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Optimizing Real-Time Energy & Asset Management

<h3><strong>Tech Stack</strong></h3><p><br></p><p>Microsoft Fabric, Notebooks, Pipeline, Lakehouse, Dataflow, Semantic Models, Power BI, SharePoint, Power Automate, Power Apps, Data Activators</p><p><br></p><p><br></p><h3><strong>Problem</strong></h3><p><br></p><p>The client, a global energy brand, wanted to utilize the large amounts of live data created by their POS systems to generate multiple reports for their management teams and alert them of emergencies.</p><p><br></p><p><br></p><h3><strong>Solution</strong></h3><p><br></p><ol><li>We created a solution in Microsoft Fabric ingesting millions of records a day into reports with alerts and custom power app applications.</li><li>The solution consisted of multiple dataflows and notebooks that cleaned and transformed the incoming real-time data into fabric Lakehouses. The real time data came from client REST APIs.</li><li>We also unified the data stored in the client’s SharePoint and other sources into One Lake in Microsoft Fabric.</li><li>We developed multiple reports which enabled the client to track their energy and carbon footprints, manage their assets, devices and tariffs and trace the maintenance of multiple POS systems worldwide.</li><li>The alerting mechanisms were created using Power Automate and directly connected to the reports providing real-time alerts and insights through emails. The alerts were segregated in multiple levels; hence they were managed differently.</li><li>We also utilized Power Apps to create dynamic applications for the client which helped them trace their maintenance efforts across the globe.</li></ol><p><br></p><p><br></p><h3><strong>Impact</strong></h3><p><br></p><p>Using the solution the client was able to effectively keep track of their carbon and energy footprints, manage their assets and devices, get insights based on location of the devices and manage tariffs &amp; other expenses.</p><p>The internal management of the client was able to take informed and wise decisions based on the multiple Power BI reports and track their assets in multiple countries.</p><p>The alerting mechanism enabled the client to take quick actions in case of emergencies.</p><p>The client was able to fully utilize their large amount of real-time data and look deeply into the working of each POS system across the globe.</p>

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Secure AI-Driven Data Visualization & Reporting

<h3><strong>Tech Stack</strong></h3><p><br></p><p>Azure OpenAI services, Azure Synaptic Analytics, Azure SQL Storage, Azure Data Factory, Microsoft 365, Python, Streamlit</p><p><br></p><p><br></p><h3><strong>Problem</strong></h3><p><br></p><p>The client needed a custom AI solution to let his employees create visuals using natural language and their own data, but he was worried about the data being leaked and used to train public AI algorithms.</p><p><br></p><p><br></p><h3><strong>Solution</strong></h3><p><br></p><ol><li>We implemented a solution for our client which didn’t require the client to share their data with the AI models, create visually appealing graphs and share them to external users, stakeholders and internal employees hassle free.&nbsp;</li><li>We used the Azure OpenAI services to access the GPT- 4 Model. We utilized the model to generate the summary reports for each visual and the python code required to generate the visuals.</li><li>We also used Azure SQL Storage, Data Factory and Synaptic Analytics to store and process client's data.</li><li>We created a custom web app to let the client visualize the graphs on any device as well as share them with external stakeholders and internal employees. We used Azure Web Services to host our Web App.&nbsp;</li><li>The web app was deeply integrated with the Microsoft 365 ecosystem letting the employees directly share the generated graphs to the platform of their preference.</li><li>The employee's credentials were also managed seamlessly by using Azure Service Principal and OAuth Services.&nbsp;</li><li>Each graph generation by the model was also accompanied by a summary of the findings from single or multiple graphs.</li></ol><p><br></p><p><br></p><h3><strong>Impact</strong></h3><p><strong>﻿</strong></p><p>The client was able to maintain his data security while being able to create visually appealing graphs and plots and sharing them with external stakeholders and internal employees.</p><p>It also increased the productivity of the employees working for the client by letting them create visuals using natural language queries.</p><p>The visuals created were shareable throughout the organization using the Microsoft 365 ecosystem. It helped the client to generate better visuals faster and perform more effectively.&nbsp;</p>

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Real-Time Predictive Analytics for Prescription Deliveries

