From Manual to Real-Time: Centralising Kipu and Dazos Data for Scalable Healthcare Insights

Client Background

The client is a growing healthcare organisation focused on addiction treatment across multiple clinics.

They rely on two main platforms:

• Kipu for EMR and clinical workflows

• Dazos for operations, scheduling, and ancillary data

Both systems were essential, but their data lived in separate silos. Reporting across both tools was slow, manual, and hard to trust.

The Problem: Manual, Delayed, And Siloed Reporting

Before working with Tenplus, the client faced several challenges:

• Data from Kipu and Dazos was exported manually from web interfaces.

• Reports were often days out of date by the time leadership received them.

• Every department used its own spreadsheets, leading to conflicting numbers.

• Clinical and operational leaders had no single view of patients, clinics, or provider performance across systems.

• There was no real-time reporting, which limited fast decision-making.

In short, the organisation had good systems, but no unified data layer to connect them.

They needed:

• Centralised, near real-time data

• Automated pipelines

• High data quality with full audit trails

• A self-service reporting layer for clinicians and operations

Solution Overview

Tenplus designed a cloud-native solution using Azure Data Factory, Azure Functions, and Databricks, with Power BI as the reporting layer.

At a high level, the architecture looked like this:

1. ADF Pipelines pulled data from Kipu and Dazos APIs using token-authenticated, paginated calls.

2. Raw JSON landed in a controlled storage layer for traceability.

3. Azure Functions cleaned and normalised the data into tabular, analytics-ready formats.

4. Databricks Notebooks applied business logic, enrichment, deduplication, and Delta Lake optimisations.

5. Curated Delta tables powered Power BI dashboards for real-time self-service analytics.

6. Monitoring and alerts used ADF logging, Log Analytics, and Microsoft Teams notifications.

Data
Analysts

Our in-house data analysts have a hands-on approach to data.

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Companies analyzed

Last year, we were able to help nearly 50 companies.

Process

Step 1: Ingesting Kipu and Dazos Data With Azure Data Factory

Step 2: Cleaning And Normalising Data With Azure Functions

Step 3: Business Logic And Transformations In Databricks

Step 4: Monitoring, Logging, And Alerting

Step 5: Real-Time Analytics With Power BI

Data designers

Our designers have an extensive background in data analysis.

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1340

Visualizations made

From presentations to marketing materials and beyond.

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Ongoing projects

Data changes, but our team stands by your side when you need us.

Results: From Manual Reports To Always-On Insights

The impact of the new platform was clear and measurable.

The organisation:

• Eliminated manual exports from Kipu and Dazos.

• Dramatically improved data freshness, from days to near real time.

• Created a single source of truth for clinical and operational reporting.

• Reduced errors and manual rework caused by spreadsheet-based reporting.

• Enabled self-service analytics for clinicians and operations staff.

• Built a scalable foundation that can support more systems and use cases in the future.

What started as a simple goal to automate reports became a long-term strategic data platform for the organisation.

⭐⭐⭐⭐⭐

“Before working with Tenplus, our reporting process was slow, manual, and full of gaps. Now we have a real-time data platform that pulls clean, trusted data from both Kipu and Dazos without our team lifting a finger. Our clinicians and operations teams depend on the dashboards every single day. Tenplus gave us clarity, speed, and a system that will support our growth for years. It has changed how we make decisions.”

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We've partnered with many household brand names to deliver insights and solutions to their problems with big data.

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Don't just take our word for it – our clients frequently stay in touch with us and work with us on future projects that require big data insights.

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I work in agricultural consulting, meaning that we often need to use big and complex datasets to justify expensive investments for our clients. Lilo's consultants helped us see the big picture with their visualization tools and expertise.

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As the in-house supplier manager, it's sometimes had to understand which manufacturers work best for our needs. Lilo's data analysis helped us identify where we could save and which manufacturers worked the best for us in the long term.

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