The greatest data strategy ever?
Workshop
Here at Data Orchard CIC we are always looking for ways in which we can support data folk in non-profit organisations. We've been running a series of webinars around data maturity which have proved popular. This showed us there was an appetite for people to put an hour aside to discuss some fairly detailed stuff about data in non-profits.
Webinars don't allow for a lot of interation with other participants though. That can be a strength, I quite like peeling potatoes and learning something new at the same time. We know that lots of data folk in non-profits can feel a bit isolated so we wondered if more workshop type events where participants get to spend time with and talk to other data folk from other organisations might go down well.
Last week we ran the first of what will be a monthly series of these workshops. We gave it the understated title of "The greatest data strategy ever". We divided the 15 attendees into three breakout groups and asked each group to discuss a series of questions. They made notes in a shared Google Doc which means I have been able to summarise the output of their discussions. These are the collected thoughts of the workshop attendees:
Why have a data strategy at all?
Data for all
To take data out of the realm of specialists. Data is important for every individual but we may need to help non-specialist become more data literate. It should help the organisation recognise data as an asset, for all, not only specialists, and acknowledge organisational value and responsibility.
Bring together
A data strategy should encourage a more holistic approach, especially around business systems and a platform approach rather than siloed. It should help you to demonstrate impact (e.g. where your donated money goes). And make people think about data from the start, rather than an afterthought. It should also help the organisation comply with the GDPR and any future regulations and trigger the sharpening up of processes more generally, whether related to data (i.e. IG) or general business processes/rules.
Align with organisational priorities
It should help data align with corporate strategy. A strategy provides higher-level buy-in and protects against data being forgotten
To give visibility around what is happening with data (it has to support the strategic objectives of an organisation). The ambition of being ‘data driven’ and ‘evidence based’ needs to become more concrete and thought through. You don’t want to create a large data structure without a plan.
Setting priorities
A strategy should establish the order in which what is needed (you can’t run before you can walk). It helps you focus on whats important and knowing where you are heading.
Establishing what is really needed from data analysis (sometimes less data is needed!) to get actual value from the data and making sense of / utilising data towards stated operational goals. Recording valuable data and not just collecting for the sake of it.
It will also guard against being overly software vendor-led in your tech/data/digital choices. And help you remain focussed on priority business goals.
Characteristics of a great data strategy
Audience
Pitched to the right audience. You need a landscape view; relatable to non-specialists - including the front-line staff and team managers. You also need to get leadership buy-in and investment. A great data strategy will have sufficient hooks and incentives for all.
Action
Actionable - a strategy document that actually commits to doing. It should contain success criteria and outcomes but with sufficient flexibility in measures of success. A great strategy has a mechanism for implementing: resources, accountability and a roadmap.
Specific
Specificity of strategies makes them workable. One organisations data strategy will be different from another's. A great data strategy has a solid connection to corporate strategy/business plan (or even to the values). It should be evidence based (and show what data has gone into the formation of the strategy).
Characteristics of a terrible data strategy
Groups clearly found this a little harder.
Not useful
Stuffed in a drawer and not used.
Glorious rhetorical/hyperbolic nonsense - cut and pasted from another strategy on the web. Too much tech talk and jargon. No organisational buy-in or commitment.
No realistic plan for delivering it (time and resource), no resources or responsibility committed.
Things you should definitely do to create a data strategy
Collaborate
Influence and connect with lots of stakeholders before starting to draft anything - deeply understand what they need and your organisation’s priorities. Educate and influence those who aren’t sold on data. Offer pain-relief data exemplars. Get closer to people furthest away from the data, sit with them to understand and coach.
Listening, talking to people in the organisation to establish the needs and what they want to do (e.g. where are they now and where do they want to go?). You need to understand the landscape. See if all the roles in the organisation are represented in the strategy.
Speak to as many people as you can before writing it, have a collaborative approach, and be visible about how a data strategy is implemented.
Be realistic
Get a good use case to show the potential of data: e.g. proof of concept - pull all your data together about your customers in one place. Show how data can solve people’s problems/service delivery. Trying to be as specific as possible - without dictating solutions, not too generic, clearly articulate a vision - aligned to other organisational strategies.
Set realistic deadlines, and manage expectations - the strategy and resulting projects won’t always solve everything, need to communicate the scope (budget, capacity). Identify pitfalls early e.g. data quality ‘elephant in the room’ might need to be dealt with first.
Be practical
Find some money!
Be generous with time - it might take more time than you think
Understand where you are now, understand where you want to be and align it to your organisation strategy
Things you should definitely avoid doing when creating a data strategy
They were a positive bunch who had a lot more to say about how you should do it rather than how you should not.
Not for the organisation
Using a generic data strategy that doesn’t align specifically with your organisation’s priorities / goals, jumping on buzzwords, or writing only for other data strategists.
Only focussing on the big picture
Not being too prescriptive so that it becomes limiting. Make sure there is flexibility in the strategy
Being pushed down a particular route by the software vendor / taking a tech-focussed viewpoint (ignoring wider data scope).
Authored in isolation. Don’t sit in a dark room and write it in isolation.
Wanting to go too quick
What can go wrong?
Bad strategy
You could recommend the wrong thing or you could discover more complexity midway and there is not enough budget to solve everything. You could find that there is no clear and timely benefit from the Strategy
It could be too long/big/detailed - needs to be a high level comms tool (how long does it take to communicate it?)
Nothing happens
Your organisation could end up spending money on the Strategy but never acting on it because:
stagnation
there was never any intention to do anything - box ticking exercise to get the shelf filled)
poor execution
Disconnected from organisation
Things will go wrong if
it doesn’t answer questions/relevance to organisation
it is technology-led (high investment/risks) - carcasses of failed CRM projects and traumatised staff from the horror.
middle to upper influence (not empowered by leadership to do it).
one-person leads/owns vision…and then leaves
expertise not connected/dispersed across team/culture of the organisation
What spin-offs can you get if things go really right?
Taking down barriers.
More confidence in data / data analysis. People developing a vision/understanding of data as a tool. Joy of empowerment for front line workers and others in being able to shape the data they need and value it brings (and ease to their jobs). Unexpected people feel like they’ve got a stake - opportunities for innovation (more diverse people/voices leading on development.
Better
Better data quality.
Efficiency gain, can do more with less resources/money
Help other organisations grow like you did/ share good practice.
What's next?
Attendees at the workshop have access to the worksheet for their own purposes and I've given you a summary of the discussions here.
We'll be holding the next workshop on 14th July at 13:00 UK time (UTC+1). The focus on this will be data fluency and participants will get the chance to try one of the excellent databasic.io workshops for themselves.
As one of the workshop participants said "Interesting topics and people leading to interesting conversations. It's really useful to have a place where these issues can be discussed with other people"
We also have a Slack team where people can continue the conversation.