Gathr

A leading US-based fintech company with a development center in India was facing difficulties in monitoring process compliance across its numerous ongoing projects. The company lacked centralized visibility to assess compliance across enterprise projects. Manual tracking of every commit, pull request (PR), and peer approval was untenable. It was also challenging to track if developers used the predefined tools and procedures for version control, source code management, peer reviews, etc.

A leading U.S.-based wireless and telecommunications service provider wanted to improve call center performance, increase customer satisfaction, and have greater insight into the activities of its call center representatives. To achieve this, the Fortune 50 Company wanted to analyze the desktop activities of the call center representatives around the clock. The client wanted to monitor desktop activities in real-time while the representatives are on duty. From an operational perspective, this meant creating a centralized system where operations personnel would be able to track idle time, track what websites are being used for how much time, track outlook usage, and track various applications being used on the desktop. The client also wanted to track desktop activities when the agent are on call, not on call, and on call and kept customer on hold.

The customer, a US-based Fortune 100 broadband connectivity company and cable operator serving more than 30 million customers, was facing several technical and business challenges with their existing data workflow. They received data from multiple sources that was fed into an SFTP server. After ETL was performed, the data was read by an Informatica workload and persisted to their Teradata data warehouse. Business analysts then accessed this data and ran queries to gather insights. The client wanted to make a strategic shift to the cloud to enhance scalability, reduce costs, improve query performance, realize a unified view, simplify management, seamlessly integrate with other cloud-native services, and automate workflows for CI/CD.

A leading cloud-based communications technology company that offers hosted contact center services needed a way to improve performance metrics, eliminate the guessing game of problem resolution and dramatically increase customer satisfaction. To attain this, they wanted a unified view into their infrastructure that would allow them to monitor calls in real-time. In the battle for consumer loyalty, the contact center is at the heart of customer care strategies. It is the central hub of communications and customer service for enterprises and is responsible for the vast majority of consumer interactions and service-related transactions in today's market. The customer service touch points—such as resolving a complaint, taking an order, renewing a warranty or up-selling a product—are pivotal in accomplishing strategic business objectives.

Insider threats are a significant cybersecurity risk to banks, becoming more frequent, harder to detect, and more complex to prevent. These threats can include employees mishandling user credentials and account data, lack of system controls, responding to phishing emails, or regulatory violations. The bank's traditional threat detection relied on setting static rule-based alerts on users' activities, which resulted in a high number of irrelevant flags when applied to thousands of users. The bank's current relational technology stack was proving to be too expensive and inflexible, limiting the bank to processing data from only 15-20% of hundreds of sensitive customer-facing and operational applications. It took almost 2 years for the solution to move a single use case to production, making it difficult for the bank to scale out.

A Fortune 500 American multinational automaker was seeking a solution to predict faults in their auto parts to proactively ensure fault-free production, thereby saving maintenance time and improving the customer experience. The company faced several challenges. Data was being generated from multiple discrete systems, all of which had to be processed simultaneously to get a complete picture. The data was in different formats like JSON, CSV, and other proprietary formats. The cutting tools had to be replaced before they reached end-of-life, affecting the production quality. Therefore, the automaker was looking for a solution that would predict in real-time, giving them enough time to replace the waned cutting tools. The data collected from multiple systems had several quality issues and missing records. This data had to be formatted, cleansed, and prepared before it could be fed into the predictive analytics models. The manufacturing unit had thousands of machines generating millions of events every minute. The automaker needed to process this massive amount of data in real-time using a single solution and shared infrastructure. Real-time alerts to floor operators and the downstream application was a crucial component. Any failure or delay in these alerts had a direct impact on the quality of parts produced.

The company, a leading media and entertainment entity with a presence in over 150 countries, was facing challenges in managing its applications, including a newly launched subscription-based streaming application. The company's internal DevOps team was responsible for managing these applications, but the company wanted to improve visibility into performance, identify areas for improvement, and gauge customer experience. However, they lacked a standard framework to measure DevOps success and relied on monthly manual reports to understand the team's health and performance. This approach had limitations in analyzing DevOps data and metrics. Furthermore, frequent bugs and a longer time to resolve issues led to a poor customer experience.

