Data and analytics services encompass the full range of work needed to collect, organise, analyse, and act on data — from engineering the pipelines that move data between systems to building the machine learning models that generate predictions, and the dashboards that make both accessible to the people who need them. DevByte builds modern data systems for healthcare, agritech, and finance organisations where data quality and governance are not optional.
Data and analytics services span a wide range that is often conflated: data engineering (the infrastructure that moves and stores data), data science (the analysis and modelling work that generates insights), machine learning (the systems that make predictions or automate decisions based on data), and business intelligence (the dashboards and reporting that make findings accessible to operational teams). Most organisations need more than one of these, but they rarely need all of them simultaneously — the right starting point depends on where your data maturity currently is.
The most common starting point, and the one that unblocks everything else, is data quality and pipeline infrastructure. An ML model trained on inconsistent, incomplete, or incorrectly labelled data will produce unreliable outputs regardless of how sophisticated the model architecture is. A BI dashboard connected to a data source that has not been properly cleaned will mislead the business leaders who rely on it. Data quality is not a technical detail — it is the foundation that determines whether everything built on top of it is trustworthy.
For regulated industries like healthcare, the data infrastructure also has to meet specific governance requirements: audit trails for data access, role-based access controls, data lineage documentation, and compliance with HIPAA or equivalent standards. We build these requirements into the data architecture from the start rather than applying them as a layer at the end.
Healthcare organisations generate enormous volumes of data — clinical notes, billing records, lab results, scheduling data, wearable device outputs — spread across systems that were not designed to communicate with each other. The data exists. The ability to aggregate it, clean it, and use it reliably does not.
The result is operational decisions made on incomplete information, predictive models that cannot be deployed in production because the training data does not reflect real-world conditions, and BI dashboards that show different numbers depending on which system was queried last. The underlying problem is not the lack of analytics capability — it is the lack of a reliable data foundation for analytics to run on.
A modern data architecture has four layers. The ingestion layer captures data from source systems — EHRs, APIs, IoT sensors, flat files — using ETL pipelines or event streaming (Kafka, Kinesis) depending on whether the data needs to be processed in batch or near real-time. The ingestion layer is responsible for reliability and completeness — data that does not make it into the pipeline reliably cannot be analysed reliably.
The storage and processing layer organises data into a structure that supports the analytical workloads that will run on top of it. For most healthcare organisations, this is a cloud data warehouse (Snowflake, BigQuery, Redshift) with a lakehouse architecture that keeps raw data accessible alongside processed, transformed data. Data lineage — the ability to trace any data point back to its original source — is implemented at this layer.
The analytics and ML layer is where models are trained, predictions are generated, and dashboards are populated. Machine learning workloads run on managed ML platforms (SageMaker, Azure ML, Vertex AI) with MLOps pipelines that handle model versioning, performance monitoring, and retraining. The output layer — dashboards, APIs, notifications — delivers results to the users and systems that need to act on them.
With a data audit. We assess your existing data sources — quality, completeness, governance, integration — and give you a clear picture of what needs to be addressed before analytics can be reliably built on top. Most organisations are closer to analytics-ready than they think — the gaps are usually specific and addressable.
Yes. We have experience integrating with Epic, Cerner, Athena, and custom clinical systems using HL7 FHIR and Qvera. Integration with your existing systems is typically the starting point for any healthcare data project.
MLOps is the practice of deploying, monitoring, and maintaining machine learning models in production. A model that was accurate when it was trained may become less accurate over time as data patterns change. MLOps includes the pipelines that detect this drift and trigger retraining before it affects outcomes.
A focused data pipeline or BI dashboard project typically takes 6 to 12 weeks. A full data infrastructure build — pipelines, warehouse, governance framework, ML layer — typically takes 3 to 9 months. We provide a specific estimate after the data audit.
Yes. All healthcare data is processed under HIPAA-compliant infrastructure with encryption at rest and in transit, role-based access controls, audit logging, and BAAs with all third-party service providers. We have maintained HIPAA compliance across all our healthcare data systems.