Question 1

What does Hopton Analytics do?

Accepted Answer

Hopton Analytics is a Microsoft data and analytics consultancy. We deliver Power BI, Microsoft Fabric, Azure Data Factory, and Microsoft Dynamics 365 Business Central reporting and analytics work for mid-market businesses across the UK and Ireland. Our work covers the full data and analytics stack, from extracting data out of operational systems through to executive dashboards, machine learning, and AI-augmented analytics. We are a delivery firm: we build the analytics estates we recommend rather than producing strategy documents and walking away.

Question 2

What technology does Hopton specialise in?

Accepted Answer

The Microsoft data and analytics stack. Power BI for visualisation, semantic modelling, and self-service. Microsoft Fabric for the unified data platform (lakehouse, warehouse, data engineering, real-time intelligence, data science). Azure Data Factory and the broader Azure data services. Microsoft Dynamics 365 Business Central for the ERP integration work that often sits underneath analytics engagements. Microsoft 365 Copilot and Power BI Copilot for AI-augmented analytics. We do not work outside the Microsoft stack as a primary technology choice.

Question 3

Why specialise in Microsoft rather than being platform-agnostic?

Accepted Answer

We built our practice on Microsoft technologies because for most UK organisations, Microsoft is the right foundation - governed, scalable, and deeply integrated with the tools your teams already use. That focus has not changed. What has changed is that we have added Pyramid Analytics to our practice, because the best answer for AI-powered decision intelligence is not always a single-vendor stack. We are now Pyramid Analytics Partner of the Year alongside our Microsoft practice - which means we can recommend the right platform for your specific situation, rather than the one that fits our preferred toolset.

Question 4

What kinds of work does Hopton not do?

Accepted Answer

We do not implement ERP systems (we work with the BC partner, not as the BC partner). We do not deliver Salesforce-specific work as a primary engagement. We do not work on non-Microsoft analytics stacks (Tableau, Qlik, Looker, Sisense) other than to migrate clients off them onto Power BI. We do not do generic IT consulting, software development, or cybersecurity work. Our scope is the Microsoft data and analytics stack, end to end.

Question 5

Where is Hopton based?

Accepted Answer

Leeds is our home and where the team is concentrated. We have a presence in London for client work and in-person engagements. Most of our work is delivered remotely with periodic in-person time on site, which suits the mid-market client base across the UK and Ireland. Geographic location of the client is rarely a constraint; we work with clients from Edinburgh to Cornwall and across Ireland.

Question 6

Does Hopton serve clients outside the UK?

Accepted Answer

Yes, but selectively. Our primary market is UK and Ireland mid-market businesses. We have worked with clients with international operations - multi-currency consolidation, cross-border data, international subsidiaries reporting up to a UK head office - and we have done specific projects for clients headquartered outside the UK. We are not a global consultancy and we are honest about that.

Question 7

What sectors does Hopton serve?

Accepted Answer

Mid-market businesses across retail, wholesale and distribution, consumer goods (FMCG), construction, recruitment, professional services (law, accountancy, consultancy), commercial healthcare, and manufacturing. The Microsoft data and analytics architectural patterns transfer across these sectors; the dashboard libraries and the data sources differ. The pattern is sector knowledge built from real delivery rather than generic capability claimed without it.

Question 8

What size of business does Hopton work with?

Accepted Answer

Mid-market is our primary focus: typically £10 million to £500 million turnover, between 50 and 2,000 staff. Smaller businesses occasionally engage us for specific projects, and we have worked with larger businesses for specific data and analytics workstreams. The mid-market focus reflects where the Analytics Acceleration Programme works best and where our team's experience is concentrated.

Question 9

What accreditations does Hopton hold?

Accepted Answer

Microsoft Partner accreditation, with active work towards the Solutions Partner for Data and AI designation. Level 3 Pyramid Analytics accreditation. The team holds individual Microsoft certifications across the data and analytics stack. Accreditations are useful as one signal of capability but the work delivered for clients is the more important reference.

Question 10

What is the Analytics Acceleration Programme (AAP)?

