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Imagine this: your team has just made a major decision based on a dashboard full of data… only to find out later that several key fields were missing, inconsistent, or outdated. Suddenly, what looked like insight was actually misleading and the cost of poor data hits both your budget and your credibility.

The truth is, every organisation has data, but not all data is good data. And in an era where decisions are increasingly data-based, quality is everything.

Butterfly Data’s new whitepaper, “What Does Good Data Look Like?”, is a practical guide for organisations who want to move from “lots of data” to trusted, actionable data that drives real results.

Why Data Quality Matters

The whitepaper breaks down the critical points where data quality truly matters:

• Impact on decision-making: Poor data leads to poor decisions. Even small errors in critical fields can create cascading effects across operations.
  
  
• Responsibility is everyone’s: Data isn’t just IT’s job. Everyone who touches data must help maintain its quality.
  
  
• Practical strategies for improvement: Learn how to implement standards, monitor datasets, and fix errors systematically.

How to Assess Your Data 

At the heart of the whitepaper is DAMA UK’s six dimensions of data quality, which provide a clear, actionable framework:

1. Completeness - Are all critical fields filled? Missing essential information can compromise outcomes.
   
   
2. Validity - Do values conform to expected rules and formats? From UK postcodes to date formats, valid data enables automation and analysis.
   
   
3. Consistency - Are the values logical across datasets? A “UK-born? = Yes” paired with “Country of Birth = Germany” is a red flag.
   
   
4. Accuracy - Does the data reflect reality? Accurate data ensures reliable decisions and prevents costly mistakes.
   
   
5. Timeliness - Is the data current and available when needed? Outdated data can mislead forecasts or operational planning.
   
   
6. Uniqueness - Are records duplicated? Unique identifiers maintain clarity and prevent errors.

The paper walks through each dimension with realistic examples that make it easy to see how these principles apply in any organisation.

Making Data Quality Real

Good data doesn’t happen by accident. The whitepaper offers practical guidance, including (but not limited to):

• Using dropdowns and calendar pop-ups to standardise data entry
  
  
• Applying null-value standards to handle missing data consistently
  
  
• Implementing both technical and process-based solutions for monitoring quality
  
  
• Learning how to safely enrich your data with third-party sources, while ensuring it’s trustworthy and fit for purpose

By combining these approaches, you can reduce errors, streamline decision-making, and save both time and money.

Transform Your Data into an Advantage

Whether you are a data analyst, a manager, or a decision-maker, this whitepaper is a guide to:

• Identifying the datasets that matter most
  
  
• Implementing organisation-wide standards for reliability
  
  
• Building confidence in your reports, dashboards, and insights
  
  
• Using enriched data to drive deeper, more meaningful understanding

Good data is complete, valid, consistent, accurate, timely, and unique. When you have it, you can make decisions faster, with confidence, and with measurable impact.

→ Download the “What Does Good Data Look Like?” whitepaper

You can also find out more about our data quality services here to see how Butterfly Data can help you implement best practices and maintain high-quality datasets.

Cities around the world are under growing pressure to manage complex transport systems, especially when faced with disruptions from extreme weather, technical failures, or large-scale events. Each incident can ripple across the rail network, causing cascading delays, passenger congestion, and communication challenges for operators and policymakers alike.

Building on our success as Global Grand Champions of the SAS Hackathon 2024, we wanted to continue pushing the boundaries of what’s possible with analytics and real-time data. We wanted to explore how analytics and real-time data could transform the way these challenges are managed. For our SAS Hackathon 2025 submission, our team developed a proof of concept for a Digital Twin of a regional rail network, built on SAS Viya.

What is the challenge?

Transport disruptions don’t happen in isolation. A single line closure, storm, or event can quickly affect multiple routes, impact passenger flows, and cause widespread knock-on effects. Yet, many transport networks lack the real-time systems needed to anticipate and model these impacts as they unfold.

