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Case Studies

Proactive Delinquency Prediction for a Leading Home Loan Provider

Business Introduction  The client is a reputable home loan company with a strong presence across multiple states in India, serving customers in major cities and towns. With over 25 years of experience, the company is deeply committed to providing affordable housing finance to middle-class families. Beyond business, they are renowned for their social responsibility efforts—supporting girls’ education, running an NGO for sheltering distressed women and children, and promoting awareness about women’s health and hygiene in underprivileged communities.  As a leader in the home loan sector, managing timely repayments is critical to their business. However, they faced significant challenges with customers missing their EMIs (Equated Monthly Installments), leading to operational inefficiencies and financial risk.  Business Objectives  The client wanted to proactively identify customers who were likely to default on their loan repayments at the beginning of each month. This would enable early follow-ups and intervention, reduce the rate of delinquency, and improve overall recovery. Relying on manual post-default follow-ups has proven ineffective and often too late to influence outcomes.  Scope of Work  Logesys was engaged to develop a predictive analytics solution capable of forecasting delinquency probabilities using the client’s historical repayment data.  Challenges & Solutions  Challenge 1: Massive Data Volume  With operations spread across India, the client provided over 100,000 records spanning a full year. Handling such a large dataset, especially Excel files, requires efficient data processing to enable accurate modeling.  Solution: Our team used scalable data wrangling techniques to clean and prepare the dataset. By leveraging Azure ML’s capabilities, we ensured that the large volume of data was processed efficiently without performance bottlenecks.  Challenge 2: Data Quality Issues  The dataset contained null values, spelling inconsistencies (e.g., “college” vs. “clg”), and outliers that threatened the integrity of any model built on it.  Solution: We conducted thorough data cleansing, including standardizing categorical variables and imputing missing values. This preprocessing step was crucial to build a reliable predictive model.  Challenge 3: Highly Imbalanced Data  About 90% of the records reflected on-time payments, leaving only 10% as delinquent cases. This imbalance risked the model being biased toward predicting non-delinquency, limiting its usefulness.  Solution: Logesys applied SMOTE (Synthetic Minority Over-sampling Technique) to artificially balance the dataset by generating synthetic examples of minority class (delinquencies). This approach enabled the model to better learn the patterns leading to defaults.   Solution  Logesys designed and implemented a robust predictive solution based on binary logistic regression to classify the likelihood of delinquency. To enhance model accuracy and feature selection, Lasso regression was also utilized.  Both models were developed in Python and executed seamlessly within Azure Machine Learning, providing scalability, automation, and easy deployment.  Results  Delivered within 3 months, the solution has empowered the client to accurately forecast delinquent borrowers ahead of the payment cycle. Key benefits include:  Proactive follow-ups and reminders to at-risk customers before EMI due dates  Significant reduction in late payments and missed installments  More than 50% decrease in delinquency rates over subsequent months  Streamlined loan recovery process, reducing manual effort and operational costs  The client has expressed high satisfaction with Logesys’ expertise, timely delivery, and impactful results.  Conclusion  This project exemplifies how data-driven insights can transform traditional financial operations. With Logesys’ predictive analytics solution, the client has shifted from reactive collection efforts to a proactive strategy—ensuring more customers pay on time, improving cash flow, and strengthening business resilience. The collaboration highlights the power of combining domain knowledge with advanced machine learning techniques to solve real-world challenges. 

