ForbiddenPlanet.com, a leading science fiction retailer in Europe, was struggling with data analysis. The company had a vast amount of data but lacked a system for effective analysis. This was particularly problematic in their fast-paced comics business, where new editions are released weekly and accurate estimations of product demand are crucial. The company's management account meetings were inefficient, with an 80-page management pack of quarterly account data that was already out of date by the time of the meeting. The lack of real-time data analysis led to meetings ending with more questions than answers.

IndianOil, the largest public sector oil and gas company in India, first turned to data analytics with QlikView in 2012. Dashboards were created, mainly for the company’s chairman and directors. During a review of its IT infrastructure, IndianOil identified an opportunity to make more use of the increasing amount of data being held throughout the organization. The company saw the potential of leveraging this data to drive business value across the organization.

Pizza Pizza, a Canadian food chain, was struggling with managing and making sense of the vast amount of data generated from its operations. The company operates over 750 locations and fulfills over 20 million orders annually, generating a database with billions of fields. The company used to rely on static reports and spreadsheets to analyze its operations, which lacked flexibility and interactivity. The reports were generated daily and if a different perspective was needed, it would take days or weeks for IT to generate a new report. The company was unable to access real-time data and lacked the ability to drill down into the details of its operations.

Sketch Books, Inc., a bookstore and stationery merchandise company with 30 branches across the Philippines, was forced to shift its focus to e-commerce due to the COVID-19 pandemic. The company needed to ensure a high level of customer service despite the shift to remote working. The challenge was to maintain the same level of service and customer satisfaction while transitioning to a new business model.

The Hillsborough County Tax Collector Office (HCTC) is responsible for collecting and distributing local and state taxes and fees to various partner organizations. They serve as an agent for their partner agencies and all of their community members to ensure that the money that flows into their office goes where it needs to go. However, they were facing challenges in managing the vast amount of data they collected and in using it to improve their efficiency and the quality of their work. They initially used Excel to handle performance metrics and insights, but it was time-consuming and prone to errors. They then tried another solution, but it did not allow them to actively manage their data or understand why certain trends were occurring.

Great Ormond Street Hospital Children's Charity was facing a decrease in the performance of warm cash appeals and needed to understand the reasons why and to explore more insight driven approaches to combat this. Previously, fundraising campaign reporting was done after each campaign by pulling data into Excel to create tables and charts, then moving these into Powerpoint for delivery and presentation. This often occurred after the next campaign was already live. The process was costly in terms of the time and resource required to prepare reports and did not allow the team to be as flexible and reactive as they would like.

Gazprom Neft, one of the top three oil-producing companies in Russia, was facing challenges in making data-based decisions due to the lack of a robust Business Intelligence (BI) unit. The company was already a year into their journey with Qlik, a data analytics platform, when the BI Department Head joined in 2013. The task was to further develop the internal Qlik-based processes. However, the allocation of resources was a major challenge, especially server resources. The company was also not always on schedule in the purchasing of new equipment. Change management was another challenge, particularly overcoming internal resistance without clear signaling from company leadership.

Nationwide Building Society, the world’s largest mutual financial institution, was facing a challenge with data management and analytics. The company's strategic vision is driven by its 15 million members, not the market or shareholders, and data underpins every one of their strategic goals. However, the company found that employees often worked within their silos or divisions and designed things for themselves, unaware that others in the organisation were trying to do something similar—often with the same data. This led to a lack of efficiency and a duplication of efforts. Furthermore, the company was using QlikView, but had licenses for only 500 people, limiting the access to data and insights for many employees.

Anurag Group of Institutions (AGI) is a large educational institution in India with a vision to maintain high academic standards and promote analytical thinking and independent judgement. The Department of Computer Science Engineering at AGI identified a serious skills gap for good data analysts in industry and business. With a forecast of 97,000 jobs that need to be filled in the area of data science, there is a huge need for skilled people. The department has been teaching 'Big Data' subjects to students and recognizes that the future for the next 30 years will be data analytics. However, they were seeking the best way to instruct students in data visualization and provide them with practical experience.

PSCU, a credit union-owned cooperative, was in the midst of a multiyear initiative to evolve and broaden its call center services when it realized the need for better data organization and visibility. The company was dealing with disparate data sources and flat reporting that lacked analysis, color-coding, and cross-functionality. The reports served specific needs but did not show all the inputs or outputs due to lack of integration. PSCU recognized the need to better organize its data to more effectively align with its business model and to provide the full suite of products and member experience required by its credit union relationships. The view needed to be cross-functional for the company to optimize and innovate where necessary.

Hendry, an asset protection firm, was facing a challenge of limited value in their services due to a narrow vision of their work. The industry was lagging behind in the adoption of technology and clients were demanding more than what was being delivered. The company realized that they needed to change and adapt to the fast-paced, technologically driven world. They wanted to provide more value to their clients than just services required within statutory law. They saw the potential of data and technology to transform their services and become a full-service advisory business.

Odyssey, a logistics and supply chain management company, was in need of a business intelligence solution that could help them keep up with competitors and add value for their customers. The solution had to be a non-cloud, on-premise solution to allow the company to maintain complete control over the operation. It also needed to be flexible enough to extend into a true 'visibility' platform on the front-end. The company spent almost all of 2016 trying different solutions during the discovery phase of the project, but none of them were the right fit.

Quooker, the creators of a tap that produces boiling water almost instantly, has been experiencing rapid growth, with a 100 percent increase every three years. To sustain this growth and expand operations across a complex global market, the company needed more precise forecasting and analytics tools.

BorgWarner, a global automotive components and parts supplier, was previously using a manual, spreadsheet-based approach to produce reports on total spend, supplier performance, and supply chain risk. This process was time-consuming, taking days to prepare for each supplier. The company faced a significant challenge in 2011 when Japanese suppliers were affected by a tsunami. BorgWarner struggled to determine the impact on their supply chain, products, and customer orders.

The article discusses the challenges faced by organizations in their journey towards data maturity. The first challenge is related to data quality. Organizations often have data quality issues, which become more apparent as they try to utilize the data. Issues can range from misspellings, inconsistent data entry methods, to software compatibility issues. Before any progress towards data-based decisions can be made, the data itself must be trustworthy. This requires creating mechanisms to ensure data quality. The second challenge is related to data literacy. Having great data is just part of the equation. Organizations must also have a genuine curiosity and the ability to ask the right questions. Most organizations are very familiar with asking, “what happened?” but the real power in analyzing data comes from more advanced questions. The final stage of intuitive growth involves reaching a level where insights about data trends lead to an ability to influence outcomes.

Transport fleets are crucial for modern commerce, and their efficiency is paramount. However, as fleets grow in size, their management becomes increasingly complex. To keep track, organizations have equipped their fleet vehicles with sensors. However, the sheer volume of telemetry data they receive is difficult to consolidate and convert into meaningful reports that point to effective business decisions. Montrans, Russia’s leading transport monitoring company, faced this challenge. It started as a fuel consumption tracker but its systems have since grown in sophistication and it wanted to move away from static reports to provide interactive fleet control and data-driven management.

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.