Create a Data Asset Register: inventory datasets, name owners, record locations and quality issues.
DATA ANALYTICS & AUTOMATION CONSULTANCY
Make your data work harder.
Your data has a story. We help you uncover it—with cleaner information, connected systems, intelligent automation and reporting that drives better business decisions.
01 Clean02 Connect03 Automate04 Understand
MYDATASTORIES / ANALYTICS DEMO↗
Performance overview SAMPLE DATA
DEC PERFORMANCE88/100↑ 26 points since Jan
DEC DATA QUALITY89%↑ 19 points since Jan
PERIOD12 monthsJan–Dec • illustrative
Monthly performance and data quality Jan–Dec · sample series
Score / % (50–100 displayed)
Performance score (0–100)Data quality (%)
Illustrative demonstration figures, not measured customer results. Source: synthetic sample data.
✦ From data to decisions
What we do / Practical data solutions
We clean your data. Connect your systems. Automate your reporting.
Do you have hundreds of datasets and no clear owner? Reports that disagree? Excel processes repeated every month? We help you find the source of the problem and build something your team can trust.
Clean errors, match records and consolidate information across Excel, SQL and business systems.
Automate repeat work and build decision-ready Power BI reports with clearly defined KPIs.
Our website charts are illustrative demonstrations unless an actual source is explicitly identified.
Explore services →BUILT AROUND THE TOOLS YOU USE
WHAT WE DOComplex data challenges.
Complex data challenges.
Practical business solutions.
From fixing unreliable data to modernising reporting across departments, we focus on outcomes you can use—not technology for its own sake.
01 / SERVICE
Data quality & cleansing
Improve accuracy, completeness, consistency and trust across critical datasets.
Explore service →02 / SERVICEData extraction & integration
Bring multiple, even difficult, source systems into a reliable reporting model.
Explore service →03 / SERVICEExcel & workflow automation
Replace repeated copying, manual consolidation and fragile spreadsheets with controlled workflows.
Explore service →04 / SERVICEPower BI & reporting
Automated, decision-ready dashboards underpinned by well-defined measures.
Explore service →05 / SERVICEMicrosoft Fabric
Plan and build a connected analytics platform with governance in mind.
Explore service →OUR APPROACHMake the foundations strong.
Make the foundations strong.
Then accelerate.
Reporting only works when the underlying data is complete, consistent and connected. We tackle the foundations and build efficient solutions that can scale with you.
How we work →01DiscoverUnderstand the business question and current data landscape.
02PrepareExtract, standardise, validate and model information.
03DeliverAutomate workflows and build clear, actionable reporting.
04ImproveDocument, govern and refine as requirements evolve.
Still spending hours preparing reports?
Tell us which processes take the most time. We’ll explore where clean data and targeted automation can make a difference.
Start a conversation ↗About MyDataStories
We listen. We understand. We solve.
We work with organisations to clean, connect and automate data, producing clear reporting that leaders can act on.
Discuss your project ↗Our Services
Practical solutions to difficult data problems.
Data cleaning, integration, workflow automation, dashboards, analytics and reporting.
Discuss your project ↗Data Quality & Cleansing
Make the numbers trustworthy.
Data profiling, duplicate detection, consistent definitions, validation rules and transparent quality scorecards.
Discuss your project ↗View data-quality visual example →Data Extraction & Integration
Connect scattered sources.
Integrate SQL databases, Excel files, business applications and cloud data into reliable reporting pipelines.
Discuss your project ↗Excel & Workflow Automation
Excel and Power Query automation
We modernise repeatable reporting workflows while keeping familiar tools where they are useful. The aim is fewer manual steps and transparent, repeatable calculations.
Discuss your project ↗- Power Query consolidation and standardisation
- Workbook audit checks and repeatable reports
- Controlled VBA and Power Automate workflows where appropriate
Excel automation — time per weekly report
Before versus after automation
Key / units: Minutes per report; lower is better. Values shown in the chart are synthetic examples, not customer results.
Illustrative example based on synthetic data; not a customer result.
Excel automation workflow. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
Power BI & Reporting
Power BI reporting that leaders can trust
We develop Power BI reporting built on well-defined measures and dependable data models, so teams can monitor operations and investigate changing performance.
Discuss your project ↗- KPI definitions, semantic models and DAX measures
- Drill-through reporting, row-level security and refresh checks
- Accessible dashboards and documented handovers
Power BI — monthly KPI trend
Illustrative performance score over time
Key / units: KPI score (0–100); monthly observations. Values shown in the chart are synthetic examples, not customer results.
Illustrative example based on synthetic data; not a customer result.
Power BI performance dashboard. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
Microsoft Fabric
A connected Microsoft Fabric data platform
Microsoft Fabric can bring together ingestion, preparation, storage and business intelligence. We help organisations plan the architecture and control the quality of data moving through it.
Discuss your project ↗- OneLake and lakehouse design
- Pipelines, dataflows and transformations
- Governance, monitoring and Power BI integration
Microsoft Fabric — data flow
Source data → OneLake → Data model → Power BI
Key / units: Four architecture stages; flow diagram, not a measured KPI. Values shown in the chart are synthetic examples, not customer results.
Illustrative example based on synthetic data; not a customer result.
Microsoft Fabric pipeline overview. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
SQL & Data Platforms
Bring disconnected systems into dependable reporting
We connect SQL-based sources and business systems, build consistent transformations, and establish checks that make datasets usable across analytics teams.
- SQL extraction and performance-aware queries
- ETL/ELT pipelines and repeatable transformations
- Data lineage, reconciliation and exception monitoring
SQL — data validation trend
Successive data-quality check runs
Key / units: Validation success rate (%) across eight runs. Values shown in the chart are synthetic examples, not customer results.
Illustrative example using synthetic data; not a customer result.
SQL validation and reconciliation. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
Python & Automation
Automate analysis with reproducible Python
We use Python to clean data, build automated checks and develop transparent forecasting and machine-learning demonstrations. The example shows a calculated signal, not an AI-rendered picture.
- Pandas data preparation and automated validation
- Scheduled analysis and reproducible Python workflows
- Forecast evaluation and explainable machine-learning models
Python — water demand pattern
Daily consumption across a sample period
Key / units: Million litres per day; illustrative daily observations. Values shown in the chart are synthetic examples, not customer results.
Illustrative example using synthetic data; not a customer result.
Python-powered analytic models. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
Data Quality & Cleansing
Know what can be trusted before reporting
Data-quality work starts with measurement: identify missing values, duplicates, unusual patterns and inconsistent business rules; then monitor improvements.
- Profiling, completeness and validity checks
- Duplicate resolution and standardised definitions
- Quality dashboards and auditable exception logs
Data quality — before and after checks
Completeness and validity improvement
Key / units: Quality score (%); illustrative before/after comparison. Values shown in the chart are synthetic examples, not customer results.
Illustrative example using synthetic data; not a customer result.
Data quality scorecard. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
CONTACT MYDATASTORIES
Discuss your data.
Tell us what you want to improve—data quality, Power BI reporting, Microsoft Fabric, SQL, Excel or Python automation. We'll start with your business challenge.
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