The Chinese Academy of Agricultural Sciences (CAAS) is a national innovation center for agricultural IT. It monitors agricultural information, risk analysis, food safety, and data acquisition. CAAS is in the process of refining big data for smart agriculture through its own application. The application handles heterogeneous data resources from a variety of data sources including local governments, commercial databases, and Internet of Things (IoT). However, the business intelligence (BI) products previously deployed by CAAS were seen as inadequate. They were generally limited to static data analysis and visualized representation, making it difficult to fulfill in-depth data interaction and analysis. CAAS wanted more specialized and powerful functionality. With such a huge volume of data, CAAS was concerned about performance load.

Volvo Group, a global manufacturer of trucks, buses, construction equipment, and marine applications, faced a significant challenge in managing its complex material flow. The company had to connect with the right suppliers for the right spare parts and find the most efficient way of getting the right parts to the right trucks at the right time. The company's existing tools, such as MS Excel, were inefficient and left a lot of room for error. The team spent an excessive amount of time gathering data and making it understandable and presentable. This inefficiency led to a culture where teams and individuals quickly became siloed, missing out on opportunities to improve workflows or identify suppliers whose products weren't up to standards.

HSBC, a global bank serving over 40 million organisations and individuals, was grappling with the complexity and scale of its data. The bank recognised the need for a strong commitment to analytics to thrive in the future. However, the challenge was not just about collecting data but making it usable and accessible across the organisation. The bank needed to ensure data quality, make data-driven decisions easier, and enable every staff member to contribute to business intelligence. The sheer size of HSBC and its commitment to data collection and analysis necessitated a simplified approach to data management.

Dataspark, a company specializing in data visualization, dashboards, and reporting specifically for Exact Online, Exact Globe, and Exact Synergy, wanted to empower Exact Software users to get even more insights from their data. To achieve this, they needed to build bespoke products and become the go-to services organization for their customers. The challenge was to find a flexible, easily accessible, and fast solution that their customers demand.

AECOM, a premier infrastructure firm, was facing challenges in managing the complex and dynamic market of coal combustion residuals (CCRs), also known as coal ash. The regulatory status of each coal ash unit was not centrally recorded, with each owner/operator maintaining its own website with all required regulatory reports. Manually gathering, analyzing, and summarizing this discrete data was complicated and arduous. Prior to 2019, AECOM was managing U.S. coal ash market data for customers in a series of spreadsheets, which had limited dimensionality and were challenging to quickly get results. The total amount of CCR material exceeds 2 billion cubic yards, making the management of this data a significant task.

Condé Nast, a dominant player in the international magazine market, embraced the digital revolution to engage with its audience in new ways. However, with its new digital applications, Condé Nast soon encountered more data than ever before. Hidden inside that data were key customer trends that could help Condé Nast create content more precisely aligned to customer appetites and seize advertising opportunities with sharper accuracy. Realizing this, Condé Nast began developing business intelligence infrastructure to make smarter use of its data, and it knew choosing the right partners in the digital transformation journey would be key.

HPE, after splitting from HP in 2015, aimed to apply end-to-end analytics to its marketing ecosystem. The company wanted to optimize demand flow through its funnel, take its digital transformation to the next level, connect the dots on marketing spend and outcomes, and transform its existing analytics system. However, the company faced challenges due to the numerous handoffs to other teams in the organization, the presence of multiple 'versions of the truth' due to different BI systems, and the complexity of connecting different connection points housed in different systems with different structures.

Benjamin Moore, a renowned paint company, was looking to transition from being a product-centric enterprise to a customer-centric one. The company wanted to improve its customer experience and needed a solution that would enable it to do so effectively.

Rackspace Technology was dealing with a chaotic data environment, which was more of a liability than an asset. The business intelligence team was spending more time fact-checking than delivering actionable insights. The company needed to transform its data into a strategic asset that could help it compete better in terms of customer experience. To achieve this, Rackspace Technology established its Global Data Office.

As a leading global brand in Quick Service Restaurants (QSR), the end-to-end supply chain is pivotal to the ongoing success of the brand. The European supply chain team are responsible for the product journey and field-to-fork performance (ethics, sustainability, quality and cost). With 6 markets, 10 distribution centres and 300 suppliers, the collection of data from multiple internal and external sources is critical and was complex. Once collected this information needs to be presented in a way that is Specific, Measureable, Achievable, Relevant and Time-bound (SMART) in order to transparently drive performance.

Corporater, a Norway-based company, was looking for a business intelligence (BI) solution to help their customers visualize and make sense of their vast and varied data. The company needed a tool that would empower end-users to see insights and take action. While Corporater typically developed software internally, they carefully weighed the option of partnering, instead of building, a visual analytics tool. They evaluated several options, considering factors such as integration simplicity, ease of doing business, reputation, and visualization capabilities.

