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Quantum Spatial, Inc. (QSI) was facing a significant challenge with their Enterprise Resource Planning (ERP) system. While the ERP was effective for many tasks, it was not capable of tracking historical data, specifically project Estimates-at-Complete (EAC). The inability to track EAC on a week-to-week basis made it difficult for the Finance team to understand variances in EAC and identify potential issues if a project EAC varied greatly from its baseline. Furthermore, the company's ERP and Customer Relationship Management (CRM) systems were not integrated, creating gaps in the project lifecycle data collection. This situation was further complicated by the fact that QSI had no formal Business Intelligence (BI) system in place, and the Enterprise Systems team was unfamiliar with Alteryx, a tool that could potentially solve their historical data problems.

The U.S. Census Bureau, a leading provider of quality data about the economy, has been relying on outdated manual processes and tools for data collection, processing, analysis, and reporting. The Bureau's data is crucial for allocating over $675 billion in federal funding to states, local communities, and businesses. However, the volume, velocity, and veracity of the Bureau's big data have been challenging to manage with legacy systems. Manual surveying and data processing are labor-intensive, time-consuming, and costly. The Bureau was in need of an innovative solution to digitally transform its data collection, analyses, and dissemination processes, particularly for the U.S. Construction Indicator. The goal was to reduce operational costs while improving the accuracy and quality of economic indicators.

Room & Board, a privately held, American retailer of modern home furnishings, faced challenges with data in marketing. The primary issue was the volume of data coming from different systems, which sometimes required real-time data streaming to feed decision management systems. Additionally, working across different departments and teams to get the necessary data and answers was a significant challenge. The shift towards digital analytics in the marketing department, due to the lack of in-person activities, made personalized email campaigns more important and relevant than ever. The company needed a solution that could blend data from different sources for a more personalized and effective approach, and speed up analytics to optimize marketing across channels and products.

Roquette, a global leader in innovative plant-based ingredients, embarked on a digital transformation project in 2018 to revolutionize their production facilities and processes. The company faced the challenge of managing and processing large datasets from 250 production processes that emitted between 500 and 3000 records every 30 seconds. The manual processing of data was time-consuming and inefficient, hindering strategic decision-making. For instance, one team was spending 100 hours manually exporting 3000 Excel databases. Furthermore, the company's 25 production sites needed to consistently operate at the highest level, requiring the assessment of the Sigma Level, a statistical term used in manufacturing to measure how much a process varies from perfection. This task was time-consuming, requiring approximately one working week of data consolidation for each plant.

SaskTel, a telecommunications company with over 100 years of experience, faced significant challenges in inventory forecasting. The company had been manually forecasting inventory, a process that was both time-consuming and inefficient. Byron Waugh, the Demand Planning and Forecasting Manager, had to manually extract data from SAP for each material number, analyze the history, and input the information into a spreadsheet. This process was not only tedious but also unsustainable given the 3,300+ active material numbers that SaskTel used. The company also faced supply chain issues due to the pandemic, with material lead times extending to 2.5-3 years. Previously, SaskTel would only order materials a few months in advance, leading to frequent rush orders and additional costs.

SearchKings, a digital advertising agency, faced three main challenges that they needed to address. The first challenge was the consolidation of data from different platforms, specifically Microsoft and Google, into a single view. This was crucial for them to have a comprehensive understanding of their data. The second challenge was leveraging data insights to better advise their customers regarding their investments. This was important for them to provide valuable advice to their customers and help them make informed decisions. The third challenge was enhancing their value proposition when working with larger organizations that required a flexible and scalable data feed as part of their solution. This was necessary for them to cater to the needs of larger organizations and provide them with a solution that could scale according to their needs.

Spar International, a global retail giant, faced a significant challenge in building a comprehensive report of its 13,000 stores spread across 48 countries. The complexity arose from the unique product naming conventions, supplier and brand relationships, and currency differences in each country. Additionally, Spar wanted to leverage Nielsen data for competitive analysis, which had its own product categorization that needed to be standardized to fit Spar's reporting. The task of finding a common denominator in all this diverse data, standardizing it, and joining it for reporting was a daunting task.

The Covid-19 pandemic has led to a significant increase in remote working worldwide. As restrictions begin to lift, employers and employees face new challenges related to commuting safely and minimizing risk. This case study focuses on the imaginary example of Acme Corp, a company with around 1,000 employees across Greater London, operating from five different offices. The company needed to answer critical questions such as: What commute options are available for our employees if they wish to avoid using public transport? How can we efficiently allocate our employees between our multiple offices? How can we help employees avoid particularly busy stations or train lines? And how should we best consolidate the number of sites that we operate to minimize the impact on employees? These questions are not only relevant to Acme Corp but also to other organizations across various industries.