Accepted Answer

The AAP is our standard delivery framework, structured in three phases. Establish (typically four weeks) covers discovery, architecture, priority dashboard agreement, and a written delivery plan. Build (typically eight to sixteen weeks depending on scope) is the core delivery: data foundations, semantic models, dashboards, governance. Continuity is the optional ongoing engagement after launch, with a defined monthly allocation of consulting days that supports the platform, extends the estate, and helps adoption.

Question 11

How does Hopton price engagements?

Accepted Answer

Most engagements are fixed-scope and fixed-price for the Establish and Build phases. Continuity is monthly retainer based on a defined consulting allocation. The day rate model is available for clients who specifically prefer time and materials. We are transparent about pricing in the proposal stage. The rate card is mid-market: meaningfully lower than big-four consultancies, materially higher than freelance contractors, and consistent with specialist Microsoft partners of our size.

Question 12

What does an engagement actually look like from the client's perspective?

Accepted Answer

A small Hopton team (typically three to five people across architecture, development, and project management) works with a counterpart team on the client side. Weekly or bi-weekly progress reviews. Working sessions on architecture and design. A clear written delivery plan with milestones. Regular access to a project communication channel (usually Teams) for ongoing dialogue between formal meetings. Most engagements involve some on-site time at key points with the bulk of the work happening remotely.

Question 13

How do we get started with Hopton?

Accepted Answer

Email hello@hoptonanalytics.com with a brief description of what you are trying to improve, your current systems, and any specific timing or budget constraints. The first conversation is exploratory, with no obligation, and usually happens within a week. If there is mutual fit, the next step is normally a four-week Establish phase that produces an architecture, a priority list, and a written delivery plan.

Question 14

What does the first conversation cover?

Accepted Answer

Your situation (size, sector, current systems, current pain points). Your aspiration - what better analytics would look like for your business. Any specific constraints (timing, budget, internal politics, existing partner relationships). What you have tried before and what worked or did not. We aim to leave the first conversation with a clear view of whether there is a fit and an honest assessment of whether we are the right partner.

Question 15

Does Hopton work with our existing Microsoft partner?

Accepted Answer

Yes, and we usually do. Most clients have an existing Microsoft partner of some description (BC partner, M365 partner, Azure partner). Hopton runs the data and analytics workstream specifically and integrates with the partners doing other work. We do not displace operational partners, and we coordinate openly so the client does not end up managing the partner-to-partner relationship themselves.

Question 16

Can Hopton provide references?

Accepted Answer

Yes, where mutually appropriate. Most of our active clients are happy to be referenced for prospective clients who are at a similar stage of evaluation. We do not publish a public client list with named logos because some clients consider their analytics investment commercially sensitive. We make references available privately when an engagement is genuinely being evaluated. Email hello@hoptonanalytics.com to discuss.

Question 17

What happens after a project ends?

Accepted Answer

We design for continuity from day one - documentation, training, and knowledge transfer are built into every engagement. The Continuity phase of the AAP provides ongoing retainer support for clients who want a long-term partner. We also offer ad hoc support for clients who prefer to manage independently with occasional specialist input.

Question 18

Do we need to already have Power BI before working with you?

Accepted Answer

No. Many clients come to us before they have invested in tooling. Part of the Establish phase is assessing your current infrastructure and recommending the right platform for your needs and budget. If Power BI is the right fit, we will build it properly from the start. If a different Microsoft platform suits you better, we will say so.

Question 19

Why do Power BI environments become ungoverned over time?

Accepted Answer

Power BI makes it very easy to publish reports quickly, which is both its strength and the source of most governance problems. Without defined metric standards, certified datasets, and workspace governance, the environment grows organically with each person applying their own definitions. The result is multiple reports showing different numbers, leadership unable to trust any of them, and data teams spending more time reconciling than analysing.

Question 20

What is a semantic model and why does it matter?

Accepted Answer

A semantic model is the governed layer that sits between your raw data and your reports. It contains the business logic, metric definitions, relationships, and security rules that all reports share. Without a well-governed semantic model, every report developer applies their own logic, which leads to inconsistency. A single, certified semantic model is the foundation of trustworthy reporting.

Question 21

How long does a Power BI implementation typically take?

Accepted Answer

A governed Power BI environment - with a proper semantic model, certified dashboards, row-level security, and adoption support - typically takes eight to twelve weeks in the Build phase, preceded by a four-week Establish phase. Simpler scopes can be faster; more complex data environments take longer. We scope precisely before committing to a timeline.

