy – the steps you take, your data quality, commercial goals, and technology investment – to show you how they influence your data reactions.

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What makes bread rise – and your data strategy work? The role of a data strategy.

For bread to rise, it needs to create carbon dioxide. This reaction occurs when the yeast starts to feed off the sugar and is accelerated when the dough is put in the oven, with the heat expanding and moving the gas to create bubbles in the dough.

But there are several factors that can stop that chemical reaction from happening. The yeast is too old, too much sugar was added, you missed out an ingredient, you didn’t prove your dough for long enough, the temperature in the oven wasn’t right…the list goes on.

The more you understand the bread-making process, and why each step is needed, the better equipped you are to avoid those mistakes.

The same goes for data. The more you have a clear roadmap to follow, and understand why you need to follow it, the easier it is to keep your data goals in sight and delivered on time (and in budget).

Data governance, for instance, is a step that is too easily missed from a data project, most often because the business either doesn’t understand why they need it, or why it needs to fall at that particular part of the journey – yet like proving bread, it is an essential step to making your data project a success.

Defining a data strategy at the very beginning will keep you on track and help you to communicate the why and when of the project, avoiding delays and mistakes later on. It’s essentially a recipe for success, that keeps everyone on the same page and moving forwards.

Are you baking a bloomer or flatbread? Creating a goal-oriented Data Strategy

Before you begin to define your data strategy, you need to know what you’re trying to achieve with your data.

If you set out to make a flatbread, but follow the recipe for a bloomer, you’re going to be disappointed: yes, you might get a ‘perfect’ loaf, with all the right chemical reactions, but it’s not what you needed or wanted. And it will make a rubbish kebab.

Once you have a vision and commercial objective for your data, you can devise a data strategy that facilitates that goal. For example, if your primary data goal is to improve marketing personalisation, your data strategy’s direction is going to be very different to if your goal is to create a more user-friendly e-commerce platform. You’ll need different data, different processes, different governance goals and different technology to facilitate it.

Too much salt? Addressing data quality

A robust data quality exercise should always be part of your data strategy: by being selective of what data you’re putting in, you are more likely to get good results out. Is the data accurate, up to date and reliable? Is there rogue or duplicate data lurking in your databases, that can skew results? Are you missing data that you need? Or is the way that data is being stored and manipulated having an impact on its quality and accuracy?

But it’s not just the data that’s missing or inaccurate that can create a problem. Data that you just don’t need finding its way into your system can also create issues. Just like a tablespoon of salt will kill the yeast/sugar reaction (as the yeast releases its water content to the salt by osmosis), trying to process unnecessary, irrelevant data will dilute your data quality and make it harder to get tangible results from your project.

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Bread maker vs skillet: getting the right technology

Another key component of your data strategy is technology, but it should never be the sole focus of your project. Your technology should be selected based on how it enables you to reach your goals with data: it shouldn’t shape your data strategy around it. So many organisations start with a piece of data software, and try to work backwards, shaping their strategy to fit with their investment. It doesn’t work. To get commercial results, you need to know what you want to create with data and select technology based on how well it delivers that goal.

It’s like buying an expensive bread maker, when what you really want is one of those aforementioned flatbreads – all of your ingredients, your process and your policies become guided by making a fluffy loaf of bread, when you don’t even really want or need it. It’s not just the financial investment in the technology that’s wasted, but the time and energy you put in to trying to make it work.

A good data strategy recognises this, and builds informed technology selection into the right phase of the project.

Has this left you hungry for more insight into creating a successful data strategy – or just craving a sandwich? We can’t help you with the latter, but we can help you get your data strategy off to a flying start.

Download our guide, 6 expert tips for building your Data Strategy, to get started.

6 expert tips for building your Data Strategy