Slingeland Ziekenhuis, a Dutch short-stay hospital, was facing challenges due to drastic changes in the healthcare sector. The partial privatisation of the hospital market required hospitals to determine the total cost of medical treatment. Additionally, hospitals were gradually losing their fixed compensation for running their facilities. Slingeland Ziekenhuis decided to restructure its internal organisation to better respond to these changing market conditions. However, the hospital's standalone reporting system was inadequate for managing the reorganised hospital through KPI measurements. The hospital needed an end-to-end management information system that could quickly answer ad hoc management questions and adequately respond to new market developments.

Colmobil, Israel's largest importer and distributor of vehicles, was executing several SAP projects in parallel across the company’s SAP landscape. This included migrating two subsidiaries’ legacy systems to SAP, which required updating testing environments to develop new business logic and properly connect all the data. However, creating a new system or refreshing an existing one with updated data for a new project was extremely time-consuming. It could take up to a week of intensive work to complete these tasks. As a result, teams always questioned whether it was worth creating a fresh system or whether they could risk working on an outdated one. Dependence on the Basis team created bottlenecks. The Application team had to rely on the Basis team to refresh a QA or Dev environment. Bottlenecks inevitably led to long lead times to create new environments. It was difficult to recreate erroneous events or research bugs. Discrepancies between the production environment and Dev or QA limited the company’s ability to address issues when they arose. Issues usually appeared in the newest customizations or development work, where the gap between production and Dev or QA existed.

Moody’s Analytics, a global provider of financial intelligence and analytical tools, needed a way to enhance their existing capabilities in order to offer new insights to their customers. They required a solution that could handle vast volumes of data about borrowers for benchmarking and risk assessment. The challenge was to present this data in an intuitive and easily accessible format that could be explored to granular levels.

Asobanca, a nonprofit institution representing 15 of the 24 biggest private banks in Ecuador, faced a significant challenge in rebuilding people's trust in the banking system following the financial crisis of 1999. The crisis, which was a combination of bad politics, bad banking, and bad luck, resulted in many banks declaring bankruptcy and 64% of the population living below the poverty line. This has led to a lingering lack of confidence in the financial sector. Additionally, the way information on the financial system is collected and distributed in the country posed a significant obstacle. Many different institutions disperse the data, each using their own methodologies of data collecting and processing, making the data often incompatible and difficult to analyze. Furthermore, data access services offered by some companies are expensive and not user-friendly, discouraging many potential users.

Nemours Children’s Health System had a big challenge around electronic information: It needed to change its culture, to move to a model where staff started pursuing data versus just receiving data. The organization wanted to shift from repeatedly building dashboards and reports to really enabling the organization. They wanted to bring all their relevant data from any system, across finance, patient care and the overall business, into one place for analysis.

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.

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.

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.

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.

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.

Takeuchi is a Japanese compact construction equipment company that has a presence in Australia, Europe, Asia, the UK, and the USA. The company is a pioneer in the construction industry, building the world's first 360° full-turn compact excavator in 1971 and the first compact track loader in 1986. Despite its leading share of the compact equipment market, Takeuchi US is constantly threatened by new players. Effective business intelligence (BI) is vital for the company to remain competitive. However, prior to 2019, there was no automated process for obtaining that data. The company relied on complex and time-consuming Excel spreadsheets and SQL reports. It could take up to two weeks for one dedicated person to assemble the mid- and year-end reviews, and additional time for those supplying the necessary data points.

Hyakunousha International Limited, a Japanese omusubi store operator, faced challenges in obtaining timely sales data from its over 100 stores. The company's existing system only provided standard reports, which then had to be manipulated to gain useful insights. This slow process of data analysis was inefficient and did not support the company's need for up-to-the-minute sales data to plan and manage its operations. Furthermore, Hyakunousha wanted to provide user-friendly tools that staff could use independently to gain the insights they needed. The company aimed to create a 'data-savvy' culture where staff routinely made use of data and data analysis to support planning and decision-making.

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.

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.

In 2006, Coface Ibérica, the Spanish division of Coface, realised the need to come up with a business intelligence (BI) solution at a local level. The company needed to take better decisions quickly and to be able to monitor certain critical activities within the company, as a complement to the corporate tools used by the group. The systems department initiated a selection process in which different manufacturers’ products could be evaluated. QlikView was the solution chosen because of its ability to permit a gradual alignment with control frameworks and the ease with which it could be deployed and managed.

Gwent Police, a force in South Wales, United Kingdom, was facing several challenges. The existing performance management system was incapable of providing information for internal stakeholders. The force needed a system that could provide crime mapping and support intelligence-led policing. The previous system was paper-based and took a whole day to compile monthly staff performance reports. Information was dispersed under different applications in the force’s Command and Control system. The force was also keen to identify the availability of appropriately trained staff and highlight where there were shortages.

