宜家团队将来自不同来源的大量数据手动管理到电子表格中，因此很难将数据转化为可操作的见解。他们希望在整个组织内扩散重要的客户数据，并将数据建立为增长的基础。

reBuy, a reCommerce company based in Germany, was looking to expand further in Europe. To achieve this, they needed to uncover insights into consumer behaviour data. They used multiple online tools to accumulate valuable data that needed to be organised and analysed regularly. However, they faced challenges with the never-ending flood of incoming data. Manual data preparation was time-consuming and distributing the final reports was tiresome. They tried to tackle their problems with an in-house solution first, transferring data to their warehouse via custom-made APIs. However, this solution proved to be error-prone and the data was not always up-to-date. Simple changes required a complete system update, which was cumbersome and costly. As a result, reBuy started looking for an out-of-the-box solution.

Havas Middle East was facing challenges in data automation and client reporting. They developed a client-facing data platform named ADAM (Automated Data Analytics Manager) to resolve these issues. The goal was to create a standard way to pull data from all platforms and across all clients, and display it in a convenient way for clients to transparently see the results of each campaign and how they relate to agreed targets. Initially, reporting was done by exporting CSV files from various platforms. This process was not efficient and had too many steps, which generated regular data errors. They tried to create a data integration tool in-house, but this proved to be inefficient, as it was difficult to keep up with the changes of the various APIs.

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.

The Austrian National Tourist Office was facing the challenge of managing large amounts of data from internal and third-party sources, especially while overseeing multiple international marketing campaigns simultaneously. The existing IT infrastructure was decentralized and designed for evaluating financial data, which was not suitable for the dynamic marketing landscape. The data collection and transformation process was manual and time-consuming, involving extraction of data from Google Analytics and the social media management tool Sprinklr, and then pasting relevant numbers into spreadsheets to derive useful information.

Carter's 的团队忙于收集和分析来自不同来源（包括 Google SA 360 和 Facebook）的大量绩效营销数据。渠道经理继续使用手动电子表格来收集数据，尽管这被认为是一项低价值且费力的任务。他们希望通过检查特定数据点来了解潜在客户和现有客户如何与营销活动互动，以提高未来的绩效。

费城 76 人队拥有一个复杂的数据生态系统，其中包含 130 个数据源，这些数据源经过管理并汇总到 Excel 电子表格中，每周需要 10 个小时来提取数据并连接到新数据源。

MediaCom, a large global media agency, offers a variety of services to their clients - from strategic consultation to the execution of ROI-driven digital campaigns. In an ever-increasing digital marketing landscape, agencies are facing a growing number of challenges - from mastering each and every new platform, to generating powerful insights from marketing campaigns. Similarly, client expectations have been revised upwards; advertising partners are no longer content with a short Excel report detailing impressions and clicks. In this data-driven world, understanding user behaviour through correlations and attributions is crucial to evaluating a campaign's performance.

达索系统严重依赖 LinkedIn 潜在客户生成表格 (LGF) 来获取新客户。他们手动收集并转换数据，花了10多天的时间才完成。处理个人身份信息 (PII) 增加了数据管理的复杂性。

Digital Matter 面临着一项艰巨的任务，即在许多不同的营销平台上为客户处理多个跨渠道营销活动。该团队严重依赖数据来决定基准、预算和投资回报率，但收集和分析数据是一个繁忙的手动过程，缺乏凝聚力的系统。 Digital Matter 的顾问们渴望有一天他们不必花时间收集数据和拼接静态报告——其中许多报告在下次提取之前就已经过时了。他们希望最大限度地减少人为错误，这进一步加剧了他们获得准确的、数字驱动的见解的能力的不确定性。

电子商务巨头 Fashionette 必须管理来自 40 多个来源的数据，导致登录各种工具、下载报告以及将数字复制到电子表格中的过程繁琐且耗时。他们需要一个解决方案将所有数据集成到一个平台中，以便全面了解其广告活动并提高投资回报率。

哈瓦斯中东公司创建了一个面向客户的数据平台——自动化数据分析管理器（ADAM），涵盖客户引导、设置目标和报告。然而，ADAM 缺乏复杂的工具来自动跨各种广告平台更新报告。该团队手动导出 CSV 文件来创建报告，这是一个压力很大、缓慢且容易出错的过程。他们还努力跟上各种 API 的不断变化。这些挫折促使该集团寻找先进的平台来彻底改革数据集成和分析，以便更快、更具战略性地为客户提供服务。

Infobip 的增长营销团队因手动数据收集流程而陷入困境，浪费了他们宝贵的时间。他们需要一个数据集成解决方案，可以自动从网站分析和广告平台提取数据并将其推送到公司范围内的 BI 工具 Qlik Sense。

Mediahub 团队在为全球客户创建数据报告和分析绩效方面面临挑战。他们的数据分散在各个广告平台上，因此很难手动跟上 API 的变化并导致延迟。

全球零售商 ODLO 缺乏统一的数据解决方案来跟踪 Google 和 Facebook 等在线渠道以及国际零售商店的营销活动绩效。该团队从各种孤立的营销工具中手动收集数据，既耗时又压力很大。他们无法准确预测投资回报率。

