HubScope

What 2025 taught us about data, systems, and blind spots Over the New Zealand summer break we’ve taken a moment to reflect on what worked, what didn’t, what we learned along the way and how all this informs our approach in 2026. Starting with what worked We helped a number of NZ Councils and Responsible Authories to better understand their current state complexity to inform Data Strategies and Transformation project sequencing. We also delivered two successful Data Migration projects: - One from MS Dynamics on Prem to an AWS Cloud Service, see our NZ Police Association customer story https://lnkd.in/emx6X9Jg . - The other from a legacy MSSQL custom database to Salesforce, see our NZ Teaching Council customer story https://lnkd.in/ekDVy_Qp . These join our Justice and Transpower customer stories https://lnkd.in/erhzM-Tp . We worked closely with data from the following endpoints, either pulling data from or pushing data to, adding to our library of IP: OpenText, SharePoint, Salesforce, MS Dynamics, Sage Accounting, VIAssets, MSSQL Bespoke Tables (including seemingly impossible to fathem auto-generated SQL tables and columns) and Microsoft Business Central. What didn't work, and how we fixed it We've struggled for some time with the use of excessive SQL scripting and leaning on inadequate integration platforms for migrations. In 2025 we partnered with Hopp as a Data Migration technology platform provider to increase our effectiency, scalability and the value we can add. We are delighted to say we also kicked off two new projects using Hopp in 2025 which will go live Q2 2026. 2025 reinforced something we’ve seen repeatedly across organisations of all sizes: Most transformation programmes start with assumptions, not understanding. We saw this repeatedly across SMB and public-sector environments: - Asset registers scattered across spreadsheets - Legacy systems still running “because something depends on them” - Risk understood emotionally, not structurally - Change planned in isolation rather than as a system - Systems were still in use long after they were assumed retired - Integrations existed that nobody could explain or confidently change - “current state” documentation was either outdated or aspirational - Operational risk was invisible until something broke None of this is due to poor people or poor intent. It’s the natural outcome of years of incremental change, staff turnover, vendor churn, and well-meaning workarounds. The biggest lesson from 2025? You can’t safely decide where to go next if you don’t truly understand where you are. That realisation has been quietly reshaping how we approach discovery, change, and execution. Most organisations don’t fail because of bad technology choices. They struggle because decisions are made without shared context. So for us 2026 is about clarity before change.

Why We Chose Hopp - Part 5 of 5: The Partnership Model Working with Hopp has been refreshingly different from enterprise platform vendors. We have direct access to their CTO. They're actively seeking our feedback. They've aligned their approach with proven methodologies like Johnny Morris's "Practical Data Migration" framework. Because they're specialists, not generalists, they've thought deeply about the problems we face every day. And because they're still building their presence in new markets like New Zealand, there's genuine partnership not just licensing agreements. Meanwhile, our Hubscope platform complements Hopp perfectly by handling what happens before and after the transformation ]the discovery that scopes the project realistically and the monitoring that proves success objectively. For organisations facing system migrations, AI enablement, or data warehouse projects: the question isn't whether you need to move data it's whether you have a framework that lets business and technical teams collaborate effectively throughout the journey, with visibility from discovery through to delivery. At Hubscope, we've found that framework. And it's changed how we approach data projects. Interested in learning more about our approach to data migration and AI readiness? Let's talk about how Hubscope and Hopp can help you build data movement capability within your organisation. #DataMigration #DataStrategy #AIReadiness #DataDiscovery #DataQuality

Why We Chose Hopp - Part 4 of 5: What We've Learned Working with Palmerston North City Council (7 million documents) and the New Zealand Police Association has reinforced some hard truths: · Count everything. Reconciliation isn't optional. Hopp dashboards show exactly what moved, what didn't, and why with drill-down capability beyond summary slides. · Iterate constantly. Steel thread first get one table working end-to-end, then scale. Weekly migrations, not monthly. · The business model emerges. Don't try to define the perfect data model upfront. It reveals itself through iteration and business conversations. · Volume changes everything. What works for thousands of records breaks at millions. Purpose-built tools handle scale. Why This Matters for AI Readiness Here's the connection everyone's missing: AI data readiness is the same problem as data migration. You need to discover your data (Hubscope), transform it into usable formats (Hopp), and monitor quality in your ingestion pipeline (Hopp). The difference is the target system. The challenges: legacy systems, undocumented structures, data quality issues are identical. Organisations rushing into AI initiatives without solving the data movement problem first will hit the same wall we've seen in failed migration projects. The Hubscope-Hopp combination addresses the full lifecycle: understanding what you have, moving it with business validation, and proving what landed where. Interested in learning more about our approach to data migration and AI readiness? Let's talk about how Hubscope and Hopp can help you build data movement capability within your organisation. #DataMigration #DataStrategy #AIReadiness #DataDiscovery #DataQuality

