In an era where accessing vast volumes of data poses no challenge, the real trouble lies in making sense of data. Disconnected systems leading to data silos often obscure a unified view, rendering meaningful insights impossible. The concept of the modern data stack (MDS) addresses these challenges by offering an integrated architecture that’s scalable, future-proof and fine-tuned for analytics.
This article aims to explain how modern data stacks enable you to navigate the data deluge, break down silos, and derive actionable insights for your business.
The modern data stack (MDS) refers to the technologies and processes that enable organizations to fully leverage data in the cloud. It provides a modular, integrated architecture to ingest, store, prepare, analyze, and visualize data.
Compared to traditional fragmented architectures, the core assumption of the modern data stack is unified access to data from across the business. By leveraging the scalability of the cloud, the MDS makes large-scale data processing and analysis financially viable for organizations of all sizes.
At the core of the modern data stack is a cloud data warehouse that serves as a repository for all data, providing analysts with a single source of truth. The data warehouse ingests and aggregates data from various online and offline sources.
These data sources include analytics, advertising platforms, testing tools, CRM systems, backend databases and more. Companies can load data from these platforms, send it to a cloud-based analytical database, and finally create a data model to merge it into comprehensive customer information.
Finally, once enriched in this way, the data is sent to various data visualization and BI tools to provide deeper insights into the performance of different activities and product features.
The key components of a modern data stack are:
This is where raw, unstructured data originates from within and outside the organization. Data sources can include:
- Databases (relational, NoSQL, etc.)
- SaaS applications (CRM, ERP, marketing platforms)
- Mobile and web apps
- IoT sensors and devices
- Social media
- Ecommerce platforms
- Streaming data from APIs
Organizations can have hundreds of fragmented data sources. The modern data stack consolidates information from these sources into a centralized repository.
Since data resides in siloed sources, the first step is ingesting it into a data lake or warehouse. Integration tools extract data from multiple sources, transform it into consistent formats, and then load it into the target repository.
Popular ingestion tools like Fivetran, Stitch, and Airbyte pull data from sources via APIs and database hooks.
This is the central repository where transformed, ready-to-analyze data is stored. Analyzed data was traditionally held in on-premise data warehouses. Modern data stacks leverage infinitely scalable cloud data warehouses like Snowflake, BigQuery, and Redshift.
Data lakes like Amazon S3 provide low-cost storage and flexibility for storing and analyzing raw, unstructured data from disparate sources. They can ingest any data type and format, and work in tandem with warehouses in most stacks.
Data transformation tools like dbt and Dataform help clean up and organize raw data from different sources into the required formats. These tools can be used to create data models, track where data comes from, and check the data’s quality before it’s moved into the warehouse.
A/B testing tools like Optimizely, VWO, and Adobe Target help evaluate and optimize digital experiences by serving different variants of a page or app to users. They provide capabilities to target test segments, analyze performance metrics, and integrate testing data with the broader data stack.
Data visualization and business intelligence tools help stakeholders explore and extract insights from analyzed data through interactive reports, dashboards, and data stories. Integration with the data warehouse provides access to clean, transformed data. Popular data visualization and BI tools include Tableau, Looker, Power BI, Mode, and Quicksight.
Web and mobile application analytics tools play an integral role in modern data stacks. They capture detailed quantitative and qualitative data on how users interact with digital platforms like websites, apps, and SaaS products.
Popular analytics tools like Google Analytics, Piwik PRO Analytics Suite, Adobe Analytics, Mixpanel, and Amplitude record user actions, events, journeys, behavior, demographics, and other engagement data points.
Analytics data enables critical user-centric analyses:
- Understanding user behavior flows and funnels.
- Analyzing feature usage and adoption.
- Attribution modeling and ROI measurement.
- Segmenting users to understand needs.
- Personalizing experiences to drive engagement.
- Predicting churn risk.
- Generating actionable user insights.
Robust web and app analytics remain crucial for understanding digital consumers. And integrating analytics data into the modern stack unlocks more impactful use cases.
Data stacks enable marketing and analytics professionals to drive impactful campaigns, customer insights, and data-driven decision-making. But they need the right tools to take full advantage of a stack’s capabilities.
Modern BI tools like Looker Studio and Tableau simplify data access through intuitive interfaces, powerful analytics, and customizable dashboards. Marketers can quickly uncover customer and campaign insights without coding. Integrating BI tools with data warehouses provides direct access to clean, transformed data.
Marketing analytics platforms like Datorama, Mixpanel, and Heap provide event tracking, funnel analysis, and attribution modeling to optimize campaigns. Tight stack integrations produce a feedback loop between marketing execution and data-driven optimization.
Tag management systems like Google Tag Manager enable marketers to self-serve tracking of events needed for analysis. Marketers can debug and update implementation tags without engineering help. Product usage analytics tools like Pendo also provide behavioral data and user segmentation.
Reverse ETL tools like Hightouch and Census simplify moving analytics data back to operational systems like Marketo and Salesforce. This reduces reliance on engineering resources for critical marketing data flows.
With the proliferation of customer data from multiple stack tools, master data management solutions like Informatica Axon provide a single source of truth for customer entities. This connects identities and provides consistent segmentation across tools.
The breadth of modern data stack tools empowers marketing experts to efficiently leverage data in driving business impact. But these experts need solutions tailored to their skill sets and use cases. Getting the right tools for marketing analytics users is key to maximizing data ROI.
