23 February 2024 | 5 minutes of reading time
In the last few years, the data landscape has been subject to many changes, with the Modern Data Stack (MDS) at the heart of these changes. Initially welcomed as a revolutionary approach to data management and analytics, the need for having an MDS has lost its significance over the need for having data activation. Now, as this evolution unfolds in front of the eyes of the data community, a debate arises over the appropriateness of the term 'modern' to describe a practice that has become commonplace. The emerging consensus is trending toward a new descriptor: the “Analytics Stack”, which signals this shift toward more activation-driven data practices.
The rise of the Modern Data Stack started with the departure from traditional, monolithic data management systems towards more flexible, cloud-based alternatives. This shift was driven by the need for scalability, agility, and the ability to leverage diverse data sources seamlessly. The MDS achieved this by integrating a suite of cloud based solutions, each serving a unique function in the data lifecycle from ingestion and storage to analysis and visualization.
Having cloud based solutions was a major leap forward. Organizations were able to enhance their flexibility and adaptability, ensuring it remained robust in the face of rapidly evolving data analytics demands. Yet, the MDS still depended a lot on running services provided by Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP). It still required strong technological knowledge for developing, implementing and maintaining of a modern data stack, making data teams strongly dependent on the availability of data engineers. With the rise of self-service cloud based Software as a Service (SaaS) solutions for ingesting, transforming, storage and exporting of data this dependency decreased significantly.
The transition from a 'modern' data architecture to an analytics architecture is not just semantic. It reflects the architecture's broader role in generating actionable data products, such as insights in dashboards, analysis, and input for activation tools, rather than just storing and processing data. This demonstrates the industry's progress toward activation-driven analytics, which focuses on extracting and leveraging data to activate business tactics that support strategies and use insights to optimize operational efficiency.
As a result, the use of SaaS tools for data processing has increased. The utilization of such tools makes implementations more effortless and less complex, which enables data teams to focus on working with the data instead of working on the data. The selection of the right tool for the job remains crucial for the outcome of the analytics stack and the adoption of such an activation platform in your organization, from usage and from a learning curve perspective.
At i-spark, we welcome the evolution from pure insights to activation driven data analytics because this creates real value for our clients in the field of eCommerce and content providers. As pioneers in this field of data activation, we have been offering our customers actionable data products using best-in-class analysis tools for several years.
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Our preferred tools that typically make up the majority of the composable data platforms that we deliver, represent a blend of functionality, scalability, and innovation. Each of these tools are selected based on our experience and their proven distinctive benefits, the flexibility to meet various use cases and their fit in the data ecosystem:
To be clear: an analytics stack does not consist of all these tools, but is often a carefully chosen selection of tools from this list tailored to the customer's requirements and wishes. These often relate to the functional and non-functional requirements and of course the available budgets.
Our journey with the actionable data started years ago, even before the term “Modern Data Stack” became popular. And over the years we used various terms, from data stack to data pack to data platform to composable, to indicate these modern data platforms. Nowadays we started to use the term Analytics Stack to indicate the full suite of tools, from ingestion to activation, and to emphasize the importance of having actionable analytics over having data. The data platform itself is an essential part of the Analytics Stack and refers to platforms such as Databricks and Snowflake where efficient data storage is combined with the abilities for data/analytics engineering together with the scalability of these platforms.
The evolution from the Modern Data Stack to the Analytics Stack signifies a shift in the data management and analytics landscape. It reflects a deeper integration of data analytics into business decision-making and more importantly the activation of data in operational processes - a trend that demands an agile, insight-driven approach to data stack construction. Our preferred tool stack embodies this philosophy, offering a comprehensive, flexible solution designed to meet the challenges of today's data-driven world.
By carefully selecting tools that not only address current needs but are also adaptable to future demands, organizations can build a data infrastructure that anticipates the evolution of data analytics. As the debate over terminology continues, one thing remains clear: the power of effective use of data is still a game-changer for businesses seeking to grow.
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