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What is the modern data stack?

Complicated data stack representing the modern data stack

And how do we see it evolving at Scribble Data?

The success of a modern business, ranging from small and medium-sized enterprises to Fortune 500 conglomerates, is now increasingly tied to how firms implement their data infrastructure. We’ve all heard the trope – “data is the new oil” of the digital economy. One thing is clear: information is power, and data analytics can be utilized to empower optimum business decisions rapidly in ever-changing markets and the overall economy. However, raw data itself carries little merit.

To make it worthwhile for businesses, data has to be refined, organized, and compiled, and the combination of technologies that transform data through these mentioned steps is what makes up the “Data Stack.” Here at Scribble Data, we enable data teams to quickly go from raw data to fit-for-purpose rich datasets that power the many business-critical decisions organizations need to make every day.


Problems With the Conventional Data Stack

Most firms have already been utilizing data for analytical purposes for ages; however, it is to be noted that today’s businesses have to make myriad complex decisions for which the data infrastructures designed in the 1990s just don’t cut it. The old world data stack comprises data warehouses that work by centralizing and consolidating large amounts of data. They are designed to support business intelligence (BI) activities, especially data analytics, to fulfill a business’s decision-making needs. Unfortunately, these legacy data tools aren’t very good at solving modern data problems. Considering the limitations of the old data stack, we see that (source):

  • Legacy tools make it harder to achieve business agility.
    Flexibility is essential. A high degree of responsiveness is needed to ensure that enterprises can manage and analyze data quickly for optimum decision-making given various constraints. Now, considering the legacy data tools, most data warehouses are maxing out on their capacity, unable to keep up with all the data requests from users. The business becomes stricken with inefficiencies as overcoming the resultant problems takes up a lot of IT time, with the problems usually compounding over time.

  • It requires a disproportionate degree of management to scale.
    Given its complexities in operation, businesses still resorting to legacy data warehouses have to continually invest in hiring a workforce to manage the old data stacks, even though they are not advancing data analytics and business agility. The nature of these legacy tools demands that more time is spent on system engineering rather than analyzing data itself. The management costs are not only high for small-scale enterprises but even for large corporations beyond the point of optimality of old data stack operation.

  • It lacks mature predictive analytical solutions.
    Legacy data tools struggle to keep up with a contemporary business’s data needs. Automated reports and summaries from software like NetSuite often lack details and specifics. The lack of flexibility means that there can be a significant amount of time between reports. Even when the details are provided, the data might not be in a format that allows for a more readable analysis across time. Furthermore, an inspection of manually exported data through spreadsheet programs like Excel can be repetitive and time-consuming.

Modern Data Stack Is the Answer!

Suppose a firm is going to get ahead of its competitors and adapt itself to the new demands resulting from data modernization. In that case, it is going to want to build a system that allows it to access and analyze data metrics at its own pace, consolidate information from multiple sources and uncover unique insights from complex data sets. In short, businesses need to revolutionize how they store, manage and learn from their data and the answer to what businesses need now is the modern data stack (MDS).

The modern data stack comprises tools that lead primary data through a processing pipeline, transforming the raw data into cleaned, well-organized, aggregated data that can be used for reporting, analytics, feature generation, and modeling. So, why is modern data stack the new industry standard? The reasoning lies partially due to the limitations of old data stacks that sprang up in response to new data demands from companies as a direct consequence of data modernization. We also see that the current data infrastructure is on the brink of transformation, driving the increased adoption and evolution of the modern data stack due to the following developments:

  • Increased adoption of cloud software
    With data warehouses maxing out on operational capacity, businesses move towards cloud-based data stores, workflow, and analytics, building a more flexible data infrastructure that forms the foundation of the modern data stack.

  • Accessible data availability
    Increased adoption of cloud-based tech and the growth of software users worldwide have generated data at an exponential rate. This shift has prompted businesses to resort to modern data stacks as the business sector becomes more “data thirsty.” The legacy data tools are simply not designed to keep up with the implementation of data-driven decision-making in every aspect of business strategy.

  • Data as a differentiator
    The most significant change occurring today is the realization that data as an asset turns out to be a far more competitive differentiator (the key to finding your target audience) than traditional tangible assets, as it allows companies to act swiftly on opportunities and threats. The modern data stack provides each functional department within a firm with easy access to pertinent data to inform key decisions.

