实例探究 > DataOrbis Creates Future-Focused Data Partnership to Serve its Global Clients

DataOrbis Creates Future-Focused Data Partnership to Serve its Global Clients

公司规模
Large Corporate
产品
  • Pyramid Analytics
  • SMART DATA platform
技术栈
  • Data Visualization
  • AI-guided Analytics
  • Predictive Analytics
实施规模
  • Enterprise-wide Deployment
影响指标
  • Customer Satisfaction
  • Productivity Improvements
  • Cost Savings
技术
  • 分析与建模 - 预测分析
  • 分析与建模 - 实时分析
  • 功能应用 - 远程监控系统
适用行业
  • 消费品
  • 零售
  • Professional Service
适用功能
  • 商业运营
  • 销售与市场营销
  • 质量保证
服务
  • 系统集成
  • 软件设计与工程服务
  • 数据科学服务
关于客户
With offices in several major gateways, DataOrbis provides data and technology solutions to some of the world’s best-known consumer brands. A Smollan-owned company, DataOrbis works across 5 continents, primarily serving manufacturers, retailers, and distributors within the FMCG (fast-moving consumer goods) industry. DataOrbis’ goal is to transform companies into businesses driven by data. They help their clients implement solutions to track performance and ultimately help them make better business decisions. DataOrbis’ solutions range from data management and visualization services to solutions that enable data-driven execution in stores. “Our clients rely on our data,” says Nicola Lapage, DataOrbis’ head of insights. “It’s an integral part of their business, and they use it to make the most important decisions in their business. So, it’s vital we have a tool that delivers well.”
挑战
DataOrbis was finding it difficult to process the amount of data they needed to process. With their previous data visualization platforms, they constantly had to break up reports into multiple subsets of data to handle the amount of data in one report. Even worse, their clients were having to log into multiple reports to see their information. The company was also challenged by the number of small report edits and changes they received from clients monthly. Minor adjustment requests came in all the time, resulting in a frustrated data visualization team who was spending valuable time making those changes. “We were finding the solution we had couldn’t quite meet the requirements of all our end users,” Nicola says. “We have quite a wide variety of end users we report to, CEOs of companies, as well as field sales and merchandising teams in stores. We need a tool and a set of reports that cater to both those kinds of end users — and everyone in between.”
解决方案
DataOrbis embarked on an intensive POC process, considering myriad tools in the data analytics market. In the end, they chose Pyramid Analytics primarily for the breadth of its capabilities, its unmatched flexibility in providing robust data access to their customers — wherever and whomever they may be — and its superior customer support. “The reason we chose to go with Pyramid is we felt they could offer what we see as a true partnership with our analytics provider,” says Tamryn Le Riche, DataOrbis’ customer success manager, who has overseen the migration to Pyramid. “The support they offer us is best in class and the licenses are much more cost effective. Their innovation in the advanced analytics and predictive analytics space is also impressive and this is where we see most of our business growth coming from in the future.” DataOrbis uses its custom built SMART DATA platform to house and process its data. The SMART DATA platform ingests, integrates and stores the data. Once harmonized this data is pushed into the Pyramid Analytics platform’s data models. DataOrbis builds all its front-end calculations and dashboards in Pyramid.
运营影响
  • Multi-source client data is now housed in a single solution with the ability to report down to a store or product level.
  • Best-in-class support surpasses client’s expectations, while cost of ownership decreases.
  • End-users can access the visualized data and make simple report changes, resulting in delighted DataOrbis clients, and a data visualization team that can spend their time on more value-adding activities.
数量效益
  • The amount of time it takes to get updated day-of data into reports has decreased by 50%–75%.

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