Modern data stack had emerged as the beacon hope when organizations were struggling with the challenges of the traditional data stack. It had an undeniable charm for organizations as it provided solutions for the TDS issues and more. Advanced analytics, streamlined workflows, predictive modeling, and real-time data insights were the MDS promises to catapult businesses into new realms of efficiency and innovation. Yet, beneath this shiny exterior lurks a complex and often costly reality. As businesses rush to integrate the latest technologies, many find themselves entangled in a web of expenses and operational chaos that could aptly be described as an “expensive mess.”
The Promise of the Modern Data Stack
The modern data stack typically encompasses a range of technologies designed to handle various aspects of data processing and analysis. This includes everything from data ingestion and storage solutions, such as data lakes and warehouses, to analytics and business intelligence tools, and increasingly, machine learning and AI-driven platforms. The promise is clear: these tools can help businesses make more informed decisions, understand customer behavior in depth, and identify trends that would be impossible to discern manually.
The Reality of Implementation
However, the implementation of these systems is far from straightforward. One of the main challenges is the integration complexity which inturn brings high resource and tool costs associated with setting up and maintaining such a sophisticated tech stack. Here are some of the hidden and not-so-hidden costs that can turn the dream into a daunting financial burden:
1. High Initial Investment
Deploying a modern data stack often requires significant upfront investment. This includes the cost of software licenses, cloud services, and perhaps most importantly, the hardware infrastructure needed to support these tools. For many small to medium enterprise, these costs can be quite high.
2. Integration Complexities
The integration of various components within the data stack can be a major challenge. Data from different sources often requires extensive cleaning and transformation to be usable, which can consume considerable resources and time. Additionally, ensuring that all components of the stack work harmoniously together requires specialized expertise that many businesses may lack internally.
3. Scaling Costs
As data volumes grow, so do the costs of storage and processing. While cloud-based solutions offer scalability, they can also lead to unpredictable expenses, especially if data usage patterns are not carefully managed. Companies can find themselves paying for excess capacity just to handle peak loads, or on the other side, struggling to scale up quickly enough to meet sudden increases in demand.
4. Talent Shortages
The modern data stack is complex and requires a range of skills to manage effectively. From data engineers and scientists to specialized IT personnel, the demand for talent in this area often outstrips supply, leading to high salaries and recruitment costs. Retaining this talent can also be expensive, as specialists may seek new opportunities in a competitive market.
5. Ongoing Maintenance and Upgrades
Technology evolves rapidly, and keeping a data stack up-to-date can require continuous investment in new software and hardware upgrades. Additionally, the need for ongoing maintenance to ensure systems operate smoothly adds further costs in terms of both time and money.
Is It All Worth It?
Even though MDS is a significant shift in data handling, promising a seamless flow from data to insights, it has resulted in a fragmented collection of tools that over complicate data pipelines. This complexity has aptly earned the ecosystem the nickname “the MAD (ML, AI, & Data) landscape.”
Yes, this image again!
The stack’s complexity not only becomes a headache for an organization, it also ends up costing big bucks as now the organization needs to invest in new tools or get new resources to simplify things.
In recent times, alternatives based on Data Fabric architecture or Dataset-As-A-Service architecture have emerged. They provide a simple solution to this complex problem without the exorbitant costs associated with a MDS.
One such platform is Knowi.
Cost Comparison
| Data Process | Modern Data Stack:Tools and potential costs | New Age: Dataset-As-A-Service |
| ETL / ELT |   $1,000 to $5,000+/month | Included |
| Data processing |   $400 to $1,000+/month | Included |
| Data transformation |   $50 to $500+/month | Included |
| Data visualization |   $500 to $3,000+/month | Included |
| Data governance |   $400 to $1,000+/month | Included |
| Maintenance with Data team | $20k to $30k+/month Data Engineer, Data Analyst, Analytics Engineer | $6,700 to $10k+/month Data Analyst |
| Total Costs | $24k to $43k+/month | $7,700 to $15k+/per month |
The Knowi Approach
Knowi is designed to streamline data management by integrating and simplifying the handling of data from disparate sources. At its core, Knowi utilizes a concept known as ‘dataset as a service,’ which functions like data virtualization while also offering its unique advantages. This system allows users to define specific data handling rules, such as direct query pushdowns or targeted queries to platforms like Snowflake or Redshift. By enabling these configurations at the dataset level, Knowi shields underlying data sources and mitigates the complexities typically associated with data aggregation and processing.
Knowi’s architecture avoids the traditional heavy lifting and shifting of data, instead facilitating transformations at the source within the virtualized layer. This flexible approach minimizes unnecessary data movement, potentially cutting costs by up to 50%. Data sets in Knowi are API-enabled and reusable, promoting an object-oriented methodology where data sets are validated and ready for further operations, such as multi-source joins and transformations. Enhanced with an NLP engine, Knowi enables natural language querying, facilitating user interaction with complex data sets. Additionally, the platform incorporates advanced BI tools, AI-driven insights, and machine learning capabilities, all designed to make data actionable. Customizable alerts, scheduled reporting, and embeddable components like dashboards and NLP bars ensure that insights are readily accessible and actionable, supporting a wide range of data-driven decisions.
