Technological evolution in the business environment, particularly in the marketing sector, is undergoing a phase of profound transformation thanks to the introduction and development of the Modern Data Stack paradigm. This new approach is proposed as a significant evolution from traditional systems, representing a contrast to the classic architecture based mainly on closed software with dedicated databases, poorly integrated and in silos.
The classical architecture incorporates management systems, such as ERP and CRM software. These tools are fundamental to the acquisition of structured data, leveraging a solid technological base hinged on relational databases. Manipulation of such data is accomplished by conventional ETL (Extract, Transform, Load) processes, which organize information according to dimensional models. These outline the dimensions and metrics crucial for analysis, facilitating the creation of interactive dashboards for monitoring business performance. In this classical setting, data rarely flow into a central data warehouse to act as a starting point for the development of specialized datamarts.
In the context of business strategies for managing and analyzing customer data, there is a confrontation between traditional approaches, typically in the hands of the IT team, and innovative solutions such as Customer Data Platforms (CDPs), tools favored by marketing teams. CDPs are distinguished by their ability to collect and organize data online, monitoring the interaction of customers and visitors on the company’s various digital channels, such as websites, e-commerce and mobile applications.
This monitoring aims to understand consumer behavior, enabling advanced segmentation based on numerous event and entity data rarely available in CRM systems.
The primary objective of such platforms is to support marketing activities through the creation of customized user segments, which can be activated on various marketing platforms. In fact, through data integration CDPs are able to unify data from different sources, facilitating the classification and identification of customers and their behaviors.
The unified data becomes crucial for the activation of campaigns on mass mailing channels, direct messaging (SMS and WhatsApp) and online advertising platforms (Google Ads, Facebook Ads, LinkedIn Ads, etc.), enabling companies to optimize their digital marketing strategies.
The choice of a CDP is based on its ability to effectively integrate transactional and browsing data. Many platforms neglect integration with offline data, favoring digital.
Separating online and offline data is, therefore, a challenge for companies seeking a holistic view of their customers and operations.
Customization and integration with other channels turn out to be additional complications in the adoption of CDPs, which tend to be perceived as closed solutions with limited opportunities for customization. The issue of data security and compliance with privacy regulations, such as the GDPR in Europe, raises other concerns related to the flexibility of these platforms in responding to constantly updating directives and integrating user consents in an effective and timely manner.
Many companies are trying to find alternatives to overcome the limitations of CDP, and the Modern Data Stack emerges as a viable solution, as it proposes the adoption of an integrated platform that overcomes the division between business functions. This new paradigm is based on the use of state-of-the-art technologies and tools capable of handling large volumes of data, both structured and unstructured, from a variety of sources. The goal is to provide a comprehensive and up-to-date view of business operations, thereby improving decision-making and the effectiveness of marketing strategies.
Through the adoption of more sophisticated data integration solutions, real-time data analytics platforms, and artificial intelligence and machine learning systems, the Modern Data Stack facilitates access to deeper, actionable insights, enabling companies to remain competitive in a rapidly changing business landscape.
Modern data stack: what it means and how it is structured
The concept of Modern Data Stack refers to an innovative architecture, configured primarily on the cloud, that serves as the backbone for enterprise data management.
This complex framework is generally based on leading cloud solutions such as Google Cloud Platform and Amazon AWS, although there are other equally viable and technically advanced platforms.
The Modern Data Stack offers organizations the ability to build a customized technology stack that connects different systems in a way that supports data integration needs efficiently and effectively. At the heart of this solution, we find a single Cloud DataWarehouse, which aggregates enterprise data and then allows all software to use it as an up-to-date, resilient and flexible database.
Flexibility and customization are crucial, as with the Modern Data Stack, companies can enable a wide range of technology tools without the need to increase system complexity and data redundancy, as solutions will interact directly with a single database.
Another key aspect of Modern Data Stack compared to traditional data warehousing projects or CDP purchases is scalability. It allows companies to gradually integrate technologies and solutions, starting with a low initial investment and scaling resources according to future needs, keeping costs proportionate to the value generated.
Data security and compliance with applicable regulations are other strengths of this architecture. By relying on technology frameworks supported by large international entities, companies can ensure high security standards. In addition, the Modern Data Stack facilitates the integration of systems related to user consent, improving permission management and regulatory compliance.