<h3><strong>Tech Stack</strong></h3><p><br></p><p>Microsoft Fabric, Power BI, EventHouse, One Lake, Dataflow, Azure ML, Azure SQl Storage, Python, MLFlow, PySpark, Scikit-Learn</p><p><br></p><p><br></p><h3><strong>Problem</strong></h3><p><br></p><p>The client wanted to figure out whether a prescription delivery would be late or not, and if it was late what were the reasons behind it being late. The client requested us to make it as easily understandable as possible.</p><p><br></p><p><br></p><h3><strong>Solution</strong></h3><p><br></p><ol><li>We created a solution for the client fully on Microsoft Fabric using services like Lakehouses, Dataflows, Notebooks and Reports. The solution used advanced ML models made using MLflow&nbsp;experiments to predict the probability of the delivery being late.</li><li>We also implemented model explanation algorithms to let us know the reasons behind a specific prescription delivery being late according to the ML model. It lets us interpret the ML model’s output in a clear way.</li><li>We created an Active Learning pipeline within Fabric to continuously train the model as new data flowed in from the client. This ensured that the model would improve and learn more as time passes.</li><li>We also utilized PySpark to create distributed computing clusters on Fabric to run our ML model experiments, model explanation algorithms and inference &amp; training scripts at high speeds.</li><li>We also created a Power BI report to visualize the results from the ML model, monitor the accuracy of the model and summarize historical data in a visually appealing manner.</li><li>We utilized Dataflow Gen 2 pipelines to transform our data at various stages of the solution, like fetching from Azure SQL Storage and feeding the data to the ML model.</li><li>The solution ran in real-time using Eventhouse in Fabric letting the client know the latest updates as soon as possible.</li></ol><p><br></p><p><br></p><h3><strong>Impact</strong></h3><p><br></p><p>Using the solution the client was able to figure out the main reasons behind the prescription deliveries being late in the past as well as the future.</p><p>The visually appealing Power BI report let the client be able to learn about the most important decision factors and look at the changes in them with each delivery.</p><p>It enabled him to expand the business, enhance operational effectiveness, and achieve high levels of customer satisfaction.</p>

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Instant Insights for Retail

<h3><strong>Tech Stack</strong></h3><p><br></p><p>Microsoft Fabric, EventStream, Lakehouse, Dataflows, Power BI, Power BI Embed API, Azure Service Principles, Azure PostgreSQL Flexible Server, .NET, ReactJS</p><p><br></p><p><br></p><h3><strong>Problem</strong></h3><p><br></p><p>The client wanted to process his real-time ERP data from multiple customers to a Power BI report included in their own web app effectively sharing his Power BI reports with self service capabilities to external stakeholders.</p><p><br></p><h3><br></h3><h3><strong>Solution</strong></h3><p><br></p><ol><li>We provided a custom web app solution made using .NET and ReactJS with a Material UI theme. It allowed the client to self-service Power BI reports from any device using the web app.</li><li>The web app could be shared with multiple people by the admin. It allowed the admin to create tenants and multiple users within each tenant. The admin could also give custom access to each tenant and user. The identity management was done using Azure Service Principles and OAuth Services.</li><li>We created multiple Lakehouses in fabric to store the data and dataflows to transform the data.</li><li>We utilized the capabilities of the Power BI Embed API to allow the safe and easy access of multiple reports.</li><li>Our solution allowed the client to share reports to external stakeholders without the need for a Power BI Premium License on their side.</li><li>Using the EventStream the client was able to visualize the data reports in near real-time speed letting him know the latest updates as soon as possible. We also utilized CDC (Change Data Capture) to reflect any changes to the data in real-time.</li></ol><p><br></p><p><br></p><h3><strong>Impact</strong></h3><p><br></p><ol><li>By using the solution effectively our client is now able to scale his business and keep the external stakeholders well informed by sharing custom reports with them.</li><li>The web app can be accessed from any device letting the users of the reports to view them anytime anywhere.</li><li>It also enhances security and control of user data since it is all stored on Azure.</li><li>By eliminating the need for the Power BI Premium License for each user of the report the client effectively reduced their operational costs by a large margin.</li><li>The client is able to segregate report views by multiple departments enabling responsibility distribution.</li><li>The unified data storage and analysis on Microsoft Fabric simplifies maintenance and further improvements.</li></ol>

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Our client, a skincare brand, faced significant challenges in effectively monitoring and reporting on sustainability metrics across its global operations.    As consumer awareness about environmental and social responsibility increased, he needed to demonstrate his commitment to sustainability and adhere to stricter regulations and industry standards.    However, the client struggled with disjointed systems for tracking various sustainability efforts, including energy consumption, waste management, and carbon emissions.    The lack of a centralized solution meant that their teams faced difficulties in accessing real-time data and generating meaningful insights on their sustainability performance.

