Every team deserves answers. Not a queue.
Business decisions should not wait for a data request to be prioritised, built, and delivered. LakeStack gives every team, marketing, finance, sales, operations, HR and product, governed, reliable data they can explore and act on themselves.
Your data team is a bottleneck. It should not be.
When every analytics request goes through a central data team, insights arrive late, decisions are made on instinct, and the data team spends its time on reports instead of infrastructure.
One governed data foundation. Every team, self-sufficient.
LakeStack makes self-service analytics possible by solving the data problem underneath it. Reliable ingestion, centralised transformation, and governed delivery to every BI tool your teams already use.
Choose your team. See what becomes possible.
When every department has access to governed, reliable data, the whole organisation makes better decisions faster. Here is what self-service analytics unlocks for each team.
Centralise spend, impression, and conversion data from every ad platform into a single view. Marketing teams identify what is working and reallocate the budget without waiting for a report.
Connect CRM, behavioural, and campaign data to build audience segments your team can refresh and activate independently, without engineering involvement.
Join data from ad platforms, email tools, and web analytics to understand the real customer journey and attribute conversions accurately across every channel.
Combine web traffic, email, and CRM data into a unified customer view that every marketer can explore and segment without requesting a custom report.
Centralise data from ERP, billing, and accounting systems into pre-built financial models. Income statements, balance sheets, and cash flow reports are always current and consistent.
Track MRR, ARR, and revenue by product, region, and segment in dashboards that update automatically. Finance teams see the current picture without waiting for a monthly extract.
Streamline month-end, quarter-end, and year-end close by centralising reconciliation data. Finance spends less time chasing numbers and more time on analysis and strategy.
Join billing, CRM, and product usage data to model LTV and predict churn. Finance and commercial teams align on the same customer value figures from the same source.
Combine CRM, engagement, and marketing data so sales leaders see deal velocity, stage conversion, and pipeline coverage updated continuously, not once a day.
Join CRM and ERP data to see where every rep stands relative to quota. Coach the right people at the right time with data your team can access without requesting a report.
Analyze customer and deal data by segment, region, and time period to surface the patterns and opportunities your team would otherwise discover too late.
Understand which outreach cadences, channels, and messages drive conversion. Give every sales rep access to the playbook the data is actually supporting.
Centralise data from ERP, WMS, field systems, and operational tools into one governed view. Operations leaders see what is happening across every location without manual data pulls.
Combine historical operational data with current demand signals to plan capacity accurately. Operations teams make resource decisions on current data, not last week's export.
Track cost per unit, throughput, and efficiency metrics across every site with dashboards that update as new operational data flows in automatically.
Join procurement, delivery, and quality data to evaluate supplier performance consistently. Procurement teams answer their own questions without waiting for a centralised report.
Join HR, engagement survey, and performance data to identify attrition risk early. People teams address issues before they become departures, without waiting for a data team to build a report.
Centralise performance, compensation, and tenure data to make review cycles faster and more consistent. HR calculates increases based on data, not instinct.
Model headcount scenarios using current organisational data and hiring pipeline information. People leaders make hiring decisions on a current, complete picture.
Track diversity, equity, and inclusion metrics consistently across the organisation using governed people data that every senior leader can access from the same source.
Centralise product telemetry, event data, and usage logs into pre-built models that product managers can explore without engineering support. Understand what users actually do, not what you think they do.
Join product usage, support, and billing data to build customer health scores that product and CS teams can monitor and act on in real time.
Track activation, engagement, and expansion metrics across the customer journey. Product teams see the data that drives growth and adjust the roadmap accordingly.
Connect product usage data with revenue data to understand which features drive expansion, retention, and upsell. Prioritise the roadmap based on what the data is telling you.
What makes self-service analytics work at LakeStack
Self-service is not a switch you flip. It is the result of a data foundation that is governed, reliable, and accessible. These are the capabilities that make it possible.
Centralise data from SaaS tools, databases, ERPs, and files without custom engineering. Every source feeds the same governed destination.
Define revenue, churn, conversion, and every other metric once. Every team works from identical definitions. No more conflicting numbers before a board meeting.
LakeStack delivers pre-modelled data to Power BI, Tableau, Looker, and any other BI tool your teams already use. No rip and replace required.
Continuous ingestion and transformation means dashboards reflect what is happening now, not what was happening at last night's batch run.
Teams explore data within governed boundaries. Access controls, role-based permissions, and audit logging are enforced throughout. Self-service does not mean ungoverned.
Every number in every dashboard is traceable back to its source. When something looks wrong, any team can investigate without needing the data team to diagnose it.
Pre-modelled, governed datasets are ready for AI and ML workloads without additional preparation. The foundation for analytics and AI is the same foundation.
LakeStack delivers governed data across cloud environments and destinations. Your team is not constrained by where the data lives or what BI tool they prefer.
Add new data sources, new teams, and new use cases without rebuilding the foundation. The platform scales with your organisation, not against it.
What happens when every team has the data they need
The impact of self-service analytics is not just speed. It is the decisions that get made correctly because the right person had the right data at the right time.
What replacing the foundation actually unlocks.
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Applify, the team behind this AI innovation, built LakeStack as a true AWS-native data foundation that lives entirely inside your AWS account, giving you full sovereignty, governed lakehouse capabilities, and production-ready AI value in weeks, without tool sprawl or external dependencies.
- Supports Agentic AI using Bedrock and SageMaker
- Uses Apache Iceberg open table format
- Enforces Lake Formation fine-grained governance
- Handles schema drift automatically every time
- Provides built-in active metadata and lineage
- Features self-healing real-time pipelines
- Eliminates all third-party tool dependencies
- Enables query flexibility with any engine
- Ensures full data sovereignty and control
- Offers automatic sensitive data classification
Frequently asked questions
Self-service analytics means business teams can explore and answer their own questions using governed, pre-modelled data without needing SQL skills or data team support. It is different from raw database access because the data has already been cleansed, modelled, and governed before it reaches the business user. Teams get trusted data to explore, not raw tables to interpret themselves.
Governance is applied at the data layer, not the access layer. Before any data reaches a BI tool or business user, it has been ingested, transformed, and governed by LakeStack. Access controls determine who can see which datasets. Audit logging tracks every query. Data lineage means every number is traceable. Teams explore within defined boundaries, not around them.
LakeStack delivers governed, pre-modelled data to any BI tool your teams already use, including Power BI, Tableau, Looker, Qlik, and others. You do not need to change your analytics tooling to benefit from a governed data foundation. LakeStack integrates with your existing stack rather than replacing it.
Metric and KPI definitions are centralised in LakeStack's transformation layer and applied before data reaches any downstream consumer. Revenue, churn, conversion, and every other business metric is defined once and enforced consistently. Finance and operations see the same number because they are both reading from the same governed model, not building their own calculations on top of raw data.
Once the underlying data foundation is in place, onboarding a new team to self-service analytics typically takes days rather than weeks. LakeStack connects to the data sources relevant to that team, applies the appropriate transformation logic, and delivers pre-modelled datasets to their BI tool of choice. Most of the setup work is in the data foundation, not in each individual team rollout.
It reduces it. When business teams can answer their own questions, the volume of ad hoc report requests to the data team drops significantly. The data team's effort shifts from building one-off reports to building and maintaining the governed foundation that makes self-service possible. That is a more scalable, higher-value use of data engineering and analytics time.
Give every team the data they need to decide, not request.
LakeStack builds the governed data foundation that makes self-service analytics real across every department, without compromising governance, reliability, or trust.