Case Studies

Delinquency Prediction of a Home Loan Company 

Business Introduction  The client is a well-established home loan company with a vast network of branches across multiple states and cities. With over 25 years of trusted service, their mission is to provide affordable housing finance solutions to middle-class families and improve access to quality housing schemes.  Beyond their core business, they are deeply committed to social causes—actively supporting girls’ education, running an NGO to shelter vulnerable women and children, and promoting health and hygiene awareness among underprivileged women.  As a leading player in the home loan market, managing repayment delinquencies has been a persistent challenge impacting their operational efficiency and growth.  Business Objectives  The client sought a forward-looking solution to predict customers likely to default on their EMI payments before the start of each month. This would allow them to intervene proactively, send timely reminders, and reduce delinquency rates—thereby improving collections and customer relationships.  Manual follow-ups after defaults were inefficient and often too late, sometimes disrupting the company’s broader business strategies.  Scope of Work  Logesys was engaged in designing and delivering a predictive analytics model that could analyze historical repayment data, identify patterns indicating probable delinquency, and provide actionable predictions for upcoming payment cycles.  Challenges & Solutions  Challenge 1: Handling Large Volumes of Data  The client provided nearly 100,000 records covering one full year, sourced from multiple branches. Processing such a large and diverse dataset in Excel format poses significant challenges for data quality and computational efficiency.  Solution:  We implemented scalable data processing workflows using Azure Machine Learning to clean, transform, and prepare this data efficiently. This ensured that the model was trained in reliable and well-structured data without performance issues.  Challenge 2: Data Quality and Consistency Issues  Inconsistencies like null fields, misspellings (e.g., “college” vs. “clg”), and outliers were prevalent. Such errors could mislead the model and reduce its prediction accuracy.  Solution:  Our data scientists performed comprehensive data cleaning and normalization, including standardizing categorical variables and imputing missing values, thereby ensuring the data fed into the model was accurate and meaningful.  Challenge 3: Imbalanced Dataset Bias  With 90% of records reflecting on-time payments, the dataset was heavily skewed towards non-delinquent cases. This imbalance risked biasing the model to predict no delinquency too often, weakening its predictive power for actual defaulters.  Solution:  Logesys applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples of the delinquency cases, balancing the workload and enabling the model to learn the subtle indicators of default more effectively.  Solution  We developed a binary logistic regression model, supplemented by Lasso regression for feature selection and improved prediction accuracy. These models were implemented in Python and deployed on Azure Machine Learning, allowing automated retraining and scalability as new data becomes available.  Results  The project was completed within three months, delivering a predictive model that accurately identifies customers at risk of missing payments in the upcoming cycle. Key outcomes include:  Early identification enabling timely customer outreach and reminders  Over 50% reduction in delinquency rates observed in the following months  Streamlined collection process, reducing manual effort and follow-up delays  Enhanced operational efficiency and improved cash flow management  The client praised the solution for its accuracy, ease of use, and tangible impact on their business.  Conclusion  With Logesys’ expertise, the client transformed their approach to loan repayment management—shifting from reactive to proactive delinquency handling. This project showcases how the power of data science and machine learning can drive real-world business improvements, supporting financial inclusion and responsible lending.  By predicting risks in advance, the client not only protects their bottom line but also strengthens customer trust and experience—fueling sustained growth in a competitive market.  View All Case Studies

Case Studies

Real-Time Audience Forecasting for a Global OOH Media Provider