Streamlining Sustainability Reporting for a Skincare Brand

<h4><strong>Tech Stack</strong></h4><p>Power BI, Power Automate, Power Apps, Azure Data Lake Storage, Azure Synapse Analytics, Azure SQL Server, Azure Analysis Services, Databricks&nbsp;</p><p><br></p><h4><strong>Problem</strong>&nbsp;</h4><p>Our client, a skincare brand, faced significant challenges in effectively monitoring and reporting on sustainability metrics across its global operations.&nbsp;&nbsp;</p><p>As consumer awareness about environmental and social responsibility increased, he needed to demonstrate his commitment to sustainability and adhere to stricter regulations and industry standards.&nbsp;&nbsp;</p><p>However, the client struggled with disjointed systems for tracking various sustainability efforts, including energy consumption, waste management, and carbon emissions.&nbsp;&nbsp;</p><p>The lack of a centralized solution meant that their teams faced difficulties in accessing real-time data and generating meaningful insights on their sustainability performance.&nbsp;</p><p><br></p><h4>Solution</h4><ol><li>To address these challenges, we implemented a solution in Microsoft Azure and Power Platform to centralize and improve its sustainability reporting efforts. We created a custom data storage, analytics and visualization platform for the client on Microsoft Azure.&nbsp;</li><li>We used automated data extraction and loading from various data silos, such as production sites, energy management systems, and environmental tracking tools, into ADLS by Azure Data Factory and enabled efficient data warehousing, visualization, and real-time analytics by using Azure Synapse Analytics.&nbsp;</li><li>We also provided interactive dashboards and reports for data exploration and informed decision-making using Power BI and automated the flows by using Power Automate providing real-time insights into sustainability metrics across their global operations.&nbsp;&nbsp;</li><li>We utilized Databricks to increase the processing speed of our pipeline while unifying our flows.&nbsp;</li><li>Using the report they can monitor key performance indicators (KPIs) such as carbon footprint reduction, water usage, recycling rates, and energy consumption in a more streamlined manner helping teams to identify areas where improvements were necessary.&nbsp;</li></ol><p><br></p><h4><strong>Impact</strong></h4><p>The solution improved the client's sustainability tracking and reporting, providing a unified data platform that eliminated silos. It enhanced transparency with clear visualizations, reduced the operational footprint, and ensured compliance with industry regulations. The cloud-based Azure architecture enabled scalability, while automated data pipelines and real-time analytics empowered faster decision-making. This led to operational efficiencies and positioned their products as eco-conscious options in the competitive skincare market.</p>

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Data-Driven Sales Optimization: Bridging the Gap Between KAMs and Customers

<p>• Analyze and compare sales performance based on evaluations from Key Account Managers (KAMs) and customer feedback.</p><p><br></p><p>• Identify discrepancies in self-assessment versus customer perceptions to drive performance improvement.</p><p><br></p><p>• Deliver a comprehensive view of sales effectiveness across the organization.</p><p><br></p><p>•Developed an interactive Power BI dashboard that visually compares KAM self-evaluations with customer feedback scores.</p><p><br></p><p>• Included drill-through capabilities for in-depth analysis, allowing managers to explore specific metrics and performance trends.</p><p><br></p><p>• Highlighted key discrepancies and provided data-driven recommendations to address performance gaps.</p><p><br></p><p>• Created a user-friendly one-pager for high-level insights and detailed views for granular understanding.</p><p><br></p><p>• Empowered managers with a clear, concise view of performance metrics for better decision-making.</p><p><br></p><p>• Facilitated targeted coaching by identifying areas where KAMs need improvement based on customer feedback.</p><p><br></p><p>• Improved the alignment between sales teams and customer expectations, leading to more effective strategies.</p><p><br></p><p>• Enabled a deeper understanding of performance trends, helping to optimize sales processes and enhance overall efficiency.</p>

Aligning KAM & Customer Feedback

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Optimizing Job Requests with Custom Power Apps: A Streamlined Engineering Workflow

<h4><strong>Streamlined Engineering Workflow:</strong></h4><p><strong> </strong>A single point of contact for managing job requests.</p><h4><strong>Initiation</strong>:</h4><p>Engineers at Industry Resources submit jobs via a Work Request Form.</p><h4><strong>Review Process</strong>:</h4><p>Jobs enter "Requirement Review" for evaluation and approval.</p><h4><strong>Transition States</strong>:</h4><p>Includes clarifying/approving jobs, reviewing completed work, and re-opening for additional tasks.</p><h4><strong>Custom Power Apps for Job Requests</strong>:</h4><p>Streamlines the creation, update, and management of job requests through an intuitive interface with seven navigable screens, offering flexibility and efficiency.</p><h4><strong>Custom Integrations and Functionalities</strong>:</h4><p>Integrates seamlessly with SharePoint Lists for real-time data management, includes advanced search and filtering, and provides status tracking with mandatory comments for transparency.</p><h4><strong>Faster Processing</strong>:</h4><p>Streamlined workflows and automated notifications accelerate job request completion.</p><h4><strong>Better Communication</strong>:</h4><p>Real-time updates and alerts keep stakeholders informed, reducing delays.</p><h4><strong>Increased Accountability</strong>:</h4><p>Mandatory comments, and real-time access ensure transparency and responsibility.</p>

