Aravo Solutions

InMobi, a company specializing in targeted mobile advertising, was grappling with the challenges of managing a complex legacy infrastructure and a multicloud data warehouse. The company's data processing requirements had escalated to 20+ terabytes per hour, leading to skyrocketing costs and the creation of data silos that hindered collaboration and data sharing. The proprietary nature of their multicloud data warehouse also posed significant challenges. InMobi's existing system was overly complex, prone to outages, and extremely costly to scale. The company realized that their current system was slowing down their ability to innovate and was keeping their engineering resources tied up in maintenance tasks. InMobi sought a single system that could address multiple issues, consolidate their disjointed systems into a single platform, and free up their engineers to focus on higher-value tasks such as developing machine learning and large language models.

Johnson & Johnson, a global consumer goods and pharmaceutical provider, faced significant challenges in managing its supply chain data. The company's growth through acquisitions led to a fragmented data system with disparate priorities and unique configurations. Data was largely being extracted and analyzed manually, limiting opportunities for speed and scalability. The disconnection was negatively impacting customer service and impeding strategic decision-making. The company also faced the challenge of optimizing inventory management and costs on a global scale, which required accurate and abundant data. The inability to understand and control spend and pricing could lead to limited identification of future strategic decisions and initiatives, potentially missing the opportunity to achieve $6MM in upside.

Fastned, a pioneer in the fast-charging infrastructure for electric vehicles (EVs), faced a significant challenge as the EV industry grew exponentially. The company started with a small footprint of charging stations and expanded as the industry took root. However, as the size of Fastned’s data grew exponentially, the company needed to migrate away from its legacy system, Redshift, which was resource intensive to scale and expensive for democratizing insights across its teams. Fastned was also faced with the pressures of building more charging stations and ensuring a continued superior user experience, which became increasingly challenging to deliver with its existing tech stack. The company's data team quickly realized that with an increase in data collection points across its network, the legacy AWS Redshift data warehouse would not be able to meet its growing needs. And while Tableau was being leveraged to deliver insights, wide-scale analytics was hindered by high costs.