Rash Curtis & Associates, a collections agency, was grappling with the challenge of losing significant agent call time daily to repetitive tasks such as comprehensive note-taking. The agents were required to swiftly move from one call to another, build relationships with consumers, and document each call meticulously, all without any assistance. This situation often led to a conflict between the need for detailed note-taking and maximizing call time within a day. Upon learning about Prodigal’s ProNotes, an AI-driven, real-time call summary assistant, Rash Curtis & Associates hypothesized that automated call note transcription could potentially reduce collection costs, standardize after-call notes, and proactively route calls to the most suitable collections teams.

HKScan, one of the largest food companies in Northern Europe, has been working on sustainability at the group level for a long time. The company has ambitious targets under its corporate responsibility programme, including achieving a carbon-neutral food chain by the end of 2040. However, the company faced challenges in collecting and managing Environmental, Social, and Governance (ESG) data across its 17 production units spread across Finland, Sweden, the Baltic States, Denmark, and Poland. The EU directive on the reporting of non-financial information, which came into force in 2017, necessitated more systematic ESG data collection. Previously, HKScan collected data manually using Excel spreadsheets, a method that was not only rigid but also prone to errors, especially given the size and geographical spread of the organization.

When David Ashirov joined Freshly, a prepared meal delivery service, the company lacked systems to measure and evaluate data. The business was largely reliant on human intuition to gauge its performance. This approach was sufficient for a startup, but as the company grew, it became clear that human intuition could not scale. Ashirov's primary challenge was to build a data fabric, a system that would connect data across the company, allowing for easy querying of every bit of data without unnecessary complications. The goal was to create a single source of information for any business question, fostering trust in the data among the company's employees.

King, a leading mobile gaming company, was facing challenges in managing business incidents in real time. The company's most popular franchise, Candy Crush, along with 17 other games, were in production. The incident management team, responsible for investigating incidents and assessing the losses incurred, needed a tool to detect and address these incidents promptly. The goal was to minimize the impact on revenue by spotting incidents as soon as possible. The team was also tasked with monitoring 18 Key Performance Indicators (KPIs) for each game, which amounted to a significant number of metrics. The challenge was not only to monitor these metrics but also to differentiate them based on the platform, build, and country. The existing tools were not sufficient for this task, as they were not adept at detecting subtle anomalies in business KPIs.

PUMA, a global eCommerce giant, was facing difficulties in monitoring all revenue aspects of their 45 eCommerce websites. They lacked a tool to distinguish what was normal or abnormal across their platforms. For instance, an issue with gift card purchases in Switzerland went unnoticed, which could have resulted in significant financial loss if discovered later. PUMA's Senior DevOps Manager, Michael Gaskin, was interested in Anodot based on the experience he had with another Anodot customer. He understood the challenges PUMA was facing and sought a solution to monitor their websites more effectively.

Tinkoff, Russia's leading fintech innovator, was facing challenges in managing the exponential surge in data due to the rise of innovative fintech and digital banking solutions. The success of their fintech model was heavily dependent on the quality of the customer experience they provided. However, monitoring, managing, and reconciling the vast amount of data was compromising their internal productivity and resources. They needed a technology that would not only guarantee the highest level of customer satisfaction but also ensure operational efficiency across their platform.

Al-Watania, a manufacturing company with over 10,001 employees, was facing significant challenges with their previous Business Intelligence (BI) solution. The system was slow, often delivered inconsistent data, and made reporting unreliable. The process of accessing data and creating reports and dashboards was crucial for the company to monitor its operations effectively. However, the slow data loading and query execution times made real-time dashboard running virtually impossible. The IT team had to rely on multiple SAP applications to develop the requested dashboards, resulting in complicated user interfaces. This complexity made it difficult for business users to navigate the system, further straining the already costly IT resources.

Forterra, a manufacturing company with 1,001-5,000 employees, was facing significant challenges with its finance department's data reporting and analytics. The department was heavily reliant on the IT team to pull even the most basic reports, leading to slow and inefficient processes. The time taken by the IT department to extract, transform, and load data from Oracle EBS and other systems could span weeks or even months. This delay created massive inefficiencies within the team. Furthermore, their existing BI and reporting tools, including Sisense, Cognos, and SAP Business Objects, were generating hundreds of reports across 30 financial dashboards. This made it extremely difficult to simplify, clarify, or consolidate critical data across various sources.

