What is a Data Strategy and do I need one?

Let’s assume you’ve been using Google Analytics in your organisation for some time and your digital marketing endeavours at least are data-driven. More and more people are starting to see the benefit in using data to get their jobs done and now everyone’s talking about Data Science or Big Data or whatever. To be clear – when we say “data” we are not talking about just Google Analytics but the proliferation of this tool means that it is very likely a starting point for your data analytics. Alongside your Google Analytics data, you’ve probably got a CRM and a bunch of other data sources that you know you should be doing something with – and gaining some kind of “competitive advantage”.

Better get a plan! If you don’t you’ll be left behind, right? Well – yes and no.

It is very easy with digital analytics to be caught up with the technical intricacies of data collection implementations and tactical analysis; quickly not seeing the woods for the trees. A strategy allows you to pull back and take stock of where you are and where you want to be. In essence it means you can identify your position, map out your surroundings and understand the route you want to take.

But stuff changes, fast. You can’t plan for every eventuality. And how your organisation is aligning their endeavours (i.e. their “stance”) will determine the shape of the roadmap for your data strategy. According to a couple of clever chaps writing at Harvard Business Review, organisations adopt offensive and a defensive stances to their data strategies; where an offensive data strategy is focused on flexibility and a defensive data strategy is focused on control. You will need a mix of both and that mix will depend on your industry sector, competitive landscape and current trends.

Acknowledging your stance and understanding this changes over time is enough. As your organisation progresses along their path of analytics maturity, your stance with regard to data strategy is likely to alter too.

To help ourselves in this planning process, we have created the “Data Strategy Canvas”:

It is still in early stages (version 0.1 in fact) but those of you familiar with the canvas approach made famous by the folks at Strategyzer (see their Business Model Canvas) will quickly grasp what the intention is here. Draw up the canvas onto a whiteboard or print it out on a big sheet of paper, then simply use post-its to capture answers in the corresponding areas.

Begin with goals in the centre of the canvas and work your way outwards. To get things started, let’s take a key principle from the Lean Analytics movement – just “measure what matters”. Take a look at your business model; do you know your customers? Do you understand their buying process? Do you know why and how they buy? And how can you be using data to improve your business?

Once you’ve determined your goals, you will need to assess the data you have available and what data you need (Tools & Tech / Data Sources). There might be a trade off; what’s the value you will likely derive from all this work? Some technical implementations can be costly and that cost simply might not measure up against the returns it could bring.

Some plans might entail investing in new IT infrastructures or new hires. But before you do, ask yourself what can you do with external help. And you will be pleasantly surprised quite what can be done with your existing systems at very little comparative technical overhead these days. 

Most importantly, can you trust your data (Weaknesses & Risks)? It is a rare thing to hear of a company that has an absolutely fault free end-to-end data pipeline. And most of the issues start right at the very beginning of that pipeline – with a misplaced tracking tag, for example. And, before you start hiring a team of analysts, can your existing team be trained and supported to get the job done to a reasonable level (Skills & Capabilities)?

Anyway, we’ve found that a Data Strategy Canvas such as this can really useful in asking those early questions.