Question 22

Can you migrate us from Tableau, Qlik, or another BI tool?

Accepted Answer

Yes. We regularly migrate clients from Tableau, Qlik, Cognos, SSRS, and Excel-based reporting onto Power BI. The migration approach depends on the complexity of the current estate, the quality of the underlying data, and the number of reports in scope. We always recommend a proper semantic model rebuild rather than a like-for-like migration, because the opportunity to improve governance is best taken at migration time.

Question 23

What is Microsoft Fabric?

Accepted Answer

Microsoft Fabric is Microsoft's unified data platform, launched in 2023. It consolidates what previously required five or more separate Azure services (Synapse Analytics, Data Factory, Power BI Premium, Purview, and more) into a single, integrated platform. Fabric covers data engineering, data warehousing, real-time intelligence, data science, and Power BI reporting - all governed through a single OneLake storage layer.

Question 24

Should we migrate from Azure Synapse to Microsoft Fabric?

Accepted Answer

For most mid-market organisations, yes - but the timing and approach depend on your current investment. Fabric supersedes Synapse Analytics and Microsoft has signalled it is the strategic direction for the platform. The migration path is well-defined for most workloads. We assess the specific components you are using, the dependencies involved, and the right sequencing before recommending a migration approach.

Question 25

How long does a Fabric implementation take?

Accepted Answer

A Fabric lakehouse or warehouse implementation, including pipelines, governance, and Power BI integration, typically takes ten to sixteen weeks in the Build phase. Migrations from existing platforms depend on the complexity of the current estate. The Establish phase (four weeks) always precedes the Build to define the architecture and scope precisely.

Question 26

What is Direct Lake mode in Microsoft Fabric?

Accepted Answer

Direct Lake is a Power BI query mode unique to Microsoft Fabric. It allows Power BI to query data directly from the OneLake delta parquet files without importing or caching the data, and without the latency of a live DirectQuery connection. The result is near-import-speed performance on data that is always current - removing the trade-off between performance and freshness.

Question 27

How does Microsoft Fabric compare to Databricks?

Accepted Answer

Both are capable data platforms. Fabric is the better choice for organisations already invested in the Microsoft ecosystem (Power BI, Azure, M365) because the integration is native and the governance model is consistent. Databricks is often the stronger choice for organisations with heavy Python/Spark workloads, data science teams who prefer open-source tooling, or multi-cloud architectures. We assess which platform fits your specific situation rather than defaulting to a position.

Question 28

What does data governance actually mean in practice?

Accepted Answer

For a mid-market organisation, data governance means: agreed definitions for every key metric (what does 'revenue' include?), clear ownership of datasets and reports (who is responsible for accuracy?), documented data lineage (where does each number come from?), access controls (who can see what?), and a process for managing changes. Governance is not a one-time project - it is the operational discipline that keeps analytics trustworthy over time.

Question 29

Why do organisations struggle with data governance?

Accepted Answer

Because governance tends to be treated as a compliance exercise rather than a delivery discipline. Most analytics projects focus on building reports and move on - the governance layer gets skipped because it adds time and does not produce a visible output. The result is an analytics estate that works at the start and degrades as the business changes. We embed governance into every engagement from day one because retrofitting it later is significantly harder.

Question 30

How do you handle data security and compliance?

Accepted Answer

Everything is built within your own Azure tenant - your data never leaves your environment. We adhere to ISO 27001-aligned practices and can work within your existing governance and compliance frameworks. Row-level security, workspace management, and endorsed datasets are standard deliverables in every Power BI engagement, not optional extras.

Question 31

What is data lineage and why does it matter?

Accepted Answer

Data lineage is the documented record of where each piece of data comes from, how it has been transformed, and where it is used. It matters because when a number looks wrong, lineage tells you exactly where to look. Without it, debugging a data quality issue means tracing the problem manually through every system and transformation. Fabric and Power BI both have built-in lineage views that we configure and maintain as part of every engagement.

Question 32

Why do most AI projects fail?