  • Sophistication in data analytics
    As companies adapt to data modernization, we see an increased demand for professions in the capacity of data scientists, data engineers, and machine learning engineers who manipulate and analyze data in these new cloud ecosystems. Unfortunately, there is a talent gap, and not enough data specialists are available to fulfill these roles. This, in turn, drives the demand for new software in a data infrastructure that can automate the relevant tasks, and the modern data stack does just that.

Components of a Modern Data Stack

A good analogy for the structure of modern data stack is cooking. Before you end up with a warm, delicious meal, you need to source your ingredients and cook everything together in certain steps to get to the end product. Much like a kitchen is essential to ensure all ingredients are in one place, cloud warehouses are the center point of the modern data stack. While companies will have different pipelines to suit their needs, general commonalities are found in the modern data stack:

  1. Data wrangling
    Through the process of data ingestion, massive raw data streams are gathered from multiple sources and are cleaned up before their insertion into the data store. This process is referred to as ELT: Extract, Load, Transform.

  2. Data storage
    Once the data has been refined and aggregated, it is stored before further analysis. Considering the limitations of spreadsheet applications and traditional data warehouses, most businesses now opt for cloud-based warehouses.

  3. Data Analysis
    At this point, organizations seek to draw meaningful insights from their data sets by funneling them through machine learning models and business analytics (BI) in collaboration with various data scientists and machine learning engineers.

Drivers of the MDS ecosystem

Data infrastructure is a $200 billion market and is rightfully expected to grow exponentially in the following decade (source). In analyzing the modern data stack, we are not only interested in the underlying data infrastructure technologies but also the new tools emerging from BI and AI/ML disciplines. For instance, data organizations are increasingly adopting “feature stores”, systems that act as the single source of truth for processed feature sets while allowing for their trustable, auditable, and schedulable generation and updating, and also surfacing contextual understanding.

Currently, BI and AI/ML tools sit at the top of the stack, coexisting in their data analytics function despite being on opposite ends of the spectrum. Unfortunately, the limitations of BI tools with more large and complex data sets, coupled with the lack of data science team’s bandwidth where the use of ML models is not feasible, means that a significant number of use cases fall in the no man’s land between AI and BI application. While the current feature stores have catered to big tech well, the fact remains that there is an entirely new market as decentralized, nimble decision-making in business functions is the need of the hour. With these cases falling into the no man’s land, Scribble Data presents a solution for these smaller businesses and their use cases since most feature stores were designed to overcome the ML challenges of tech giants.

Scribble Data’s modular feature store: Enrich takes care of business’s ML and Sub-ML use cases. We resort to a Sub-ML approach in scenarios where it is not feasible for companies to opt for complex ML models given time and cost constraints. Instead of building a model from scratch, we simultaneously follow a series of incremental steps to discover the problem, value, and approach. Enrich not only builds and prepares data sets and features, but it also enables data practitioners to understand its context and consume data assets and features across teams – internal and external, technical and non-technical. It also provides a features app store that integrates with customers’ existing data stacks, aiding in metadata management and cataloging, with a user-friendly, low-code configurable interface. The feature apps provide multiple functionalities such as metadata management, data cataloging, metrics apps to search and store KPIs and metrics across the organization, and many more. At Scribble Data, we ensure that the small businesses and their use cases are not abandoned in the no man’s land.

Looking ahead

The modern data stack is an exciting space with many new vendors entering the market every day. However, the modern stack shouldn’t just cater to the needs of tech giants but also to the wider set of individuals within the business and focus more on business outcomes. It is very likely that soon, it will become verticalized, becoming increasingly gelled in with small and medium scale enterprises through the implementation of the Sub-ML use case approach.

According to researchers at Gartner, 70% of the organizations will shift their focus from big to small and wide data by 2025, and here at Scribble Data, we’re betting on Sub-ML to take advantage of this shift towards small and comprehensive data. We are already seeing 8x faster time to value compared to the traditional ML models as our customers continue to deploy Sub-ML use cases. The verticalization of the modern data stack is evident as we access the diverse nature of our customers. Ranging from large enterprises to mid-market, small and medium scale businesses.

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