In summary, the Modern Data Stack enables companies to manage data efficiently, supporting innovation and growth in today’s digital environment through a flexible, scalable and secure cloud-based framework that exposes us to various services classifiable in the following six macro areas.
Data Collection
In the context of Data Collection, we include the entire process of accessing information from both online and offline sources, embracing a range of data that varies in terms of structuring. Data characterized by a high degree of organization facilitate the implementation of Extract, Transform and Load procedures due to their predisposition to standardization.
Less structured data, while presenting initial integrative challenges, find in the Modern Data Stack, the necessary tools to be effectively organized through the use of advanced reconciliation and classification techniques. Thus, even information that at first glance may seem ill-fitting with existing systems can now be properly structured and incorporated, contributing significantly to overall analytical capacity.
Data Processing
In the context of data management, the Data Processing operation is critical to ensure that data, once extracted from various sources, are also properly processed to meet specific business needs. Transformation plays a key role in converting raw data into a more meaningful and manageable format, thus enabling more effective analysis and the generation of actionable insights.
To facilitate the ETL process, tools designed to automate and simplify individual steps have emerged on the market.
Data Storage
Modern data warehousing solutions overcome the technological limitations traditionally associated with the analysis of large volumes of information. Platforms such as Google BigQuery and Amazon Redshift represent the vanguard in this area, offering systems no longer anchored exclusively to classical relational databases, but optimized for data warehousing and advanced data analysis. With the adoption of cloud computing, both multinationals and small businesses can now access scalable and flexible infrastructures that were previously precluded due to capacity or cost limitations. The democratization of access to such technologies enables every entity to take full advantage of the potential offered by data analytics, fostering an unprecedented era of innovation in data storage and business intelligence.
Data Visualization
The adoption of interchangeable data visualization tools allows users to select and customize the tools that best suit the specific needs of the project or organization. This modular approach also promotes continuous innovation, enabling the integration of new technologies and methodologies as they become commercially available.
Data Governance
By implementing data governance solutions, companies can not only mitigate the risks of data breaches and noncompliance, but also optimize the management of information assets, thereby improving operational efficiency and fostering a culture of data security within the organization.
Data Pipeline
It includes all those orchestration systems designed to precisely coordinate a set of interconnected activities, ensuring that data flows are constantly updated and synchronized. In addition, these systems offer the flexibility to adapt to new data integration requirements, ensuring that organizations remain competitive in the constantly evolving digital age.
Conclusion
Therefore, the ability to integrate and harmonize data collected through online and offline channels emerges as an indispensable pillar for business strategies. In particular, the analysis of web browsing data, currently dominated by advanced solutions such as Google Analytics, is merged with traditional CRM and ERP systems to create a single picture of the customer.
The cornerstone of this integration lies in the ability to identify a unique ID or “reconciliation key,” which allows a customer’s online behavior to be linked to their actions in the physical world after they have been acquired as a customer.
Identifiers such as CRM Client IDs or hashed emails serve to generate a unique digital footprint that not only ensures complete traceability of the customer journey across online and offline channels but also opens the way for broader strategic thinking. It is, therefore, a critical element for companies that aim to fully understand their customers’ needs and behaviors in order to offer highly personalized services and improve the effectiveness of their marketing and sales strategies.
At Bytek, we distinguish ourselves by implementing significant advances in data management and analysis. Bytek Prediction Platform allows data from different sources to be collected and centralized and then enriched using artificial intelligence algorithms for classification, predictive analysis and segmentation. Subsequently, the enriched data is fed back into the company’s databases, marketing automation and advertising platforms, following the logic of reverse ETL, thus providing the company with valuable information for business purposes and for optimizing internal operations.
A crucial point of our strategy concerns data privacy and security. We ensure that exposed information is always anonymized, ensuring that no personally identifiable information is ever stored within our systems.
Our approach, based on the Modern Data Stack concept, seeks to meet the emerging demand in the global business landscape for greater flexibility and technological adaptability. By creating a modular technology stack that can be configured to the specific needs of the business and the ability to integrate predictive artificial intelligence and segmentation algorithms efficiently, we maximize the value of data and promote more targeted and effective business strategies.
