Four ways to make your data work harder for you

How to make your data work for you – four ways of optimising your data to ensure that you’re avoiding siloing and extracting potential value from your data.

Paul Rubens | 7th Nov 19
Four ways to make your data work harder for you

Organisations of all sizes are generating and storing more data than ever before, and thanks to innovations in data science and analytics that data has huge potential value. But to extract the value from that data you need to put it to work – otherwise you’re faced with ballooning storage costs with nothing to show for it.

Here are four ways to make your data work harder for you:

1.) Get rid of data silos

A data silo is a data storage system which is used by one person, team, or department within an organisation which is difficult or impossible for other people in the organisation to access. Data silos may exist because the people who collect the data feel they own it and want to control it, and in many cases they do not believe it could be used anywhere else within the organisation.

Data silos are bad because:

  • The data can only be used by a small section of the organisation, and its value is limited by the ways that that section puts it to use
  • The potential for that data to enhance the value of other data in the organisation when combined with it is lost
  • The same data may end up being duplicated in another silo, with all the duplication in storage costs that this entails
  • Even when data can be extracted from a silo, this is likely to be unnecessarily cumbersome, limiting the frequency that the data is put to work in other parts of an organisation.

For all these reasons it makes sense to get rid of data silos and collect all the data that you need in a central data repository – sometimes called a data lake – which is available to everyone in the organisation. There may be some cost involved in this, but it is key to unlocking the value of your data.

2. Become data-driven

Being data-driven means using your charity’s data and information derived from that data – rather than anecdotal evidence, personal experience, or gut feeling – to guide everything from the overall direction of the organisation right down to the definition of individual campaigns and fundraising drives.

To do this you need to understand what data you require to answer questions like “what is the best way to target 18-24 year olds”, what data your organisation already has that can help you, and what data you need to collect.

Understanding this is made much easier if you have already removed data silos so that all the data in your organisation is visible and readily available.

3. Let your data talk

Putting your data to work to help provide answers to your questions is one way to extract value from it. But there’s another important way that your data can work for you which many organisations overlook: Big Data analysis.

This involves analysing the data that’s been centralised in a data lake to uncover patterns and correlations that you may not have expected or previously thought to look for. From these patterns and correlations you can draw useful insights that can be used to get valuable results.

Here’s an example: Your data shows that 2,000 mobile users clicked your “Donate Now” button last week, and data analytics shows you that 73% were using Android devices. But it also produces the insight that Android users are 20% less likely than iPhone users to complete the donation process. Figuring out why – perhaps the payment process is visually confusing on smaller Android devices – and fixing the problem could instantly lead to a jump in donations.

4. Employ a data expert

The figuring out process in the example above is key, and to do this successfully you need people – specifically people who understand data and know how to turn insights into actions. What’s important is that you have experts who understand your organisation and who are impartial.

That’s important because sometimes the data will tell you things that particular departments don’t want to hear – but if your data is to work for you effectively it’s imperative that is listened to and acted on, not ignored and swept under the carpet.