Our challenge was to design a solution that could:

• Simulate and forecast the impact of rail disruptions in real time
• Enable operators to reroute services dynamically
• Forecast passenger numbers during peak hours or events
• Estimate financial and passenger impacts of delays and cancellations
• Support faster, data-informed decision-making

Our solution: The Event Horizon Digital Twin for the rail network

Using SAS Viya, we built a Digital Twin that integrates live data streams with forecasting, optimisation, and intelligent decisioning models to replicate the behaviour of a rail network under stress.

This system can:

• Predict disruptions caused by weather events, such as floods or storms, using live APIs and historical data
• Model the cascading effects of delays or closures across the wider network
• Suggest alternative routes or transport modes for passengers
• Forecast passenger volumes during events or peak travel times
• Automatically trigger alerts and rerouting workflows through Agentic AI

During the hackathon, we focused on one regional network to demonstrate how a framework like this could reveal where, when, and how disruptions matter most - not just for individual stations, but across the entire connected system.

How the Digital Twin works

Our approach brought together multiple SAS Viya components:

• SAS Studio – Data preparation and processing
• SAS Visual Analytics – Real-time dashboard and visualisation
• Model Studio & Model Manager – Predictive and forecasting model development
• Event Stream Processing – Integrating live API feeds (e.g. flood warnings)
• Visual Forecasting – Predicting passenger numbers at peak times and during major events
• Optimisation – Identifying the most efficient alternative routes
• Intelligent Decisioning – Automating alert workflows and rerouting logic

For example, when a flood warning API triggers an alert, the system immediately identifies the high-risk area, recalculates the most efficient routes using optimisation models, forecasts the impact on passengers, and automatically generates alerts to operators and customers.

All this information is displayed through a single Visual Analytics dashboard that updates as new data arrives, providing a unified, command-and-control view for transport planners and decision-makers.

The expected impact

By combining real-time data with predictive modelling and automation, our Digital Twin demonstrates how cities can move from reactive crisis management to proactive, data-driven transport planning.

Potential outcomes include:

• Reduced disruption impact through faster decision-making and automated rerouting
• Improved passenger experience with timely alerts and alternative travel options
• Stronger operational resilience for transport operators and emergency services
• Data-backed infrastructure investment decisions for policymakers

Ultimately, our goal is to help cities become more resilient, connected, and sustainable, ensuring public transport continues to run smoothly, even when disruption strikes.

About the SAS Hackathon

The SAS Hackathon brings together teams from around the world to collaborate on data-driven solutions that tackle real-world problems. Using SAS Viya’s powerful analytics, machine learning, and AI capabilities, participants design and prototype solutions across industries.

We are proud to have contributed an innovative approach to improving transport resilience and look forward to exploring how this concept could evolve into real-world applications for public infrastructure and mobility planning, and look forward to SAS announcing this year's winners in December.

We’re proud to announce that Sara Boltman, co-founder of Butterfly Data, has recently joined the DAMA UK Committee. This is a great milestone that reflects both her individual expertise and Butterfly Data’s deep commitment to data management excellence.

About DAMA UK

DAMA UK is the UK chapter of the Data Management Association International (DAMA International), a not-for-profit organisation dedicated to advancing the understanding, development, and practice of data management. The association promotes professional standards across key disciplines including data governance, data quality, data architecture, metadata, and analytics.

As part of the committee, Sara will contribute to shaping best practices, fostering knowledge sharing within the data community, and supporting DAMA UK’s mission to raise data standards across industries. She will also sit on the Corporate Accounts sub-committee.

Aligned Values and Shared Principles

Our approach to building data solutions has always been closely aligned with DAMA’s principles. Whether it’s developing robust data governance frameworks, ensuring high-quality data pipelines, or designing sustainable data architectures, we embed these standards into every project we deliver.

Sara’s appointment to the DAMA UK Committee strengthens our connection to the wider data management community and ensures we continue to stay at the forefront of industry best practice, to help our clients create data solutions that are not just useful, but trusted, well-managed, and transformative.