{"id":28329,"date":"2022-11-25T12:44:27","date_gmt":"2022-11-25T07:14:27","guid":{"rendered":"https:\/\/blazeclan.com\/studies\/data-lake-solution-with-400-metrics-helped-customer-make-incisive-decisions-in-real-time-2\/"},"modified":"2023-03-02T11:37:00","modified_gmt":"2023-03-02T06:07:00","slug":"data-lake-solution-with-400-metrics-helped-customer-make-incisive-decisions-in-real-time","status":"publish","type":"case_study","link":"https:\/\/blazeclan.com\/anz\/studies\/data-lake-solution-with-400-metrics-helped-customer-make-incisive-decisions-in-real-time\/","title":{"rendered":"Data Lake Solution with 400+ Metrics Helped Customer Make Incisive Decisions in Real-Time"},"content":{"rendered":"

The customer is a travel and lifestyle company. It gives the members with access to the widest network of travel and lifestyle partners. This means the members will earn rewards across everything, ranging from airlines and hotel bookings to shopping, dining and more globally.<\/p>\n

The Need for a Data Lake with Extended Metrics<\/h3>\n

There are 10+ categories based on which a loyalty member can accrue\/redeem miles. The data for all these categories are stored in their on-premise database without being utilized. Also, there were intricate challenges in gathering data from third-party vendors. The data is needed to be stored in a common repository, as a data lake, and then connected at a member level. This common repository is necessary to get a holistic view of all loyal customer interactions and drive business insights out of the structured or semi-structured data. The customer wants to utilize that data by leveraging their artificial intelligence platform to perform advanced analytics on that data for making informed decisions.<\/p>\n

The Solution<\/h3>\n

To achieve the roadmap of the customer, Blazeclan helped them create, gather and operationalize their scattered data into a single Cloud-based Data Lake Solution. This will enable their business users and data scientists in deriving descriptive and prescriptive insights. Blazeclan set up a data extraction, loading & transformation pipeline for the data ingested from multiple sources. The solution approach also involved creating 400+ metrics (key performance indicators) giving a complete persona of the journey of their 10 million+ loyalty members.<\/p>\n

Ingested Data Sources<\/h3>\n
    \n
  1. Real-time analytics and detailed segmentation across all of the customer\u2019s marketing channels.<\/li>\n
  2. Captured all activities and transactions being done by customer\u2019s member across all 10+ categories.<\/li>\n
  3. Online and offline data from a unified view of audiences were captured.<\/li>\n
  4. Captured the data for the accrual and redemption flight bookings.<\/li>\n
  5. Captured flight search data of the members from their flight platform.<\/li>\n
  6. Stitching (mapping) of the online and offline data.<\/li>\n<\/ol>\n

    The data of the members\u2019 financial transactions on the wallet. Given below is the Data Flow Diagram, which shows how we generated metrics and enabled the customer\u2019s team to drive actionable insights.<\/p>\n

    \"\"<\/p>\n

    Architecture Diagram – Optimized Solution<\/h3>\n

    \"\"<\/p>\n

    Key Benefits Achieved by the Customer<\/h3>\n

    Repository for Everything:<\/strong> All types of raw, structured\/unstructured, cleaned, processed, snapshot, extended metrics are all stored at the same place. This meets the purpose of enriched data being stored at a common pool for the leverage of advanced analytics and insights.<\/p>\n

    Cost-Effective:<\/strong> Serverless and managed services helped the customer eliminate the extra cost spent on on-premise hardware infrastructure and its maintenance.<\/p>\n

    Serves all types of Users:<\/strong> All types of business users like business stakeholders, data scientists, technical users can now access the enriched data stored at a common pool using AWS services.<\/p>\n

    Robust Ecosystem for Next-Generation Solutions:<\/strong> For all the advanced next-generation technology, this ecosystem will serve as an input to drive business solutions and accelerate growth.<\/p>\n

    \"\"<\/p>\n

    Tech Stack<\/h3>\n\n\n\n\n\n\n\n
    Amazon S3<\/td>\nAWS Lambda<\/td>\nAWS EMR<\/td>\n<\/tr>\n
    AWS KMS<\/td>\nAWS Glue<\/td>\nAWS CloudWatch<\/td>\n<\/tr>\n
    API Gateway<\/td>\nRDS Aurora<\/td>\nAWS Step Function<\/td>\n<\/tr>\n
    Amazon SQS<\/td>\nAmazon SNS<\/td>\nAWS Secret Manager<\/td>\n<\/tr>\n
    AWS DynamoDB<\/td>\nAWS Athena<\/td>\nAWS IAM<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

    The customer is a travel and lifestyle company. It gives the members with access to the widest network of travel and lifestyle partners. This means the members will earn rewards across everything, ranging from airlines and hotel bookings to shopping, dining and more globally. The Need for a Data Lake with Extended Metrics There are […]<\/p>\n","protected":false},"featured_media":12283,"parent":0,"menu_order":0,"template":"","case_category":[3876,3921],"case_tags":[4226,4151,4106,4161,4146],"class_list":["post-28329","case_study","type-case_study","status-publish","has-post-thumbnail","hentry","case_category-big-data-analytics-anz","case_category-datalake-anz","case_tags-automation-anz","case_tags-big-data-anz","case_tags-cloud-anz","case_tags-data-analytics-anz","case_tags-data-lake-anz"],"acf":[],"yoast_head":"\nData Lake Solution Helps Make Incisive Decisions in Real-Time<\/title>\n<meta name=\"description\" content=\"Data Lake Solution with 400+ Metrics Helped Customer Make Incisive Decisions in Real-Time\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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