While serious data analysis is often associated with big organisations, large commercial and not-for-profits looking for an edge, the tools and techniques now available put it within range for smaller NFPs.
The point is that every organisation – big and small – has access to data, it’s just a matter of having a strategy to deal with it effectively. It means working out what should be collected and what should be done with it and, of course, being aware of the privacy laws.
"It’s all about using the data to understand your clients or members", says Dr Rami Mukhtar, big data project leader at NICTA, Australia’s information, communications and technology research centre.
He offers these tips for improving your data analysis:
1. Collect stakeholder data generated by every interaction
It’s critical to collect and store data indefinitely rather than temporarily because you may have new ideas about how to use the data later. Ensuring the accuracy and currency of your data is paramount.
2. Construct a detailed view of stakeholder behaviour
Use answers to questions such as: how do stakeholders use your web page, when did they answer an outbound call, what month did they donate money, or whatever targets are important for your organisation.
3. Analyse stakeholder data
Data analysis tools can help to determine the best way to interact with a stakeholder. For example: how frequently they should be contacted, which channel should be used (such as call centre, SMS, email or direct mail) and what the message should be.
4. Personalise communication strategies
For each stakeholder, there is an answer to each of these questions that will help to achieve the organisation’s goals such as increasing donations, enhancing interactions with members or improving client service.
5. Harness the expertise in-house
Identify the person in your organisation who understands the organisation’s data and how to apply data analysis tools. They are a valuable asset! Many organisations are now hiring data scientists for their skills in analysing data, particularly big data that they don't already have in-house.
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