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Data is everywhere, in everything we do. But many of us don’t capture this data, let alone analyse it. By doing so though, charities can identify efficiencies, save money and make more informed decisions. All of this will directly enable charities to better pursue their activities in line with their charitable purpose, ultimately benefiting their beneficiaries.

What is Data Analytics?

Data Analytics is not a technology, it is a concept. It refers to the use of certain technologies, skill sets and processes for the exploration, evaluation, and investigation of business operations.

Data analytics is the process by which insights are extracted from operational, finance and other forms of electronic data, internal or external to the organisation. The insights can be historical, real-time, predictive or risk focused.

Why use Data Analytics?

There are several key reasons for organisations to use Data Analytics.

• Provide invaluable insight
• Save money (eg cost recovery, reduce revenue leakage)
• Identify control issues
• Improve operational efficiency
• Look for fraudulent activity
• Test 100% of a data population
• Informed decision making

Over the last 20 years or so, there has been a huge upsurge in the volume of data being stored. As such, the use of Data Analytics is the only efficient way of gaining insight from this myriad of data. Charities can easily drill down in their data and gain insights to help drive their organisation.

Where can Data Analytics help charities?

Analytics can help charities provide better insight into the data they hold, which in turn helps with better decision making and taking action.

For example, data from the Finance, payroll and CRM systems can help in the following areas:

• Identify and quantify any control issues within key financial controls
• Identify potentially fraudulent activity in the core expenditure areas, such as Purchase to Pay and Payroll
• Provide better insight into fund-raising activities e.g. for charity retailers, analysing shop revenue and gross margin by period and region, and identifying any trends like poor performing shops
• Identify potential unclaimed and repairable Gift Aid income
• Cost recovery in identifying duplicate supplier payments
• Identify data quality issues across IT systems

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