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NACEX, a leading courier service operating in Spain, Portugal, and Andorra, was facing challenges in speeding up parcel deliveries from distribution centers to the last mile. The company, which handles special requirement deliveries such as pharmaceuticals, relied on franchisees with over 1,500 vehicles to sort and deliver parcels. The standard receiving process involved using a dedicated device to scan the barcode on every parcel for verification and sorting. The company wanted to improve scan speeds and the type of information they could record for faster delivery and improved customer service. With last mile drivers scanning over five million barcodes every month, superfast scanning was critical. Additionally, they needed to maintain proof of delivery records for each delivery, which included photographing the parcel and the recipient’s signature, or scanning it. Drivers delivering pharmaceutical items had to abide by stringent temperature requirements and delivery times, and needed to be alerted in advance of any updates.

Capitol Boiler Works, a mechanical service and contracting company based in Springfield, Virginia, was facing a significant challenge in managing its vast inventory of over 5,000 asset items. These items included phones, computers, printers, power tools, and refrigerants. The company was struggling with a high rate of lost and stolen equipment, which was negatively impacting its operations and bottom line. The lack of visibility and control over their inventory made it difficult to hold employees accountable for missing equipment. The company needed an effective solution to increase visibility, manage inventory and assets, and ensure employee accountability for equipment loss.

AtLink Services, a wireless internet service provider based in Oklahoma City, was facing a significant challenge as it grew. The company, which serves over 15,000 Oklahomans and is dedicated to providing exceptional wireless internet service to rural communities, needed to enhance its processes and procedures to manage Inventory and Assets more effectively. The company was looking for a cost-effective way to accurately track the equipment used for providing wireless internet. The ability to track unique and industry-specific equipment was crucial. In their industry, equipment is identified using a MAC Address, and they needed to capture both a Serial # and MAC Address for each piece of equipment in their assets.

Surface mining in the North Bohemian brown coal basin presented a unique challenge due to the presence of unexploded Nazi ammunition from the second World War. The control cabin of the K10000/K74 mining machine was located dangerously close to the excavator, posing a risk of potential explosions that could endanger the operator’s life and health. The mission was to move the controls to a safer location within the mining machine and use a camera system to provide the operator with the same view as from the original cabin. This nonstandard requirement posed a significant challenge for the integrator, as it had to devise a unique custom-made solution without compromising the mining machine’s functions or possibilities.

As datasets get bigger and data sources more varied, complexity increases and work processes become more time-consuming. Demyst clients need help identifying which external data attributes are predictive in marketing, risk, and portfolio management use cases across the vast ocean of external data.

The challenges are as follows:Challenges in conventional B2B trading practice Challenges in validating the trustworthiness of the counterparties Loss of actual and potential revenues Lengthy & expensive dispute resolution Missed sales opportunities & overhead costs for B2B vendors

Litium, a scalable ecommerce platform, was seeking to enhance its customer package by adding a Content Delivery Network (CDN). The company's goal was to provide its B2B and B2C clients with a fast, flexible, and scalable sales platform that could support their growth. However, the legacy CDN providers were not a good fit for Litium's needs as they were cumbersome and time-consuming to manage and service. Furthermore, Litium's customers, who are based around the world, needed a solution that could maintain backend operations in Europe while allowing sales in the US and other parts of the world without any degradation in response time.

LoveHolidays, one of the largest and fastest-growing travel brands in the UK, faced a significant challenge in maintaining its website performance to meet the high demand. With over a million users each month and an average of 4,000 requests every second in 2019, the company needed to ensure a seamless user experience to maintain and increase its conversion rates. The company's previous architecture was composed of various legacy systems, which added an unnecessary level of instability and lack of scalability. Furthermore, the company needed to optimize its hotel images, a critical factor in engaging potential customers and boosting conversions. Slow-loading images not only deterred potential customers but also negatively impacted the site's SEO performance.

Edgemesh, a web acceleration company, was faced with the challenge of delivering fast and reliable websites for their customers, particularly in the ecommerce sector. Their customers required instantaneous response times and high-quality immersive experiences for their shoppers. Edgemesh was responding to 4-5 billion resource-level requests daily and needed a solution that could handle this volume while improving performance. The company's primary focus was on performance, and they needed a solution that could meet this need effectively. The challenge was to find a solution that could execute code at the edge near their customers at blazingly fast speeds, enabling highly personalized and interactive user experiences without any noticeable latency.

