The Company contacted our data consultant to provide a Data Integration and Analytics assessment for the CFO and CIO. The first step was to collaborate with executives to create a roadmap and identify what data was needed to achieve their analytics requirements. This included evaluating existing technical architecture, data integrations and data stores. Next we identified the necessary data to provide a clear view between marketing and finance by tying it to CRM customer data to provide the right insights for critical decision making.
Since 2017, the firm’s US operation faced a significant change to its digital transformation strategy as they were acquired and needed to conform to the Corporate strategy. They sought out external expertise to advise them on ways to use technology and IoT data to allow production leads to track and adjust the process as needed. They were looking for a proactive real-time approach to utilize their manufacturing and quality data to correct their manufacturing process deficiencies when a problem was detected. To implement this AI-based approach, our data consultant had to identify and analyze all the possible data sources during the manufacturing process and design a centralized data extraction, transformation, and load (ETL) process across the entire manufacturing system nodes.
The company’s management approved a digital transformation strategy and budget to grow their data practice and data analytics for customers’ asset management. The goal was to increase customer retention and grow the preventive care side of the business. The CFO brought in our data consulting expert to advise them on ways to use data and technology to track and analyze customer’s needs. They wanted to offer a direct way for customers to add services and emergency requests, as well as show the before and after results of the services received. To implement this data-driven approach, the data expert needed to identify and analyze all possible data sources during the servicing process, contract SLA’s and then design a centralized data extraction, transformation, and load (ETL) process across the multiple applications and environments used to track services and orders.
When a leading global retailer in the health and wellness industry experienced challenges in gathering and accessing information, they needed help creating a single source of truth in order to make better strategic and operational decisions. Internal data was dispersed across various different sources and was only available to a few high-level executives in the company. That made it very difficult for others within the organization to get their questions answered on time, if at all.
Gett.com is the largest provider of on-demand mobility in Europe — far surpassing Uber — across four countries, 100+ cities, and servicing 7,000 global corporations. Gett.com wanted to expand upon their current B2B services with a new and user-friendly platform, so they turned to Sphere Software to help build One Transport from the ground-up. One Transport is a web ordering and management platform that provides companies across 1,500 cities and 35 countries, access up to 200,000 different vehicles, from black taxis to executive cars.
Lands’ End was trying to find a more effective way to extract actionable insights from terabytes of customer data. Their massive operational database lacked the functionalities required by their marketing team to produce the analytics needed to reach new customer segments.
Rebel wanted to develop chat bots that would not only assist their internal customer service representatives but also enhance their customer’s experience while lowering costs. However, before Rebel’s chatbot could assist customers, they needed to first enrich its knowledge-base and train the chatbot by utilizing the expertise of their customer service agents.
Proclivity Media wanted to build a prototype of its advertising technology analytics platform. With an algorithm in mind, they needed a team with the analytics experience necessary to build a scalable front and back-end system that would allow them to establish proof of concept with a custom analytics prototype.
Gett’s current online system allows them to accept and process delivery orders from their vendor partners. However, Gett needed to develop an API-based service that accepts orders from their partner’s disparate systems and places them into the Gett system. In addition, this service was required to be implemented as an isolated SaaS with a reporting interface, provide statistics on KPIs, handle 100,000 orders per day, and have a 99.8% uptime.
An angel investor was approached by a five-person start-up in Tel Aviv with an innovative social media data harvesting application. The investor asked us to evaluate the technology and team competence. Since the desired seed investment was relatively small, the investor asked us to move much quicker than usual and present an informal recommendation within three days.