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Analytics-Powered Dashboarding for a Top Furniture Brand’s Market Research


Our client, IKEA is one of the largest furniture manufacturers in the global home appliances industry. To enable continuous development, the company needed a dashboarding solution. This will allow regional managers, marketers, to process historical, current, and expected data to generate sales and marketing insights. This would facilitate more efficient resource allocation and decision-making in the company’s furniture businesses. By teaming up with OrangeMantra, our client built an advanced dashboarding platform to drive marketing insights in India using a powerful combination of big data technology together with retail expertise.


Retail & FMCG


Dashboards & Analytics

Our Process

Our team of developers and DevOps engineers are working hand in hand with our client’s requirements. We have conducted planning sessions, to generate and discuss new ideas, knowledge sharing. To date, our collaboration has resulted in a full-fledged Dashboarding solution that has scaled and helped with real-time market insights. As the data analytics platform and its functional specifications regularly evolve, our client plans to expand our technical partnership by hiring more OraangeMantra specialists.


Regular sessions with stakeholders were performed to keep a check on the business goals and required analytics metrics. Analysis of existing data sources were done with additional data integration requirements.


Collaboration with IKEA on design elements was done to build an engaging and user-friendly dashboard interface. Focused on adding key performance indicators (KPIs) and appealing visualizations.


Creation of functionality to forecast demand, define delivery times, and identify possible challenges was the next step. Testing for the refinement of dashboard to achieve accuracy was performed.


Rigorous testing of data relevancy, accuracy, dashboard operations, were performed. Gradual deployment with continuous monitoring was done to eliminate issues or optimizations.

The Problem

Limited automation and scalability as well as the use of legacy technologies stalled the growth of their business expansion. As the company wanted to move to the next level by leveraging the most advanced technologies for market analysis and processing, it needed a seasoned technical partner. They chose OrangeMantra to streamline and future-proof their solution based on our proven expertise in data analytics, data engineering, and custom dashboarding solutions.

Our Role

  • Conceptualization
  • Design
  • Development
  • Deployment

Project Challenges

1. Sluggish Data Updates

Providing real-time data updates was a real challenge, especially when dealing with large datasets with manual operations.

2. Low Quality Data

Inconsistent data resulted in inaccurate insights and visualizations for the client. Moreover, challenges were present to balance visually appealing design with functional efficiency.


With the aim of ensuring consistent, automotive and centralized decision-making for their business, we helped IKEA to build a sophisticated dashboarding platform providing real-time visibility into operations and validating data. Powered by big data predictive analytics, this solution revealed opportunities for improving our client’s business in specific regions and increase sales efforts. Besides aggregating and analyzing information, the dashboarding solution being developed by the OrangeMantra team represents data in a meaningful manner. Visual help sales, and marketing experts on opportunities that have yet to be activated. Backed by reliable data, this set of tools also provides inventory and sales predictions used by our client to avoid overstocks and stockouts.

1. Improved Data Quality

Addition of data validation processes and automation resulted in improved data quality and consistency, ensuring clients to have accurate and reliable insights.

2. Scalable Architecture

The dashboard architecture was designed to scale efficiently, managing large data volumes without impacting performance, ensuring responsiveness even data grows.

3. Real-time Data Updates

Utilization of automation, caching mechanisms, and fast data processing frameworks enabled real-time updates, providing users accurate information.

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