A Fortune 5 company was grappling with the challenge of managing and analyzing data from multiple systems across the organization. The company was heavily reliant on static, homegrown reports that took hours to run, often forcing users to run these reports overnight or during weekends to avoid slowing down critical applications. The process of building new reports using the company’s legacy reporting tools, including SAP Business Objects and Informatica, was time-consuming, taking between 8-12 weeks. These tools only delivered static, graphical reports at specific times to specific people and required considerable maintenance and special skill sets. Furthermore, they did not provide access to queries, drill down or data filtering capabilities for non-technical users.

In 2018, a Fortune 50 media and technology company was grappling with an outdated financial data analytics environment that was nearing its end-of-life. The company's key objectives were to reduce the time taken to run financial reports, increase data availability and refresh rates, speed up development time, and enhance the user-friendliness of the data analytics experience. Upgrading the existing systems would necessitate the addition of 29 new servers, a solution that was not only costly but also insufficient to truly advance the company. The legacy BI tool, primarily used as an extract and pivot tool, was not providing enough value and was even referred to as “the million dollar download tool” among employees, indicating the urgent need for a more effective change.

GC Services, a business process outsourcing company with 30 offices across the US, faced significant challenges in mining their vast data for actionable insights into client and internal staffing needs. Their initial attempt at business intelligence (BI) using traditional data warehousing failed due to the extensive data transformation required, poor performance on large table joins, and the inability to combine data from SQL Server, Microsoft Excel, and text files. The company needed a solution that could quickly aggregate data with near real-time updates and be user-friendly enough for non-technical users to drill down at multiple levels.

Smiths Medical, a global medical device manufacturer, was facing challenges with its legacy data pipelines and processes. The company desired more granular visibility into its business systems to improve its bottom line and identify new growth opportunities. However, the existing data pipelines and processes were not able to deliver timely and actionable insights. The IT team was unable to provide the necessary data-driven insights due to the limitations of the legacy systems. This lack of timely and granular insight was hindering the company's ability to make agile and data-driven decisions.

The world's largest coffee retailer was facing a significant challenge in gaining comprehensive, timely access to data across all its 32,000 locations. The company needed to understand the cost of goods sold at a granular, transactional level, by product, region, store, and week, to improve operations and profitability. The retailer also wanted to enhance visibility into its multiple lines of business to optimize the supply chain and make informed decisions about product placement. Additionally, the company required a solution that could integrate seamlessly with Microsoft, one of their primary technology providers.

The Sales Comp, Finance, and IT teams at a Fortune 100 technology company were struggling with the accumulation, synthesis, and understanding of data needed to support specific queries and manual processes relating to sales commissions. The data-intensive activities were time-consuming and prone to error, with various systems including SFDC, Oracle, Snowflake, and Kafka serving as data sources. The company was heavily dependent on IT resources and a few core developers, leading to weeks of delays in preparing and summarizing data. Reports took minutes or even hours to run due to manual data refreshes and unwieldy SQL Server data schemas. Low replication with the company’s Snowflake data warehouse often resulted in unreliable data. In 2020, a change in the company’s commission data led to an explosion of transactions, exacerbating the situation. The time required to transform data to insights increased to 4-6 weeks, and the average time needed to resolve approximately 3,000 commission disputes ballooned to 40 days. Traditional approaches like increasing staffing were no longer viable solutions, necessitating a fundamental transformation.

Nortek, a global manufacturing and technology company with over 10,000 products and millions of connected systems, faced significant challenges due to its complex business structure and multiple new acquisitions. The rapid advances in IoT and smart home technologies added market pressure to deliver value, which required real-time insights. However, to analyze their Oracle NetSuite ERP data, they had to dump it into Microsoft Excel and then manually apply pivot tables and lookup functions. They also used business intelligence (BI) tools like Tableau or Microsoft Power BI, which required keeping the data warehouse up to date and structured correctly. This process was time-consuming and diverted IT resources from delivering business analytics. The company was in dire need of a more efficient solution.