The Department of Defense (DoD), the largest business entity in the United States, faced significant challenges in managing and analyzing its vast data resources. With a 2019 budget of $686 billion and an estimated 3.4 million employees, the DoD generates an enormous amount of data. However, the defense sector's unique business drivers, policies, constraints, and operational challenges often make it difficult for traditional consultancies, solutions providers, and product vendors to understand and address its needs. The DoD's existing data collection, storage, and analysis processes were heavily paper-driven and lacked the necessary frameworks for effective data management and analysis. The data existed in hundreds of different places and lacked standardization across various lines of business. This situation made it difficult to increase collaboration, information sharing, and data-driven action. The challenge was to change the perception of data within the DoD and highlight the value it could provide.

Predict Crypto, a project by a student at the University of Colorado Boulder, aimed to predict, trade, and research the cryptocurrency markets. The project required a fully automated solution capable of dynamically pulling the latest data from a database, producing a new set of predictive models daily, and executing real trades on the live cryptocurrency markets every hour. The challenge was to navigate the volatile world of cryptocurrencies and observe patterns within the markets to make medium-term predictions. The goal was to cash in on these trends better than a simple 'HODL' (Hold On for Dear Life) strategy, which involves putting the investment away and forgetting about it for a couple of years.

Vector Logistics, a Sales, Merchandising, and Supply Chain partner in Southern Africa, was facing a significant challenge in managing multiple data sets within the customer analytics area. The company was spending a substantial amount of time manually extracting files from SAP and consolidating them with files received in email attachments using Microsoft Excel and the Power Query functions. This process was repeated approximately 4 days per month and upon ad-hoc requests, such as employee changes including new hires, resignations, and role changes. The challenge was to create an automated workflow that would significantly reduce manual efforts in extracting, mapping, and cleansing data that was previously done using Power Query. The company also needed to map unstructured IRI/Shoprite/PNP Portal data to SAP BW data, thereby reducing efforts involved with cleansing data previously completed using 'text to columns', 'vlookup' and other manual interventions.

Verato, a startup specializing in patient identity resolution and master person index, recognized the need for higher-quality data in the healthcare market. The company uses a unique cloud-based solution that employs algorithms, or Referential Matching, to match vendor databases with patient data. This solution helps hospitals match medical records and patient data with greater accuracy, improving patient care. However, Verato faced a significant challenge in analyzing vast volumes of patient identity data. The company had no standard tool in place to analyze millions of records quickly and efficiently. Much of the work was being done in Python scripts, which did not allow for ad-hoc data analysis. Furthermore, Verato had to process ad-hoc requests on tight deadlines, with each customer providing 600 million rows of data. The company needed a solution that could automate workflows, process data inputs in a timely fashion, and connect to their Slack files and Mongo database.

Veritone, Inc., an artificial intelligence technology company, was facing challenges in managing and analyzing large datasets for their advertising campaigns. The company provides tools and SaaS products to help TV and radio stations prove advertising attribution to their clients. However, they were using Excel spreadsheets for all their data blending, which was not only time-consuming but also limited in terms of flexibility to connect to different data sources. This process was subject to delays and slowed their ability to deliver enhanced analysis and reports to their clients. Additionally, Veritone wanted to develop a foundational Uplift Study to show the effectiveness of broadcast advertising compared to digital. This required analyzing and correlating data points from 250 radio and TV campaigns, a task that proved to be daunting due to the sheer volume of data.

Vodafone New Zealand, a telecom service provider, was grappling with the increasing complexity of managing customer demands in a rapidly evolving technological environment. The industry's shift towards digital-first solutions and simplicity for customers was putting immense pressure on the services Vodafone needed to provide. This included new service locations, increased traffic demand, and faster services, all of which required accurate modelling, efficient monitoring of complex networks, and smart capital expenditure on network builds. The company was also dealing with a vast amount of data from various sources, which was being handled differently by different teams. This approach was manageable when demand was low and network complexity was under control, but as customer needs grew, the company needed to simplify its processes and be proactively prepared for change.

