Data Analytics Consulting and Business Intelligence Services

PS3G provides data analytics consulting and business intelligence services for organizations of every size: the metric definitions, data model, pipelines, dashboards and predictive models that turn the data in your systems into numbers people act on. Whether you are a founder reporting from a billing system and a spreadsheet, an operations lead with an ERP and a CRM that disagree, or an IT director adding a new domain to a corporate warehouse, the work follows the same sequence. We build on SQL Server, Snowflake, Databricks and Microsoft Fabric, visualize in Power BI or Tableau, and hand you a model your own people can extend.

From reports nobody trusts to numbers people act on

Most reporting problems are not visualization problems. The sales dashboard and the general ledger disagree because they define revenue differently. The weekly report is late because one person assembles it from exports, and the board pack has three versions of the same metric because every team filtered the data its own way. Adding a prettier chart to that situation changes nothing.

Our analytics consulting starts by settling what the numbers mean and where they come from, then builds the model and the pipelines that produce them the same way every time. The dashboard is the last step, and by then it is mostly done. The outcome is a set of reports that finance, operations and leadership read from the same screen without arguing about the inputs.

The approach fits any size. A small company gets a governed semantic model on two or three sources and its first dashboards. A growing company gets a warehouse, a metric dictionary and a team trained to extend both. A division inside a larger organization gets a new domain modeled inside the security and procurement rules it already works under.

What our data analytics consulting delivers

Dashboards and KPI reporting

Dashboard development services for executives, operations, sales, finance and clinical teams: KPI definitions, the semantic model behind them, the visuals, row-level security, refresh schedules and the handover documentation. Every number on the screen traces back to a definition the metric owner approved.

Self-service BI

A certified semantic model, naming conventions, workspace structure and training so analysts and managers can build their own reports on governed data instead of raw tables. Self-service works when the model is right; we spend our time there.

Business intelligence reporting and operational reports

Scheduled and paginated reports (statements, invoices, compliance and regulatory reports) in SQL Server Reporting Services or Power BI Report Builder, alongside the interactive dashboards. Migration of legacy Cognos, MicroStrategy, Business Objects and Crystal reports to Power BI or Tableau when you are ready to retire them.

Data warehouse or lakehouse

A star-schema warehouse on SQL Server or Azure SQL for most organizations, or a lakehouse on Snowflake, Databricks or Microsoft Fabric when volumes, sources and concurrency call for it. Pipelines in Azure Data Factory, Fabric, SSIS or Informatica, transformations in dbt or SQL, with quality checks built in. The larger platform work is described on our data engineering services page.

Predictive analytics and models

Forecasting, churn and risk scoring, demand planning and anomaly detection built in Python, SQL and the native tooling of Fabric, Databricks and Azure Machine Learning. Models are delivered into the same dashboards your teams already use, with monitoring so you know when they drift. When a use case calls for generative AI or agents, our AI development team takes it from the same data foundation.

Data strategy consulting

An assessment of your current reporting, sources, data quality and skills, followed by a written roadmap: which metrics first, which platform, which engagement model, and what your own team needs to run it afterward.

Tools we work with

Layer	Tools	When we recommend them
Visualization and reporting	Power BI, Tableau, SQL Server Reporting Services, Power BI Report Builder, Excel with Power Query	Power BI for Microsoft-centric organizations; Tableau for analyst-led teams and mixed stacks; SSRS and paginated reports for operational printouts
Legacy BI	Cognos, MicroStrategy, Business Objects, Crystal Reports	Support and maintenance while they run; migration to Power BI or Tableau when the business is ready
Warehouse and lakehouse	SQL Server, Azure SQL, Snowflake, Databricks, Microsoft Fabric, PostgreSQL	SQL Server or Azure SQL as the default; Snowflake, Databricks or Fabric for larger volumes, many sources or machine learning
Pipelines and transformation	Azure Data Factory, Fabric Dataflows and pipelines, SSIS, Informatica, Fivetran, dbt	Chosen to match the platform; dbt for versioned, tested transformations
Models	Python, R, Azure Machine Learning, Databricks ML, Fabric Data Science	Forecasting, scoring and anomaly detection delivered into existing dashboards
Source systems	Dynamics 365, SAP, Oracle, QuickBooks, Salesforce, HubSpot, Shopify, custom databases and APIs	Any system with a database, an export or an API, including the ones on our ERP implementation page

Choosing between the two leading tools is the most common question we get, so we wrote it up: Power BI vs Tableau: Which Should a Growing Company Choose?. Teams already committed to the Microsoft stack can go straight to our Power BI consulting page.

