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The Denodo Platform has improved our agility around data engineering, data integration, and data processing, while enabling us to innovate more quickly with a 360-degree view of our products.”
Rupesh More
Former Enterprise Data Architect

Konecranes is an industry-leading group of lifting operations, with a broad customer base that includes engineering and process industries, shipyards, and ports. The company delivers productivity-enhancing solutions and services for lifting equipment of all sizes. In 2021, the group had a market turnover of $3.2 billion and employed 16,500 people in 50 countries. Konecranes considers improving the safety and productivity of its customers and their operations as its main goal, for which it leverages data and analytics to report on, monitor, and optimize the performance of millions of lifting devices.

Empowering Analytics

Konecranes had been struggling to leverage data holistically and optimally from its various data sources with different data formats. The traditional extract, transform, and load (ETL) development for data integration and processing involved a long development cycle, resulting in a longer waiting time for data access for data science projects. Even though ETL processes were available at the company, this did not turn out to be the most effective method for complex data integrations across cloud and on-premises data sources, and it did not support reusable assets for data processing and transformation.

To successfully cope with its data challenges and enable enterprise-wide digitalization and data analytics, Konecranes devised a strategy to build a next-generation data platform, internally referred to as the Konecranes Data Platform. The architecture of this platform is centered around a logical data fabric built on the Denodo Platform, which consists of aggregating data from different kinds of data sources like ERP, CRM, supply chain, or operational data from the factory floor, and then making the processed form of that data available to a variety of reporting tools in the needed formats and at the required speed and automation level. The processed data is not only used to serve traditional reporting requirements such as BI but also the new needs of self-service, data science explorations through APIs, and the micro-service-driven development of digital services.

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