Data analytics services for numbers your teams can trust
We connect your systems, clean and model the data, and build dashboards and forecasts so leaders stop debating whose spreadsheet is right and start acting on the numbers.
ZedBlu offers data analytics services that start with the plumbing and end with decisions. We pull data from your applications, databases, spreadsheets, ad platforms and payment systems into a central warehouse, model it around how your business works, and deliver dashboards, reports and forecasts people actually open.
Good analytics depends on definitions as much as technology. Before building a single chart, we agree with finance, sales and operations on what counts as revenue, an active customer or a delivered order, then encode those rules in tested data models so every report uses the same logic.
Our team combines data engineers, analysts and software developers. That means we can also embed analytics inside your product, build data APIs for your customers, or add machine learning once your historical data is good enough to support it.
What our Data & Analytics team delivers
Data engineering and pipelines
Data engineering services that move data reliably from source systems into your warehouse. ETL pipeline development in Python includes scheduling, retries, incremental loads and data quality checks, with alerts when a source changes its format or a load fails.
Data warehouse development
A central warehouse on PostgreSQL or a managed cloud service on AWS, Azure or Google Cloud, designed with clear layers for raw, cleaned and business-ready data. Dimensional models keep reporting fast and understandable, and access controls keep sensitive fields restricted.
Power BI dashboards and reports
Power BI consulting and dashboard development services: data models, DAX measures, row-level security, refresh schedules and dashboards designed around specific decisions. We also train your analysts to build new reports confidently on the governed model.
Embedded and customer-facing analytics
Dashboards inside your own web app or SaaS product, so customers see their usage, performance or spend without exporting data. We build them with secure multi-tenant data access, fast queries and charts that match your product's design.
BI strategy and KPI frameworks
Business intelligence services for companies starting out or cleaning up: an inventory of data sources, a KPI framework, an owner for each metric, a prioritized roadmap of reports and recommendations on tools and running costs.
Predictive analytics
Forecast demand, predict churn, score leads or flag anomalies with models built in Python using TensorFlow or PyTorch. We validate every model against historical data, explain which factors drive its predictions and monitor accuracy after deployment.
Data migration and consolidation
Merge data from acquired companies, legacy systems or duplicate tools into one clean dataset. We profile and deduplicate records, map fields, reconcile totals with finance and document every transformation so the result can be audited.
Plain-English questions with AI
Let managers ask questions such as which region missed its target last month and get answers from your governed data model. We use language models from OpenAI or Anthropic with guardrails, so queries run only against approved tables and metrics.
What you get
Numbers everyone agrees on
Shared definitions, tested data models and a single source for each metric end the debate over whose figure is correct, so meetings focus on what to do next.
Less manual reporting
Automated pipelines and scheduled refreshes replace the weekly spreadsheet assembly that eats analyst time, freeing your team to focus on the analysis that actually informs decisions.
Faster answers to new questions
A well-modeled warehouse lets analysts answer a new business question in hours rather than waiting days for a developer to write a custom export from each system.
Secure and auditable data
Role-based access, row-level security, encrypted storage and documented lineage show who can see what and where each number came from, which supports audits and data protection requirements.
Our process
Tools & tech we use
Use cases
- Sales and pipeline dashboards combining CRM and finance data
- Inventory and demand forecasting for retail and distribution
- Marketing attribution across ad platforms and web analytics
- Operations dashboards for field service and logistics teams
- Churn and lifetime value analysis for subscription businesses
- Financial consolidation across branches or subsidiaries
- Usage analytics embedded in SaaS products
Ready-made solutions to launch faster
Related services
Data & Analytics: FAQ
How much do data analytics services cost?
Cost is driven by the number and complexity of data sources, data quality, the volume of history to load, the number of dashboards and whether you need predictive models. A focused project, such as one dashboard suite on a few clean sources, is far smaller than a company-wide warehouse with dozens of integrations. We scope in phases and quote each phase at a fixed price after a free consultation.
How long does a data warehouse and dashboard project take?
A first set of dashboards on a few sources typically takes 4–8 weeks. A company-wide warehouse with many sources is delivered in phases over several months, with useful dashboards released along the way rather than at the end. Data quality is a major variable, and the audit in the first weeks shows how much cleanup is needed.
Do we need a data warehouse, or can Power BI connect directly to our systems?
For a few clean sources and modest data volumes, Power BI can connect directly, and that may be enough. A warehouse becomes worthwhile when you combine many systems, need history that source systems do not keep, want consistent definitions across reports or face slow refreshes. We will recommend the lighter option whenever it is sufficient.
What is the difference between data engineering and data analytics?
Data engineering builds the pipelines and warehouse that collect, clean and store data reliably. Data analytics uses that prepared data to answer questions through dashboards, reports, statistical analysis and predictive models. Both matter: analysis on unreliable data produces misleading answers, and pipelines without analysis produce no decisions. Our data analytics services cover both, so one team owns the path from source to insight.
Can you work with our existing data and tools?
Yes. We connect to common databases, cloud applications, spreadsheets and APIs, and we can build on your existing Power BI workspace or cloud account rather than starting over. If a source has no API, we use secure database connections, scheduled exports or file drops. Everything runs in your environment, and you own the code, models and documentation.
Can we use AI on our business data safely?
Yes, with the right controls. We prepare a governed data model first, then let AI tools query only approved tables and metrics, with the same row-level security as your dashboards. Sensitive fields can be masked, and the AI provider is set up so your records are excluded from model training. Each AI-generated answer can show the query behind it, so users can verify the result.
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