Data architecture is evolving, and we are now at a point where data platforms are composed of multiple layers, each serving a specific function. However, with our data needs rising exponentially, layered data architecture has ended up becoming a jigsaw puzzle with no central source of truth to tether all these layers together into a single, irrefutable source of truth.

Furthermore, not all data have the same structure, shape, or size, and different types of data need to be handled differently. This, in turn, leads to a wide variability in how data platforms collect, store, process, and maintain the data. Various applications, in turn, have to retrieve this data from diverse environments. These processes also tend to be recurring in nature, i.e. they need to be repeated for each domain or business team, causing further challenges for data platforms in keeping the data flow consistent and unentangled.

We at FORMCEPT have been at the forefront of solving this problem ever since the beginning of our journey. In this blog, we take the bull by the horns and demonstrate a smarter, simpler, and radically innovative data architecture that lies at the heart of everything we do.

Introduction: What Are ‘Layers’ in Data Architecture?

At the outset, let's clarify what a layer is. In the context of data architecture, a layer is a functional component with a specific role or set of tasks within the data platform. The common layers in data platforms include the Sourcing Layer, the Integration Layer, the Processing Layer, the Storage Layer, the Analytics Layer, the Visualization Layer, the Security Layer, and the Data Governance Layer.

The following image from this article by Deloitte shows the various layers in a typical data platform.

Image source: Deloitte, Link to Page

However, this ‘layer on layer’ architecture is not the cure-all it was once thought to be. This is evident in the fact that even with the most sophisticated (and shockingly expensive) data tools flooding the market, the majority of enterprises are flailing about in the dark.

Truth be told, in the absence of a single, authoritative source of truth backed by robust metadata, the data teams are stuck in a maze of data layers that are neither integrated nor updated in real time.

This means that data teams often have to reinvent the process of making the right data available to the right user every time a new data request arrives, i.e. whenever a new product, insight, or report requires the integration of a new data source or the aggregation of existing data sources in a new way. The problem is further compounded when there is attrition in the data team. This is because the knowledge about which pipelines need to be changed (and how) remains compartmentalized with only a few members of the data team.

Data teams get bombarded with data requests from multiple domain teams. This often results in the modification of multiple data flows to accommodate the changes in the data sources as requested independently by these teams.

How Does Data Get Corrupted as it Passes Through Multiple Layers?

When there is no common thread connecting the different layers, multiple versions of the same data exist simultaneously. This introduces fragility at the very roots of the data lifecycle, making debugging extremely challenging. When data changes hands indiscriminately and gets corrupted at its very core, even the most advanced and sophisticated algorithms won’t yield accurate results.

Below are some of the common ways in which data corruption may occur in the absence of a Global Data Definition which represents the Domain Ontology for all enterprise data across all layers.

Schema Breakage: Most data tools today are schema-dependent. If the schema of a dataset changes at any one location and is not updated in other locations, downstream processes break down, and ETL process disruptions occur.
Semantic Drift: Semantic drift happens when the meaning or context of the same piece of data varies across multiple layers due to the absence of a Global Data Definition that represents the Domain Ontology. Additionally, semantic drift is also induced by a static ontology. Modern data platforms must evolve their ontologies by continuously adapting to the shifts in the nuances of the relevant domain.
Data Unavailability: Logical errors in data access may occur due to structural or infrastructure problems affecting the platform's stability. Such errors may originate at the source, during pipeline development, or even across downstream processes. Logical errors that affect just-in-time data availability are usually closely linked to the lack of a universal definition of data.
Erosion of Trust: Ensuring alignment between data and stakeholders is crucial to securing trust in data. This requires consolidating data changes centrally, reconciling conflicting data between multiple sources, and embedding business logic within each layer of the data architecture.

Is Tedious Debugging the Answer?

The short answer is–no.

Here is the long answer: Many data platforms take the flawed approach of tediously cleaning the data after the above issues have already crept in. Instead of starting with a global data definition right at the beginning, they take a post-facto approach.

This method of post-facto debugging adopted by most platforms has failed and also caused massive expenditures to enterprises. By the time the cleaning process begins, it is already too late. The data is already an enormous, unmanageable mess by then.

Rather than focusing extensively on testing and scrutinizing the data itself, the way forward is to transform the way data is sourced, processed, and utilized across various layers using a Global Data Definition that represents the Domain Ontology and is backed by robust metadata.

This is where MECBot comes in.

