TimeXtender a Finalist for 2020 Microsoft Partner of the Year
BELLEVUE, Wash. — July 13, 2020 — TimeXtender today announced it has been named a finalist of Data Estate Modernization 2020 Microsoft Partner of the...
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Greg Horton, Head of Global Marketing, TimeXtender June 25, 2019
We are extremely pleased to share that TimeXtender has been named a finalist in the 2019 Microsoft Data Estate Modernization Partner of the Year Award.
The Microsoft Partner of the Year Awards are presented in several categories, with winners chosen in 2019 from a set of more than 2,900 entrants from 115 countries worldwide. TimeXtender was recognized for providing outstanding solutions and services in Data Estate Modernization.
The Data Estate Modernization Partner of the Year Award recognizes partners that deliver outstanding solutions based on the Microsoft Data Platform. Finalists have empowered a customer with a solution that takes advantage of the leading capabilities of Microsoft data platform across on-prem and Azure for mission critical applications with a focus on high availability, performance or high scale or hybrid applications leveraging SQL Server and/or a set of Azure Data services.
In our award submission, we highlighted how Komatsu Australia used Discovery Hub to build a modern data estate on Azure SQL Database Managed Instance.
Komatsu Australia wanted to use data to differentiate themselves as a company while helping customers increase productivity, do their jobs better and increase their ROI on Komatsu machines. To unlock the transformative power in their data they needed a highly efficient platform with which to modernize their data estate while aggregating, making sense of, and dealing with exceptions in the data. They turned to TimeXtender and Microsoft to rapidly build a new, scalable, modern data estate on Azure SQL Database Managed Instance that also leveraged Azure Analysis Services, Azure ExpressRoute and Power BI.
Construction is an extremely data-rich industry, and in Australia alone, Komatsu has more than 30,000 machines in daily operation that stream IoT data on productivity, machine condition, and other factors back to head office. These machines each have thousands of parts in a bill of material along with sales, current inventory and other data points, contained in a Microsoft Dynamics AX system. While still more data is collected for each machine through hydraulic fluid sampling during active maintenance.
“The fuel for innovation and digital disruption in our business and our industry is data,” says Todd Connolly, General Manager, Construction Solutions at Komatsu Australia. “But data itself is not enough—we need to turn that data into actionable information like recommendations on when to replace parts to optimize machine operation.”
With this statement as a starting point, data from three business critical systems were identified as a must-have, and with the possibility of additional internal and external data sources needing to be added, Komatsu’s modern data estate needed to support future growth from both a source and complexity perspective. In addition, it was desired that this solution would demonstrate rapid time-to-value for the business. TimeXtender’s Discovery Hub proved to be the best-suited data management platform because of its ease of use, Microsoft-aligned cloud strategy and powerful Dynamics AX adapter.
Discovery Hub was used to seamlessly connect to all the data sources, transform and move data into an Azure SQL Database Managed Instance, build data models and then catalog and document data for analytics and business intelligence. From there, it was used to build, publish and populate semantic models with data in Azure Analysis Services, through which insights are made available for reporting and analytics in Power BI, giving employees access to all relevant data from multiple sources. “We were able to deploy our TimeXtender solution into production on Azure SQL Database Managed Instance in a matter of weeks. We immediately realized a 49% cost savings and a 25-30% performance improvement, and the promise of applying artificial intelligence through machine learning to our data is an exciting opportunity for us,” said John Steele.
With over several terabytes of data, and volumes growing by around a million records per day, Azure SQL Database Managed Instance was determined to be the best choice in terms of scalability, cost, and performance. Easy access to current, trustworthy data also enhances decision making, explains Nipun Sharma, Analytics Architect, Business Technology & Systems at Komatsu Australia. “We used to run a stock report once a week on Saturdays, because it took 8 to 10 hours—we just couldn’t do that on a weekday,” he says. “Now that employees have data available on a daily basis, they can analyze it and better manage and move around inventory in response to market and customer needs.”
“This consolidation also makes it possible to import that data into platforms where we can work with it in ways that we couldn’t do on a mainframe. The accessibility and visibility we have now makes it possible to perform advanced analytics and extract more value from the data.” says John Steele, General Manager, Business Technology & Systems at Komatsu Australia.
With a successfully modernized data estate laid down in record time and users gaining insight from the modern data warehouse and analysis models, this data asset is now being further leveraged as part of Komatsu’s AI strategy. Data from the newly modernized data estate, feeds into the supply chain optimization Machine Learning model to better plan and predict availability of parts in remote regions based on machine data, sales data, current inventory levels and maintenance data.
While the combination of Discovery Hub and Azure Data Service provides a major technical benefit, the biggest business benefit lies in the fact that the solution has empowered Komatsu’s decision makers and enabled instant access to data for real-time visibility into operations. This has significantly improved accessibility to data and resulted in more informed decision making.
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