Accepted Answer

Trust, not intelligence. Gartner reports that eighty-five per cent of AI projects fail to deliver business value. The technology usually works. The decisions do not follow because stakeholders cannot verify what the AI told them, cannot trace the evidence, and rationally refuse to act on confident-sounding outputs that arrive with no provenance. The fix is a trust framework that travels with every AI output, not better models.

Question 33

Is AI just a rebranding of statistics?

Accepted Answer

Largely, yes. Most of what gets sold as AI in analytics today has been delivering value for decades under different names: machine learning, statistics, applied maths. RFM segmentation, market basket analysis, propensity modelling, time-series forecasting, anomaly detection, regression, clustering. All of these are AI in 2026 marketing language. Generative AI is genuinely new in the last three years. Agents combine both. The underlying technology lineage is older than the buzzword.

Question 34

Is our organisation ready for AI?

Accepted Answer

Probably not yet, in the sense most leadership teams mean it. AI on bad data does not produce useful outputs. It produces confident-sounding rubbish at scale, faster than a human team could produce it manually. Most mid-market analytics environments need foundation work first: extracting data from legacy systems, building certified semantic models, governance, the trust framework. The largest part of most AI engagements is the work underneath the model, not the model itself.

Question 35

What is the single operating rule for AI outputs?

Accepted Answer

If it cannot cite, it cannot conclude. Nick Kelly's shortest version of the trust framework. If an AI output cannot cite where its evidence came from, it is not allowed to conclude anything. It can produce a draft, raise a flag, suggest a hypothesis, but it cannot make a recommendation that anyone is expected to act on. This rule does most of the heavy lifting. It rules out almost every AI Said So failure and forces the data lineage that prevents Silent Changes.

Question 36

What is the Model Trust One-Pager?

Accepted Answer

A single page, structured into seven blocks, that sits alongside any AI output going into a decision. The seven blocks are: the decision, sources, freshness, limits, confidence, recommended action, and change history. It travels alongside the AI output, not in an audit log buried somewhere else. The stakeholder receiving the output sees both at once. Everything they need to act safely is on the page in front of them.

Question 37

What is the AI Said So failure mode?

Accepted Answer

An AI output appears with confidence and no evidence trail. The recommendation is clear, the language is decisive, and there is no way for the recipient to check what it is based on. They cannot tell what data was used, how recent it is, what was excluded, or what assumptions are baked in. The rational response, and the one we see almost universally, is to ignore the output. Once that has happened, the credibility damage extends to everything downstream.

Question 38

What is the Silent Changes failure mode?

Accepted Answer

The model produces a recommendation in March. It produces a different recommendation in June. Nobody can explain why. The training data shifted, or a parameter was tuned, or a feature was deprecated, but the change happened silently. The first time, the stakeholder questions the model. The second time, they question the team running it. By the third, they have stopped engaging entirely. The fix is not to stop changing models. It is to make every change visible, dated, and explained.

Question 39

What is the Pocket Failures failure mode?

Accepted Answer

The model is right on average. It performs well in tests. The accuracy metric looks strong. But it is wrong in the exact segment that matters most: the high-value customers, the new store format, the region with different behaviour. Stakeholders remember pocket failures forever. A model that is ninety-two per cent accurate overall but wrong about the top ten customers will be remembered as the model that got the top ten customers wrong.

Question 40

What is the licensing for Copilot in Power BI and Fabric?

Accepted Answer

Power BI Copilot and Fabric Copilot run on Fabric capacity. Since April 2025 the minimum capacity is F2 (around £200 per month). Before that change, the minimum was F64. The reduction is significant for mid-market organisations that were previously priced out. The capacity covers all Fabric workloads, not just Copilot, so the cost should be assessed against the whole platform value, not Copilot in isolation.

Question 41

Are we ready for Microsoft Fabric?

Accepted Answer

If you scored nine or above, you are likely ready to consider Fabric. The Building stage is where structured platform investment usually starts paying back. Below nine, fix the foundations first. A new platform amplifies the problems, not solves them. The Fabric Adoption Playbook covers the platform decision in detail; the assessment tells you whether to start that conversation now or later.

Question 42

What is anomaly detection?

Accepted Answer

A pattern recognition technique that identifies observations differing significantly from expected behaviour. The output is typically a flag (anomaly or not) or a score (how unusual the observation is) per record or per time period. Anomaly detection is one of the most useful and underused ML techniques in mid-market analytics.

