Organizations have gone through significant digital transformation over time, geared toward the integration and optimization of internal processes through the adoption of advanced technology systems. This path commonly begins with the implementation of an ERP system, such as SAP, renowned for its ability to effectively manage business data. The progression toward more complex digitization leads companies to adopt CRM systems to refine customer relationship management and, in parallel, to invest in marketing and digital marketing strategies. The evolution continues with the exploration of automations and the addition of analytical tools, such as Google Analytics and CMP systems for consensus management, highlighting the complexity and challenges in integrating different technologies.
This multiplicity of solutions, each with its own databases and data models, introduces significant difficulties in harmonizing business information.
In addition, in the past, synchronization of data between different platforms required the implementation of batch or on-demand synchronization processes, procedures that added additional layers of complexity to the business workflow.
Today, synchronization processes have improved but new challenges have been added, such as consent management, which must be consistent across all systems. The segregation of data into isolated silos makes it difficult to keep information up-to-date and consistent, requiring considerable effort on the part of IT teams to integrate and update data.
The adoption of a Modern Data Stack represents a strategic shift in this direction, proposing a model in which a single cloud database serves as the central core of integration. This approach facilitates smooth communication between the different technologies adopted by the enterprise, enabling more responsive and agile data management. Instead of operating in independent silos, data are centralized at a single point of truth, facilitating real-time updating and access to information.
In the following paragraphs, we will explore a number of use cases that benefit from adopting a Modern Data Stack as their technology infrastructure.
Dynamic control of user consents
The introduction of the General Data Protection Regulation (GDPR) has significantly redefined the privacy landscape, marking a turning point for companies operating in the digital sector. This regulatory evolution, along with the recent Digital Acts, has placed particular focus on tech giants such as Google, which handle considerable volumes of user data. These regulations require these entities to obtain explicit consents from users, given their dominant position in the market. As a result, companies are now required to update their consent policies, making them mandatory and compliant with new specifications.
Managing user consents presents itself as a complex and dynamic challenge, requiring constant attention to changes in user behavior and new consents provided through different interfaces. This complexity extends to the need to synchronize consents obtained through various channels, such as physical stores and websites, to ensure that user preferences are respected across all platforms.
Implementing a Modern Data Stack that centralizes user consents in a single holistic hub emerges as a strategic solution to simplify consent management. Such an approach not only facilitates compliance with current regulations but also makes companies more agile in adapting to future legislative developments.
In addition, the adoption of consent mode v2 is greatly affecting practices such as customer matching, a technique that allows companies to send data sets to ad platforms to optimize ad retargeting. Recent changes introduced by Google require that companies provide not only user data but also confirmation that users have explicitly consented to the sharing of their data and ad personalization.
Consent management platforms that interact with solutions such as Google BigQuery and CRM systems are key to synchronizing consents and dynamically managing user preferences. This allows data to be lawfully transferred to advertising players, ensuring that retargeting is always based on valid and up-to-date consents.
Failure to adapt to these dynamics not only risks undermining brand trust but also carries legal implications. Flexibility in managing consents, therefore, becomes essential to navigate the evolving regulatory and technological landscape, ensuring the ability to adapt quickly to changes in the marketplace and user needs.
Performance optimization of automated campaigns
Large digital advertising platforms are proposing with increasing insistence the adoption of automated advertising campaigns. Although these offer the advantage of simplified management and potential optimization based on advanced algorithms, a significant dilemma emerges regarding the alignment between business goals and values and actual campaign performance. The discrepancy between companies’ expectations and the results achieved by automated campaigns can be attributed to the lack of awareness of algorithms regarding the specific goals and values that companies intend to pursue. This situation manifests itself, for example, when a company finds itself prioritizing the sale of products with higher margins, without this principle being effectively reflected in the strategies of automated advertising campaigns.
In this context, there emerges the need for constructive dialogue and operational synergy between two historically disjointed domains: the ERP, repository of product cost and margin information, and advertising campaign management platforms. The challenge is to effectively integrate these two realities so that data can be transferred for campaign optimization.