The Washington State Department of Health was faced with the challenge of managing and analyzing a massive influx of data from various sources such as hospitals, schools, and clinics due to the COVID-19 pandemic. Their traditional processes were overwhelmed and the use of virtual machines did not provide a solution. The department's data systems, which had been underfunded for the past 50 years, were built for single purposes, overly customized, and lacked interoperability. This resulted in a lengthy and complex process to clean, transform, standardize, and restructure data before it could be queried. The lack of tools to simplify or centralize this process led to a long time to insight and a great amount of duplicative work done by agency analysts.

The TRX Public Realm project, located in a 70-acre district in the city center of Kuala Lumpur, aimed to seamlessly integrate into its neighborhood. The project's centerpiece is a 10-acre park designed across multiple levels, including the roof of a shopping mall. The park's design aimed to create visual and physical connectivity between the mall and the park, integrating it into the urban fabric. The park was also designed to be porous to the larger landscape around it, allowing for a public space that is fully integrated into the urban fabric. The project faced challenges in implementing complicated civil infrastructure projects, such as box tunnels with super-elevations, which are more challenging compared to other types of building projects. The project also aimed to reduce any possible redundancies and uncertainties that would eventually lead to project cost overruns.

The DVM Group, a prominent architecture practice in Hungary, was tasked with designing the Szervita Square Building in Budapest. The challenge was to create a structure that would not only fit in with the historic architectural styles of the surrounding area but also meet the stringent requirements for LEED Platinum certification. The design involved hundreds of curved and polished panels to create a gleaming façade, which required meticulous attention to detail. Furthermore, the design had to go through multiple iterations due to the site's prestigious location and the LEED-certification requirement. The complexity of the design and the need for sustainability resulted in an extraordinary amount of data, which needed to be managed effectively.

In 2013, CARENE, a Saint-Nazaire developer, selected Coste Architecture’s design for their Aquatic Center. The design was not only compliant with the program but also demonstrated energy and environmental proposals, and an innovative architectural proposal – 3 wavy roofs supported by poles. The challenge was to manage the project effectively, especially considering the technical innovations involved, such as the structural columns in seismic zones. The project also required close collaboration with water and energy experts due to the high environmental quality energy requirements of the aquatic center. The architectural proposal of three roofs in the form of waves, supported by structural laminaria shaped columns in a seismic zone, called for true collaboration with structural engineers specializing in Ultra High Performance Fiber Concrete.

The Duke Ellington School of the Arts, a renowned public school in the District of Columbia, was housed in an aging building that was originally built in the 1890s. Despite several rounds of renovation and repair, the building needed a major overhaul to meet the demands of the 21st century. The school was ready to modernize in 2011 and held an international design competition to solicit concepts from leading firms worldwide. The challenge was to create a design that would preserve the historical elements of the building while incorporating modern features. The project was not only complex but also had a tight deadline that could not be extended.

HCF and Associates, a boutique architectural consultancy based in Singapore, faced a challenge with their residential project, House 15. The project was located in an exclusive gated community, with the building lot facing the sea and measuring 700 square meters. The site, while valuable, was relatively small and had restrictive set-backs and height limitations. The design of House 15 was geometrically derived, featuring three distinct blocks situated around a central atrium. The division of the floor plan was conventional with public spaces on the first floor, and more private spaces in the basement and on the second floor. The challenge was to implement a Building Information Modeling (BIM)-integrated design process that would minimize time and resource consumption, and positively impact the thought process and productive mindset of the architects.

Vestas, a global leader in sustainable energy solutions, faced a complex challenge in optimizing their shipment patterns to save costs. The Service Analytics team at Vestas had to consider not only external, customer-facing products, but also internal stakeholders across the Operations, Finance, Supply Chain, and Commercial teams. All of these teams worked together to answer big questions for the company such as how and when to deliver a turbine part from point A to point B. The team recognized that a more robust data operation could help them simplify and improve logistical challenges. They understood that data science-based solutions in predictive asset maintenance, field capacity planning, inventory management, demand and supply forecasting, and price planning would provide critical support to the internal customers of Vestas. However, until that point, the data team ran a traditional business intelligence (BI) based analytics operation, querying BI-dashboards, deriving insights, and building data products in a less automated manner.

Home Credit International (HCI), a global consumer financial provider, recognized the potential of Machine Learning (ML) models in financial institutions, particularly in risk-related use cases. However, they faced challenges in deploying ML models efficiently. The time to delivery was long and access to data was limited. HCI’s internal research revealed that nearly 80% of the time spent on data science-related tasks was dedicated to collecting datasets and cleaning and organizing the data, leaving only about 20% of the time for core tasks like building training sets, mining data, and refining algorithms. In 2021, the average delivery time of an AI initiative, from prototype to production, was more than seven months. The biggest blocker for more efficient use of AI/ML was access to data, followed by the need for a proper AI/ML environment.