Shutterfly, a manufacturing and retail company with over 10,000 employees, was facing significant challenges with its inventory management. The supply chain management and procurement team were spending hours manually compiling inventory data to identify part numbers that required attention. This inefficient process often led to urgent issues, such as stockouts, which negatively impacted Shutterfly's customers and put undue pressure on the buyers and planners. The lack of access to accurate inventory data also resulted in E&O expenses, with unnecessary stock costing the company both money and space. The data lag and lack of visibility were also problematic for Shutterfly's leadership, as rigid legacy reports hindered any real understanding of how the organization was performing.

Solutions by stc, a technology company with 1,001-5,000 employees, was facing significant challenges due to siloed data and systems. The company was using multiple data sources to monitor client-side network nodes, each containing millions of records. When a client reported a network issue, agents had to investigate five separate systems to identify the problem, a process that could take hours. This delay often led to exceeding KPIs for response and resolution time. Additionally, the company was using Microsoft SQL Server Reporting Services (SSRS) to develop weekly and monthly reports about each client's nodes and network status. However, each report took two to three days to complete, making it nearly impossible to provide timely updates while clients were on the line.

17LIVE, a live streaming company founded in Taiwan in 2015, has rapidly expanded its presence worldwide, offering a diverse range of content to more than 50 million registered users from 133 countries. However, as the company grew, it faced challenges in maintaining the quality of its streaming services. Initially, 17LIVE deployed its live streaming application on a public cloud platform that didn’t have a data center in Taiwan. As its business in Taiwan grew, the company realized that it needed to enhance the scalability capability of its app infrastructure and lower the network latency of its streaming services. The company also wanted to optimize its operations and the content performance on its live video streaming platform.

33Bondi, a technology, product, and design consultancy based in Sydney, Australia, was faced with the challenge of delivering powerful advertising, marketing, technology, and transactional solutions to clients within tight timeframes. The company needed to manage spiky demand, particularly when sending out large volumes of emails to a user base of approximately 20 million. In one specific case, a bicycle insurance company wanted to launch a web presence in Australia, the United Kingdom, and the United States within 12 weeks, and needed to partner with a prominent social network for athletes. This required 33Bondi to create multinational web presence incorporating full transactional capabilities within a very short period. The company also needed to ensure that the solutions they provided met key security and compliance requirements, and instilled confidence in their clients.

Horizon Leisure Centres, a not-for-profit organization operating premier leisure centres in the UK, faced a significant challenge in complying with the General Data Protection Regulation (GDPR). The organization needed to identify and classify the data stored across more than 500,000 folders and subfolders and protect it in accordance with GDPR requirements. This was a daunting task, as the organization also had to be ready to satisfy requests from data subjects, such as processing restrictions and erasures. The challenge was not only to avoid penalties for non-compliance but also to ensure that the sensitive data was secure and only accessible to management.

Hull College, a large educational institution with over 15,000 students enrolled each year, faced a significant challenge in managing and securing the millions of files containing students' personal information. The college needed to meet GDPR data retention requirements, which required full visibility into all these files and enforcement of appropriate retention periods. The IT team sought to automate the data discovery and classification processes to ensure accuracy and avoid disrupting other IT projects. Additionally, the college needed to promptly address data subject access requests (DSARs). Given the vast number of files, manual search for regulated information was impractical, necessitating automation. The IT team also aimed to monitor unusual activities and receive alerts for potentially harmful ones, such as privilege escalation, attempts to access financial or HR data, and suspicious file deletions.

IDB Bank, a New York-based private and commercial bank, faced a significant challenge in securing customer data and ensuring compliance in an increasingly digital and threat-prone banking environment. The banking sector is a prime target for cybercriminals due to the sensitive and valuable data it holds. Any compromise to this data can severely impact a bank's reputation and customer loyalty. The emergence of progressive digital technologies and their associated security risks further complicate data protection in the banking sector. David Smithers, CIO at IDB Bank, was tasked with transforming the organization into the 'bank of the future' over a five-year period. This transformation presented a major security challenge. To address this, IDB Bank conducted a thorough market and technical review to evaluate potential solutions in the System, File Integrity Monitoring, and Change Control space.

