IKEA, a global home furnishing chain, was grappling with an outdated and siloed data process. The company was manually managing large amounts of data from various sources, services, and agencies, which was labor-intensive and inefficient. This data included not only the usual localized website and sales data but also crucial customer data from the IKEA Family Loyalty program. The data team aimed to disseminate this data across the organization, allowing other departments such as finance and e-commerce to gain access. However, the existing process made it challenging to turn this vision into reality. The company needed a solution that would establish data and facts as the foundation to accelerate and enhance IKEA’s overall growth.

Carter’s, a leading children’s clothing brand in America, was facing a significant challenge in managing and analyzing vast amounts of performance marketing data from various sources, including Google SA 360 and Facebook. The team was overwhelmed with the manual task of collecting data using spreadsheets, a process that was not only laborious but also considered low-value. The need for a more efficient approach to manage and process data was evident, as the team sought to understand customer interactions with their campaigns and improve future performance. The goal was to examine specific data points to gain insights into potential and existing customers' behavior.

The Philadelphia 76ers, a top NBA franchise under Harris Blitzer Sports & Entertainment (HBSE), faced a significant challenge in managing their complex data ecosystems. These ecosystems included online advertising platforms, ticketing systems, and audience engagement data. The data team was tasked with managing and aggregating 130 diverse data sources into an Excel spreadsheet, a process that was chaotic, overwhelming, and time-consuming. It took the team 10 hours a week just to extract the data and up to a few weeks to connect to a new data source. The urgency of this task was further amplified by the fixed timelines of the events-driven industry, necessitating the ability to pull and manage data quickly.

Colgate-Palmolive's Online Acceleration Center of Excellence in London was focused on transforming its digital offering in markets throughout the EU. However, reporting on key digital advertising parameters was largely manual. Local media teams logged into various ad platforms, collected data, and inserted them into Excel templates to create bi-weekly reports. These reports were then sent to brand managers in respective markets and the local agency teams, with no automated flow of information. The process was slow and time-consuming, involving over 25 people in the EU region. Furthermore, the data was practically out of date by the time it arrived with the analysts and decision makers. The reports were distributed via email, clogging up inboxes and increasing the possibility of a data discrepancy.

The Philadelphia 76ers, a part of Harris Blitzer Sports & Entertainment’s (HBSE) portfolio, had a data ecosystem consisting of over 130 data sources. Managing and expanding this data infrastructure was becoming increasingly difficult and time-consuming. The process of connecting to a new data source could take weeks, severely limiting the bandwidth of the data team. The fixed timelines surrounding live events made flexibility and agility of data operations paramount. The existing data consolidation procedure was not scalable and was preventing the team from fully focusing on uncovering the insights the data could provide.

IKEA Austria was facing challenges in consolidating data from various sources, which was crucial for understanding the needs of its customers and preparing for future growth. The company was dealing with multiple data sources and using the services of several agencies, which led to data silos and unavailability of data. Data quality was also a significant issue, with different KPIs and naming conventions used for campaigns on different channels, making reporting on campaign performance extremely difficult. The company was also facing challenges in terms of data accessibility, with global teams at IKEA having to wait for days, even weeks, for the information they needed.

Dassault Systèmes, a global software company, relied heavily on LinkedIn lead generation forms (LGF) to acquire new customers. They ran high volumes of campaigns from over 20 different markets. The challenge was to convert this campaign information into usable datasets for the sales team, which involved manually collecting and transforming the data. Each dataset had to be downloaded, formatted with tracking, and imported into Adobe Campaign, a process that took more than 10 days to complete. This time-consuming process hindered the company's ability to move quickly. Additionally, the lead forms contained personally identifiable information (PII), which required prioritizing customer security, adding to the complexity of handling the data. With such demanding data complexities, Dassault Systèmes needed a faster and more efficient solution to save time and produce usable, accurate data.

Digital Matter, a digital marketing agency, was grappling with the challenge of managing multiple cross-channel campaigns on various marketing platforms for its clients. The team heavily relied on data to set benchmarks, budgets, and ROI, but the process of gathering and analyzing this data was chaotic and manual, lacking a cohesive system. The consultants at Digital Matter were spending a significant amount of time collecting data and creating static reports, many of which were outdated by the time of the next extraction. The manual process also increased the risk of human error, which further fueled uncertainty in their ability to derive accurate, numbers-driven insights.