The traditional, old-fashioned Data Stack-based solution faces inherent problems related to real-time synchronization, user consent management, and the need to integrate data with additional information from different business areas. To overcome these challenges and implement truly effective advertising campaigns in the age of hyper-personalization, it is essential to implement a synchronization system that operates in real time or, at the very least, within a short time frame. Such a system is not limited to sequential data updates, but adopts an update model based on listening to business events, thus ensuring the immediate availability of relevant information to optimize campaigns in response to specific marketing stimuli.
This evolution implies a substantial rapprochement between IT and marketing teams, which are working closely together to fully understand different use cases and to make the most of the potential offered by the technology. The close interaction between these two businesses not only facilitates knowledge sharing and optimization of marketing strategies, but also marks a crucial step toward a future in which campaign performance can effectively reflect corporate goals and values. The restructuring of the data management system, centered on the Modern Data Stack, together with an ongoing dialogue between the IT and marketing departments, thus emerges as a key element in the success of advertising campaigns.
Creating a personalized user experience on the website
One of the most ambitious and sought-after challenges is the implementation of hyper-personalization. This concept refers to the creation of highly personalized Web experiences for users, based on their specific characteristics and preferences. Although it is a highly desired goal of companies, many face substantial difficulties in actually achieving it due to technical and organizational complexities.
The heart of the problem lies in the difficulty of synchronizing detailed information about users with their experience on the Web in real time. Ideally, companies would like to be able to recognize a key user with one or more marked interests and as soon as he or she accesses the website, personalize the content displayed according to his or her specific needs and preferences. However, only a limited number of companies succeed in translating this aspiration into reality.
To effectively enable this type of use case, it is essential to have a Modern Data Stack. By applying artificial intelligence models to centralized data, valuable information such as interests, cluster membership, scoring and Customer Lifetime Value ratings can be obtained, which can be used to modulate the Web experience in a highly personalized way.
This personalization strategy, once the exclusive prerogative of giants such as Amazon and Netflix, is also becoming accessible to small and medium-sized businesses thanks to the transition to more agile and less expensive technology stacks. In fact, the adoption of a Modern Data Stack not only significantly reduces the investment required to implement hyper-personalization, but also increases the ability of companies to successfully activate these strategies, thereby democratizing access to a level of personalization that was previously considered prohibitively expensive for anyone outside of the big players in the market.
Generation of dashboards on customer actions and business performance
In a technology ecosystem where systems and platforms are often disconnected and require continuous updates, creating integrative dashboards can prove challenging. However, by centralizing data on a CDW, companies can overcome these obstacles, facilitating the correlation of disparate information and significantly accelerating decision-making.
This tool enables companies to identify the most effective marketing campaigns by identifying those that generate the “top clients.” Such seemingly simple insight reveals its crucial importance for business strategies.
The adoption of a comprehensive Customer Data Platform, equipped with state-of-the-art artificial intelligence algorithms, promises to further elevate an organization’s analytical capabilities. These advanced systems are designed to work optimally with data collected directly from the CDP. However, it is crucial to recognize that the effectiveness of such algorithms is inherently linked to the quality and completeness of the data on which they operate. Limitations in integrating data from different sources, can significantly impair the performance of the algorithms, making the investment less fruitful than potentially possible.
In the face of these challenges, it becomes clear that the quality of artificial intelligence algorithms and the availability of diverse data streams are both crucial. Without proper centralization of data, even the most advanced algorithms risk delivering biased results, significantly limiting their added value to the business. Against this backdrop, an organization’s data management strategy cannot ignore a thorough consideration of the choice of technologies and their integration in order to take full advantage of the potential offered by artificial intelligence and first-party data.
This is the great challenge facing modern companies: balancing high technology quality with the need for a cohesive and integrated data ecosystem to effectively transform information into strategic insights.
