Unlocking a Single View: Solving the Puzzle of Disconnected Data 

Every organisation collects data but few truly harness its full potential. Disconnected systems, scattered sources, and inconsistent formats make it difficult to unlock a single unified view . Whether you’re in local government, housing, healthcare, policing or financial services, you’ve probably faced the pain of disconnected systems, duplicate records, and unclear data ownership. 

Following on from our webinar ‘Unlocking a Single View’ this blog dives into what exactly that means, why it matters, and how the powerful (and free) open-source tool Splink can help, overcoming your data challenges. 

What is a single view, and why does it matter?

Firstly, in order to utilise the power of a single view, you first need to understand what exactly a single view is.  Simply put, a single view is your organisation’s ability bring everything together – like a person, property or tenant – into one accurate, single profile. 

To work effectively, the view needs to be:  

• Complete: Pulling together data from systems like your CRM, finance, and service delivery. 

• Accurate: Able to detect and reflect changes like a new phone number or change of address. 

• Accessible: Ready at the point of need, and accessible to who needs it – no more rummaging through multiple systems or spreadsheets. 

Without this? Organisations stay frustrated, held back by fragmented data, users fall through the cracks, and time and money are lost, ultimately slowing down data-driven insights.  

The cost of disconnected data

Without this data matching capabilities, the cost across all sectors is sizeable, emphasising the need for data-driven insights. 

• £17 billion is spent by public services every year, addressing the consequences of late intervention. 

• £219 billion: The cost of fraud to the UK economy – much of it avoidable through better data sharing. 

• £20 million or up to 4% of the annual worldwide turnover of the preceding financial year, whichever is greater: The potential size of a GDPR fine for mishandling personal data. 

Fragmented data makes it increasingly difficult in today’s data driven world to identify vulnerable individuals, detect fraud, or provide joined-up services – leading to delays, duplication, and missed opportunities.  

How to get a single view

Before you can build a single view, you need to know who’s who. That’s where record matching comes in. 

Here are the common approaches: 

• Deterministic Matching: Exact matches (e.g., Name = Name). Simple, but fragile. 

• Fuzzy Matching: Allows for near matches (e.g., “Oliver” ≈ “Olive”). 

• Probabilistic Matching: Assigns a confidence score to each potential match based on multiple fields (e.g., name, DOB, postcode). 

The third method is the most robust – especially when data is messy, inconsistent, or comes from multiple sources. 

Which technologies are best to gain a single view? 

Gaining a unified view of your data can be aided with technologies, accelerating your data matching.  Splink, a Python Spark package that implements the Fellegi-Sunter model of record linking, is trusted for high-speed, high accuracy data matching.   

Funded by the Ministry of Justice, it can bring many benefits to your data matching process, and is:  

• Fast: Matches millions of records in minutes. 

• Transparent: Easy to explain both the output and matching logic.  Interactive visualisations and partial match weights are also provided. 

• Flexible and scalable: The synergy with existing leading technologies such as Microsoft and Databricks is a key benefit. As a Microsoft partner, and winner of Partner of the Year for Community Response 2024/25, as well as a Databricks partner, we can provide this expert guidance, making the implementation as smooth as possible.  

• Free: Fully open source, try and test Splink for yourself here.

How Splink works

Splink uses probabilistic record linkage, assigning scores to similarities and differences between records. It considers: 

• Name similarity (using methods like Levenshtein and Jaro-Winkler) 

• Date of birth proximity 

• Postcode sectors 

• Data frequency (e.g., “Oliver” is common, “Barnabas” is rare) 

It then applies blocking rules to compare only likely matches – —reducing billions of comparisons to manageable numbers. Finally, it tunes weightings and thresholds to identify the most confident matches. 

The result? A registry of matched IDs you can trust to power analytics, digital services, and AI. 

Key takeaways

Matching data records is just the beginning.  Once you’ve implemented an accurate data foundation, without disconnected and fragmented data, you can make more data-driven decisions, and provide truly –joined up customer experiences, using the power of data for good.   

As AI adoption accelerates, it’s never been more important to ensure your underlying data is solid, and you can move to the next stage without being held back by fragmented data. 

Ready to get started? How Simpson Associates can help

Are you ready to embark on your Splink data linkage journey, but ensure where to start? Don’t worry! Our experts at Simpson Associates know exactly how to help you integrate this into your organisation. With the right guidance, you can leverage this effectively to achieve your goals and drive success.  

Missed out on our webinar? Watch our recording of ‘Unlocking a Single View’ here.  Reach out to us via live chat if you have any further data matching questions.