A+E Networks, a global entertainment company, was facing challenges in managing its streaming media services. With a portfolio of seven popular video brands, the company reaches 335 million people worldwide. However, the increasing demand for more viewers, more bandwidth, and more content was putting pressure on developers to deliver frequently and fast. The company's configuration management was largely manual and became slow and cumbersome as their portfolio grew. A build across each site could take hours, and as they added programming and moved to microservices, the time only expanded. A+E Networks also lacked visibility with their legacy Content Delivery Network (CDN), having to rely on professional services with no view of what was happening behind the scenes. The company wanted to empower developers to implement services and upgrades quickly and manage traffic spikes with a multi-CDN model.

Slate Magazine, a digital media pioneer, faced significant challenges with their legacy Content Delivery Network (CDN). The CDN lacked the agility to purge content and complete updates at the speed required by the rapidly evolving digital media landscape. The process of waiting for a change to go live could take as much as 15 minutes, which was highly inefficient in the fast-paced digital media industry. Additionally, the legacy CDN charged on a per-service basis and required XML for updates, adding to the workload of Slate's already busy team of developers. These challenges prompted Slate to seek a more efficient and agile solution.

Finn AI, a provider of AI-powered virtual assistance for banks and credit unions, faced a significant challenge in securing their business-critical APIs. The company, which uses natural language processing technology (NLP) to enable conversational AI technology for financial institutions, needed a solution that would provide visibility into API discovery attempts by malicious threat actors and the ability to stop unusual activity against these APIs. Despite having a relatively small attack surface due to the absence of a client-side frontend, Finn AI's APIs still required effective protection. The company sought a solution that would install easily, scale effectively, be light on resources, and provide protection against OWASP Top 10 and zero-day exploit attempts.

The Erasmus University Rotterdam Student Center, designed by Powerhouse Company and De Zwarte Hond, was envisioned to be a vibrant meeting point on the campus where research meets business and science meets culture. The main objectives for the design of the Student Center were sustainability, usability, transparency, and intimacy. The challenge was to create a building that could adapt to the changing needs of the students and the environment. The building needed to be flexible in use, energy-efficient, and able to moderate the amount of natural light and heat according to the sun’s position. The design also needed to accommodate 'dark space' programs that require an absence of daylight, without compromising the transparency of the building.

Action, Europe’s fastest growing non-food discount retailer, faced a significant challenge in managing the vast inflow of data from its over 2,300 stores across 11 countries. The company needed to track various aspects such as consumption patterns, product placement, and supply chain disruptions, which varied according to local, national, and international trends. The existing architecture was not sufficient to handle the data efficiently and provide accurate forecasting models for demand and sales in new and existing markets. The company also faced issues with data access and quality, costly and complex processes, lack of visibility and control, and operationalization and business impact. The use of Excel for gathering, sorting, manipulating, and modeling data was proving to be a bottleneck for the speedy and efficient deployment of data analytics and models.

Convex Insurance, a company that heavily relies on data for decision making, was facing challenges with its traditional data handling methods. The company's diverse team of actuaries, architects, and business analysts needed a more efficient way to collaborate and extract value from their data. The use of spreadsheets was no longer sufficient due to the enormous volume of data and the complexity of the data pipelines. The company needed a solution that could accommodate the varying levels of technical expertise within the team and facilitate effective communication and collaboration.

JK Lakshmi Cement, a decades-old manufacturing firm in the cement industry, was facing significant challenges in improving operational efficiency and accelerating analytics reporting. The company was bottlenecked by a lack of data- and tech-savviness, with only a few people tasked with building reports for the entire organization. This limited the number of reports that could be created, hindering the company's ability to make data-driven decisions. The team was also struggling with scarce and underutilized data experts, and their data processes lacked operationalization and the ability to make a strong business impact. They were in need of a platform that could boost the efficiency of its coders and allow for cross-team collaboration with line-of-business users.

Rabobank, a leading Dutch bank, was faced with the challenge of keeping up with the rapid pace of technological change in the banking sector. According to a 2020 PwC report, 81% of banking CEOs expressed concern about the speed of technological change, more than any other industry sector. Rabobank, however, chose to embrace this change and transform their organization to move with the pace of innovation. The bank had been on their data journey since 2011, and while they had the support from both the executive level and the people implementing the technology and processes, they needed to further streamline their approach to data transformation.