WinWire

One of the main challenges was that the company required a centralized data warehouse for unified reporting and analytical requirements. The data was not consolidated properly and its structure was not suitable for user requirements. Besides, reporting process was extremely slow. Individual systems had expanded to the point that information was no longer stored and presented in the centralized manner that could facilitate efficient organizational decisions across the company as a whole. It was also becoming obvious that a data warehouse should better enable internal and external user access to data and increase the performance of analysis and reporting activities. To evolve its offering from simple data feeds to self-service BI, the company needed a new robust platform that could enable users come out of excel mindset and could take them to exploratory mode for better and faster decision making. The objective was to integrate the line of business applications (On-Premise and SAAS).

The medical management solution provider was looking to transform its Pharmacy automation solutions, automate their medication dispensing processes, improve inventory control, eliminate errors during medication dispensing in the pursuit to achieve better patient outcomes, and free up the pharmacists for more clinically focused initiatives.They wanted to adopt an intelligent solution that automates workflow, streamlines pharmacy operations, and improves safety to optimize the entire medication management process. Their process was currently disconnected and time-consuming and leading to:Medication-related problems account for 40% of hospital re-admission.75% of pharmacy time goes into non-clinical activities.There was a dependency on manual and inefficient processes that were prone to errors. Medical dispensing and central pharmacy were not automated and were not regulated.

The company had over 10,000+ suppliers spread across the globe. The finance team of the company handles queries from these suppliers related to payments and purchase orders. As the company started outgrowing, the accounts payable team started spending the majority of their time answering the queries by suppliers leading to operational inefficiencies & expenses, poor supplier relationships, and more extended query time management.Suppliers expect an intuitive and seamless experience. When an organization fails to engage its suppliers properly, they run the risk of inefficient supply operations and high processing costs.The company was looking to build a solution to automate the process to optimize resource utilization and improve supplier relationships.A leading electric car manufacturer which is already using next-gen technologies wanted to harness the power of Chatbot-driven Artificial Intelligence and Cognitive Services to replace FAQs that will be managed by finance/human agents.

The company needed a more streamlined approach that would facilitate their employees and users to avail information from their help desk team. The company identified these as areas that need improvement and could result in a better employee experience and productivity.The company employees/users reach out to the help desk team for IT support. The help desk team responds to a user request by sending a relevant article from the existing knowledge base.In the last few years, the company has witnessed significant growth in users and their queries. The number of monthly tickets created by the help desk team in Service Now has increased substantially and is expected to grow further impacting the team’s productivity, and support cost.The company needed a way to lower the number of tickets hitting the help desk. However, what they didn’t want is to reduce the quality of the service.A well-trained conversational bot was required to automate responses for FAQs leveraging the power of Artificial Intelligence (AI), Natural Language Processing, and Cognitive Services.

The customer had embarked on a multiyear initiative focused on moving their Big Data platform from Cloudera Hadoop On-Prem instance to Cloudera Data Platform (CDP) on Azure. As a first step, they wanted to explore the prioritized MapReduce jobs in the current state and consider migration to Spark before moving the workloads to Azure Cloud.They had initially created a solution with Hadoop Map Reduce engine and Hive Queries (HQL). The current setup had the following challenges: Slower code execution speedHigher storage requirementDifficult to maintain workflowsThe newer solution they envisioned should address all issues mentioned above and wanted a revamped approach to processing Big Data. They were looking for a partner that could support them in converting identified MapReduce Jobs to Spark as they wanted to reduce the execution and processing time of Jobs as it was impacting their business performance.Eventually, it will enable them to move their Big Data platform from Cloudera Hadoop On-Prem instance to Cloudera Data Platform (CDP) on Azure.