Business Introduction  Our client is a leading out-of-home (OOH) media intelligence provider, helping brands measure their visibility and engagement across commercials, especially on television. By analyzing audience and advert data, they enable marketing teams to assess media value and fine-tune their omnichannel campaigns. Their work helps global brands understand when, where, and how to invest in TV ads for maximum reach.  However, despite their extensive brand partnerships, one recurring roadblock was delaying campaign decisions—audience data from TV channels arrived 14 days after a commercial aired. These delays made timely audience analysis nearly impossible, leaving marketing strategies reactive instead of proactive.  The client approached Logesys to address this data lag and build a system that could accurately forecast audience volume in near real-time, enabling brands to adjust their TV media budgets ahead of time rather than after the fact.  Business Objectives  The objective was clear: develop a predictive model that could forecast audience volume by time slot, allowing brands to make quick, confident decisions on when and where to invest in commercials.  To achieve this, the Logesys team needed to design a machine learning solution that could handle high-frequency data, generate timely predictions, and work at scale—offering insights that were as close to life as possible.    Scope of Work  Logesys was tasked with analyzing one year’s worth of historical audience data, predicting audience engagement for upcoming time slots across TV channels, and delivering this through a user-friendly dashboard for brand teams to act on.  Challenges & Solutions  Challenge 1: Massive Data Volume  Audience data was recorded at 15-minute intervals for every channel. This meant over 35,000 data points per channel per year—and there were dozens of channels. Processing this volume quickly and accurately was essential for real-time forecasting.  Solution:  We implemented an efficient data ingestion and preprocessing workflow. Raw data was stored in SQL Server and processed in manageable batches. We used intelligent flags to tag weekends, special events, and holidays, allowing the model to better understand viewer behavior patterns over time.  Challenge 2: Time-Sensitive Analysis  Audience patterns shift rapidly—especially around weekends, public holidays, or special events. The model had to adapt quickly and respond to these fluctuations, as even minor lags could invalidate forecast usefulness.  Solution:  We designed a fast, adaptive pipeline to process incoming data continuously and incorporate it into the model’s predictions. Predictions were refreshed frequently, ensuring that stakeholders always had the most relevant forecast available when planning media buys.  Challenge 3: Time Series Models Lacked Accuracy  Conventional time series forecasting models like ARIMA and LSTM failed to deliver the accuracy required. The predictions fluctuated too widely and couldn’t reliably support strategic ad placements.  Solution:  Logesys pivoted to a Random Forest Regression approach. While not a traditional time series method, Random Forests are excellent at handling large, complex datasets. We extracted granular time-based features—like day, hour, weekend flags, and more—from a single timestamp column, essentially converting the data into a structured time-series-friendly format. This dramatically improved prediction accuracy and stability.  Solution  Our final solution combined robust data processing with a custom machine learning pipeline powered by Random Forest Regression. By deriving time-based variables and integrating behavioral patterns (e.g., increased weekend viewership), the model was able to predict audience volumes with 90% accuracy compared to the actuals that arrived two weeks later.  A comprehensive Power BI dashboard was built to visualize the predictions. Users can view historical audience data alongside forecasts for specific time slots, days, and channels—making it easier to fine-tune ad strategies.  Results  The project was delivered in 3 months, and the results have been nothing short of transformational:  90% forecast accuracy for audience volumes, eliminating the 14-day decision lag  Faster, data-backed ad placement decisions across brands and marketing teams  Optimized media budgets, with brands reallocating spend based on forecasted reach  Full visibility into high-impact time slots, such as holidays and weekends  Dashboards accessible across devices, supporting real-time collaboration  A scalable machine learning system that improves continuously as more data flows in  Over the last year, the solution has continued to support smarter media planning—driving better ROI for brands and giving the client a competitive edge in audience analytics.  Conclusion  With this predictive forecasting system, Logesys helped the client close the gap between audience behavior and advertising action. Brands no longer have to wait two weeks to know if their ad was seen—they can now plan in advance with data-driven confidence.  This case highlights how modern machine learning, when paired with practical business needs, can turn delayed insights into proactive strategies—saving time, money, and missed opportunities.  The client’s marketing team has praised the solution not only for its accuracy but for the way it has reshaped their entire decision-making process. And as the system continues to evolve, the precision—and the value it delivers—will only grow stronger.  View All Case Studies