Fashionette, a leading retailer specializing in designer handbags for German-speaking markets, was facing a significant challenge in managing and integrating data from over 40 sources. The company's data integration tools were not keeping pace with the rapid business growth. The process of logging into various tools, downloading reports, and manually copying numbers into sheets was tedious and time-consuming. The company sought a solution that would integrate all the data into a single, sophisticated platform that would serve departments across the organization. The goal was to gain a comprehensive view of their entire advertising activities, enabling them to analyze and strategically increase ROI, ensuring the company's continued growth and success.

Havas Middle East, a global agency, had developed a client-facing data platform called Automated Data Analytics Manager (ADAM). ADAM was designed to handle client onboarding, target setting, and reporting. However, the platform lacked advanced tools to automate updated reports across various ad platforms. The team had to manually export CSV files to create reports, a process that was slow, stressful, and prone to errors. They also found it challenging to keep up with the constant changes from various APIs. These issues prompted the team to search for a more advanced platform that could improve data integration and analysis, enabling them to serve clients more quickly and strategically.

Infobip, a global leader in omnichannel communication, was struggling with manual data collection processes. The growth marketing team was spending valuable time gathering information for lead acquisition and conversion reporting from Google Analytics and various ad platforms such as Facebook, LinkedIn, and Google. As a global company, Infobip’s marketing analytics required two advertising accounts with Facebook in different currencies, making it difficult to gain a holistic view of their advertising spend. The lack of a data platform that could automatically pull the data and provide reports led the team to rely on outdated spreadsheets to merge the aggregated data. This process required the support of Infobip’s business intelligence (BI) data engineers, who invested 10 to 15 hours a month just to consolidate and turn the information into usable datasets. The company needed a data integration solution that would eliminate such manual efforts, that could collect data from website analytics and ad platforms and push it to the company-wide BI tool Qlik Sense.

Mediahub, a leading global media planning and buying agency, was grappling with the stress of creating data reports and analyzing performance for its global clients. The agency's growing data was scattered across various advertising platforms, making it difficult to manually keep up with API changes that required constant monitoring and updating. These API connectors often broke, causing delays and requiring data engineers to spend time and effort fixing them. Despite having a full-time engineer to develop an in-house solution, Mediahub needed to automate the data acquisition and management process to alleviate the frustration and inefficiencies.

ODLO, a global retailer specializing in sports clothing, was facing a significant challenge in tracking and analyzing the performance of their marketing campaigns across various online channels and international retail stores. The team was manually collecting data from disparate marketing tools, a process that was not only time-consuming but also prone to errors. The lack of a consolidated data solution made it difficult to clean, prepare, and report the data, thereby weakening the team's ability to accurately predict return on investment (ROI). The absence of a unified platform for data management was hindering their operational efficiency and decision-making capabilities.

Seven.One Entertainment Group, a German-based innovator in the ad tech industry, was managing over 100 marketing campaigns involving siloed sources of data, including TV, Google and Bing search ads, print, and social channels. The team was manually gathering and breaking down the information using spreadsheets and PDF files, a process that was not only time-consuming but also made it difficult to examine results quickly. Reports were only issued once a week rather than daily, and the company lacked an accurate, overall view across online and offline campaign strategies, leading to uncertainty in decision-making.

Superbet, a leading sports betting and gaming operator, was facing challenges in measuring the effectiveness of their marketing efforts due to the lack of a dedicated marketing data team. They were using various platforms for their operations, but tracking and updating across these platforms was proving to be time-consuming and at times, impossible due to limited resources. The company operates across multiple geographies, with localized campaigns in place across various channels. Therefore, being able to track, analyze, and improve their marketing programs was crucial for their continued growth. The challenge was to find a solution that could streamline their process, improve the speed and accuracy of their marketing data and analysis, and save time.

UPC Switzerland, a dominant player in the telecommunications sector, was struggling with the collection and understanding of large amounts of data from various channels and platforms. The team lacked an automated platform to manage the information, resulting in a tedious process of copying and pasting sales and marketing performance data into individual spreadsheets. The company wanted to issue accurate, updated reports to understand campaign performance rather than spend time and resources on manual data processes. The challenge was to find a solution that could automate and streamline the data management process, enabling the team to focus on analyzing the data and making informed decisions.