Data consultancy

Data consultancy

We deliver data consulting to organisations that have outgrown spreadsheets. Our team handles data strategy, infrastructure, analytics, and governance.

Overview

One foundation. Every answer.

Shipshape Data operates as a data consultancy located in the UK. We provide data consulting services that transition you from disconnected systems to a managed data foundation equipped with supporting analytics. These services comprise data strategy, data engineering, data science, and business intelligence, all delivered by a single team for the whole process.

The majority of data problems seem to be reporting problems. Examples are dashboards that users distrust, manual weekly exports, or departments presenting conflicting revenue figures. These issues typically arise from the absence of a single governed foundation. We start by creating a cloud data warehouse that employs a medallion architecture and a semantic layer. This produces a single business definition that acts as the source for each report, model, and AI tool.

Artificial intelligence remains relevant even when it is not your current priority. All AI capabilities we build depend on this same foundation. Engaging us for data consulting guarantees your systems are ready for AI as an integral part of the build process instead of a later addition.

Medallion architecture diagram showing sources feeding bronze, silver, and gold data layers, a governed semantic layer, and the dashboards, AI models, and apps built on top
The medallion architecture places raw data into the bronze layer. The data is then cleaned and transferred to the silver and gold layers. All later processes rely on a single governed definition.
What you get

Our data consulting services

Data strategy consulting

Determine which data to collect, where to store it, and which decisions it will support. Arrange these tasks so the first phase creates sufficient value to finance the next step.

Data engineering and infrastructure foundations

We have set up a warehouse, pipelines, and a medallion architecture with data migrated cleanly. More details are available in the sections on the data foundation and data migration.

Analytics and BI consultancy

Your team can independently access dashboards and self-serve reporting. These tools draw from a single data source. Refer to AI forecasting.

Data science and data governance

Predictive models deliver value via practical application, and governance keeps data accurate, secure, and compliant as the system expands.

Proof

Evidence of effectiveness

We knew what we wanted to achieve, but wanted specialist support to accelerate the journey and bring external expertise.

Chris Pullinger, Financial Controller, Smarter Services
Read the Smarter Services case study

7 systems, unified

Smarter Services swapped its fragmented reporting for a single governed dashboard within 14 weeks.

1.5 days a week

returned to the Smarter Services team once manual reporting stopped.

60% of tickets

It was handled to a human standard at 1NCE, using data that the foundation made trustworthy.

Method

The engagement process

  1. List your current assets

    Systems, sources, quality, and ownership are mapped openly. You will know what your data can support before any build begins.

  2. Strategy is linked to decisions

    We pinpoint the specific decisions your business must make and base our strategy on them. This guarantees the final output is a practical delivery plan rather than a presentation.

  3. Build the foundation

    Warehouses, pipelines, medallion layers, and the semantic layer constitute the foundational components of data architecture. This infrastructure sets the reliability of all later data processes.

  4. Provide the analytics

    We will create dashboards, reports, and models atop the foundation. These will be delivered to each team individually once we have set agreed definitions.

  5. Govern and hand over

    We keep access rules, lineage, and documentation inside the platform. Our team stays engaged after the initial launch to ensure your staff can run the system independently.

Analytics and science

Data analytics and data science consultancy

After the data foundation is set up, the analytics layer delivers practical results for businesses. Our data analytics consultancy backs the reports your teams use daily: dashboards that individuals access independently, consistent self-service data, and forecasts for planning. Analytics consulting works best when all charts draw from the semantic layer, which offers a single, consistent definition of revenue, margin, and customer, allowing different departments to reach the same conclusions.

Data science consulting starts after reporting is finished. We deliver demand forecasting, churn and risk scoring, anomaly detection, and standard machine learning. These methods frequently outperform language models when applied to structured data. We advise our clients on which problems need a model and which only need a standard report. Many data science projects today are costly ways to obtain information that a basic dashboard could have delivered at no extra cost.

Both systems share the same underlying infrastructure. This means that finishing an analytics project positions you to start using AI without rebuilding your systems.

Kept in order

Data management and governance consulting

Data management is the aspect of data consulting that ensures a project can pass an audit. Our consultancy work concentrates on ownership, quality, and the data lifecycle. We define who is responsible for each dataset, how to measure accuracy, and how to handle data that is no longer needed.

Data governance consulting makes sure that rules are enforced via technical systems rather than staying as goals. Access controls, data lineage, and retention policies are embedded in the platform, the data warehouse, and the semantic layer. This configuration guarantees that governance stays active without needing manual oversight. This approach sets the practical scope of the work. Row-level security rules work best when they reside in the semantic layer instead of individual applications. For organisations in the UK, this method offers specific ways to meet GDPR obligations and data protection requirements. It also enables boards to handle risk management with technical evidence rather than static policy documents.

FAQ

Frequently asked questions

What is the role of a data consultancy?

A data consultancy helps you turn fragmented operational data into a reliable resource for your business: a plan for data collection and its purpose, a managed storage system, and the analytics, data science, and business intelligence applications built from it. At Shipshape Data, a single team delivers these four components.

What are the differences between data strategy and data governance?

Data strategy defines which data a business needs and how to use it. Data governance sets the rules and controls that keep this data accurate, secure, and compliant during use. Both are required. A strategy lacks direction without governance, and governance lacks purpose without a strategy.

Which platforms do you use?

We build on the modern cloud data stack, including Snowflake, Databricks, Google BigQuery, and Microsoft Fabric, with dbt and Fivetran in the pipeline layer. We recommend the platform that fits your estate instead of one that gives us a margin: we do not resell licences.

Do you provide business intelligence consulting services?

Indeed. Typically, the first visible outcomes of the project are dashboards, self-serve reporting, and forecasting. They are constructed on a semantic layer so that each team applies the same definitions to their data.

What is the typical duration of a data consulting project?

Typically, a specific initial result can be reached within a few weeks. In the Smarter Services engagement, we combined seven systems into one live dashboard that includes established governance. The project required 14 weeks to finish. Since that time, the organisation saves 1.5 days of manual reporting each week.

Is this service suitable for small businesses?

Indeed. The majority of our clients are mid-sized organisations whose data requirements have outgrown the limits of spreadsheets and specialised software. This architecture works well for both small and large operations. The key requirement is that the system offers a reliable foundation for the specific decisions you need to make.

When evaluating data consulting firms, begin with the distinguishing question: will they demonstrate what your data can support before suggesting what to build? That is our initial deliverable. Talk to us and describe what your data looks like today.

Next steps

What you can expect when you contact us

  1. Please describe the problem to us

    Complete the form briefly. We will send a personal response, usually within one working day.

  2. A discussion concerning the project scope

    We will allocate thirty minutes to discuss your systems, your reporting, and the decisions you derive from your data. There will be neither a slide deck nor a sales pitch.

  3. A straightforward reply

    We will deliver a written document that outlines your foundation requirements, our initial build plan, and the total cost.

Not prepared to discuss this yet? You may review a data engagement example: the Smarter Services case study outlines the full 14-week process.

Start at your core.

Tell us where your data is today and what you want AI to do. We'll come back with a straight answer on what your foundation needs and where the quickest real win is.

Talk to us