VTM, an IT communications provider, was faced with the challenge of ensuring fast, secure, and reliable communication for the mission-critical industries it serves, including content streaming, water management, energy, and emergency services. The company needed to deliver end-to-end interconnection that accelerates machine-to-machine (M2M) data traffic to its customers without going over the public internet. The challenge was to provide low-latency connectivity, ensuring high-performance, secure communication with no interruptions. The industries served by VTM, such as fire, ambulance, and environmental safety services, require continuous communication across their IoT devices, as any disruption in service could potentially lead to loss of life.

Linktree, an Australian SaaS startup, had created a unique niche for itself as a 'link as a service' provider. With over 16 million global users, the platform was experiencing a steady growth in views, reaching close to 1 billion views per month. However, as consumer behaviors shifted, Linktree faced the challenge of maintaining uptime, managing traffic, and delivering profiles with low latency on a global scale. The company was also preparing for a potential traffic spike, as it aimed to support 4 million new users in 3 months. Linktree's small team had big growth goals, and they needed a solution that could improve their security posture and scale seamlessly. They had initially used another vendor's WAF, but it was not comprehensive enough for their needs.

Orange, a leading telecommunications company, was facing challenges in its client services department's data science team. The team was primarily performing ad-hoc analysis and had limited capacity to work on complex machine learning-based projects. The challenges were twofold: tooling and hiring. The existing tool was proprietary and could only be used by statisticians or data scientists, making data access difficult and hindering project initiation. It was also not equipped to support machine learning-based data projects. On the hiring front, Orange struggled to attract fresh, ambitious data scientists due to the tooling challenge. Young data scientists preferred jobs where they could work with open-source tools like Python or R. New hires had to learn the legacy tool, which took months before they could start being productive.

Ansaldo Energia, a nearly 200-year-old company manufacturing generators and turbines for thermoelectric power plants in over 90 countries, faced a significant challenge with data quality. The company's main source of resource planning data, SAP, was being polluted with inaccurate data from various departments, negatively impacting the supply chain. This inaccurate data often led to incorrect orders, disrupting the manufacturing schedule and the planning department’s budget. Guglielmo Mantero and his team at Ansaldo Energia were tasked with creating a set of KPIs to ensure the accuracy of their ERP and SCP data. However, they needed a new method to monitor these KPIs, focusing on data quality and the real impact of inaccurate or outdated information on Ansaldo Energia’s manufacturing processes.

Bank of America, a global financial services provider, faced a significant challenge in its regulatory testing process. The Enterprise Testing Team at the bank was responsible for ensuring that all regulators were notified of any applicable transactions. However, given the volume of transactions, which ran into millions every minute, manually preparing and cleansing this data for quality assurance (QA) was a slow and laborious process. The team could only confirm that the appropriate regulators had been notified up to two months after the transaction had occurred. This delay not only made the process inefficient but also left the bank vulnerable to costly regulatory fines in case of any system failure that required correction.

BT Group, the UK’s leading telecommunications and network provider, had to comply with strict reporting regulations due to its public service nature. The company was using over 140 legacy Excel models to run its regulatory processes, a process they named the ‘Cascade’. The Cascade required a large team and took up to four weeks for a single run, and had to be run multiple times before the reports were ready to be published each year. This process was not only time-consuming but also resource-intensive. Additionally, BT faced challenges with cumbersome change control due to complex logic buried deep in cell formulas, making it difficult to audit changes in Excel. The risk of errors was high with 140 complex Excel models, and even very small errors needed to be reported to regulators. Knowledge management was also a challenge as Excel isn’t the best tool for annotation and knowledge transfers, especially with 1,500 unique data inputs requiring specific methodologies.

Dubai International Airport, known for its high volume of transfer traffic, faced a significant challenge in managing passenger flows. The airport experienced peaks in passenger volumes throughout the day, with immigration halls and transfer security checkpoints fluctuating between being completely empty and overcrowded within short periods. While the airport could plan for expected passenger load profiles, changes to flight arrival times could significantly impact the actual passenger load profile. The Airport Operations Control Center (AOCC) had access to real-time queue information from sensors and cameras, but there was a need for advanced passenger load predictions and resource requirements. This would enable operational teams to collaborate with other stakeholders and proactively open more security or immigration lanes, in anticipation of passenger arrivals. The challenge was to predict passenger load profiles and manage resources effectively to prevent overcrowding and long queues.