Case Studies

Driving Strategic Product Bundling for a Leading Pharma & Wellness Retailer

Business Introduction  The client is a prominent pharmaceutical and wellness retail chain with a widespread presence across major cities and towns. Along with prescription medicines, they offer a wide variety of FMCG products focused on wellness, beauty, and personal hygiene—available via physical stores, an e-commerce website, and a mobile app. Their stores operate 24/7, ensuring healthcare access around the clock.  With over two decades of excellence in retail and numerous awards—such as Best Omnichannel Retailer and Most Admired Healthcare Retailer—the client continues to expand rapidly. As the business scaled, so did their need for intelligent marketing strategies that could increase revenue, optimize promotions, and identify which products to bundle together in campaigns.  To move beyond gutfeel and guesswork in their bundling approach, the client sought a data-driven solution. That’s when Logesys was brought in—to unlock patterns in massive volumes of transaction data and power smarter bundled promotions.    Business Objectives  The core goal was to perform market basket analysis on recent sales data to identify profitable product combinations that could be bundled together helping the marketing team craft better campaigns, clear excess inventory, and increase average transaction values.  The analysis needed to:  Identify which products are frequently bought together  Recommend bundles of 2 to 4 items with strong buying patterns  Exclude combinations that wouldn’t add business value (e.g., two top-selling items that already perform well individually)  Handle 20+ million records with high efficiency  Scope of Work  Logesys was entrusted to design and implement an end-to-end machine learning solution that would take six months of transactional data, analyze patterns, and deliver actionable bundling insights via an interactive Power BI dashboard.    Challenges & Solutions  Challenge 1: Massive Transactional Data  With over 20 million records across just two quarters, the volume of historical data was overwhelming. Extracting relevant relationships manually or through traditional tools was simply not scalable.  Solution:  Logesys utilized PySpark within Azure Databricks for efficient parallel data processing. By distributing the load across multiple nodes, preprocessing that once took hours was now completed in a fraction of the time—improving speed tenfold. This allowed us to consistently analyze recent sales and deliver real-time suggestions for campaign planning.  Challenge 2: No Readymade BI or ML System  The client relied on their team’s experience and judgment to decide on bundle promotions. No automated solution or predictive system was in place to drive decisions with data.  Solution:  We built a custom machine learning model leveraging the Apriori algorithm in Python. Apriori is widely known for its precision in market basket analysis. It identified associations between items and suggested bundles based on support and confidence metrics, focusing on pairs, triads, and quartets of products that frequently appear together.   Challenge 3: Performance and Accuracy Concerns  Off-the-shelf analytics platforms either lacked the scale or failed to provide usable, timely results. Accuracy in detecting strong bundle patterns without overloading the system was critical.  Solution:  Rather than using a plug-and-play tool, we customized Python libraries and integrated them with PySpark to handle complex computations on large datasets. We also cleansed the data thoroughly—removing nulls, correcting invalid item codes, and excluding expired or unavailable products—ensuring only valid and actionable records entered the model.  Solution  The final solution delivered by Logesys included:  Data processed using PySpark on Azure Databricks for high-speed handling  A machine learning model using the Apriori algorithm to determine frequently co-purchased items  Recommendations tuned to exclude bundles that don’t add incremental value (e.g., high performers that sell well independently)  Fresh insights every cycle based on the last 6 months of sales data  Dashboard & Reporting  A dynamic Power BI dashboard was developed to visualize bundling suggestions, confidence levels, and promotional priorities. The dashboard allowed the marketing team to:  Identify high-confidence product bundles for in-store and online promotions  Filter suggestions by product category, frequency of sale, and sales region  View historical bundle performance versus predicted outcomes  It’s fully responsive and accessible across laptops, tablets, and iPads, making it easy to use for marketing managers on the go.  Results  Logesys delivered the project in just two months, with dashboard rollouts completed in three phases. The solution has enabled the client to:  Launch highly targeted bundle promotions with better returns  Improve revenue by bundling underperforming products with popular ones  Reduce manual guesswork and align promotions with actual customer behavior  Facilitate faster stock clearance through bundled discounts  Create separate promotional strategies for online and in-store campaigns  An especially unique outcome was the non-intrusive strategy—the model focused on bundle opportunities only where it made business sense. High-selling products were kept unbundled when bundling would offer no additional gain.  Conclusion  Through this project, Logesys helped a leading pharma and wellness retailer move from instinct-led marketing to data-driven promotional campaigns. By combining machine learning with enterprise-scale data processing, we empower their team to create bundles that convert—without wasting budget or inventory.  As a result, their bundling campaigns are now smarter, faster, and significantly more profitable. And with a system that evolves over time, the client is well-positioned to continue driving growth through strategic product insights.  View All Case Studies

