The need was to study the extensive set of data acquired from the product and build the data analytics application to provide actionable insights.

To build the data analytics system a methodical approach was adopted where understanding & use of the story derived from the acquired data was used to define the goals, covering-

Creating data storage of acquired data

• Defining business goal definition in mutual discussions with the client
  • Ideation & defining broad goals like improved performance, better operations and more
• Exploring & preparing data for analysis to include- data normalization, filtering duplicate or erroneous data, and creating derived data (e.g., time durations, scaling, conversions, etc.)
• Exploring the data set by creating visualizations for various trends, co-relations, and dependencies
• Creating the ability to gather insights from the data that can be used to feed further steps to meet business goals
  • Usage insights- such as usage patterns with respect to time of the day
  • Performance insights- such as steamer temperature drops with respect to door status open time
• Building algorithms
  • Analytical – algorithms based on gathered insights to validate & correct the insights by applying insights to the larger dataset
  • Predictive Deployment and Evaluation- algorithms based on outcomes of analytical algorithms which will predict error or usage conditions and warn users
  • Control Deployment and Evaluation- algorithms based on outcomes of analytical and predictive algorithms which will correct or control the error conditions automatically

• Evaluation and reporting of results of the deployed algorithms to check the achievement of business goals and to further train the algorithms