Our approach: definitions first, then pipelines, then visuals

1. Discovery and metric dictionary. We interview the people who use the reports, pick the ten to fifteen metrics that matter, and write a definition, formula, grain, source and owner for each. Disputes are settled here, not in the dashboard review.
2. Source inventory and data model. We map each metric to its source fields, design the facts and dimensions, and decide where the model lives.
3. Pipelines and quality checks. We build the loads, the transformations and the tests for the critical fields, so a bad load is caught before anyone sees a wrong number.
4. Semantic model. Measures from the dictionary are defined once, in the model, with row-level security and naming conventions.
5. Dashboards and reconciliation. We build the visuals and reconcile every number against your current reports, explaining each difference in writing.
6. Rollout and handover. Workspace structure, refresh schedules, training for builders and viewers, documentation, and a backlog for the next sources.

Request a fixed-scope quote for your first dashboards or your warehouse.

Data governance built in

Analytics without governance produces fast, confident, wrong answers. Every engagement includes the minimum governance that keeps the numbers trustworthy: named metric owners, a published dictionary, access rules in the model, quality checks on the critical fields, lineage from source to report, and a retention rule for the data we land. When you need the wider program (policies, stewardship, catalogs such as Microsoft Purview), our data governance services team runs it alongside the analytics work.

Engagement models

Model	What it looks like	Typical fit
Fixed-scope project	A defined deliverable (metric dictionary plus first dashboards; a warehouse on two sources) with a written scope and price	A first version, a migration, a specific domain
Dedicated team or staff augmentation	A BI developer, data engineer or analyst working inside your team on a monthly basis; scale up or down	Growing companies building out reporting over several quarters
Managed services	We run the pipelines, refreshes, model changes and report requests under defined SLAs	Organizations that want reporting to work without hiring a data team
Advisory	Assessment, roadmap, tool selection, fractional head of data	Leaders deciding what to build and in what order

How we start: a first conversation, a short assessment of your sources and reports, and a written proposal. No minimum budget; the scope is sized to what you need now.

Why PS3G

• Over a decade of software and data work, with a US headquarters in Wilmington, Delaware and a delivery center in India for extended coverage.
• We build and run our own products, including our HIPAA-compliant EHR and telehealth platform, DocVilla, so we report on clinical, scheduling and billing data for medical practices across multiple states every day and know what it takes to keep those numbers right.
• Microsoft-certified and open-source-certified engineers: Power BI, SQL Server, Azure and Fabric alongside dbt, Python, Snowflake and Databricks. You choose the stack; we are vendor-neutral.
• Full lifecycle in one place: consulting, data engineering, BI, AI and governance, backed by a testing center of excellence for data validation and regression testing.

Related services

• Big data and data engineering services: pipelines, warehouses and lakehouses that feed the analytics.
• Power BI consulting: modeling, DAX, governance and training on the Microsoft stack.
• Data governance services: owners, definitions, quality and lineage that keep reports trustworthy.
• AI development: models, agents and automation built on the same data foundation.
• ERP implementation and consulting: Dynamics 365 and SAP systems that are usually the biggest analytics source.
• All solutions: the full list of what we build and run.

Guides on this topic

• Power BI vs Tableau: Which Should a Growing Company Choose?
• Business Intelligence Reporting: From Spreadsheets to Dashboards

Frequently asked questions

What does a data analytics consultant actually do?

A data analytics consultant works out what your organization needs to measure, finds where that data lives, designs the model and pipelines that bring it together, builds the dashboards and reports, and trains your team to use and extend them. The consulting part is the definitions, the roadmap and the tool choices; the delivery part is the engineering and the visuals.

Should we use Power BI or Tableau?

Power BI is usually the better fit for organizations on Microsoft 365 and Azure, for finance-heavy reporting, and where viewer licensing cost matters. Tableau suits analyst-led teams and mixed technology stacks. Both connect to SQL Server, Snowflake and Databricks. We are vendor-neutral and will recommend one after looking at your stack, your builders and your audience.

Do we need a data warehouse before we can have dashboards?

Not always. A small organization with two or three sources can model everything in a Power BI semantic model and treat it as its first warehouse. A separate warehouse or lakehouse becomes necessary when sources multiply, when history must be kept beyond what the source systems retain, when several tools need the same model, or when refresh times grow.

How long does it take to get a first dashboard?

Scope decides. A first set of dashboards on one or two clean sources is a small fixed-scope project measured in weeks, most of it spent on definitions and reconciliation rather than visuals. A warehouse with many sources is delivered in phases. The proposal states the timeline for your scope in writing before any work starts.

Can you work with our existing Cognos, SSRS or Crystal reports?

Yes. We support and maintain legacy reporting while it is still needed, and we migrate it to Power BI or Tableau when you are ready, report by report, reconciling each one against the original. Operational reports that need exact layouts move to SSRS or Power BI Report Builder rather than being forced into a dashboard.

Is predictive analytics realistic for a smaller company?

Often, yes, once the reporting foundation exists. Forecasting demand or cash, scoring leads or churn, and flagging anomalies are achievable with modest data volumes when the history is clean and the definitions are agreed. We start with a short proof of concept against your real data and only move to production when it shows value.