Here is a list of some standard tools used in modern data stacks beyond just data warehouses:
|Data ingestion||Fivetran, Stitch, Airbyte, Kafka|
|Data transformation||dbt, Dataform, Trifacta|
|BI and data visualization||Tableau, Looker Studio, Power BI, Quicksight, Mode|
|Reverse ETL||Hightouch, Census, Hevo, RudderStack|
|Data science and machine learning||Databricks, DataRobot, H2O.ai, Anaconda|
|Data governance||Collibra, Alation, Atlan, Immuta|
|Web analytics||Google Analytics, Piwik PRO Analytics Suite, Adobe Analytics, Mixpanel, Amplitude|
|Product analytics||Pendo, FullStory, Hotjar, Amplitude|
|A/B testing||Optimizely, VWO, Conductrics, Adobe Target|
Having the right tools for each capability allows organizations to build a comprehensive modern data stack tailored to their use cases. The key is integrating these technologies into a modular, scalable architecture.
Constructing a high-performance modern data stack involves carefully evaluating and selecting the right set of technologies across capabilities.
Here are some recommended best practices:
Start by identifying the types of data and sources you need to integrate, the volume and velocity of data, your analytics and reporting needs, downstream use cases, and technical skill levels in your organization. This foundational understanding allows you to define gaps the data stack must address.
Factor in expectations for data volume growth, spikes in usage, new data sources or pipelines, and potential analytics use cases. Choose technologies that can scale flexibly through modular architecture and cloud-native support.
Look for modern tools with intuitive interfaces, self-service access, and low-code capabilities. Complex tools with steep learning curves will severely limit adoption across skill sets.
Security, access controls, data lifecycle management, and compliance with regulations should be top considerations. Identify tools that can fulfill governance requirements out-of-the-box.
Shortlist tools that provide pre-built connectivity and seamless integration with other components needed in your stack. Avoid solutions that operate in silos.
Design your data stack architecture in a modular way so capabilities can be swapped in and out as needed. Use open standards to prevent vendor lock-in. Build APIs and microservices for flexibility.
Leverage free trials and pilots with real datasets from your organization to validate technology choices across parameters. Get hands-on experience before committing to a tool. Carefully assessing your needs and mapping solutions prepares you to build a future-proof modern data stack. Remember to always put business outcomes at the core of your focus.
So why should your business invest in building a modern data stack? Here are some of the key benefits:
- Scalability. The cloud data warehouse can scale up or down on demand, providing flexibility as data volumes and analytics needs change. There is no longer a need for costly on-premise data warehouse infrastructure.
- Agility. New data sources can be quickly ingested and transformations and analyses applied without disrupting existing flows. Faster iteration means faster insights.
- Single source of truth. All data lives in the centralized data warehouse, providing a unified view across units and functions – no more siloed data repositories giving conflicting numbers.
- Deeper analytics. Large amounts of granular data enable more sophisticated analytics using techniques like machine learning. You can drive better insights and decisions.
- Productivity. Less time spent collecting and reconciling data means more time focused on value-added analysis. Self-service analytics reduces dependency on IT and data teams.
- Cost efficiency. Consolidated architecture and leveraging the cloud reduces costs compared to traditional fragmented systems. It lets you free up resources for high-impact projects.
To derive actionable insights from data, modern organizations build robust data stacks. However, a fundamental aspect of this endeavor is selecting the right analytics platform.
As analytics influencer Timo Dechau points out, placing Google at the core of your data management operations may be a viable strategy. However, he also stresses that this is contingent upon the acceptance of the Google Analytics 4 (GA4) architecture, which is much more intricate compared to its predecessor, Google Analytics 3 (GA3). Moreover, sharing data with Google is another consideration that might sway the decision for companies operating in data-sensitive industries.
For those deterred by GA4’s complexity or having reservations about sharing data with Google, the market presents several alternative analytics platforms that resonate with the standard concepts inherent in GA3. These alternatives offer a semblance of familiarity and often provide additional features or different pricing models that might be more aligned with individual or organizational preferences.
The emergence of Google Analytics 4 alternatives reflects a market response to varying business preferences and requirements. For years, Google Analytics has been the industry’s gold standard. However, many businesses reject the complexity of GA4, often in favor of simpler, more privacy-centric alternatives. Deciding what platform to use requires a thorough understanding of the technical merits of each of these platforms and the broader organizational, ethical, and compliance landscapes in which these tools will operate.
If you want to explore GA alternatives, check out our tool comparisons:
By managing data collection, storage, analysis, and visualization, modern data stacks empower organizations to transcend the chaos of data silos.
They help facilitate a coherent understanding and leveraging of data for a whole scope of organizational functions that include:
- Operational analytics. Monitoring and analyzing operational metrics to enhance efficiency and productivity.
- Customer analytics. Understanding customer behavior to improve engagement and drive retention.
- Financial analytics. Assessing financial performance to ensure fiscal health and inform budgeting.
- Supply chain analytics. Optimizing supply chain operations to reduce costs and improve responsiveness.
- Product analytics. Evaluating product usage and performance to guide development and marketing strategies.
- Regulatory compliance and reporting. Ensuring adherence to regulatory requirements and facilitating accurate, timely reporting.
- Predictive and prescriptive analytics. Leveraging historical data to forecast trends and prescribe actionable strategies.
The volume of data generated today is exploding, suggesting a wealth of information at every analyst’s fingertips. But in reality, many organizations struggle to make sense of the data they ingest.
Silos of data spread across disconnected systems prevent you from creating a unified view and make gaining insights difficult. With more data come issues connected with security, access controls, data lifecycle management, and compliance with regulations.
The modern data stack is instrumental in not only breaking down data silos but also in promoting a culture of data-driven decision-making, fostering a continuum of improvement and innovation across organizational domains.