To get the accurate, reliable data you need to make informed decisions, create better experiences, tighten up your operations and make your organisation as effective and financially efficient as possible, you need to build a data management foundation that doesn’t just tackle technology but changes the way you manage data for good – from infrastructure to cultural acceptance.
Every organisation will need a different approach to data management, depending on their goals, their structure, the data they have and the purpose of their organisation, but there are some steps that every community-focussed organisation will need to take to build a strong data management foundation.
Data strategy: what do you want to achieve with data?
You’d be surprised how many organisations launch into a data management project without a strategy. It rarely ends well, at best resulting in outcomes that aren’t fully relevant to organisational goals, at worst wasting significant investments on the wrong processes and technologies.
Before you do anything, ask: what do we want to achieve with our data? Think short and long term – are you looking to improve customer insights, to open self-service portals? Do you want more detailed financial data, for reporting and compliance but also to improve operational efficiency? It’s important that this isn’t left for one person to decide – your data strategy needs to be a collaborative effort amongst all key stakeholders, not isolated to an IT or technology team.
While the details of your strategy will depend on what you want to achieve both urgently and in the future, many of the foundations will remain the same. You’ll always need good quality data, data governance and a strong data culture, but how you apply them and the technology you use will change. Once you’ve asked that initial question, you’ll be able to outline exactly what the rest of your strategy will look like – from addressing the relevance of your existing data to changing organisational attitudes to data.
Data Quality: is your data up to the task?
Poor data quality has been blamed for rent non-compliance in Housing Associations, financial strain and overwork in the NHS, mistrust in government departments and local councils and countless other mistakes, miscalculations and misestimates across community-based organisations. As organisations become more reliant on data, poor data quality opens up more opportunities for errors in every area.
At the risk of stating the obvious, the quality of your data determines the quality of its output – or, as we have said before, “rubbish in, rubbish out”. Data quality is determined by how complete, unique, consistent, valid, accurate and timely your data is. If your data is riddled with duplicates, missing fields, out-of-date information and invalid data formats, you won’t be able to trust the reporting, decision-making, automation and other actions that come from it.
Say your goal is to establish a self-service portal for your users (tenants, patients, donors, etc). You want to improve customer experience, but you also want to use the data you collect to guide decision-making on services. To do so, you need to make sure that your data is:
- Complete – the same critical data is available on every customer, with no missing information.
- Unique – the customer is not duplicated, either as a whole data set or a partial data match.
- Consistent – data meets the same criteria and is consistently available, e.g., every customer has the same key details available.
- Valid – the data meets your organisation’s data rules and is in the right format for processing.
- Accurate – the data reflects reality. For example, data on disabilities in housing association tenants accurately reflects the person’s real-world experience.
- Timely – the information has been updated or checked recently, such as new contact details or name changes.
If your data doesn’t meet those attributes, then that self-service portal won’t work – whether it’s by providing users with the wrong options and wrong information, or by prohibiting internal access to that information where it’s needed most.
A data quality initiative is the first step to tackling data quality, but it won’t maintain it over time. For that, you need Data Governance.