From MI you cannot trust to reporting that drives real decisions. Root-cause diagnosis, not platform sales. For mid-market leadership teams who need board-ready data foundations - built in weeks, not months.
A data strategy is a plan that defines what data an organisation needs to run and grow the business, how that data will be collected and maintained reliably, and how it will be used to support decisions at every level from operations to the board. Assured Velocity helps mid-market leadership teams restore board confidence in reporting and build the data foundation that commercial decisions depend on.
When board questions cannot be answered with confidence from management information, the problem is almost always upstream, in processes, system design, or data ownership, not in the report itself.
Spreadsheet-dependent reporting that worked at 50 people does not work at 200. The point at which it breaks is usually a board question that nobody can answer rather than a planned migration.
When finance, operations, and sales systems hold different versions of the same number, the reconciliation effort becomes a permanent overhead and the underlying cause is never addressed.
Data quality problems that have no owner are data quality problems that do not get fixed. Establishing clear ownership is a governance decision, not a technical one, and it belongs at leadership level.
Organisations planning AI or advanced analytics investments that have not addressed their data quality foundations are building on ground that will not hold. The foundation work is not glamorous but it is what determines whether the investment pays off.
Data held without clear governance, retention policy, or access control creates regulatory exposure that compounds over time. An independent view of where that risk sits is a board-level requirement, not an IT one.
Engagements are scoped to the board's actual question. Typical work covers:
"We had invested in a BI platform but the board still did not trust the numbers. The problem was not the platform, it was three process gaps upstream. Assured Velocity found them in two weeks."
"Our Series B investors asked a straightforward question about NRR by cohort. We couldn't answer it cleanly. Assured Velocity found the root cause in four days and gave us a roadmap to fix the data architecture."
| Product | Fee | Duration | |
|---|---|---|---|
| Velocity Readiness Survey | Free | Instant | Learn more → |
| Business Rapid Diagnostic | Bespoke | 2-6 weeks | Learn more → |
| Focussed Functional Diagnostic | Bespoke | 2-6 weeks | Learn more → |
Start with a 30-minute call to confirm fit and agree what a useful first step looks like.
“Regulatory reporting had three teams producing three different numbers. The root cause was process, not people. Fixed in six weeks.”
“The board could not reconcile operational performance with financial reporting. Assured Velocity found the break in the data pipeline within two weeks.”
“Our MI had lost the board’s confidence. The data strategy redesign gave us reporting we could stand behind for the first time.”
“We had the data. We just could not produce a view the board trusted. The problem was architecture, not effort.”
“The 14-day Business Review was the most useful £7k we have spent. It gave us a clear answer on what to fix and in what order.”
“They gave us a data architecture that was designed around what the board actually needed to see. Not what the systems could produce.”
“Regulatory reporting had three teams producing three different numbers. The root cause was process, not people. Fixed in six weeks.”
“The board could not reconcile operational performance with financial reporting. Assured Velocity found the break in the data pipeline within two weeks.”
“Our MI had lost the board’s confidence. The data strategy redesign gave us reporting we could stand behind for the first time.”
“We had the data. We just could not produce a view the board trusted. The problem was architecture, not effort.”
“The 14-day Business Review was the most useful £7k we have spent. It gave us a clear answer on what to fix and in what order.”
“They gave us a data architecture that was designed around what the board actually needed to see. Not what the systems could produce.”
A data strategy defines how your organisation will collect, manage, govern, and use data to achieve its business objectives. If your leadership team regularly cannot get reliable answers to basic operational questions, if different teams are working from different numbers, or if you are considering AI or advanced analytics, a data strategy is worth having. If data is not yet a constraint on decisions, the investment can wait.
The most common issues are: data trapped in disconnected systems with no single source of truth, reporting that takes too long and is often disputed, poor data quality that makes analytics unreliable, no ownership or governance for master data, and an inability to answer simple questions like "what is our actual margin by customer" without a manual data exercise.
A data warehouse stores structured, pre-processed data optimised for reporting and BI. A data lake stores raw data in its original format, typically for analytics and data science workloads. A data lakehouse combines elements of both - structured enough for BI, flexible enough for analytics. For most mid-market businesses, a well-designed data warehouse or modern cloud analytics platform is sufficient.
A focused data foundation - a clean, reliable reporting layer covering your core business metrics - can be operational within eight to sixteen weeks if data sources are accessible and a business owner is engaged. More ambitious programmes involving multiple source systems, complex data modelling, or self-service analytics capability typically take six to twelve months.
Data governance is the set of policies, ownership structures, and processes that ensure data is accurate, consistent, and used appropriately. For smaller organisations, a lightweight governance framework - defining who owns which data, what the authoritative source for each key metric is, and how data quality issues are raised and resolved - is usually sufficient. Enterprise-scale governance frameworks are overkill for most mid-market businesses.
Data quality work starts with understanding what decisions the data needs to support and what level of quality is genuinely required for those decisions. We then profile the data to understand the actual quality position, identify root causes (usually process or system issues rather than pure data problems), and prioritise remediation by business impact. Trying to achieve perfect data quality across everything is a trap.
Yes. We can lead requirements definition, vendor selection, and implementation governance for BI and analytics platforms. Common platforms we work with include Power BI, Tableau, Looker, and Qlik, alongside modern cloud data platforms such as Snowflake, Databricks, and the major cloud provider analytics stacks. We are vendor-agnostic.
At a minimum, you need data that is accessible (not locked in systems that cannot be queried), sufficiently complete for the use case, reasonably clean, and governed well enough that you trust its provenance. Most organisations overestimate their data readiness for AI. A data readiness assessment before committing to AI tooling spend is usually a worthwhile investment.
Data regulation shapes what data you can collect, how long you can retain it, where it can be stored, and how it must be protected. A data strategy needs to be designed with regulatory compliance as a constraint, not retrofitted after the fact. This is particularly important if you are working with personal data, financial data, or data in regulated sectors.
We provide both strategy and technical delivery. Depending on the engagement, this might include data architecture design, ETL pipeline development, data modelling, dashboard build, and team capability development alongside the strategic advisory. We do not produce strategies that we cannot also deliver.