Data Quality & Management: Understanding data integrity to ensure success
Event Archive
Incomplete, inaccurate, duplicate, inconsistent and outdated. Do these terms describe your data?
Poor data quality can severely hinder your ability to make informed strategic decisions at your non-profit organisation.
So, how can you begin to tackle data quality issues?
To help answer this question, we were joined by Dr. Nuna Staniaszek, Director of Communications at IOM3 (Institute of Materials, Minerals & Mining) to share how they:
- Reduced their (dirty) data volume by two-thirds all whilst enhancing its quality
- Consolidated data from four major sources (and countless spreadsheets!) into one CRM
- Reduced 100+ duplicate member and event delegate accounts to single digits
- Streamlined hundreds of thousands of contact records to tens of thousands with improved accuracy
We were also be joined by Marios Chrysanthou from Bluelight who shared his expertise on plans and strategies to mitigate risks when your data set lacks integrity.
Throughout the webinar, delegates had the opportunity to put their questions to Nuna, Marios and Hart Square’s Alan Perestrello.
Knowing that many of these answers will be valuable to others in the NFP sector, we have complied and detailed the responses given by our speakers into three common themes below.
If you are looking for more information on understanding data integrity to ensure success, you can access the full webinar recording by completing the form on this page, or by contacting us at hello@hartsquare.co.uk.
Theme 1: Prioritising and managing data quality
Q. How Do You Prioritise Data Quality?
Hart Square: Prioritising data quality starts with an audit or discovery phase. Map and prioritise your data assets to identify which ones are the most critical. It’s essential not to try and tackle everything at once; focus on the areas that will have the most significant impact. Track data issues centrally perhaps through a help desk to gather statistics and find root causes. This method helps you to address not just symptoms but also the underlying problems enabling more effective long-term solutions.
Marios Chrysanthou: The critical aspect is having some ownership. This could be an individual or a team. Somebody’s got to want to do it and be able to take that responsibility on. Beyond that, it’s prioritising. For instance, the Institute prioritised membership and finance data because their renewals were the next checkpoint. Additionally, senior management always asking for reports and stats to drive decisions highlights the importance of data quality. Showing them the value of allocating resources and taking data quality seriously is crucial.
Dr. Nuna Staniaszek: It definitely needs a champion. We’ve done it by having a team where everyone contributes a little bit. It’s important to show people how data quality impacts their job and to flag up any issues. It’s about spreading the responsibility within teams and using a mix of carrot and stick approaches to emphasize the importance of data quality.
Q. What Are Some Challenges in Balancing Flexibility Versus Integrity in Data Management?
Marios Chrysanthou: There’s always a trade-off, especially in new digital projects where different teams have separate priorities. For instance, the web team might focus on user experience while the data team prioritises data cleanliness. Aligning teams and setting a clear data model is key. Deciding whether to allow users to link themselves to an organisation or use free-text inputs impacts data cleanliness and user experience.
Theme 2: Handling duplications
Q. How Do You Handle Duplication of Organisations, Different Departments in Organisations, or Different Offices?
Marios Chrysanthou: Systems now can manage more sophisticated structures and data models. Having clean organisations and hierarchical structures helps. For instance, you can have a parent organisation with child relationships for different branches or locations.
Dr. Nuna Staniaszek: This is a significant challenge, especially in universities with different departments and postcodes. We’ve implemented a hierarchy with the HQ or main part of the organisation as the parent and others as children. Deduplication has mainly been done by postcode, which requires human intervention to decide which entries are genuine.
Hart Square: Human intervention is crucial. While scripts and automation can help, humans provide the necessary context to make informed decisions about data quality.
Q.What Tools or Methods Do You Use to Handle Duplicate Data?
Marios Chrysanthou: One approach is setting out a perfect customer picture according to your data model and then auditing what you realistically have. This helps prioritise how to handle your data.
Hart Square: Tools are essential but must be used in context. Tracking data issues and creating KPIs from them helps understand why data quality issues happen, enabling you to address the root cause rather than just the symptoms.
Theme 3: Building a data quality culture
Q. How Did You Foster a Culture of Data Responsibility Among Your Team?
Dr. Nuna Staniaszek: Our legacy database had a lot of frustration around its usability, which highlighted the importance of good data quality. Showing people how accessing good quality data helps them in their roles, especially in reporting, has been key. Ensuring everyone understands that “rubbish in, rubbish out” and that good data quality enables us to get more accurate information.
Q. How Do You Decide How Many Privileges to Give Colleagues? Can Everyone Delete or Merge Records? How Do You Balance the Risks vs Rewards in Having More People Able to Make Irreversible Changes?
Dr. Nuna Staniaszek: We have a limited number of users who can delete data, but most can deactivate records, which allows for rollbacks if errors occur. Initially, more users could delete data, but after some issues, we restricted this to minimise risk. Deactivation allows for review and ensures data can be confidently removed if necessary. Training and ensuring users understand the importance of these actions is key to maintaining a balance.