Why We Chose Hopp - Part 3 of 5 : Why Specialist Tools Matter We evaluated several platforms that can handle data migration. Most were integration platforms or ETL tools that included migration. Hopp took the opposite approach and is built from the ground up for migration alone. Here's what that focus delivers: 1. Clean Architecture That Enables Conversation: Hopp enforces a logical structure: source mapping, business object model, and target system mapping. When transformation rules live in one maintainable place instead of scattered across thousands of lines of SQL, business users can actually participate. You can show them their real data flowing through the system and get immediate feedback: "We didn't mean that folder structure" becomes "let's adjust the rule and try again." Technical teams own the mechanism. Business users own the rules. And both can see what's happening. 2. Fail Fast, Iterate Often: Instead of discovering data quality issues six months into a project, Hopp validates against your business model at every stage. Invalid transaction? No matching policy? The system flags it immediately. This changes project dynamics. We run weekly migrations, gathering feedback and proving the mechanism works long before go-live. No crossed fingers. No surprises. 3. Built for Volume and Complexity: Purpose-built tools handle what general platforms can't. What works for thousands of records breaks at millions. What looks clean with simple transformations becomes unmaintainable when business rules compound. Hopp was designed for the scale and complexity of real enterprise migrations, because that's all they do. Interested in learning more about our approach to data migration and AI readiness? Let's talk about how Hubscope and Hopp can help you build data movement capability within your organisation. #DataMigration #DataStrategy #AIReadiness #DataDiscovery #DataQuality

Why We Chose Hopp - Part 2 of 5: The Hub-Hopp-Hub Sandwich This is where our partnership with Hopp becomes powerful: Phase 1: Discovery with Hubscope Before any data moves, we need to understand what we're dealing with. Hubscope.io automatically profiles your source databases, analyses structure and relationships, and provides interactive reports based on metadata—all without exposing sensitive data values. We're cataloguing applications, mapping data landscapes, and identifying patterns that reveal how systems actually work versus how people think they work. We also map the interdependencies between application to understand the impact of change at a macro data in motion perspective as well as at a data at rest perspective. This discovery phase is critical. You can't estimate migration effort without knowing if your tables are sensibly named or called "table_underscore_1_underscore_18" with 300,000 meaningless records. You need to surface those fault lines in the data—the system migration that happened in 2012, the undocumented business rules embedded in application logic, the tables that haven't followed standard patterns. A key to unlock the data puzzle can come down to mapping each field the user sees to where the data is stored. On rare occasions fields names are self explanatory but you can never assume that will be the case. Phase 2: Transformation with Hopp Once we understand the source landscape, Hopp handles the actual data movement with a framework purpose-built for migration, not retrofitted on top of a data integration platform. This is where business rules get defined, data gets validated against business object models, and weekly iterations prove the mechanism works before go-live. Phase 3: Monitoring with Hubscope After migration, Hubscope monitors the target system. This gives stakeholders end-to-end traceability, not just a PowerPoint with red-amber-green status indicators that turn red the day before go-live. HubScope.io allows all of the metrics generated from each stage of the data migration into an interactive dashboard and reconciliation report. This allows both technical and business stakeholder the opportunity to review each iterative and decide whether to repeat with changes or prepare for a final rehearsal and go live cutover. This sandwich approach means discovery and monitoring bookend the transformation, creating accountability at every stage and ensuring nothing gets lost in translation. Interested in learning more about our approach to data migration and AI readiness? Let's talk about how Hubscope and Hopp can help you build data movement capability within your organisation. #DataMigration #DataStrategy #AIReadiness #DataDiscovery #DataQuality

Excited to see where HubScope goes in 2026, I formed the company in 2012 and it's been a rollercoaster of a ride so far. We're doubling down on providing Data Landscape insights to new clients and continuing to provide a Data Migration competency that has evolved out of recent successes at NZ Teaching Council and NZ Police Association.

Why We Chose Hopp - Part 1 of 5: Rethinking Data Migration for the AI Era A 5-part-post by Steve Dickinson, CEO HubScope After 20+ years in data migration, I've learned one fundamental truth: the technical challenge isn't moving data it's managing the conversation around it. Most organisations approach data migration as a one-off technical exercise. They assemble a team, write thousands of lines of SQL, work in isolation for months, then cross their fingers on go-live day. I've been that stressed developer and I've felt the isolation this approach. Technical teams can grow to fulfil the demand but I’ve also I've seen the mess that gets left behind when technical teams make business decisions without business input. The core problem, technical people talk a different language to business experts and neither side knows where to start bringing the gap. That's why at Hubscope, we've partnered with Hopp not because we needed another integration platform, but because we needed a framework that separates technical execution from business ownership while combining both into a single, transparent workflow. From Data Migration to Data Movement Here's the shift I'm seeing, data migration isn't just about moving from System A to System B anymore. It's about data readiness preparing your most valuable asset (your data) for whatever comes next, whether that's a new system, an AI initiative, or a data warehouse project. The challenge is identical across all these scenarios: discovering where your data lives, understanding its quality, and transforming it into something usable. Legacy systems don't document themselves. Data that's accumulated over decades doesn't follow neat patterns. And the people who built these systems? They've long since moved on. Data Readiness and Enablement Data Migrations and Data Pipeline project are challenging given the uncertainlty that might exists around your current state. We are announcing an limited, NZ specific offer to help. Warch our for similar offers in other regions. The new year is right around the corner and for many NZ Organisations, December and January offer a rare moment of breathing space. We’re using this window to support a very limited number of prospects with a free Data Landscape Assessment Report designed to help you get ahead of 2026’s digital and data priorities including data migration and AI related projects. Read our offer below: https://lnkd.in/eD8s4Fv9 Interested in learning more about our approach to data migration and AI readiness? Let's talk about how Hubscope and Hopp can help you build data movement capability within your organisation. #DataMigration #DataStrategy #AIReadiness #DataDiscovery #DataQuality

We're officially on LinkedIn! Organisations shouldn't have to wrestle with outdated systems and scattered data. That's why HubScope exists. We're here to make digital transformation practical, not painful. With over ten years of experience, we build solutions designed to grow and adapt alongside our clients. If solving data challenges interests you and your team, follow along as we share insights and experiences. We look forward to collaborating with you.