Case Studies

From Fragmented Data to Clear Reports: A Multi-Channel Seller’s Journey 

About the Company  The client is a leading Indian retail company renowned for designing, manufacturing, and selling an extensive range of high-quality home décor and furnishing products. Their portfolio also includes beautifully crafted and affordable kitchenware, dining accessories, and ethnic clothing items, positioning them as a holistic home lifestyle brand.  With strong roots in India, the company has established a growing global footprint, operating across 9 countries including the United States, Canada, Australia, and several nations in Europe. The client reaches customers worldwide via major online marketplaces like Amazon and eBay, in addition to their own local eCommerce platforms in India.    Business Challenges  As the business scaled up operations internationally and diversified across eCommerce platforms, it began encountering major data-related challenges. These issues stemmed from fragmented data sources, inconsistent formats, and an over-reliance on manual data handling processes. Below are the key obstacles the client was facing:  Diverse and Disparate Data Channels Each country-specific storefront on Amazon and eBay generates its own dataset, producing multiple streams of raw monthly data. While these platforms do provide customized reports, they come at an additional cost. Even when available, integrating and consolidating this disparate data proved to be a significant hurdle for the client’s internal teams. On top of that, data from their proprietary eCommerce websites and other internal systems further contributed to the complexity.  High Manual Intervention To manage the inflow of business data, the team relied heavily on manual processes. PHP scripts were used to extract and massage the data before dumping it into a database. This labor-intensive method was time-consuming, prone to human error, and not scalable.  Inconsistent Data Granularity The granularity of data across different platforms varied significantly. This posed a serious challenge in aligning and consolidating information from all sources for comprehensive reporting. Even with tools like Tableau, producing effective, unified reports remained difficult due to incompatible datasets.  Lack of a Unified View of Revenue One of the most pressing concerns for the executive team was the absence of a consolidated view of the profit and loss (P&L) statements.  Platforms like Amazon charge sellers over 30 different types of fees, including fulfillment, advertising, and referral fees. Additionally, costs incurred through inventory management, logistics, and bank charges created layers of complexity. Without a unified view, identifying cost-saving opportunities and understanding true profitability was nearly impossible.    Solution Delivered by Logesys  Understanding the multifaceted nature of the client’s business and its growing data needs, the Logesys team conducted a thorough analysis of all available data sources. The objective was to create a unified data model that allows for effective consolidation, cleansing, and reporting.  Data Consolidation & Cleansing We began by defining a common level of granularity across all datasets to ensure smooth integration. Data from Amazon, eBay, and other internal systems were extracted and merged. This data was then cleaned using advanced SQL scripts to eliminate duplicates, inconsistencies, and junk values.  Once the data was refined, it was consolidated into a centralized Microsoft SQL Server database. This acted as a single source of truth for the reporting layer.  Interactive Dashboards Using Power BI To transform raw data into actionable insights, Power BI was implemented as the visualization and analytics layer. Our team developed customized dashboards that deliver real-time visibility into KPIs across finance, operations, inventory, and logistics. These dashboards were tailored for specific teams, including the executive board, finance department, and inventory management, and were fully accessible via the web and mobile.    Technology Stack and Timelines  Visualization & BI Tool: Microsoft Power BI  Database: Microsoft SQL Server  The entire project—from initial requirement of gathering to solution design, development, testing, and go-live—was completed within a span of approximately 3.5 months. Since deployment, the system has been operating seamlessly and has delivered tangible business value.  Reporting Capabilities by Team  Key Benefits Delivered  Common Benefits Across the Organization  Fully automated reporting, eliminating manual intervention  Highly visual dashboards with maps, charts, and color-coded insights  Web and mobile accessibility for on-the-go decision-making  Tight data governance and security protocols  Daily data refreshes for up-to-date insights  24/7 technical support provided by Logesys  Unveiling Hidden Insights  With data merged from various geographies and channels for multiple years, the analytics solution provided deeper business intelligence:  Inventory Optimization: Reports highlighted costs associated with damaged goods, transit losses, and returns—allowing better inventory rotation between Amazon fulfillment centers and the client’s warehouses.  Root-Cause Analysis: Product performance reports reveal common reasons for order cancellations and returns—such as delayed shipments, product damage, and incorrect deliveries.  Financial Transparency: Detailed finance dashboards enabled the team to analyze advertising costs, keyword performance, and promotional expenses—uncovering spend areas that were not delivering results.  Strategic Planning: The executive team could finally view comprehensive P&L statements, helping them spot surprising gains and unexpected losses. They could also pinpoint hidden expenses such as import duties and banking charges, which previously went unnoticed.    Results & Business Impact  The Power BI-powered analytics solution implemented by Logesys revolutionized the client’s decision-making process. Key outcomes included:  Unified, real-time insights accessible across departments  Elimination of manual data preparation efforts  Streamlined inventory and financial operations  Improved transparency into expenses and profits  Better strategic planning based on accurate, holistic data  The project was delivered within a remarkably short timeline of 3.5 months, including intensive testing and fine-tuning. Since deployment, the system has proven to be indispensable for the client’s operational and financial management.    If you’re looking to unlock similar value from your business data and streamline reporting across complex eCommerce platforms, get in touch with Logesys today. We specialize in implementing tailored analytics solutions that turn fragmented data into strategic advantage.  View All Case Studies

Case Studies

Streamlining Omnichannel Operations and Supply Chain Visibility for a Middle Eastern Retail Gian

