![]() With bottlenecks throughout its BI deployment, and data and teams siloed, eMAG’s BI department went on a mission to modernize its data and analytics landscape. As such, anyone wanting to analyse the data in a way not covered by existing reports had to export it to Excel and then use VLOOKUP and various other processes to utilize it, making for a protracted and tedious process. ![]() The company also had instances of shadow IT within the organization which were creating divergent processes, applications and forecasting methods – causing a further misalignment between business intelligence (BI) and the business team.Īnother challenge for eMAG was that its data was kept in silos and could only be viewed in BI reports based on extracts. ![]() Previously, eMAG's workflows and applications were based on open-source components which meant each element had its own lifecycle without integration. Keen on applying more Artificial intelligence and Machine Learning to mature its analytics, eMAG found itself hindered by silos and a fragmented landscape. From an architectural perspective, driving insight and value from the growing data estate was becoming increasingly unfeasible. Ever-increasing web trafficįirst and foremost, growing web traffic was proving problematic. The company also understood the importance of freeing up its data scientists so that they could spend most of their time focusing on results and solutions rather than building data sets. EMAG is a fast-growing online marketplace and ecommerce leader in south-eastern Europe headquartered in Romania.įrom its early days as a start-up, eMAG understood that in order to speed up its growth it needed enterprise-grade tools and support.
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