--- title: "Data Strategy Consulting | Smart Data" url: https://www.smartdata.net/services/data-consultancy description: "Data strategy consulting from engineers who also build the platform: a data strategy assessment, a roadmap and the move to Fabric, Databricks or Snowflake." source: "Smart Data (smartdata.net). Markdown copy of the page; the HTML page is canonical." --- Modern data platforms # Data strategy consulting We assess where your data lives, what it can support today and what has to change, then design the platform and the order to build it in. For most companies on Microsoft, that means an affordable, complete platform on Fabric first, with governance and security from the start. Platforms we build on - [Microsoft FabricIncluding Power BI](https://www.smartdata.net/services/microsoft-fabric-consulting) - [DatabricksLakehouse builds and migration](https://www.smartdata.net/services/databricks-consulting) - [SnowflakeArchitecture and optimization](https://www.smartdata.net/services/snowflake-consulting) Our data services ## Five areas, five places to start These are the five areas our data readiness assessment scores. Each has a focused first project. Integration ### Data integration and accessibility Data spread across an ERP, a CRM and a dozen spreadsheets can’t be trusted or reused. We get the sources into one governed platform, automated and monitored for freshness. A first project: a focused data integration pilot on one priority data flow. - ETL/ELT pipelines on Azure Data Factory, Fabric Data Factory, Airflow or custom orchestration - Legacy warehouse migrations from SQL Server, Oracle, Teradata, AS/400 and Azure Synapse - Old and new run in parallel, so production reporting keeps running Tools we work in - Microsoft Fabric - Databricks - Snowflake - Azure Data Factory Quality and traceability ### Data quality and traceability When a pipeline fails quietly, the dashboards keep showing numbers and the numbers are wrong. We agree the critical measures, put validation into the pipelines and keep the source history, so every number can be traced. A first project: data quality and modeling discovery. - Validation and freshness checks that catch issues before they reach dashboards - dbt transformation layer with automated testing and generated documentation - Master data management and entity resolution across source systems - dbt - Microsoft Purview - Unity Catalog Governance and security Governance is an operating model, with owners, rules and monitoring that keep running after go-live. We name data owners, set access by how sensitive the data is and build the lineage that shows where every number came from. A first project: a governance and security foundation. - Data owners and classification set around the team structure you already have - Role-based access, masking and row-level security for sensitive data - Lineage that makes audit and compliance straightforward BI and adoption ### Business intelligence and adoption The hard part of BI is the data layer under the dashboards, the one that lets finance and operations see the same number for the same question. Then people have to use it. We start from one business decision, build reusable measures and train the people who read the reports. A first project: a BI and adoption pilot. - Power BI on Fabric, Tableau or Looker on governed semantic models - Self-service analytics that reduces the backlog on your data team - Executive reporting that aligns financial, operational and customer metrics - Power BI - Tableau - Looker Operations and scalability ### Platform operations and scalability A platform in production needs controlled releases, monitoring and someone watching capacity and cost as usage grows. We set that up, or run it for you through managed services with P1 to P4 support. A first project: a production readiness assessment. - Source control, CI/CD and controlled deployment for data - Monitoring, incident response and pipeline maintenance - Capacity and cost reviews as usage grows Want your own read on the five areas first?[Take the data readiness assessment](https://www.smartdata.net/data-readiness-assessment) Three decisions, one architecture ## Which platform fits your estate? The architecture stays the same whichever platform you choose, and we build on all three, so the recommendation follows your estate. ### Microsoft Fabric is usually the shorter path Your data, identity and licensing already live with Microsoft, and Power BI reads straight from Fabric. Start there. If the data science grows, Databricks works alongside it, so what you’ve built carries over. How this connects to AI ## Not sure your data is ready for AI? Data strategy work often begins with an AI readiness question. Our AI Enablement Guide covers how to evaluate your data before investing in AI. Our process ## How we deliver data projects Every engagement starts with understanding what you have, what you need and where the gaps are. Then we build it once and serve every consumer. ### 01Discovery and architecture review We map your current data sources, pipelines, platform architecture and quality baselines. The first phase stands on its own: you keep the roadmap whether or not you go further. Deliverable A prioritized roadmap that identifies the highest-impact improvements and the dependencies between them. ### 02Architecture and design We design the target architecture from what the assessment found: the platform, the pipeline design, the data model and the governance rules, each tied to a business requirement you can measure. ### 03Build and migrate Pipelines deployed, data migrated, transformations tested, dashboards connected and governance controls configured. Old and new run in parallel, so production reporting keeps running through the cutover. ### 04Ongoing support and optimization Ongoing We transfer ownership to your internal teams with documented procedures, monitoring dashboards and runbooks in place. If you’d rather we kept running it, managed services covers that. Industries ## Industries we work in ### Healthcare Patient, pharmacy and clinical data systems held to HIPAA, including where AI is allowed to touch them. - Medicaid programs - Pharmacy services - Clinical documentation [Healthcare](https://www.smartdata.net/industries/healthcare) ### Manufacturing and industrial ERP, shop-floor and supplier data on one platform, so plant reporting and finance reporting reconcile. - SAP and ERP reporting - Shop-floor