About the Company  The client is a prominent UAE-based retail conglomerate operating across 9 countries in the Middle East, managing a vast network of over 1,800 stores. The company transitioned to an omnichannel retail model three years ago to enhance customer service and convenience. Today, customer-facing and inventory-related operations—including order management, inventory tracking, and shipment management—are available through multiple touchpoints: website, mobile apps, kiosks, and in-store systems.  Their foray into online retail began five years ago, and the company now generates approximately 20% of its total sales through online channels. This omnichannel expansion significantly elevated their customer experience and brand reach, positioning them as a retail powerhouse in the region. However, complexity came with scale, particularly in managing and analyzing operational and supply chain data.    Key Challenges  Despite their success, the client faced several data-related challenges that hindered efficient decision-making and operational optimization:  Diverse Data Channels Data was streaming in from various digital platforms spread across multiple countries. This fragmented data structure made it difficult for teams to access a consolidated view of operations, impacting analysis across inventory, delivery performance, and order management.  Inadequate Visibility into Store Contribution The omnichannel strategy allowed customers to browse via one channel, purchase via another, and fulfill orders via a third (e.g., order online, pick up in-store). While this boosted customer convenience and online sales, it blurred visibility into how physical stores influenced digital transactions. Teams struggled to differentiate between direct online sales and store-initiated online conversions.  Scale of Operations and Product Diversity Internally, the organization was divided into more than 20 distinct business units, each termed as a “concept.” Every unit operated individual e-commerce sites per country, often in both English and local languages. The scale and diversity of product offerings created a highly varied dataset, which was difficult to manage using the existing analytics solution.    Objectives  The Logesys team conducted a comprehensive assessment and defined the following objectives:  Consolidate data across all digital and physical channels to enable unified insights.  Equip the operations team with tools to optimize inventory, order processing, shipment, and delivery workflows.  Enable demand forecasting and track current sales trends to anticipate future order volumes.  Improve last-mile delivery efficiency and identify hidden costs or operational bottlenecks.    The Solution  Following a deep dive into the client’s architecture and needs, Logesys proposed implementing QlikView as the primary data visualization and analytics platform. Chosen for its scalability and real-time dashboard refresh capabilities, QlikView is ideally suited to handle the client’s large and complex datasets.  Dashboard Design & Implementation  The final solution consisted of three customizable dashboards, built with usability in mind. These dashboards empower end-users—including non-technical staff—to analyze and interact with data without requiring analytics expertise. Benefits Delivered  The dashboards delivered significant value across operational and strategic dimensions:  Functional Benefits  Real-time dashboards with customization options tailored to various departments  Secure access through the company’s intranet, ensuring data protection  Intuitive, user-friendly visuals for effortless understanding  Automated report generation with up-to-date data  24/7 support provided by the Logesys team  Strategic Insights Uncovered  Behavioral patterns and preferences of customers across geographies  Courier partner performance benchmarking and improvement opportunities  Enhanced order flow visibility across all channels  Identification of bottlenecks in fulfillment workflows  Quantification of operational overheads and hidden financial drains, especially in inventory and logistics  Project Execution & Results  The project was delivered in three structured phases over a span of 10 months, with each phase focusing on distinct functionality and dashboard deployment.  Final reports and dashboards were made accessible via tablets, laptops, and iPads, ensuring widespread adoption and ease of use across the organization.  The implementation resulted in significant operational optimization and financial savings through better visibility into areas previously overlooked or under-analyzed.  Perhaps most importantly, the project received enthusiastic feedback from the client’s internal teams. The level of insight, accessibility, and support provided by Logesys not only met but exceeded expectations.  Conclusion  In a rapidly evolving retail landscape where omnichannel strategies are essential, data can be both a challenge and a catalyst for growth. This case study is testament to how the right analytics platform, tailored dashboards, and expert implementation can transform operational chaos into actionable insights.  Logesys continues to be a trusted partner for retailers looking to consolidate complex data streams and turn them into powerful tools for growth, efficiency, and customer satisfaction.  Looking to optimize your omnichannel data and elevate your retail performance?  Contact Logesys today for a tailored consultation. Let’s turn your retail data into your strongest competitive edge.    View All Case Studies

Case Studies

Transforming Retail Analytics for Times Square by Unifying Its Data Platform with Microsoft Fabric 

