What is Data, and why do they matter?

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What is Data?

What does ‘data’ actually mean, and why should you care? Think of data as the raw information that fuels smart decision-making for your business. Data isn't just ‘numbers’ (although it often comes in that form). It's any quantifiable information that gives insight into your business. It could be:

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How to make data useful? Context

For data to be useful, it needs context. Numbers on their own are meaningless; traffic figures to a website are only valuable, for example, if you are able to track them over time, benchmark them against competitor or best-in-class metrics, or gain meaningful insights as to how they change based on other actions you take.

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Endless reams of user feedback on your product or service are significantly more valuable when you have the context of the specific customer experience that elicited them, the profile of the individuals in question, or information about where in the user journey the feedback arises from.

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Businesses often underestimate the planning needed to produce valuable data. Tracking and analysis have to be consciously built into your business model - gathering useful, actionable data doesn’t happen by accident.

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It’s not uncommon for us to come across non-technical companies who develop and launch a product and who will assume that this will magically generate useful data automatically,  rather than realising that data capture is something that has to be planned into a system for it to meaningfully exist - without this planning, you might have numbers, but you won’t have data.

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Making Data commercial

A useful analogy to deploy here is a museum or art gallery.

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At the most basic level, any venue will gather basic ticket sales numbers - but that doesn’t constitute ‘useful data’ per se. All this will tell you is ‘how many tickets have been sold’ - not how many people actually visited, when they visited, what they engaged with, how they interacted with the space, whether they made supplementary purchases, whether they used any facilities…

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A lot of what we do when working with businesses or founders is understanding the underlying commercial objectives, constructing the correct questions which measure success against those objectives (KPIs) and then architecting and implementing the data solutions which answer those questions  - which is part of the foundational work of scoping and building a business.

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Why is this important? It may seem obvious, but it bears repeating.

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As a business, your focus when it comes to data should be on working out WHAT data is going to help you make the best decisions to optimise your business, and then determining HOW you need to modify your business, at a systemic/operational/infrastructural level, to maximise your ability to capture and analyse that data.

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What does good data look like?

What does ‘good data’ look like? It will, of course, depend on your business and what you are looking to optimise right now, but in general we can say that useful data has three specific qualities:

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Every business will have a different approach to data collection and use - but the following are some principles we’ve arrived at over the years which we believe are universally-applicable and which will help you ensure you’re looking at the question of ‘what to do with all this data?'

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Data may challenge assumptions. Use it as a guide, not an echo chamber.

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Decide early on what you want to measure, why it matters, and how you'll analyse it. This will help you avoid falling into the all-too-easy trap of making kneejerk decisions based on limited or misleading datasets.

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What will you DO with the data? How will it impact your decision-making? Knowing how you will feed data into your decision-making processes, and when, helps you avoid making hasty judgements.

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Data interpretation can be complex, and not everyone is an expert at analysis - even technical people. If you’re uncertain about what a dataset is saying, seek advice and opinions from people who have expertise and insight - because coming to the wrong conclusion can be more damaging to a business than coming to no conclusion at all, and it’s worth taking more time to come to a better, more considered, analysis.

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