Manufacturing & Industrial Engineering
Australia
End to End Project Lifecycle Management
During the initial round tables, the client shared several challenges while scaling its data and analytics capabilities. These limitations impacted data accessibility, decision-making, and overall operational efficiency.
Fragmented Data Across Enterprise Systems:
Disparate data across SAP, FSM, SharePoint, and other platforms led to silos, inconsistencies, and a lack of a unified data view.
Manual & Time-Intensive Reporting:
Heavy reliance on manual data extraction, reconciliation, and consolidation resulted in prolonged reporting cycles of up to two weeks.
Limited Business Visibility:
Restricted access to critical KPIs constrained timely insights and slowed management’s decision-making across finance and operations.
Data Quality & Governance Gaps:
Absence of standardized data models, validation mechanisms, and governance frameworks reduced data reliability and trust.
Scalability & Performance Challenges:
Existing systems struggled to efficiently process and manage 50 to 100M+ records annually alongside growing analytics demands.
Operational Inefficiencies:
High dependency on manual workflows increased turnaround time, introduced errors, and elevated effort across teams.
Throughout the discovery workshop and solution design phases, DataToBiz delivered an end-to-end analytics transformation using Microsoft Fabric and Power BI. The approach focused on unifying data, enabling faster insights, and building a scalable, future-ready analytics ecosystem:
Unified Data Platform with Microsoft Fabric:
Established a centralized Fabric Lakehouse (OneLake), integrating SAP, FSM, and operational systems into a single, governed, and reliable data environment.
Near Live Data Availability:
Enabled faster data access and processing, reducing reporting cycles from two weeks to just one day and significantly improving business responsiveness.
Automated Data Pipelines:
Implemented seamless data ingestion and transformation workflows, eliminating manual effort while ensuring consistent and high-quality data delivery.
Self-Service Analytics with Power BI:
Replaced static reporting with interactive, role-based Power BI dashboards, empowering users with drill-down and self-service analytics for faster decision-making.
Data Governance, Security & Reliability:
Established strong governance, monitoring, and security frameworks to ensure data accuracy, compliance, and high system availability.
Scalable & Future-Ready Architecture:
Designed a robust data foundation capable of supporting advanced analytics, AI/ML use cases, and evolving business needs at scale.
The solution was built on a cloud-native Microsoft Fabric analytics stack, designed to balance scale, speed, and governance without overcomplicating the core.
Data Ingestion:
Automated pipelines using Fabric Dataflows Gen2, Pipelines, and Notebooks pulled data from SAP S/4HANA, SAP FSM, SharePoint, and flat files, enabling consistent batch processing.
Centralized Data Platform:
A Fabric Lakehouse on OneLake, structured using a Bronze–Silver–Gold architecture, created a single, scalable, and governed data foundation.
Data Processing & Transformation:
Fabric Notebooks (Spark) and pipelines handled large-scale data transformation, cleansing, and KPI standardization across systems.
Semantic Layer:
Power BI Semantic Models with DAX and Row-Level Security (RLS) ensured governed, role-based access to business-ready data.
Visualization & Reporting:
Power BI Service in Direct Lake mode enabled near-real-time dashboards for financial and operational insights.
Automation & Orchestration:
End-to-end ETL workflows automated daily data refreshes, reducing manual effort and improving data timeliness.
Monitoring & Governance:
Integrated Azure AD, monitoring, and alerting frameworks ensured data quality, security, and high system reliability.
Accelerated Reporting Cycles
Reporting timelines were reduced from 10 to 14 days to under 24 hours, enabling faster access to critical insights and improving responsiveness across finance and operations.
Reduced Manual Effort
Over 20 reports were automated and standardized, significantly cutting down manual data consolidation and freeing teams to focus on analysis and decision support.
Expanded Data Accessibility
A centralized OneLake platform enabled 60+ business users with secure, role-based access to consistent and trusted data across functions.
Standardized & Scalable Reporting
Implemented 10+ business-critical dashboards, bringing consistency in KPI definitions and enabling scalable reporting across departments.
Reliable & High-Availability Platform
Automated pipelines, monitoring, and alerts ensured consistent data delivery with 99.9% availability and minimal operational disruption.
Faster, Insight-Led Decisions
Near real-time Power BI dashboards improved visibility into key metrics, helping stakeholders make quicker, more informed decisions with reduced IT dependency.
Conclusion
With a unified Microsoft Fabric and Power BI platform in place, the organization evolved into a more agile and insight-driven enterprise. The outcome is faster reporting, improved efficiency, stronger data governance, and a scalable analytics foundation ready to support future growth and advanced use cases.
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DataToBiz is a good choice for someone who wants a partner who understands the product journey, requirements, and delivery expectations. They have the experience and agility to understand what’s possible and deliver to the client’s expectations. They‘re really passionate people, and they know what they’re doing!
We’re impressed with DataToBiz’s professionalism, dedicated support, and consistent follow-ups. Their team maintains clear communication through online meetings, ensuring a smooth and reliable collaboration.
A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.
DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.
Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.
DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.
If your stack includes a tool not listed here, the team can assess compatibility during discovery.
Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.
Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.
The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.