integration - Supplier portals [Manufacturing and industrial](https://www.smartdata.net/industries/manufacturing) ### Retail and distribution Order, inventory, credit and customer data connected across portals, branches and the platforms behind them. - Vendor and customer portals - Order and credit systems - Data platforms [Retail and distribution](https://www.smartdata.net/industries/retail) ### Financial services Bank data platforms, reporting that holds up to an audit and managed support for the financial systems the business runs on. - Bank data platforms - Managed ERP support - Legacy migration [Financial services](https://www.smartdata.net/industries/banking-financial-services) ### Insurance Claims, member and policy systems for health plans and insurers. - Health plans - Claims data - Member apps [Insurance](https://www.smartdata.net/industries/insurance) Case studies ## Recent data work ### SAP ECC 6.0 to Microsoft Fabric for a global industrial group Six operating entities on one SAP ECC 6.0 system, brought into Fabric with no RFC bulk extraction and entity-level security. The client's Basis and SAP teams found the process much easier than they expected. - SAP Integration - Lakehouse Architecture [SAP ECC 6.0 to Microsoft Fabric for a global industrial group](https://www.smartdata.net/case-studies/sap-ecc-microsoft-fabric-industrial) $16M+ ### in lost Medicaid revenue returned to the health plan Hired to add a state-mandated code to a health plan’s nursing facility file, our team noticed the process was mishandling records. The plan confirmed it, filed corrections and recovered $16M+. - SQL Server [A routine Medicaid code update that surfaced $16M+ in lost revenue: in lost Medicaid revenue returned to the health plan](https://www.smartdata.net/case-studies/nfloc-platform-rewrite) ### Reducing data quality issues across four migrated programs Turnover had taken the onboarding knowledge with it. Recurring data quality issues across SSIS and MuleSoft feeds were fixed, onboarding was documented and four programs were migrated. - ETL & Data Pipelines - SSIS - MuleSoft [Reducing data quality issues across four migrated programs](https://www.smartdata.net/case-studies/enterprise-grade-data-quality-for-a-pharma-services-provider) Next step ## Talk to us about scope One call about your systems and what needs to change. You get a written proposal if the work fits. A few practical questions ## Work through the decisions Questions we hear on first calls. Data advisory work covers how your data is stored, moved, governed and used: modernizing legacy platforms, engineering pipelines, analytics architecture, AI readiness and governance, plus ongoing support. We also build and run what we recommend, so the roadmap is one we know how to deliver. Cost follows scope. A focused data assessment comes first, and it sizes the migration or modernization by the number of source systems and what depends on them. Managed services are a monthly contract set by the systems covered and the response times. You get a written scope with the timeline and cost model before work begins. Usually, yes. For most companies already on Microsoft, Fabric is the affordable way to get a complete platform, with governance and security included, starting with the sources that matter most. When the data work grows, Databricks can be added alongside it. The features rarely decide it. Ask what it costs to leave in three years, who owns the data model once the vendor’s tooling owns the pipeline, which parts of the stack you are renting and which you are building, and what breaks first when the data volume doubles. We keep the architecture the same on Fabric, Databricks and Snowflake, so those answers stay in your favor. Usually when a platform migration needs experience the team doesn’t have yet, when the team is at capacity with day-to-day work, when the timeline is shorter than hiring allows, or when leadership wants an outside read on the current architecture. Many clients split the work: we build the foundation and their team maintains and extends it. Healthcare, manufacturing, distribution and retail, financial services and insurance. Healthcare runs deepest: more than ten years of HIPAA-governed software and data work for one Medicaid health plan, plus pharmacy services and clinical software organizations. Microsoft Fabric, Databricks and Snowflake for the platform; Azure Data Factory, Fabric Data Factory and Airflow for orchestration; dbt for transformation and modeling; Power BI, Tableau and Looker for reporting; and hosted, private and local language models for AI, routed by task. The choice follows the environment you already run. We have been putting systems into production since 2006, and we build on Fabric, Databricks and Snowflake, so the recommendation follows your estate rather than a partnership. We aim for one Gold use case in 90 days, not a full warehouse rebuild, and we’ll tell you when a simpler setup will do. With 230+ people we can staff a platform migration. Related services ## Where data strategy connects ### AI development AI-assisted modernization, AI on your data and model features built into the applications you already run. [AI development](https://www.smartdata.net/services/ai-development) ### Data engineering Pipelines, warehouses and reporting on Fabric, Databricks or Snowflake, run in parallel with the old ones until the numbers match. [Data engineering](https://www.smartdata.net/services/data-engineering) ### Microsoft Fabric consulting Architecture, migration off SSIS-era pipelines and Power BI on Fabric for Microsoft-heavy estates. [Microsoft Fabric consulting](https://www.smartdata.net/services/microsoft-fabric-consulting) ### Healthcare data analytics HIPAA-governed data platforms, clinical analytics and compliance reporting for healthcare organizations. [Healthcare data analytics](https://www.smartdata.net/industries/healthcare) ## Where does your data live today? Tell us which systems hold the data and what the business needs from it. Here is what happens after you reach out. ### A short call about your data estate The systems, the reporting that depends on them and the decision you are trying to make. ### An engineer looks at what’s running Scope comes from your systems as they are, before anything is priced. ### You get a written proposal Covering scope, team, approach and price. [info@smartdata.net](mailto:info@smartdata.net)[(937) 886-9166](tel:+19378869166)