Insight Credit Union was faced with the challenge of securing various types of confidential data stored on their file servers. The credit union needed a solution that could detect anomalous activity that could potentially lead to a security breach. Additionally, they needed a system that could accelerate the investigation and remediation of incidents and operational issues. The challenge was to find a solution that could provide real-time alerts on suspicious activities, identify high-risk accounts, and mitigate potential ransomware attacks.

Lake Michigan Credit Union was faced with the challenge of improving compliance with security protocols in line with federal regulations such as those set by the FDIC. A key requirement was to maintain a comprehensive record of changes made to Active Directory. The credit union also needed to detect and prevent malicious activity across critical systems to avoid security breaches or business downtime. The process of providing auditors with the necessary information was time-consuming, often taking several hours to locate the requested data.

Landspitali University Hospital was faced with the challenge of improving the security of patients’ and employees’ Personally Identifiable Information (PII) and Protected Health Information (PHI) to comply with the General Data Protection Regulation (GDPR) and the Icelandic Data Protection Act. This included ensuring that only authorized staff could access this data. The hospital also needed to secure other sensitive information, such as contracts, medical devices, and research documents, which was crucial for retaining the hospital’s research licenses and ensuring uninterrupted patient care. The hospital had to control 8,500 user objects daily, and the auditing process was time-consuming, taking up to 59 hours per audit.

Lockheed Martin, a contractor/subcontractor of the Department of Defense (DoD), was required to comply with 110 security controls defined in NIST Special Publication 800-171, with a focus on network access and administrator privileges. The company needed to establish a company-wide program to meet the DoD requirements under (DFARS) 252.204-7012. The challenge was to find a highly scalable solution that could integrate multi-factor authentication (MFA) and dynamic privileged access, meet compliance requirements, and minimize impact on ongoing operations. Existing password vault solutions did not provide dynamic privileged access, and building an in-house solution was deemed expensive and time-consuming.

Mission Capital Advisors, a boutique investment bank, faced a significant challenge in proving its reliability to clients by successfully passing at least two audits per month. The company needed to secure personally identifiable information (PII) and financial data of its customers, which was a critical requirement given the sensitive nature of the financial services industry. Additionally, the company sought to gain a better understanding of how users interacted with sensitive data and monitor their activity to prevent misuse. The challenge was not only to secure the data but also to ensure that the access permissions of employees did not exceed their job functions, thereby reducing the risk of data being overexposed or mishandled.

Mott MacDonald, a global engineering, management, and development consultancy, faced a significant challenge in enhancing data management processes across their SharePoint infrastructure. The company aimed to encourage knowledge sharing among its 16,000 employees spread across 150 countries. The task was to automate the discovery, classification, and tagging of tens of thousands of files to enable fast search and provide better service and long-lasting value to customers. The challenge was not only to streamline the data management process but also to ensure that the solution could handle the vast amount of data generated by the company and its employees.

PAL, a leading manufacturer of woodworking machinery, faced a significant challenge in securing their sensitive data and intellectual property. The company relied heavily on Windows file servers, Exchange Online, and SharePoint systems to store and process this data. They also employed third-party developers to support their ERP system, which required close monitoring to prevent any abuse of privileges or incorrect changes that could disrupt operations. The existing two-person IT team had been using manual log monitoring to identify security issues and conduct audits. However, this process was inefficient, cumbersome, and often delivered inaccurate results, leaving them unable to promptly detect incidents. The need for a more efficient and accurate system to audit their critical systems and ensure the accountability of contractors was evident.

PayPoint, a company that processes billions of dollars’ worth of payments each year, recognized the critical need to protect large volumes of sensitive data and improve their IT environment due to a changing threat landscape and regulatory environment. They also faced the challenge of a cybersecurity skills shortage, a common issue in their industry. PayPoint was required to adhere to PCI DSS compliance, which mandates organizations to ensure that various file tracking and monitoring systems are in place. They already had a File Integrity Monitoring (FIM) solution, but as they were going through an IT transformation, they needed to increase their FIM services, which would have significantly increased their costs. Increasing FIM services with PayPoint’s previous provider was problematic due to the high costs associated with processing client transactions.