Business Overview  Times Square Group, headquartered in Dubai, is a leading retail fashion conglomerate operating across the Middle East. With over 50 stores and 10 business entities, the group offers a wide range of products—from everyday fashion to luxury bridal couture. As part of its digital transformation journey, Times Square partnered with Logesys to modernize its data platform using Microsoft Fabric.    Current Scenario  Times Square’s operational data was spread across systems like Dynamics NAV and Business Central, which were primarily designed for transactional processing—not analytics. Reporting workflows were manual, slow, and error-prone, relying heavily on tools like Excel and PowerPoint. The lack of a unified, analytics-ready data platform made it difficult for business leaders to access timely insights.  Recognizing the need for a scalable, automated, and governed data infrastructure, Times Square decided to adopt Microsoft Fabric to unify its data estate and enable real-time reporting.  Project Objective  Logesys was tasked with designing and implementing a modern data platform that could:  Consolidate data from NAV and Business Central into a unified lake-centric architecture.  Automate data ingestion, transformation, and reporting workflows.  Enable real-time, interactive dashboards using Power BI.  Ensure data governance, security, and compliance across all layers.  Build a scalable foundation for future analytics and AI initiatives.    Scope of Work  Ingest raw data from NAV and Business Central into OneLake using Fabric Data Pipelines.  Organize data using the Medallion Architecture (Bronze → Silver → Gold).  Transform and enrich data using PySpark notebooks within Fabric Workspaces.  Build Semantic Models to define business logic and KPIs.  Visualize insights through Power BI dashboards connected to the Gold layer.  Implement role-based access control and data lineage tracking using Microsoft Entra ID and Purview.  Data Flow Design & Technical Solution  The solution was built using the following components:  Component  Purpose  Fabric OneLake  Unified data lake for storing raw, cleaned, and aggregated datasets.  PySpark Notebooks    Automated data transformation, cleansing, and enrichment  Data Pipelines  Scheduled ingestion from source systems into OneLake.  Semantic Models  Business logic layer for consistent KPIs and metrics  Power BI  Interactive dashboards for real-time decision-making.        The architecture followed the Medallion pattern:    Bronze Layer: Raw data ingested from NAV/BC into OneLake as Delta tables.  Silver Layer: Cleaned and standardized data using PySpark (null handling, deduplication, joins).  Gold Layer: Aggregated, business-ready datasets with KPIs and metrics for reporting.    Solution Design  Data Extraction  Tool: Fabric Data Pipelines  Description: Automated ingestion from NAV and Business Central into OneLake. Delta format used for schema preservation and incremental loads.  Data Transformation  Tool: PySpark  Description: Scalable processing of large datasets. Tasks included cleansing, normalization, enrichment, and joining with reference data (e.g., region, currency).  Data Warehousing  Tool: OneLake (Lakehouse Architecture)  Description: Centralized storage with support for versioning, partitioning, and schema evolution. Enabled seamless integration with Power BI and Purview.  Reporting & Visualization  Tool: Power BI + Semantic Models  Description: Dashboards built on top of Gold layer using semantic definitions. Ensured consistent KPIs across departments and near real-time insights.    Challenges & Solutions  Challenge  Solution  Data Integrity  Rigorous analysis and normalization using PySpark to ensure schema accuracy.  Data Quality & Consistency  Validation protocols and metadata tagging across Silver and Gold layers.  Scalability for Large Volumes  Delta format and OneLake’s architecture enabled efficient handling of data.  Governance & Access Control  Role-based access via Entra ID and lineage tracking via Purview.    Business Impact  Unified data platform with lake-centric architecture.  Automated data lifecycle management using Medallion Architecture.  Scalable transformations with PySpark.  Secure and governed access across departments.  Real-time reporting with Power BI dashboards.  Reduced manual effort and improved data accuracy.  Conclusion  Times Square now operates on a modern, automated data platform powered by Microsoft Fabric. Business leaders can access real-time insights, make faster decisions, and rely on consistent, governed data across the organization. The transformation has laid a strong foundation for future analytics, AI, and business growth.  View All Case Studies

Case Studies

Automating Complex Market Analytics for an FMCG Leader

Business OverviewThe client is a leading global Fast-Moving Consumer Goods (FMCG) company. They rely heavily on detailed market reports to understand product performance, sales trends, and consumer behavior. These reports, often sourced from data providers like WSP, are crucial for making strategic decisions about product placement, marketing campaigns, and inventory management. Project Objective The central objective of this project was to transform the client’s market analytics process from a time-consuming, manual workflow into an efficient, automated system. The team’s previous method of downloading and filtering reports from WSP was labor-intensive and delayed decision-making. The new automated system aimed to:Ingest raw data and automatically create comprehensive market mappings.Integrate multi-level product, period, and market data into a cohesive model.Apply complex calculations to generate key sales metrics automatically.Produce detailed, accurate reports with monthly, quarterly, year-to-date (YTD), and moving annual total (MAT) summaries in a single run. Challenges & Solutions Challenge 1: Time-Consuming Manual Process The manual process of downloading and filtering reports from WSP required a deep understanding of multiple market hierarchies and consumed significant hours of a data analyst’s time. This extensive manual effort was highly inefficient and a major bottleneck in the reporting cycle.Solution: An automated process was implemented to ingest raw, flat CSV files directly from a designated location. This solution eliminated the need for manual data retrieval and filtering. By automating the entire data flow—from ingestion and transformation to final report generation—the time required to produce a complete report was drastically reduced from hours to mere minutes. Challenge 2: Complex and Inconsistent Data Mappings The client’s market data involved intricate hierarchies and product mappings that varied across different reports and categories. Maintaining data accuracy and consistency was a constant struggle due to the complexity of manually aligning these disparate datasets.Solution: The solution included an automated process for creating a detailed market mapping file. This process automatically derived and combined markets based on a pre-defined hierarchical structure (e.g., zones, states, cities), ensuring that all data was consistent and standardized. A robust data model was built to integrate product, period, and market hierarchies, which served as a single source of truth for all subsequent analysis and reporting. Challenge 3: Diverse and Complex Metric Calculations The project required the calculation of a wide array of specific metrics, each with its own unique formula. These calculations had to be applied consistently across different levels—markets, time periods, and products—to provide meaningful insights.Solution: The automated system was designed with a modular approach to handle distinct calculation logics. It applied a series of specific formulas for each metric and period (e.g., monthly, quarterly, YTD, MAT) on the integrated data model. This ensured that all metric-based outputs were calculated accurately and consistently, providing clear, reliable insights into market dynamics and performance trends. Implementation and Outcomes The solution was developed as a robust, executable Python script. It utilized the Pandas library for all data ingestion, modeling, and transformation tasks. The script was designed to read input data from CSV files, merge different DataFrames to create a unified data model, perform all necessary data transformations and calculations, and finally export the clean, processed data into a final CSV report.The implementation delivered significant value and tangible benefits:Drastically Reduced Time: The report preparation time was slashed from several hours to just a few minutes, enabling faster access to critical market insights.Improved Accuracy and Consistency: By eliminating manual intervention, the automated system ensured that data was accurate and consistent across all reports, leading to more reliable analysis.Actionable Insights: The final CSV reports provided a comprehensive view of product and market performance, complete with monthly, quarterly, and annual summaries, empowering stakeholders to make more informed, data-driven decisions.Scalable Framework: The automated solution is highly scalable and adaptable, allowing it to easily accommodate new markets, products, or reporting requirements as the business evolves