Question 43

Where does anomaly detection pay back fastest?

Accepted Answer

Five recurring patterns. Finance integrity (unusual journals, expense outliers, supplier payment anomalies). Operations monitoring (equipment, process, service quality). Supply chain (delivery exceptions, demand spikes, stock anomalies). Customer behaviour (unusual purchase patterns, account compromise). Cyber security (unusual access patterns, data exfiltration).

Question 44

How is anomaly detection built in Microsoft Fabric?

Accepted Answer

Through Fabric Data Science notebooks for the modelling, with deployment patterns ranging from scheduled batch evaluation to streaming evaluation through Real-Time Intelligence. The standard implementation: a notebook trains the model on historical data, persists it, and runs scoring as a scheduled pipeline against new data. Anomaly scores are written to a Delta table in the lakehouse.

Question 45

What is Fabric Copilot Capacity (FCC)?

Accepted Answer

A separate capability that lets you point all Copilot usage across the tenant to one centralised capacity for billing and management. Still requires F64 or higher because it is designed to be shared across many workspaces. Most mid-market clients do not need this until they are operating at scale. Useful when you have many distinct Power BI workspaces and want to centralise the Copilot cost rather than carry it on each capacity.

Question 46

When are ML methods worth the additional complexity?

Accepted Answer

Three patterns. Multivariate anomalies where unusualness depends on combinations of features (a customer's transaction is anomalous in this geographic location at this time even if neither factor alone is unusual). Highly seasonal data with complex patterns that simple statistical methods cannot capture. Use cases where the cost of false positives is high enough to justify the better precision of ML methods.

Question 47

What is isolation forest and why is it popular?

Accepted Answer

Isolation Forest is an ML technique that identifies anomalies based on how easily a record can be 'isolated' from the rest through random splits. Anomalous records are easier to isolate (fewer splits needed) than normal records. The technique handles multivariate data well, runs efficiently on large datasets, and produces interpretable scores. It is one of the standard go-to ML techniques for general-purpose anomaly detection.

Question 48

Why Leeds?

Accepted Answer

Leeds has a strong technology and financial services community, a mature Microsoft partner ecosystem, and excellent transport links to London, Manchester, and the rest of the UK. The team is largely Leeds-based and we have built our internal culture and operating rhythm around the city. The cost base is materially lower than London, which we pass through to clients as more competitive day rates without compromising on the calibre of the team.

Question 49

Can we visit your offices?

Accepted Answer

Yes, we welcome visitors to our Leeds base for in-person workshops, discovery sessions, and project reviews. The London presence is more flexible and we typically meet London clients at their offices or at central London locations rather than at a fixed Hopton London office. Email hello@hoptonanalytics.com to arrange a visit. Who Hopton is

Question 50

Who runs the day-to-day operation?

Accepted Answer

The leadership team includes Simon Devine (Managing Director), Shauna Duffy (Director of Delivery and People, responsible for client delivery and team), Bryn Jones (commercial lead), and senior practice leads across data architecture, project management, and reporting. The structure is deliberately flat for a firm of our size. Clients work directly with the people who do the work; we do not run a model where a senior consultant pitches and a junior team delivers.

Question 51

Does Hopton publish case studies?

Accepted Answer

Selectively. We publish anonymised composites in our whitepapers and FAQs based on real engagements, with details obscured to protect client confidentiality. Named case studies are available where clients have explicitly agreed to public reference. Most of our client work is not published because the analytical advantage being created is the kind of thing clients prefer not to advertise.

Question 52

Does Hopton publish frameworks publicly?

Accepted Answer

Yes, and deliberately. Our governance framework, our trust framework for AI outputs (the Model Trust One-Pager and the Trust Storytelling Delivery Checklist), the Business Value Question, the Project Gate, the Analytics Maturity Model, and the FP&A Power BI playbook are all published in our whitepapers and FAQs. We share working templates with clients on request. Publishing the frameworks costs us nothing and helps the broader Microsoft data community.

Question 53

What is the Hopton Analytics Maturity Model?

Accepted Answer

A self-assessment framework across five dimensions of analytics capability, each scored at three levels. It takes about fifteen minutes to complete. The output is a clear picture of where you are strong, where the gaps are, and what to address first. We built it from patterns we see across mid-market organisations during our own client assessments. It is the first thing we work through with every new client.

