Making sure your data does the job
Charities have been facing substantial challenges since 2020. With Covid-19 impacting several aspects of our lives, increasing need for better healthcare support, for greater support for isolated people, the need to manage mental health and wellbeing, and raising funds for medical research, charities’ operations have required more agility and speed. With the world facing political and economic uncertainties, charities will need to be prepared and build a more sustainable and scalable model.
The pandemic taught us that the greatest weapon against uncertainty is information. Companies which had already begun their data and digital transformations before Covid fared far better as the market shifted. For charities to be able to transition to the next era, having the right data strategy in place will be mandatory but according to a 2021 report from Salesforce, 76% of charities lack a data strategy. What does the digital imperative look like for charities?
REAL-TIME ANALYTICS AND INSIGHTS. Many charities operating in an ever-evolving digital world are using CRM (customer relationship management) systems that were implemented over a decade ago. These systems have been designed primarily for traditional direct marketing activities, which means all data from digital communications platforms must be manually loaded.
Growing data volume
As the volume of data coming from these sources grows, and the demand to use them increases, charities are struggling to improve the speed of ingestion thus limiting efficiency. What’s more an over-reliance on humans for loading, identifying file issues and re-uploading is leading to diminished data quality.
Much like public sector services, charities are held to a much higher standard than private businesses. They are expected to act in favour of the people and for good reason – this is their raison d'être. As such, ensuring all critical decisions across the charity are based on quality and trustworthy data is essential in today’s world.
Without chief data measures such as accuracy, completeness, consistency and validity, the data ingested into the CRM system – or any other system – can only offer a partial or biased view. This is what we call “bad data” as its use can result in a negative impact on operations, fundraising efforts, donor experiences and, worse still, may lead to discrimination.
If we take the example of donations, for charities, the ways of giving have evolved. Not only do donors need relational, emotive points in time and communications that resonate with each of them individually, but also a diverse range of channels that allow them to either fundraise in fun ways or donate impulsively if the moment takes them.
As such, traditional methods of marketing – direct mail and even email – don’t tend to resonate with these younger audiences. Instead, charities are being forced to meet them where they reside digitally. To meet this new need, many charities are beginning to adopt technologies that allow them to fully harness the power of internal and external data and undertake quality data-driven campaigns.
Access to trusted data
Effective analytics and real-time access to trusted data can power charities to better serve increasing demand. This is the journey that housing and homeless charity Shelter has taken. It has been able integrate 14 income systems, 28 data feeds and more than one million records, enabling the charity to better serve those in need. Shelter is now more effective at raising money, has better visibility in relation to local housing stock and has deeper connections with local landlords.
MEASURING DATA QUALITY. The categories of data quality dimensions cover a number of metrics that indicate the overall quality of files, databases, data lakes and data warehouses. Academic research describes up to 10 data quality dimensions — sometimes more — but, in practice, there are five that are critical to most users: completeness, timeliness, accuracy, consistency, and accessibility.
- Completeness: is the data sufficiently complete for its intended use?
- Accuracy: is the data correct, reliable and/or certified by some governance body? Data provenance and lineage — where data originates and how it has been used — may also fall into this dimension, as certain sources are deemed more accurate or trustworthy than others.
- Timeliness: is this the most recent data? Is it recent enough to be relevant for its intended use?
- Consistency: does the data maintain a consistent format throughout the dataset? Does it stay the same between updates and versions? Is it sufficiently consistent with the other datasets to allow joins or enrichments?
- Accessibility: is the data easily retrievable by the people who need it?
Poor decision making
Each of these dimensions corresponds to a challenge for analytics: if the data doesn’t provide a clear and accurate picture of reality, it will lead to poor decision making, missed opportunities, increased cost, or compliance risks. In addition to these common dimensions, specific business domain dimensions are usually added as well, typically compliance dimensions.
At the end, this makes measuring data quality quite a complex, multi-dimensional problem. To add to the challenge, the volume and diversity of data sources have long surpassed the ability for human curation. This is why, for each of these dimensions, data quality methodologies define metrics that can be computed, and then combined, to automate an objective measure of the quality of the data.
More subjective measures can still be added in the mix, too, typically by asking users to provide a rating, or through governance workflows. But even this manual work tends to be increasingly complemented by machine learning and artificial intelligence.
Managing data quality throughout the data life cycle, including implementing data observability rules and practices, will ensure that everyone in the charity has access to healthy data.
Mental Health Concern, a charity dedicated to providing free mental health services, has started an organisation-wide data transformation to be able to deliver integrated, healthy data for timely decision making. The charity now connects multiple systems and data sources so it can make reliable information more accessible and more trustworthy for stakeholders across the company. Everyone from the charity’s leaders to the therapists working with patients now has data-driven insights at their fingertips, and that helps them make more informed decisions and consider new approaches.
GOVERNANCE AT THE HEART OF THE DATA STRATEGY. As any other businesses, charities have to deal with the ever-evolving regulatory landscape, and meeting compliance and security requirements is a challenge for them too.
Interestingly, when GDPR was enacted, a lot of charities were worried that parts of their database would become unusable because they hadn’t acquired customer consent. Instead, they had to think about less intrusive ways to reach their donors without sparking a mass shift to unsubscribe.
Long term winner
What many quickly realised was that this approach is the key to becoming a long-term winner, creating a personal, ongoing relationship with their donors, rather than through an event base approach. For this, data must be used. But it must be used wisely, ensuring that all insights are based on quality and trustworthy data to ensure continued engagement.
Having the right data governance strategy in place is the key to preventing data privacy risks. It is mandatory to ensure that data is trusted, controlled and that the right people have access to the data they need.
In 2021, HIV Scotland was fined by the authorities for sending out an email that contained personal information. So to be better prepared for the future of data privacy, charities need to build a data privacy by design approach by implementing first a data literacy programme that will drive a greater data culture, ensuring that there is a common understanding of data across the organisation.
Making sure that everyone shares the same definition of data they use is key to establishing a proper data governance strategy. Defining access and rights by establishing the right rules depending on the profile and the data usage is also a mandatory step. If you work in fundraising, you don’t need to have access to the same data as the HR department. Finally, the data integration and management technologies need to be able to scale and adapt to the development of the activities or the infrastructure.
Having a unified platform like a data fabric where data processing modules can be added based on the evolution of the use cases – like APIs (application planning interfaces) to meet data sharing objectives or self-service data preparation to empower staff with managing the data they use. This is a requirement which charities need to consider in the early stages of their planning.