JPMorgan Chase & Co., one of the oldest and most respected financial institutions in the United States, was facing a significant challenge in managing its active projects. The organization was using JIRA for their project management needs, but the limitations of the software's canned dashboards made it difficult to extract data in a manner that presented hierarchical information of all tickets in the queue. Due to JIRA server limitations, only a limited number of records from a given JIRA project could be pulled at one time. This lack of visibility into everyday tasks and the complexity of pulling data at a large scale was a significant issue for the organization. The challenge was to find a solution that could provide improved oversight into the organization’s active projects and automate the ideation and project creation phases of more than 10,000 projects over the past two years.

KPMG, one of the 'Big Four' accounting firms, has a long-standing reputation for delivering results for clients across 146 countries and territories. However, the firm recognized the need to digitally transform its operations to maintain its competitive edge and continue to provide high-quality services. The challenge was to streamline processes, optimize compliance, and help organizations transform their functions for the future. This was part of KPMG’s Tax Reimagined initiative, which aimed to help tax leaders embrace disruption, seize new opportunities, and drive greater value. To achieve this, KPMG needed to change the way its people work and interact daily, necessitating an investment in data and analytics platforms. The firm also needed to eliminate human error and automate its processes of entering and accessing data and performing quantitative analysis. The existing system was slow and lacked transparency, with some calculations taking up to 30 minutes to complete in Excel.

The challenge was to match credit and debit transactions in a single dataset made up of 24 million records. The dataset did not include any identifier that might match debit transactions (sales), and credit transactions (returns). Each debit transaction with a corresponding credit transaction had to be removed from the data, and isolated into a separate stream of records without that single identifier already mentioned missing. The legacy process was time-consuming and prone to errors, involving filtering for high count values, and finding their exact credit value. This process was performed manually, copying and pasting the debit row with sales and the credit row into a separate excel spreadsheet, and removing the “matched” rows from the larger dataset.

McLaren Racing, a globally recognized sports entity, has a rich heritage of innovation and success in Formula 1 racing. However, with over 20 race weekends in the Formula 1 calendar, each generating 1.5 TB of data, the ability to collect, process, and act on that data is crucial. The challenge was to make data-driven decisions at tremendous speed to improve performance both on and off the track. The team needed to analyze data from 300 telemetry sensors on each race car, generating 100,000 data parameters. Furthermore, the introduction of a strict budget cap of $145M by the governing body for world motor sport, the FIA, added another layer of complexity. The team needed to control operating costs while driving performance enhancements.

Monoprix, a major French distribution chain, was in the process of digitalizing its operations and wanted to leverage data to optimize its promotional campaigns. The company was looking for a solution that could forecast the sale of promotional products, anticipate supplies, reduce stock shortages, and analyze customer behavior. The challenge was to find a tool that could be used by business analysts without the need for coding skills. The company was also undergoing a complete overhaul of the architecture of its promotional system, which required the industrialization of its promotion production.

NTUC Income, a leading digital and multi-channel insurer in Singapore, was facing challenges in managing and analyzing large volumes of data. The actuarial team at Income deals with data extensively on a daily basis, covering all aspects of data extraction, data preparation, data visualization, and data modeling. They relied on tools such as Microsoft Excel and Access, as well as some programming languages such as SQL, VBA, or R. However, data came from many different sources and in various sizes and formats. They used multiple tools to converge the data, which often created many silos of data processes. This resulted in data reconciliation issues in their end analysis and reports. Another problem they faced was that some of the data processing tools they used were not effective in handling huge volumes of data and required significant time for their analysts to manually customize the data to serve insights to multiple stakeholders. They were lacking in audit trail and documentation logs, which made it difficult for a new analyst to trace data errors or make enhancement to the existing data processes.

Siemens Energy, a global force in power generation and transmission, faced a significant data management challenge. The company's Transmission unit, with 36 geographically distributed factories, generated a vast amount of production, logistics management, and financial data. Each factory had its own database, adding to the complexity of data management. The company also had to pull information from various sources such as SAP, Salesforce, and Amazon Web Services. The organization was spending a significant amount of time consolidating data, checking its validity, and ensuring the proper functioning of formulas. The lack of data analytics and the complexity of data management prevented the organization from drawing full value from its data. The company needed a solution that would provide access and consolidation to gain full visibility into the company's data estate and automate the process to simplify it.

