Business cases

Problem Definition

  • Retail Service Organization that deals with delicacies, glass, ceramic, crystal and earthenware gift items, souvenirs and other types of items.
  • Collects large volume of sales and inventory data, however not using effectively the data assets. Reporting environment done in Excel proving not scalable enough for the information needs.
  • Unable to provide timely and accurate reports and insights to senior and board members.

Our Solution Approach

  • BI assessment and roadmap definition.
  • Prioritization on the information needs.
  • Focus on a centralized Data Warehouse approach in order to integrate all aspects of the business.
  • ETL developed on Open Source BI tools (Pentaho Data Integration).
  • Dashboards and reports developed in Qlikview.

Key Benefits

  • Dashboards and reports delivery in a timely and accurate fashion, always reflecting the latest day of sales data and providing actionable insights.
  • Sales performance measuring.
  • Data meshing capabilities on all data aspects of the organization.
  • Scalable architecture.

Problem Definition

  • European leader in electrical, mechanical, HVAC, energy and communication system services
  • Company is challenged with the optimization  and maintenance of their automated machines
  • Existing OLTP solution has data storage and historical data challenges
  • Explore open source driven Big Data solutions

Our Solution Approach

  • Pilot phase with a sample of the pressing lines sensor data
  • Leveraged Open source technologies – MongoDB, JSON and ETL Technologies
  • MongoDB based data storage layer to store the historical sensor data reading
  • Exploratory data analysis to detect patterns & hypotheses suggestion for machine operation optimization

Key Benefits

  • Predictive asset management and equipment maintenance
  • Optimize machine operation costs using Big Data Analytics
  • Ability to handle and generate insights from the most granular minute level data

Problem Definition

  • One of the largest power distribution company in India operating for the last 15 years with 400+ substations
  • Company is challenged with Unscheduled Interchange penalties due to excessive drawal of energy
  • Existing OLTP solution has data storage and historical data challenges
  • Explore open source driven Big Data solutions

Our Solution Approach

  • Pilot phase with a sample of Substation distribution and consumption data
  • Leveraged Open source technologies -Hadoop & HTML5/Javascript
  • Hadoop based data storage layer to store the historical meter reading and substation distribution data
  • Exploratory data analysis to detect patterns & hypotheses suggestion for energy drawal phenomena

Key Benefits

  • Predictive asset management and equipment maintenance
  • Reduce unscheduled interchange penalties using Big Data Analytics
  • Ability to handle and generate insights from the most granular minute level data (~180 TB)

Problem Definition

  • Largest retail service organization in Netherlands with 4500+ entrepreneurs and franchisees in sectors Fashion, Sport, Living & Media.
  • Collects large volume of sales & inventory data from its clients however not using effectively the data assets.
  • Unable to provide timely, high-quality, accurate, actionable insights to its customers

Our Solution Approach

  • BI Assessment and roadmap definition; recommended to monetize the information assets
  • Program Management  (Quick wins vs Strategic solutions)
  • Focus on Federated Data mart architecture
  • Metadata driven ETL solution (Microsoft SQL Server Integration Services)
  • MicroStrategy  based mobile intelligence, dashboard reporting implementation

Key Benefits

  • Centralized Competence center for BI has been formed
  • Foundation layer and BI program plan envisioning the information monetization
  • Scalable architecture with standardized master & transaction layer provide ability to build single EDW over a period of time
  • Intuitive dashboards provide actionable insights

PROBLEM DEFINITION

  • Water company supplying drinking water to over 2 million residents and businesses in The Netherlands
  • Around 800 employees ensure clean and safe drinking water.
  • Seeking the best quality and service
  • Collects considerable volume of customer service data (speech) through their Customer Service Center
  • Water company had no experience with the application of BIG DATA.
  • Pilot to assess the possibilities of carrying out a structural quality analysis of customer inquiries through data mining

OUR SOLUTION APPROACH

  • Build a language model on the voice data (about 27,000 WAV files) from a period of four weeks
  • Analyzed email (1,500 e-mails) and other available meta data
  • Speech Analysis based on language model, voice data and meta data
  • Presentation of the first results to evaluate the initial results and adjust the language model
  • The actual depth analysis. The core of customer question.
  • Present the final report and discuss options for next phase(s)
  • Used technologies; Kibana, Elastic Search

KEY BENEFITS

  • Better insight in the organisations processes and the usability of a periodical analysis of Voice Data
  • Envision the information monetization
  • Better understanding of Big Data and the quality benefits of speech analysis

Problem Definition

  • Food services and facilities management giant in 80 countries across the globe with the consolidated revenue of 18 billion euros
  • The current Excel based solution to capture service performance data in 150+ sites for 56 clients is manual, error prone and has scalable and historical reporting challenges

Data-Storage

Our Solution Approach

  • Layered Architecture provides flexibility and scalability
  • User friendly , easy navigational web based UI screens using .NET (MVC based approach with JQuery)
  • SSAS cubes to provide analytical and adhoc capability
  • Rich Dashboards & Scorecards built on SSRS and Performance Point Server

Key Benefits

  • Robust and flexible data model to capture KPIs  at customizable hierarchy levels of granularity
  • Ability to scale up to 2000+ sites
  • Dashboards & scorecards provide actual vs. target KPIs to various stakeholders
  • Self-service BI provides the end user to slice & dice the site information at preferred levels of hierarchy