Move to Modern Analytics
“Our data is everywhere and nobody trusts the reports”
Data locked in silos. Excel spreadsheets, legacy data warehouses, and third-party tools that don't talk to each other. Decision-makers wait days for reports that are already stale by the time they arrive.
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Unified data lake with dashboards your stakeholders trust.
Automated pipelines feeding real-time dashboards. Data accessible to everyone who needs it, governed for everyone who doesn't. Decisions based on facts, not gut feel.
Your data should be your competitive advantage. Instead, it’s a liability. Scattered across systems that don’t talk to each other, locked in formats that only one person understands, and summarised in reports that nobody fully trusts.
The irony is that you’re drowning in data while starving for insight. Every decision involves someone exporting a spreadsheet, manually reconciling numbers, and presenting findings that are already out of date.
Why data projects stall
Most analytics initiatives fail not because the technology is wrong, but because the foundation is missing.
You can’t build dashboards on data you can’t find. You can’t find data that isn’t catalogued. You can’t catalogue data that isn’t governed. And you can’t govern data when nobody has mapped what exists, where it lives, or who owns it.
This dependency chain is why data projects drag on for months. Teams jump to the exciting part. Dashboards and visualisations, without sorting out the plumbing underneath. Six months later, nobody trusts the dashboards because the data feeding them is inconsistent.
How we do it
We start with the data, not the dashboards. The visualisation layer is the last thing we build. Because it’s only as good as what feeds it.
Source inventory and quality assessment. Every data source mapped. Format, freshness, ownership, quality issues. You can’t integrate what you haven’t catalogued. This step alone often reveals duplicate sources, stale feeds, and orphaned datasets nobody knew existed.
Automated pipelines with AWS Glue. Data extracted, transformed, and loaded into a centralised data lake on S3. Pipelines run on schedule or in real time with Kinesis. No more manual exports. No more spreadsheet reconciliation.
Governed access with Lake Formation. Fine-grained access control down to the column level. Your analysts get self-service access to what they need. Sensitive data stays protected. Every query is auditable.
Dashboards built on trusted data. QuickSight dashboards designed with your stakeholders, validated against existing reports, and deployed only after sign-off. The numbers are right because the pipeline is right.
What's usually in the way
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Data scattered across incompatible systems
Your data lives in a dozen places. CRM, ERP, spreadsheets, SaaS tools, legacy databases. Each has its own format, its own update schedule, and its own version of the truth.
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No data governance or cataloguing
Nobody knows what data you have, where it lives, or who's allowed to see it. Sensitive data is mixed with operational data. There's no catalogue, no lineage, no access control worth the name.
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Existing reports are undocumented tribal knowledge
Your current reports were built by people who've since moved on. Nobody knows the logic behind the numbers. Stakeholders don't trust them, but there's nothing better to replace them with.
What we resolve
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Data source inventory and integration strategy
We map every data source. Format, freshness, quality, ownership, and design an integration strategy that brings everything into a single lake. AWS Glue handles the ETL. You get one source of truth.
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Lake Formation governance with fine-grained access
AWS Lake Formation provides cataloguing, lineage tracking, and column-level access control. Your data team can find what they need. Everyone else sees only what they're authorised to.
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Report validation and migration with stakeholder sign-off
Existing reports are reverse-engineered, documented, and rebuilt in QuickSight. Each one validated against the old output before stakeholders sign off. No silent changes to the numbers.
Frequently asked questions
What does a modern data platform actually do for us?
It brings every data source into one governed lake on AWS, with dashboards your stakeholders trust. No more reconciling figures across five spreadsheets. No more reports that are stale by the time they land. Decision-makers see the same numbers as everyone else, in close to real time.
Do we have to abandon our existing BI tools?
No. The lake sits underneath your BI layer. If your team uses Power BI, Tableau, or Looker today, they can keep using them and connect to AWS data sources. Most clients also adopt QuickSight for dashboards aimed at non-analysts, because the per-user pricing is fairer at scale.
How long before we get useful dashboards?
The first dashboards typically land within 8-12 weeks: the highest-priority report, validated against the old version, signed off by the stakeholder. Full data lake migration runs 6-12 months as more sources come in. Value lands incrementally, not at the end.
What about real-time data?
We use Amazon Kinesis or Managed Streaming for Kafka where the business genuinely needs sub-minute latency. Most reporting doesn't, and we say so. Real-time costs more to build and operate, so we apply it only where the decision speed justifies it.
Who controls access to the data once it's on AWS?
You do. AWS Lake Formation handles cataloguing, lineage, and fine-grained access control down to column level. Your data team can find what they need. Finance sees finance data. HR sees HR data. Nobody sees what they shouldn't. Everything audited.
Ready to take the next step?
No obligation, just a clear conversation about where you are and what's possible.