Siemens, a global company operating in 85 countries, faced a significant challenge in consolidating financial data from across the company. The process was complex, involving the integration of financial data with external market data, productivity data, and detailed data on customers or products. The finance department was tasked with calculating numerous KPIs, growth rates, and margins, which were then aggregated through a regional hierarchy and business segmentation. This entire process was conducted using spreadsheets, leading to a high risk of errors in the complex formulas used. Furthermore, any slight change in the analytical question required the controller to redo the entire analysis, a time-consuming and labor-intensive process. The company also faced difficulties in maintaining and updating a manual data preparation process that involved 3,000 lines of VBA code, which was prone to errors and hard to hand over to another person.

TerraVeta, a Geospatial information firm, was faced with the challenge of manually creating aviation charts. These charts, which provide essential information to pilots such as safety warnings, terrain contours, communication frequencies, geospatial data, and other instructions, had to be constructed with precision and attention to detail. The process was time-consuming and labor-intensive, with technicians having to extract information from a database piece-by-piece and individually plot each element. The challenge was to automate this process, reducing the time spent on menial tasks and allowing aviation experts to focus on larger conceptual issues.

West Marine, the nation's largest retailer of boating and marine supply products, was facing a significant challenge in managing and analyzing their customer data. Despite having 240 stores nationwide, both on the retail and wholesale distribution side, they were heavily reliant on a third-party vendor for data management. This external database was not only costly but also disconnected, providing reports in Excel that did not offer a comprehensive view of the customer journey. The main issue was the inability to cleanse, enrich, and analyze their own customer data, which was hindering their mission to outfit, educate, and inspire anyone interested in the water lifestyle. The company was seeking a solution that would allow them to utilize their own resources, own their customer data, and trust their data with confidence.

The second largest telecom operator in Pakistan, with 40.7 million active subscribers, 27% data users and approximately 1.13 BN USD revenue, was facing challenges in maintaining its customer base and minimizing churn attributed to poor network services. Customer feedback indicated that poor network experience was the key reason for churn and dissatisfaction. The marketing, sales, and network teams were making decisions in silos, using disparate data sets. The company lacked the technical expertise to integrate, build, and manage analytical capability on large data volumes related to network experience. They needed help in deploying analytics in their network to generate actionable insights.

84.51°, a retail data science, insights, and media company, was faced with the challenge of creating more personalized and valuable experiences for shoppers across the path to purchase. This included the entire customer journey, from initial awareness to activation, retention, and beyond. The company's goal was to leverage 1st-party retail data from over 62 million U.S. households to fuel a more customer-centric journey. However, the sheer volume of data and the complexity of creating personalized experiences for such a large customer base presented a significant challenge. The company needed a solution that would enable better production, deployment, and interpretation of data science models to meet its objectives.

Flexiti, a rapidly growing company in Canada, is recognized as the country's leading provider of point-of-sale financing with buy-now, pay-later solutions. Despite its success, the company faced a significant challenge. It sought to empower its talented risk and analytics team to gain greater visibility into data more quickly. The need for faster and more efficient data insights was crucial to maintain its competitive edge and continue its growth trajectory. The challenge was not only to speed up the data analysis process but also to ensure the accuracy and reliability of the insights derived from the data.

Cater Care, a leading Australian contract catering, accommodation, and facility management service provider, was facing significant challenges in managing its IT service delivery and HR processes. The company's internal support teams, including IT, Quality, Marketing, and HR, were in dire need of a robust ITSM solution to streamline processes and enhance collaboration. The IT team, without a prior ITSM solution, was struggling to manage IT-related requests and issues manually, which was hindering their ability to scale with the company's growth. Repetitive tasks were consuming most of the agents' time, leaving them unable to focus on tickets that required expertise. Similarly, the HR team was also facing difficulties in managing 200 tickets per month each, entirely over emails, without a proper ticketing solution. The lack of a reporting system resulted in poor SLA management and no feedback for performance improvements. The company was in urgent need of a powerful, centralized, and flexible solution to overcome the lack of coordination among teams and improve the way support functions interact with the front lines of the business.