Case Studies

Empowering Pharmaceutical Analytics for Market Leadership

Business Overview A US-based pharmaceutical company needed to analyze the performance of its drugs against competitors to identify growth opportunities and improve market access. The company’s business users were dependent on vendor and IT teams for custom reports, which hindered their ability to conduct ad-hoc analysis and get timely insights. They needed a self-service business intelligence (BI) platform to empower them to analyze key metrics on their own. Business Challenges The company faced several key challenges due to its reliance on external teams and legacy reporting methods. The business users were unable to perform real-time analysis on their own, which led to delays in critical decision-making. The process for generating reports was rigid and not adaptable to changing business needs. Specifically, the problems included: Approach and Key Metrics To address these requirements, a comprehensive BI solution was developed using QlikView. This approach involved aggregating and linking data from multiple sources to create a unified analytical platform. The main data source was rolling 24-month prescription (Rx) data from IMS. This was integrated with other key data sources for HCPs (ZIP codes, specialties, decile ratings), national and regional accounts (payers), formulary access (Preferred/Non-Preferred/Covered/Not Covered), and regional hierarchy structures. The solution enabled business users to analyze several key metrics: Benefits Obtained The implementation of the QlikView dashboard provided significant benefits, transforming the way business users accessed and analyzed drug performance data.

Case Studies

Digital Transformation in Ports & Logistics for a Leading Terminal Operator 

Business Introduction  JM Baxi, a century-old leader in container port and terminal operations, with multiple locations across India. Renowned for operational expertise and scale, the organization manages high-volume cargo handling, crane operations, and terminal throughput for domestic and international trade.  While core port operations were already partially automated, the client sought to modernize further by adopting advanced data engineering and business intelligence (BI) capabilities. The goal was to leverage real-time data to improve operational efficiency, reduce vessel turnaround times, increase material handling capacity, and enhance customer satisfaction.  Business Objectives  The transformation initiative aimed to:  Scope of Work  Logesys was engaged to design and implement a centralized analytics infrastructure, covering:  The engagement began with requirement-gathering workshops involving stakeholders from sales, operations, finance, and port operations teams to ensure the solution addressed cross-departmental needs.  Challenges & Solutions  Challenge 1: Decentralized and Fragmented Data Each port location maintained its own operational records, making performance tracking inconsistent and time-consuming.  Solution: A centralized data warehouse was established, serving as the single source of truth for all terminals. This enabled standardized reporting and consolidated performance monitoring.  Challenge 2: Manual, Error-Prone MIS Reporting Relying on Excel for complex calculations and large datasets was labor-intensive and prone to human error.  Solution: Automated ETL pipelines were developed to handle data ingestion, transformation, and loading without manual intervention. This reduced reporting lead times and eliminated repetitive tasks.  Challenge 3: Multiple Data Sources and Formats Data arrived from diverse operational systems, creating integration challenges.  Solution: A robust data integration framework was implemented, capable of consolidating and harmonizing data from all sources into the centralized warehouse for uniform analysis.  Solution  The final architecture combined data engineering and BI capabilities to deliver real-time, high-accuracy insights:  Results  The solution delivered significant operational and strategic benefits:  Conclusion  Delivered in just 90 days, the project empowered the client with a robust, future-ready analytics ecosystem. The centralized data warehouse, automated pipelines, and interactive dashboards not only improved operational performance but also enhanced agility in responding to changing demands.  With the ability to access insights on any device, at any time, leadership and operations teams are now better equipped to make swift, informed decisions—cementing the organization’s competitive edge in the rapidly evolving ports and logistics sector. 

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