SDI, a digital supply chain company, was facing challenges in managing and utilizing its data effectively. The company had siloed areas of expertise, with knowledge spread out across the organization. This resulted in a reliance on tribal knowledge and information sharing, which was not efficient or effective. The company was unable to leverage data effectively between accounts, which could have led to shorter lead times and quicker turnaround for customers. Furthermore, the company was using outdated tools like Excel for data management and had not been exposed to enterprise-level BI solutions.

Generali, one of the world’s largest insurance companies, was facing a challenge with their application landscape. They had two-speed IT in place. The core legacy business application landscape had become more and more complex over time, and as a result had lost agility. On the other hand, newer customer facing and channel applications like portals were fast and serving different expectations. The task was two-fold. Firstly, to remove the complexity that had become visible to customers and enable them to independently access accurate information in real time, in a channel of their choice. The second strand was establishing new IT processes and improving development efficiency. They needed a solution that would connect two worlds to make them work more efficiently and cohesively for the business.

The Asthma Control Test Qlik Sense application was built to assist in trending scores documented by the Medical Assistants within the clinic flowsheet. The challenge prior to the application was referencing a prior week lookback of data via an excel report. There simply could not be a meaningful conversation involving the ACT documentation without being able to easily show what was occurring.

The traditional undergraduate education in the US is facing a decline, and the competition is increasing, especially with the advent of Covid-19 which has forced many institutions to move online. Budgets are tight, particularly for on-the-ground organizations that face the additional cost of maintaining buildings. It is vital that colleges and universities maximize returns when they are making large investments like opening a new campus or satellite. They must be sure that the site is right and must know the demographics of the students they aim to recruit. They must also pinpoint the best focus for their marketing spend. Obtaining this information requires the analysis of millions of lines of data, billions of highly complex calculations and input from the latest machine learning technologies.

CSC ServiceWorks (CSCSW) services laundry locations and air services at gas stations across the United States. These services are often overlooked because when they’re working well, no one thinks twice about them. But these are historically coin-based industries. As currency has increasingly moved into the digital sphere, these services are less convenient for the general public. About a decade ago, the payment technology for laundromats and air machines started to shift so that people could pay with a swipe of a card. It was a nice leap forward. As the years rolled on, the rise of the Internet of Things (IoT) made smart payments with mobile devices possible, connecting information on these payments to a main network. Now, we can monitor all of our machines from a central location, provide convenient app based payment, and offer a higher level of service. Just as CSCSW served a traditional coin industry, we had a traditional on-premise data warehouse platform when I joined the company as Chief Digital Officer in 2019. We have developers for these warehouses, who would run and share reports with others when necessary. It was functional in the past, but as we gained more data this wasn’t a viable solution. With connected machines, our thirst for data and analytics within the company grew exponentially. We needed to have easier access to the data within the organization so people could get the information they needed on their own, and developers could focus on more important work besides pulling queries and running reports. We needed a more efficient way of dealing with data and driving value for our consumers, clients and employees.

Tenaga Nasional Berhad (TNB), the largest electricity utility in Malaysia, is aiming to become a leading provider of sustainable energy solutions by 2025. To support this goal, TNB's Group Finance Division initiated the creation of a centralized Business Performance Management (BPM) dashboard. The dashboard was designed to increase the focus on growth and profitability, transforming finance from process manager to value creator. However, the challenge was to ensure that the dashboard displayed accurate and timely information, provided the correct picture through data visualizations, and was delivered to the right stakeholders.

China’s rapid technological development has created a need for trained data analysts but there is a skills gap in the recruitment market for students with strong data literacy capabilities. One organization working to plug that gap is Xi’an Jiaotong-Liverpool University (XJTLU), an international joint university based in Suzhou, Jiangsu, China. The university recognized the need to provide hands-on data analytics experience to its students to prepare them for the job market.

J.B. Hunt, one of the largest transportation and logistics companies in North America, was seeking to increase efficiency and customer responsiveness by gaining real-time insights into its operations and assets. However, the company faced the challenge of not impacting production systems while doing so. They had implemented a Microsoft Azure Databricks data lake but needed to accelerate the flow of analytics-ready data into the lake.

Weber State University (WSU) began using Qlik AutoML in late Q1 2020, right as the pandemic took hold in the US. As Covid-19 wreaked havoc, the university had difficult decisions to make about where to allot resources in the midst of self-described “survival mode.” A combination of the right power at the right price made Qlik AutoML a viable choice even in uncertain times as a method to empower, support, and retain their students in a quickly-changing world. After a trial month with Qlik AutoML, decision-makers saw value thanks to Qlik AutoML’s ability to translate something complicated to something understandable, making it easier to share with stakeholders with and without technology backgrounds. With limited funding, Qlik AutoML was a more attractive option because of its affordability compared to competitors and consultants that can incur “astronomical expenses,” according to WSU’s Heather Chapman, Director, Academic Analytics.

