Data Analytics That Turns Scattered Data Into Decisions
Nexlantic builds the data pipelines, warehouses, dashboards and predictive models that let your teams measure what matters and act on it, using your existing systems as the starting point.
- Data Pipelines
- Business Intelligence
- Predictive Models
- KPI Dashboards
Data Analytics that fits how your business runs
Data usually exists; it is just spread across an ERP, a CRM, a few SaaS tools and a lot of spreadsheets, each with its own definitions. Analytics work starts by bringing that data into one governed place with clear ownership, tested transformations and documented metrics. From there dashboards, alerts and models become reliable rather than something an analyst rebuilds every month.
We design and build data pipelines with tools such as Azure Data Factory, Python and dbt, land data in warehouses like Azure SQL, PostgreSQL or BigQuery, and model it for reporting in Power BI or custom dashboards. Where the question is about the future, we add forecasting, anomaly detection and segmentation models in Python, evaluated against holdout data before anyone makes a decision on them.
Every engagement produces something your team can run: pipeline code in version control, data quality checks that fail loudly, metric definitions in one place, and training for the analysts who will own it. We work across finance, healthcare, logistics, retail and manufacturing data, from New York and Islamabad.
Why teams choose Nexlantic for data analytics
Trusted metrics
Shared definitions and tested transformations mean every dashboard reports the same number for the same question.
Automated pipelines
Scheduled, monitored data flows replace manual exports, so reports are current when the day starts.
See problems early
Data quality checks and anomaly alerts flag missing loads and unusual values before they reach a board report.
Forecast, not just report
Predictive models for demand, churn, risk and capacity give planners a view ahead with measured accuracy.
What's included
Everything a data analytics engagement covers, from the first workshop to handover.
- Data audit and analytics roadmap
- Pipeline development and orchestration
- Data warehouse or lakehouse design
- Metric layer and KPI definitions
- Dashboards in Power BI or custom web apps
- Data quality monitoring and alerts
- Forecasting and predictive models
- Documentation and analyst enablement
Our data analytics process
Four focused stages, each with a clear deliverable and a decision point for your team.
- 01
Audit the data
We catalogue sources, owners, quality issues and the questions each team needs answered, then prioritise the roadmap.
- 02
Build the foundation
Pipelines, warehouse schema, transformations and quality checks are built in code with version control and tests.
- 03
Deliver the analytics
Dashboards, reports and models are built iteratively with the teams who will use them, validated against known figures.
- 04
Operationalise
Monitoring, documentation, access controls and training make the platform something your analysts run and extend.
Technologies we use
The tools we reach for on data analytics work, chosen per project rather than by habit.
- Python
- SQL
- dbt
- Azure Data Factory
- Azure SQL
- PostgreSQL
- BigQuery
- Microsoft Fabric
- Power BI
- Pandas
- scikit-learn
- Apache Airflow
Data Analytics for your sector
Finance software
Secure fintech applications, payment processing, and regulatory-compliant financial platforms.
Learn moreERP Systems software
Custom ERP platforms and integrations that put finance, inventory, purchasing, HR and operations on one system of record.
Learn moreRetail software
E-commerce platforms, inventory management, and omnichannel experiences that drive sales.
Learn moreCRM Systems software
Custom CRM platforms, sales pipelines, customer portals and integrations with Salesforce, HubSpot and Dynamics 365.
Learn more
Often paired with
Power BI
Interactive dashboards and enterprise reporting that turn raw data into clear, actionable business insights.
Learn moreAI Solutions
Machine learning models, natural language processing, computer vision, and intelligent automation for your products.
Learn moreCloud Solutions
Cloud architecture, migration, and DevOps on AWS, Azure, and GCP. Infrastructure that scales with your business.
Learn more
Data Analytics questions, answered
Still unsure? Book a 30-minute call and we will walk through your situation.
Data audits, pipeline and warehouse development, metric definitions, dashboards and reports, data quality monitoring, forecasting and predictive models, plus documentation and training so your team can own the platform.
Not necessarily. We can start with your existing databases and tools and introduce a warehouse or lakehouse when data volume, source count or reporting needs justify it. The roadmap from the initial audit sets out when that step makes sense.
Python, SQL, dbt and Azure Data Factory for pipelines; Azure SQL, PostgreSQL and BigQuery for storage; Power BI or custom web dashboards for reporting; and Python libraries such as scikit-learn for models. We fit the stack to what your team already runs.
Transformations are tested, metric definitions are agreed with finance and operations, and results are reconciled against known reports before launch. Data quality checks then run with every load and alert when something is missing or out of range.
Ready to start your data analytics project?
Tell us what you are building. We reply within one business day with next steps and a proposed team.