Question 54

What are the five dimensions in the model?

Accepted Answer

Data Architecture, Reporting, Governance, Skills, and Adoption. Each one matters. A weakness in any single dimension limits the value you get from the other four. Strong architecture and reporting with no governance produces chaos. Good reports with no adoption produces expensive shelfware. The shape of your scores across the five dimensions matters more than the total.

Question 55

How does the scoring work?

Accepted Answer

Each dimension scores 1, 2 or 3. Total range is 5 to 15. Be honest. If you are between two levels, pick the lower one. An inflated score gives you a false picture and leads to bad decisions. The total tells you the stage. The shape of your individual scores tells you what to fix.

Question 56

What does my total score mean?

Accepted Answer

Five to eight is the Foundation stage: significant gaps, focus on basics before investing in new platforms. Nine to eleven is the Building stage: some foundations in place but inconsistent, you are a candidate for structured platform investment with governance and skills attention alongside. Twelve to fifteen is the Scaling stage: foundations are solid, focus on execution and expansion.

Question 57

What are the five profiles?

Accepted Answer

The Spreadsheet Business (Profile A): score 5-7, Level 1 across most dimensions. Tool-Rich, Governance-Poor (Profile B): score 8-10, the most common profile we see. Single-Person Dependency (Profile C): score 9-11, good capability resting on one person. Good Foundations, No Adoption (Profile D): score 10-12, the platform is good but nobody uses it. Ready to Scale (Profile E): score 12-15, foundations solid, focus on execution and expansion.

Question 58

Who is the assessment for?

Accepted Answer

Senior leaders, IT directors, finance directors, and anyone responsible for data and analytics decisions. You do not need a technical background to complete it. It works particularly well when two or three people from the same organisation complete it independently and compare. The gaps between their answers are often as revealing as the scores themselves.

Question 59

How does Gartner's data on analytics maturity compare?

Accepted Answer

Gartner puts eighty-seven per cent of organisations at low BI and analytics maturity. That is not a criticism. It reflects how most mid-market businesses have grown: pragmatically, with spreadsheets and manual processes, solving problems as they arise. The point is that low maturity is normal. The mistake is investing in new platforms before recognising that the constraint is foundations, not tools. The five dimensions

Question 60

What is the average score across mid-market organisations?

Accepted Answer

9.2 out of 15, based on the organisations we assess. That puts the average mid-market business in the Building stage. The most common single profile is Profile B (Tool-Rich, Governance-Poor), where Power BI is in use, dashboards exist, but governance is largely absent and reports have multiplied without coordination.

Question 61

Why do most data governance initiatives fail?

Accepted Answer

Five recurring patterns. First, the 50-page policy document that becomes evidence governance exists rather than something that shapes behaviour. Second, waiting for a Chief Data Officer or formal committee before starting. Third, treating governance as a separate workstream from delivery. Fourth, confusing governance with control, which kills delivery and gets routed around. Fifth, no review cadence, so the framework drifts out of date and becomes irrelevant.

Question 62

Does data governance need a Chief Data Officer?

Accepted Answer

No, particularly not in mid-market organisations. Governance does not need senior structure to begin. It needs a small set of rules, a few people who agree to follow them, and the discipline to keep doing so. Waiting for a CDO is one of the most common reasons governance does not start. The environment continues to sprawl while the role is being scoped, and the longer the wait, the harder the eventual cleanup.

Question 63

Should governance be a separate workstream from delivery?

Accepted Answer

No. Governance must be embedded in delivery, applied to the dataset being deployed this week, the report being promoted, the model being shared. Anything else is theatre. A governance team that writes rules separately from the delivery team that builds reports rarely produces governance that anyone follows. The rules have to live where the work happens. The Business Value Question

Question 64

What is the Business Value Question?

Accepted Answer

The single discipline that anchors every piece of work we do. Six blocks: the decision, the decision owner, the action, the measurement, the deadline, and the cost of inaction. We complete the BVQ with the client at the start of every engagement, every proposal, every sprint. If we cannot complete it, the work stays in the framing stage. It does not start until it can. The BVQ prevents technically impressive work that changes nothing in practice.

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