Introducing MECBot

Solving Enterprise Data Problems with a Single Source of Truth

MECBot by FORMCEPT is a premier data excellence platform designed to address critical enterprise data challenges comprehensively. MECBot facilitates insight-driven decision-making without dependencies on underlying databases or data structures. This distinguished feature has positioned MECBot as the preferred data analytics solution for numerous Fortune 1000 clients worldwide, spanning industries such as Banking, Insurance, Retail, Sports, Healthcare, and beyond.

Here is a quick video that explains what MECBot can do.

MECBot keeps all enterprise data and data assets pristine, refreshed, usable, and replicable by design. Below are the key components that go into the making of our award-winning product.

MECBot’s Domain Ontology

In MECBot, the data journey begins with an extensible and continuously evolving Domain Ontology which serves as the Global Data Definition for the entire enterprise and provides a centralized and standardized way of organizing data from the perspective of the domain of the business. Domain Ontology lies at the heart of MECBot, and consists of 3 key elements – 'Domain', ‘Entity’, and ‘Attribute’.

For example, in the case of an enterprise in the retail industry, its Domain Retail can be defined by entities such as ‘Customers’, ‘Products’, ‘Stores’, etc. Each entity can further be defined by specific attributes. For example, the entity ‘Customers’ may be defined by attributes such as ‘id’, ‘name’, ‘age’, ‘gender’, and ‘location’, etc.

In MECBot’s Domain Ontology (as shown above), a logical collection of attributes constitutes an ‘Entity’, and a set of ‘Entities’ and their relationships form the domain. This way, even if the underlying attributes are the same for any two or more entities, the moment a value is assigned, a unique ‘Fact’ about each entity is obtained.

While the underlying definition of attributes is the same across entities, the values associated with them at a given point in time establish a unique ‘fact’ about an entity. MECBot's Domain Ontology thus defines all the ‘facts’ using a Global Data Definition and is fundamentally different from traditional databases. With this approach, MECBot eliminates the problems associated with redundant data structures.

At the core of MECBot lies its ability to establish a Global Data Definition for the entire enterprise that evolves over time to keep up with the changes in the data ecosystem. It forms the bedrock of all data or ‘facts, seamlessly unifying and contextualizing the enterprise data at scale.

Employing advanced graph technology, MECBot aggregates enterprise data and comprehends their real-world connections. This data is enriched further through linked Knowledge Bases, culminating in the creation of the Enterprise Knowledge Graph which serves as a dependable source of truth and is securely accessible at all times. Graph technology actualizes a global, comprehensive definition for the entire domain. This enables us to effectively employ the domain ontology across the enterprise without any breaks, gaps, or lapses.

Leveraging advanced metadata capabilities, MECBot enriches data with vital context, including its origin, destination, modifications, contributors, quality, and format. This way MECBot harmonizes disparate data into a unified, dependable, and cohesive data layer, facilitating just-in-time decision-making throughout the organization's lifecycle.

How Does MECBot’s Single Source of Truth Address the Data Corruption Problem?

MECBot offers a consolidated view of the organization's data resources, serving as the trusted and authoritative source for all data governance needs within the organization. At any point in time, MECBot answers 6 key questions about enterprise data:

Where is the data coming from?
Where is it being used? By whom?
What changes have been made to the data?
When were those changes made? By whom?
What are the various data artifacts?
Who are the related data stakeholders?

Thus, MECBot acts as the guardian of all enterprise data, guaranteeing uniform access to data for diverse users through a secure conduit and ensuring round-the-clock observability of data. This empowers decision-makers with accurate, dependable, and just-in-time decision-making on a broad spectrum.

With its focus on prioritizing business needs, MECBot doesn't rely on underlying databases or data structures. It caters to various data user personas from a common, trusted, unified data layer that complies with ISO 27001 and SOC2 Type 2.

Below are the key ways in which MECBot mitigates the risks associated with complex, layered architecture:

Efficient Data Management: MECBot provides a unified view of an organization's data assets, making data management more efficient. It organizes data, establishes connections, and offers essential metadata context for simplified governance, analysis, and discovery.
Improved Decision Support: MECBot uncovers complex data patterns, enabling deeper insights into business operations. This facilitates accurate decision-making just in time.
Promotes Collaboration: MECBot encourages collaboration across teams by offering a shared foundation for a single source of truth. This fosters cohesive data interpretation and analysis, promoting a more productive work environment.
Scalable and Cost-Effective: MECBot is flexible and scalable, adapting to changing data needs efficiently, and optimizes data management processes by reducing silos, which leads to significant cost savings.

Conclusion

Did you know? Switching to MECBot has led to substantial reductions in data engineering efforts and analytics costs for enterprises while boosting their ROI by 3X or more. Experience MECBot today and revolutionize your business intelligence!

Learn more about MECBot here, or request a demo to learn more about how we can address your data needs.