Kuveyt Türk Katılım Bankası, a leading risk participation bank in Turkey, was facing challenges with its operational reporting system. The system was poorly organized and record keeping was dispersed. The bank was generating reports directly from the online transaction processing system in a list format, which lacked graphics or data visualization. This made it difficult for managers to interpret the content of the reports. In 2008, the bank created a data warehouse but immediately recognized the need for a reporting solution to provide visual and user-friendly reports with graphic representations and comparative tables to facilitate analysis.

Lifetime Brands, a leading designer, developer, and marketer of a broad range of nationally branded consumer products used in the home, was facing significant challenges in terms of visibility into supply chain management and sales analysis. The company had grown organically and through acquisitions, leading to a complex landscape of disparate enterprise software systems. This made consolidated reporting for all company brands a significant challenge. The company had recently replaced its legacy Great Plains application with an implementation of SAP R/3. While the new system provided a view into current operations, it could not incorporate historical data, which made comparison and analysis a difficult process. The out-of-the-box reporting capabilities from SAP were limited and would not provide the level of detailed analysis that Lifetime Brands desired.

Ochsner Health System, a leading healthcare organization, was facing challenges in shifting from a reactive mode to proactive decision-making. They wanted to gain a complete understanding of the performance metrics of their clinical operations. The organization was struggling to monitor surgical room utilization and turnaround times to maximize capacity. They also wanted to identify long turnaround times between cases in the operating room to streamline the process. Another challenge was to measure surgical room utilization and its impact on revenue generation as 70% of revenue was driven from surgeries. They also needed to assess the number of procedures and costs by case for manual surgery versus potential for robotic surgery.

PT. Kapal Api Global, a manufacturer and distributor of Fast Moving Consumer Goods (FMCG), was facing performance issues when analyzing huge amounts of data (1.5 billion records, 200 million rows). There was a 2-3 week gap needed to prepare analysis, which was affecting decision making. The conventional reporting tools they were using were unable to cope with the huge data sets and frequent user changes to analytical requirements. The company has a distribution footprint that includes more than 500,000 stores nationwide, mostly consisting of small family-owned shops.

The Sistema de Emergencias Médicas (SEM) is a public enterprise within the Health Department assigned to the Servicio Catalán de la Salud (Health Department of Catalonia). It is responsible for handling outpatient casualties and emergencies as well as emergency medical transport via the emergency number. In the past year alone it has dealt with over four million calls. As part of its strategy for continuing improvement, the SEM decided to develop a system to increase actual speed and efficiency ratios in its service to the public. The system required a tool that would support the development of management control methodologies and, specifically, enable scorecard implementation across an organisation employing 701 staff and 3,720 associates with an annual budget of €245.7 million.

Sibley Heart Center Cardiology, one of the largest pediatric cardiology practices in the U.S., was facing challenges in managing and analyzing its vast database of patient records. The practice was considering hiring an additional full-time person to handle reporting requests for its leadership team. Standard end-of-month reporting took a few days, while ad hoc requests from senior management and physicians took up to a week or more to fulfill. The reports and hard-coded structure of Sibley’s systems couldn’t deliver basic analysis. A person would have to make several queries, which alone would take hours to generate, export data to Microsoft Excel and “reverse engineer” an answer. This limitation led to a situation where Sibley staff would not ask many questions due to the difficulty in obtaining data.

Teck Cominco Limited, a diversified mining company, needed a platform for turning large volumes of transactional data into usable business information. The company's ERP solution met all of management’s data capture requirements, but its cumbersome reporting capabilities proved inhibitive to providing the key performance indicator (KPI) information necessary for quickly recognizing trends and driving improvements. Temporary, Excel-based systems were implemented to deliver KPI data to management. These unsustainable, labor-intensive systems required IT managers to dedicate one day per month to prepare and distribute reports for process capability and maintenance KPI reviews. The reports provided only static views of information with a narrow focus on a single topic from a single subject area. Access to and value of overall information needed to be significantly and quickly improved.

University of Maryland University College (UMUC) was facing the challenge of organizing and deriving insights from four core datasets, totaling 10 terabytes. These datasets included data related to online student activities, student enrollments, financials, and customer relationship management. The data was stored in a mix of on-premises Oracle and SQL Server databases, Salesforce, and other cloud-based service applications. UMUC’s challenge was to aggregate these disparate data sources, normalize the data, and then load it into a data warehouse for analysis. This was a classic ‘data integration challenge’ that included extracting data from source systems, staging the data in a relational database and applying transformations, loading data into a data warehouse, and running analytics and providing a visualization layer.

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.

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.

VodafoneZiggo, a merger of two prominent providers in the Netherlands, faced a complex environment with many legacy tools, different techniques, and processes. The company had over 10,000 reports, leading to many versions of the truth. The company needed a tool that would enable it to merge all its data and processes. The company's ambition was to create the most enjoyable digital customer experience, blending the best of technology and human interaction in a personal, instant, and easy way. They also aimed to be the employer of choice.

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 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.

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.