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Automated process of interactive queries with data analytics






Santiago, Chile

Use Case

Data Analytics


Kaufmann is a leading company in the automotive segment, maintaining its leadership in service and quality in the transportation industry for over 65 years. They have a regional presence with operations in Chile, Peru, Costa Rica, Nicaragua, and Panama. Kaufmann caters to the requirements of automobiles, light trucks, semi-trucks, and heavy trucks, as well as specialized vehicles and equipment, minibuses and vans, buses and bus chassis, and various mobility services and maintenance solutions for different industries.

Business Challenges

Kaufmann's mission is to always be close to its customers and provide them with the highest quality of service. To achieve this, advanced technology is used to efficiently manage the fleet, primarily in the spare parts line.

Kaufmann needs to centralize information from multiple internal sources such as business units and affiliated companies, as well as external sources like marketing agencies, digital metrics, insurance companies, and civil registries from different countries.

Another challenge is to have quick and scalable availability of business data in a structured format that allows for practical querying, as several business processes rely on this availability to make informed decisions.

In response to this situation, ARKHO proposed:

The creation of an automated process for interactive queries to facilitate data analysis from Amazon S3, enabling Kaufmann to monitor different points in its value chain, such as:

  • The fleet's history to know its whereabouts, mileage, and proactively anticipate possible failures.

  • Future implementation of intelligent scheduling for fleet maintenance.

  • Ability to reduce the time involved in executing commercial integrations and user interactions.

  • Reduced operational costs.

  • Create assets and IT technology in an automated manner to keep operational costs to a minimum.

  • Support for business metrics and analytics. Integrate third-party tools to provide key metrics for business operations and services.


ARKHO developed a data extraction process using various AWS services (Lambda, DynamoDB, Glue, Step Functions) that allows cleaning and structuring the data in typical data analytics stages, making it available in a user-friendly tool for exploration using Athena.

All the information can be connected to Kaufmann's SaaS system and enables feeding different data models for business decision-making and predictive analytics purposes.

  1. The initial user from Kaufmann utilizes SFTP to upload CSV files.

  2. Data ingestion and transformation: Using SFTP, data is obtained and stored in raw format in an S3 bucket. A Lambda function is triggered by events to extract, transform, and load the data into Kaufmann's Data Lake. In this step, new KPIs are created, which are used by Quicksight dashboards and date-related information. The goal is to eventually have historical sales data per dealership.

  3. To maintain dealership data compliance with standards, governance, and security, all data is added to Kaufmann's Data Lake.

  4. After the data is successfully analyzed, new data is inserted into Athena tables, which feed into Quicksight. Quicksight's SPICE data is updated through a scheduled daily refresh at 5 a.m.

  5. After the data is loaded into Quicksight, a scheduled email is sent to end users.

  6. Security is considered at all levels. CloudWatch is used to track resources, particularly if the Lambda function encounters any errors. CloudTrail is used to monitor user activity, IAM roles are utilized to manage access and user policies, and MFA is implemented to comply with security standards.

  7. The end users consist of dealership users and data analysts from Kaufmann's team. They can review these dashboards to analyze their daily performance.

Results and Benefits

Kaufmann has achieved an efficient solution that integrates centralized information relevant to the business with user-friendly tools for the end user. This solution enables agile analytics with timely responses to demand.

With this solution, Kaufmann has been able to predict and detect the needs of its customers for spare parts in a timely manner, ensuring that they have the necessary stock to maintain excellent customer service.

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