Awais Aslam

I designed and implemented a big data project to handle the daily ingestion of 240 million rows of data generated by electricity meters. Also, I created and developed a data warehouse on Azure Synapse that manages a total volume of around 430 billion rows. This solution has allowed business users to gauge electricity usage patterns to better forecast demand and generation of power and assist the organization in complying with California state laws.

Using the Prophet algorithm, I developed a forecasting AI model using Azure AutoML to predict depletion and shipments. It involves:

• Data collection from SAP and VIP.
• Feature engineering.
• Forecasting model identification using AutoML.
• Analytics on Power BI.

We designed and built an AI model to determine whether or not a drinking incident occurred based on telemetry project portfolio management (PPM) data coming from wearable devices. This model eliminates all noises, such as sanitizer on the skin, perfume in the air, etc. The challenge was handled by applying classification models, labeling, and comparing values to breathalyzer results. The project also involved:

• Gathered data in both controlled and uncontrolled contexts.
• Information obtained from a breathalyzer and wearable devices.
• It detects drinking episodes within a thirty-minute span.

This mission-critical data ingress enables millions of pulse ingestion in Azure Cosmos DB. This is done by implementing a data platform to ingest telemetry data on alcohol levels in the blood from wearable devices and transmit this information to Azure Cosmos DB. On top of this data, near-real-time and batch reporting is available.

For the near-real-time analytics, we employ analytical stores on collections and then consume these data through an Azure Synapse link, which subsequently displays information in Power BI. On the other hand, for batch analytics, we put in place archiving mechanisms that migrate data from Cosmos to hot or cold storage and finally to Azure Synapse data warehouse for reporting. Finally, all of the Power BI analytics were embedded in an ISV application built using Node.js.

I created a Power BI connector for NetSuite to retrieve data from saved searches and workbooks. It was packaged with a number of reports, including:

• A budget to actual variance analysis.
• Commission calculation analytics.
• Accounts receivable aging tabulated via an aged receivables report.
• An aging inventory report.
• An order summary report.
• Portfolio management.
• Warehouse management analytics.

I implemented a data solution, which detects email threads and identifies spam in emails, received in Microsoft Dynamics 365 CRM. Based on the response time of emails, which can be both automatic acknowledgment or manual response, it calculates the percentage of emails and maps it to the defined service-level agreement (SLA).

I implemented call rotation and campaign analytics to assess the impact of each campaign and the contribution of corporate social responsibility (CSR) to revenue generation. We used Power BI for analytics to get data from Microsoft Dataverse and perform transformations before presenting analytics for CSR performance.

I created a comprehensive set of native database applications to monitor the massive warehouses and archival processes. These applications can:

• Capture deadlock history, long-running connections, slow running queries, and space used by databases.
• Back up and index management jobs.
• Monitor and kill blocking sessions.
• Utilize the Notification Framework.
• Conduct the SQL Server Integration Services (SSIS) log analysis.

I participated in implementing the National Bank of Kuwait (NBK) data warehouse (DWH). I designed and implemented ETLs to populate various datamarts of DWHs as well as semantic layers for multiple business units for ad-hoc analysis and reporting on top of the data warehouse.

We developed a complete suite of analytics for the construction industry. Users can manage projects on the ground and monitor the performance of each project through Power BI dashboards. It also helps subcontractors and vendors manage their equipment and workforce on site. Power BI apps are available in Microsoft AppSource.

The Market Void Identification project aims to uncover untapped market opportunities in all states of the United States by analyzing depletion data from the VIP database, market channel data from Nielsen, and sale invoices from various retail chains. By comparing the actual depletion of products with the estimated market potential, this project seeks to identify regions where consumer demand is not adequately met, leading to potential market voids.

The project begins by collecting and preprocessing the data from VIP, Nielsen, and retail chain sources using Azure Data Factory. It will then integrate the datasets, aligning them geographically for state-level analysis. The market potential will be estimated using Nielsen data, which provides insights into consumer behavior and purchasing patterns.

Through a comprehensive comparison of depletion and market potential, the Power BI report highlights areas with significant gaps between supply and demand, indicating potential market voids. These findings help businesses identify expansion opportunities, refine marketing strategies, and optimize product distribution to better cater to consumer needs in specific regions.