七一娱乐集团管理了 100 多个营销活动，涉及孤立的数据源，因此难以快速检查结果，并且缺乏对线上和线下活动策略的准确整体了解。

如果没有专门的营销数据团队，Superbet 无法快速衡量其营销工作的有效性。他们正在使用一系列其他平台，但跨各种 API 进行跟踪和更新非常耗时，有时由于资源有限而无法实现。

电缆巨头 UPC Switzerland 努力收集和理解来自各种渠道和平台的大量数据。该团队缺乏管理信息的自动化平台，因此他们不得不将销售和营销绩效数据复制并粘贴到各个电子表格中。他们希望快速发布准确、更新的报告以了解活动绩效，而不是在手动数据处理上花费时间和资源。

Fashionette, a German ecommerce company specializing in designer handbags, aimed to become a digital pioneer in its core markets. The management team envisioned smart data as the driving force behind this ambition to accelerate decision making, as well as further growth and innovation. The marketing strategy needed a highly sophisticated tool to truly realize such an ambitious goal. For its data-driven daily business, the marketing team needed to integrate over 40 different data sources. To paint a holistic picture of all advertising activities and to analyze their ROI, it was necessary to connect data from all these marketing tools within one system. Combining such data with sales and logistics KPIs was essential for the management team to continue to make the right decisions and lead the company in the right direction.

UPC Switzerland was facing several challenges in their marketing department. The manual collection of performance data from each campaign was time-consuming and prone to errors. There was no standardised approach to budget planning and forecasting, and the company was unable to efficiently connect data from different channels. This resulted in inconsistent and unreliable marketing reports.

iProspect Canada, a performance-focused media agency and part of the dentsu global network, was looking for ways to deliver greater value to clients, based on data. They were employing a lot of manual work on harmonizing data from various data sources, the first challenge was to standardize and automate their reporting processes. This process would include going to each of the platforms, extracting reports and CSV files, and combining them in Excel. This was very tedious and time-consuming, so they wanted to remove this manual segment and move towards a more efficient, automated data environment that would save time, but also let the data speak for itself. With a proper infrastructure in place, they would then need to work out how to get the most valuable insights from the data in the most efficient manner possible.

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.

AnalytixLive, a U.S. data analytics company, was hit hard by the COVID-19 pandemic. The company, which provided revenue management services to the live entertainment industry, saw its business come to a halt as live events were cancelled worldwide. However, as live entertainment shifted to streaming video services and social media, the team saw an opportunity to support their clients with advanced analytics of the engagement data coming from these platforms. The challenge was to seamlessly acquire the new and diverse datasets, which would allow them to successfully pivot their business in this direction.

Digilant, a digital media company, was facing challenges in providing timely reports to its clients. The process was time-consuming and involved manually collecting data from various platforms, importing it into Google Sheets, and then creating client-facing PowerPoint presentations. This process was not only slow but also prone to errors, causing issues both internally and with clients. The team needed a scalable solution that could automate the collection of necessary client data and then visualize and structure it in reports.

Hotspex’s Media Ad-Ops team faced a challenge when it came to collecting campaign performance data from various ad platforms. Most of these were extracted manually, and used for different reports. It was time consuming and impacted the team's ability to focus its attention where it mattered most – identifying insights and optimizing media investment.

ODLO, a sportswear company with a wide retail network and an online store, was struggling with monitoring and analyzing marketing data across different channels and ad platforms. This was time-consuming and difficult, leading to insufficient control over daily marketing spend and difficulty in finding the right marketing mix and efficiently steering the allocated budget. The company's data management difficulties stemmed from the siloed marketing tool landscape, making it tough to collect, process and analyze marketing data. This environment is a mixture of Google Analytics, Google Ads, Facebook Ads, Bing Ads, and other popular platforms, while Microsoft Excel spreadsheets were used to assemble the data manually from all these sources and create reports. Meanwhile, other sectors in the company were building and analyzing transactional reports, drawing information from order management and ERP systems.

Mediahub, a global media planning and buying agency, was facing daily operational difficulties with handling ad performance data for multiple clients coming from multiple advertising platforms. The agency had employed a full-time data engineer to build an in-house solution with API connectors to fetch data from popular ad platforms and store it into the internal database. However, these APIs would often change or update, sometimes overnight and without prior warning, which would break these connectors. The data engineer had to spend a lot of time and effort in fixing them.

Vodafone Italy’s digital marketing team was facing a challenge in linking online prospect activities with lead, sales, and activation data handled offline by many call centers using different CRMs. In a highly competitive market, data reporting and analysis for decision making was often manual and time-consuming. The goal was to unify offline and online data sources and hence extract and analyze actual campaign performances quickly, easily and in a self-service way for various stakeholders.

Business was booming for Cosmonauts & Kings, a political digital agency. The Berlin-based agency was rapidly expanding, but their data infrastructure did not match the pace of growth. Their teams were spending too much time wrangling client data into spreadsheets and creating pivot tables to examine the data and produce reports. They craved a better solution to seamlessly connect and analyze the data from various advertising platforms.