CSC Financial Co., Ltd (China Securities) needed to better understand asset movements in real-time to maximize investment strategies. It also wanted to provide its clients with a real-time display that would clearly show the status of their portfolio from the China Securities application and other investment channels. The company had to deal with large data volumes and many databases which required up to 18 replication tasks. Frequent system upgrades also meant that replication environments had to be constantly rebuilt and replicating from heterogeneous databases was a cumbersome operation.

Urban Outfitters, a lifestyle retailer with 650 stores across the USA, Europe, and the UK, was struggling with siloed data across different systems and technologies. This made it difficult to gain daily visibility of actionable data, a problem further complicated by different time zones and the varying operating styles of the three business brands. The Covid-19 pandemic added another layer of complexity as the company had to manage store closures and differing pandemic strategies. The company's biggest roadblock was the inability to easily explore information to gain the necessary insights, resulting in reports that had to be manually compiled, consuming a significant amount of administrative time.

Naylor Association Solutions was facing a challenge in their marketing-sales processes, particularly in lead scoring and qualification. The company was using a CMS that required account executives to fill out nearly 30 different data fields, which were then used by the marketing automation platform for scoring and qualification. This process was time-consuming and frustrating for the salespeople, who viewed many of the fields as unnecessary. On the other hand, the marketing team couldn't provide a better justification for the data other than they needed it for their processes.

Beyond 12, a technology-based services organization, aims to increase the number of low-income and first-generation students who graduate from U.S. colleges and universities. However, they lacked a data scientist on staff who could build a predictive analytics model. They needed a robust in-house tool managed by their existing engineering staff for a viable and scalable solution. The objective was to refine Beyond 12’s approach to addressing their analytics needs and to provide coaches with a bird’s-eye view of their students. This would enable them to provide the right assistance to the right students at the right time.

Chef Works, a supplier for hospitality businesses, was impacted by the economic climate driven by the global Covid-19 pandemic. The company experienced a reduction in bandwidth and a growing need to effectively practice data science with a lower time commitment. The pandemic left many businesses in the hospitality industry on uncertain ground, and Chef Works needed to make the best use of their time and resources to not only sustain themselves through these changes, but to continue to innovate in ways their customers have come to expect at a price point that would be agreeable to once-lucrative businesses now strained for cash. Chef Works understood the grim reality that, while many of their customers would see the other side of the pandemic with business in-tact, many would not. To better support those that would survive, and to understand which businesses those were most likely to be, Chef Works turned to the power of data science.

The Department of Education, Skills and Employment (DESE) in Australia is tasked with a broad undertaking that impacts the lives of every Australian citizen. It accumulates huge amounts of data from multiple sources, including early childhood care providers, schools, universities, registered training organizations and employment services providers. Understanding this data is essential for delivering a world-class education, skills and training and employment support infrastructure. However, DESE faced challenges with its data analytics approach. It needed to future-proof its approach with a scalable SaaS platform. The department also needed to eliminate data time lags to understand the current reality and provide data in an accessible, digestible and engaging format to end users.

Yellow Brick Road (YBR) was struggling with their existing business intelligence (BI) tool. The company was initially using Excel for data analysis and reporting, which led to significant delays in data delivery. The sales teams needed timely information to make decisions regarding sales targets and strategies, but the company didn’t have insights into loans until they received the commission statements from the banks—a delay of around eight weeks from the time of the actual sale. The delayed insights were always a challenge in making informed decisions to improve the sales KPIs, and the stacks of Excel sheets received from the lenders in different formats didn’t help with a timely and meaningful presentation. The company then implemented an emerging reporting software, which was a step up from Excel and a step in the right direction. However, not having local technical support and the tool's exorbitant pricing made them lean towards a more established tool with a local presence.

De Swinhove Groep, a healthcare provider for senior citizens in the Netherlands, was facing major challenges due to the rising costs in the care sector. The Dutch government was promoting homecare and shifting the responsibility for the use of budgets towards care institutions. This meant that care providers like De Swinhove Groep had to minimize building operation costs and optimize Total Cost of Ownership. The group wanted to develop a high-grade information organization and quickly integrate changes in management control information. They needed a business partner that was familiar with complex healthcare processes as well as business intelligence.

Systemair, a leading ventilation company with operations mainly in Europe and North America, faced several challenges. The company needed to share valuable business data locked in its Movex ERP system with more than 25 subsidiaries throughout Europe. The goal was to simplify and speed up data analysis enterprise-wide for improved decision-making. Additionally, Systemair wanted tighter control over business processes and operations to ensure 24x7 product availability for customers. As the business complexity grew, the existing system of analyzing data with simple SQL queries and Excel became unworkable.

Target Express, the largest independent express delivery company in the UK, was looking to enhance its industry-leading image as an open, proactive business partner. The company wanted to quickly unlock data stored in various systems to provide superior service and share detailed logistical information with customers beyond standard industry KPIs. A key account recognized that a huge repository of information residing in Target Express’ systems would be valuable for conducting their business more effectively. The customer requested access to the data which would disclose what it cost them to operate in a specific area, move specific products, during a specific time of year, and numerous other operational insights.