A leading media and entertainment company with a presence in over 150 countries and a headcount of over 3000 employees faced several challenges in managing their applications. They had recently launched a subscription-based streaming application, in addition to their existing apps that required frequent updates. Their internal DevOps team was responsible for managing these applications, but the company wanted to improve visibility into performance, identify areas for improvement, optimize costs, and assess customer experience. They lacked a standard framework to measure DevOps success and relied on monthly manual reports to understand the health and performance of the team. They also faced limitations in analyzing the DevOps data and metrics. Frequent bugs and a longer time to resolve issues led to a poor customer experience.

The client, a major telecom company providing nationwide telecom services, was in need of a system that could perform real-time, multi-lingual classification and sentiment analysis of text data. They were looking for a solution that allows storing, indexing, and querying PetaBytes (PBs) of data with a very high throughput. The critical requirements included the ability to ingest and parse a high volume of data [250M (15 TB) records/day] of varied types such as weblogs, email, chat, and files. They also needed to apply real-time multi-lingual classification and sentiment analysis with very high accuracy (four nines), store metadata and raw binary data for querying, and meet a Query SLA of 5s on cold data.

The customer, a cable TV and telecom provider operating in over nine US states and serving nearly 5 million customers, was facing intense competition from traditional players and new digital players like Netflix, Amazon Prime, Roku, and more. These digital players were using predictive analytics and machine learning to deliver highly personalized, contextual, and content-driven interactions. The customer was experiencing a steady decline in demand and high churn rates. They lacked proactive and contextualized customer service, with their data analytics restricted to a historical analysis of a limited set of monthly calls. The absence of real-time dashboards and lack of customer data enrichment prohibited contextualization. Their technology stack was not equipped to analyze large volumes of disparate data in real-time.

Enterprises need to analyze large volumes of data from various sources in real-time to make strategic business decisions. They often create custom frameworks to process these large data sets, which can lead to technical debt and dependency on IT teams who understand the historical choices made during the initial platform designs. This can risk impacting businesses and increase customization costs. The customer, a leading security and intelligence software provider, wanted to modernize their existing big data applications. They were looking for an easy-to-use and scalable solution that could process 1.5 billion transactions generated per day from multiple real-time feeds. They needed a near-zero-code solution for ETL processing jobs that could perform real-time ingestion and complex processing, ensure high throughput while indexing and storing, and detect anomalies in transactions.

The auto insurance industry is increasingly investing in connected car solutions to offer simplified, transparent, and flexible products and pricing options. Usage-based insurance is a voluntary, behavior-based insurance program that uses analytics to create highly personalized and dynamic plans based not only on the driver’s age and other demographics, but also accounts for the driver’s behavior, risks related to a vehicle, and external factors such as driving conditions and weather. A leading auto insurance provider chose Gathr to ingest, transform, enrich, analyze and store automotive telematics data in real-time to build an end-to-end analytics application for driver profiling & individual risk assessment, and subsequently offer dynamic, usage-based, plans to its customers.

The company, a leading IT services and consulting provider catering to B2B sales, marketing, and customer success departments, was facing difficulties in optimizing and making the most of its cloud investments. The challenges included expensive VM sprawl, limited visibility into resource consumption and costs, and a lack of readiness for migration to a containerized environment. The company has a significant presence, with over 3000 employees and operations in over 170 countries. However, these challenges were hindering its ability to fully leverage its cloud infrastructure and achieve cost-efficiency, scalability, and availability goals.

The company, a global leader in insurance broking and risk advisory with a presence in 130 countries and a headcount of 45,000, was facing difficulties in monitoring its cloud resources. The challenges included a lack of centralized cloud cost monitoring, control over dangling and idle resources, visibility into cloud costs for development teams, and anomaly detection and predictive capabilities.

The airline was experiencing a massive growth of high-speed data coming in from various online and offline customer touch points and operational systems; nearly 5TB of data was coming into its systems every day at an input data velocity of 7,000 events/second. The massive volume of data limited data searches to only two days of data logs; preventing analysis of customer behavior patterns and anomaly detection based on a longer and more relevant time window. The traditional technology stack was unable to manage the rapidly growing volume of high-speed data.