Writing a data strategy can be like walking a tightrope, requiring a fine balance of data management know-how and business acumen. It needs to be clear and concise, avoiding technical language and focussing on business outcomes. So what should be in it?
Here’s a five-point guide on what to include:
1. Relevance: Set out the role of data within your organisation and how it intertwines with strategic objectives and business aspirations. Canvassing widely for input to get this right is key. Speaking with each department and working together to identify the part data plays in achieving current and future goals can help create a sense of ownership of the strategy by the whole business. Don’t stop there, though – your customers need a voice and not just about the data you hold about them. Engaging user groups to understand what they want to see in the way data is used or made available and being open about your plans can foster trust and keep customers loyal.
2. Ambition: Where do you want to get to and what will this look like? Setting the ambition will be informed by your engagement with colleagues and external users. Ask questions like:
a) How widely do you want data to be available, by when and to whom?
b) Do staff have the relevant data knowledge and skills? (DataIQ research has found analysing customer data is the biggest marketing challenge for 29%, for example.)
c) What about the way data is managed – do you want to make improvements here, for example to roles, responsibilities and processes or technical infrastructure? (Legacy systems are the biggest data management challenge for 54%, according to DataIQ research.)
Setting out clear commitments and milestones for achieving these will help everyone to know the part they need to play. The data office, if one exists, has an important role here – as the data management professionals, they are well placed to advise on best practice.
3.Scope: It may seem obvious, after all, data is data, right? But clearly stating which type of data the strategy applies to will support implementation later on. For example, does it apply to all data held within your organisation or just certain subsets, eg, personal data, customer data, operational data? Taking an holistic approach will ensure the ambition, processes and behaviours are aligned across the organisation and you’re more likely to be successful in embedding a data culture. Note that this doesn’t mean you need to implement across all departments at the same time – you can phase it!
4. Principles: Setting out the principles to which you will work will be your guiding light during implementation. There are a wealth of principles for data out there to draw from – data protection principles, open data principles and FAIR data principles are just a few examples. The key is to get them right for your organisation in terms of ambition and language.
5. Measuring success: So, you’ve set out where you want to get to, what data you will be using and how you will work to get there. But how will you know when you’ve succeeded? Including measures of success is fundamental for any objective-setting exercise. Be outcome-focused and include how frequently you will review the strategy and track progress against it. Benchmarking performance prior to implementation can really help to demonstrate the progress of the organisation or of individual departments.
However you create your strategy, and whatever you include, be sure to write it in your specific context with input from a wide range of colleagues, customers and potential customers. Only by doing this will you be able to walk that tightrope and navigate the high wire of data improvement.
Deborah Yates is a senior consultant at the Open Data Institute and committee member of the Data Management Association (DAMA) UK.