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Building a Scalable, Unified Data Warehouse with Microsoft Fabric

<h4><strong>Data Warehouse:</strong></h4><p><strong> </strong>Build a scalable, unified data warehouse to integrate data from multiple sources, including SAP, APIs, web scraping, SFTP, and Sage.</p><p><br></p><h4><strong>Data Integration &amp; Transformation:</strong></h4><p><strong> </strong>Implement ETL processes for data extraction, transformation, and loading, ensuring data is standardized and prepared for analytics.</p><p><br></p><h4><strong>Data Security: </strong></h4><p>Ensure secure access to sensitive data with role-based access control, encryption, and managed identities.</p><p><br></p><h4><strong>Scalability: </strong></h4><p>Design the system to scale with increasing data volumes and user demands, maintaining high performance.</p><p><br></p><h4><strong>Data Warehouse</strong>: </h4><p>Use Microsoft Fabric to build a unified, scalable data warehouse that integrates data from sources like SAP, APIs, web scraping, SFTP, and Sage.</p><p><br></p><h4><strong>Notebooks</strong>:</h4><p> Leverage Microsoft Fabric Notebooks for data exploration, analysis, and transformation, enabling interactive and reproducible analytics.</p><p><br></p><h4><strong>Pipelines</strong>:</h4><p> Set up Data Pipelines in Microsoft Fabric to automate ETL processes, ensuring data is extracted, transformed, and loaded efficiently. Pipelines can also schedule data loads and run transformation tasks.</p><p><br></p><h4><strong>Data Activator</strong>: </h4><p>Use Data Activator to trigger specific actions when certain conditions are met, such as changes in data or thresholds being reached. </p><p><br></p><h4><strong>Data Quality Monitoring and Notifications:</strong> </h4><p>Real-time notifications are sent through Microsoft Teams or Outlook for every data ingestion cycle. This allows for quick error detection and resolution, maintaining high data quality standards</p><p><br></p><h4><strong>Efficiency</strong>: </h4><p>Streamlined data integration with incremental loads, reducing processing time and resource usage.</p><p><br></p><h4><strong>Data Quality</strong>:</h4><p> Ensured clean, non-duplicated data for accurate analysis.</p><p><br></p><h4><strong>Shortcuts</strong>:</h4><p> Used shortcuts to avoid duplication and integrate Dynamics 365 data efficiently.</p><p><br></p><h4><strong>Scalability</strong>:</h4><p> Solution scales to handle growing data while preventing duplication.</p><p><br></p><h4><strong>Cost Savings</strong>:</h4><p> Reduced redundancy lowers storage and processing costs.</p><p><br></p><h4><strong>Faster Insights</strong>:</h4><p> Real-time triggers and optimized flows enable quicker decision-making.</p>

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Efficient Data Management with Automation

<h4><strong>Data Integration and Standardization:</strong></h4><p><strong> </strong>Consolidated and standardized diverse customer data from multiple sources into a single unified format for efficient analysis and insights generation.</p><p><br></p><h4><strong>Data Warehouse Solution Development:</strong></h4><p><strong> </strong>Designed and implemented a data warehouse solution capable of handling various data formats, ensuring seamless data processing and accessibility for analysis.</p><p><br></p><h4><strong>Automation of Reporting and Maintenance:</strong></h4><p><strong> </strong>Automated weekly data reporting and maintenance workflows, ensuring consistent, timely, and accurate delivery of insights with minimal manual intervention.</p><p><br></p><h4><strong>Azure Integration Services: </strong></h4><p>Leveraged Azure Synapse to automate the ETL (Extract, Transform, Load) process, integrating and transforming customer data into a consistent format stored in Azure SQL Server for easy access and analysis.</p><p><br></p><h4><strong>Azure SQL Server Data Warehouse:</strong></h4><p><strong> </strong>Built a scalable and high-performance Azure SQL Data Warehouse to handle diverse data types (e.g., structured customer records, semi-structured transactional data) while optimizing for query performance and analytical workloads.</p><p><br></p><h4><strong>Efficiency Gains:</strong></h4><p><strong> </strong>Automation reduced manual data handling, saving time and enabling faster insights.</p><p><br></p><h4><strong>Scalability:</strong></h4><p><strong> </strong>Robust infrastructure to handle growing data volumes, ensuring faster processing.</p><p><br></p><h4><strong>Improved Insights:</strong></h4><p><strong> </strong>Centralized data and automated reporting for better decision-making and enhanced customer experience.</p>

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