This project monitors the impact of price promotions on beverages across seasons. Leveraging retail data from multiple chains and promotional information, it analyzes the effects of promotions on sales and consumer behavior.

Power BI creates interactive dashboards for real-time insights, aiding data-driven decisions. Azure Synapse handles big data analytics and warehousing, efficiently processing large-scale retail data for in-depth analysis. Its seamless integration with Power BI enhances data flow.

Azure Data Factory orchestrates data pipelines, automating data movement and transformation for faster, more efficient analysis. The data-driven approach provides valuable insights into consumer preferences and behaviors during different seasons.

Optimized pricing strategies maximize sales and revenue. The project examines SKU interactions within the same brand, refining cross-selling and brand loyalty tactics.

The goal is to offer actionable recommendations to stakeholders, enabling informed decisions on promotions and yielding the best results for various seasons and product categories. Utilizing Power BI, Azure Synapse, and Azure Data Factory enhances marketing strategies and competitiveness in the market.

This project was built using VIP, Salient, and retail data from major retail chains. It aims to merge VIP depletions with retail stores' scanned data to predict future stock-outs. The project generates stock-out alerts on the Microsoft Power BI dashboard by monitoring depletions and sales trends for all SKUs using historical data.
The project predicts potential stock-outs in advance by integrating VIP, Salient, and retail data. This proactive approach helps retailers and suppliers prevent inventory shortages, optimize supply chain management, and maintain customer satisfaction.

The main objective of this project is to streamline the onboarding process for new suppliers in distribution companies. By utilizing Microsoft Power Automate flows and the Microsoft Power BI dashboard, the project facilitates efficient supplier management and SKU integration. The system assists suppliers in submitting all the required official documents and uploading pricing details for their items.
Power Automate flows are critical in automating and orchestrating the onboarding process. They guide suppliers through the necessary steps, ensuring they provide all the essential documents and pricing information for their products. This automated approach saves time and reduces manual errors, expediting supplier integration.

The Power BI dashboard acts as a central monitoring tool for the onboarding process. It provides real-time updates on the status of each supplier and SKU integration. Distribution companies can track the progress of multiple suppliers simultaneously, ensuring the timely completion of onboarding tasks.

This project's primary focus is to efficiently collect pricing information from suppliers using Microsoft Power Apps while assisting distributors in maintaining a comprehensive record of internal pricing for items. Additionally, the project aims to identify and address discrepancies between the pricing set by suppliers and the actual invoiced price to retail chains. Furthermore, the project facilitates distributors in establishing and maintaining a centralized pricing database for all items. This central repository allows for easy access and management of pricing data, empowering distributors to make more informed pricing decisions and strategies. A crucial aspect of the project is its capability to compare the pricing set by suppliers with the actual invoiced prices of retail chains. By identifying discrepancies between these two data sets, distributors can promptly address pricing inconsistencies, preventing potential revenue loss or disputes with retail partners.

The core objective of this project is to proactively alert stakeholders before on-shelf expiry dates for items in retail stores. To achieve this, the project leverages VIP data, which provides information on depletion units to stores, and utilizes retail (scanned) data to measure the velocity of units per store. By analyzing historical information from each store, the system identifies items at risk of expiring on the shelf due to low sales velocity despite their significant current volume in-store.

By issuing timely alerts, the project enables stakeholders to take proactive measures to address the identified high-risk items. Distributors and store managers can make informed decisions, such as implementing targeted promotions, adjusting pricing, or relocating items to increase visibility and sales velocity.

Successfully implemented a robust supplier pricing tool leveraging the capabilities of Power Apps, designed to streamline the collaboration between distributors and suppliers in the product pricing workflow. This tool integrates seamlessly with Power Automate flows to efficiently manage the approval and rejection processes.

Key Features:
1. User-Friendly Interface.
2. Integrated Power Automate Flows: Utilizing Power Automate, our solution automates the entire approval workflow. As Suppliers set product pricing, the system efficiently manages the approval and rejection processes, enhancing speed and accuracy.
3. Bulk Update/Edit/Approve/Reject.

Bulk Inserts: Simplify adding new pricing entries with the bulk insert functionality. This feature facilitates the efficient onboarding of new products and pricing information in large quantities.

Approval Dashboard: A dedicated dashboard provides a consolidated view of all pricing approvals, making it easy for authorized personnel to monitor and manage the entire approval pipeline.