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Data Architecture

Over the past decade, businesses have been flooded with an avalanche of data. Some estimates indicate that the total global volume of data is expected to grow to 181 zettabytes by 2025. This data wave has created compelling strategic opportunities for adaptive companies primed to influence decision-making and control operational change.

Here we are, a decade later, engrossed with countless technologies, applications, solutions, data-aggregation platforms and visualization techniques. However, many businesses lack the infrastructure to fulfill the promises that came with the emergence of big data. According to Harvard Business Review, less than half of an organization’s structured data is actively used in making decisions.

The problem isn’t data. The problem is the cumbersome processes, obsolete technologies and inflexible legacy infrastructure that have seen too much investment for organizations to shed. This is why businesses need a full-spectrum data architecture that can sustain the requirements of every facet of an organization—from R&D to marketing to culture.

What is Data Architecture?

Data architecture is a blueprint for standardizing the process of data collection, storage, transformation, distribution and usage. It is an effectual data strategy that sets rules and policies to determine how data is stored, processed and accessed within an organization.

Even today, IT teams oversee the flow of data in many organizations. Suppose a business analyst needs to analyze historical and real-time data to pinpoint trends and anomalies and make informed decisions. For that, they ought to collaborate with the IT team to get access to a data stream. Sometimes, even after lingering for a certain period, it’s still not guaranteed that the analyst got what they asked. This is one of the many inconvenient steps that could result in overlooked opportunities and create innumerable bottlenecks for individuals by confining their ability to analyze data effectively.

Organizations need to understand that data architecture is more critical than the data itself. A modern data architecture can align IT and business users to make them function on a common ground. It can democratize data access to enterprise-wide users and entrust them to make fast decisions.

Data Architecture Evolution: The Past, Present and the Future

The transition in the data architecture paradigm has transformed how enterprises harness, operate, process and derive insights from data. Let’s see how it has evolved over the years:

The Era of Hierarchical and Network Databases

In the 1970s, data was handled using hierarchical and network databases. The data in these models was accumulated in either one-to-many or many-to-many relationships.

In the hierarchical model, data was structured in a tree-like format. In this format, every child node has only one parent, but a parent can have numerous children. The model efficiently regained data but fell short in addressing complex or interrelated data. On the other hand, the network model was devised to handle complex relationships by allowing the child node to have multiple parents.

Both models had stringent schemas that made them incompetent to handle ad-hoc queries and for organizations requiring flexible data management.

The Revolution of the Relational Model

The 1970s also witnessed the inception of the relational model that first introduced the concept of data independence. Edgar Frank Codd, a computer scientist and mathematician, was the first to devise the notion of separating the logical organization of data from its physical storage. It uses a set of tables to represent data and its relationship. Each table has multiple columns, and each column has a distinctive name. This model depicted complex data with flexibility using a declarative language – SQL.

The Arrival of Client-Server Architecture

The early 90s witnessed the rise of a client-server model that drove away from centralized mainframe systems towards distributed systems. The architecture split accountabilities between servers and clients, meaning that the database software could live on a server and data could be obtainable on client machines for users and applications. The model enriched scalability and accessibility to govern growing data volumes and user concurrency.

The Emergence of Traditional Data Warehousing

In the late 90s, organizations acknowledged that they needed a data architecture that could help them perform strategic data analysis for data-driven decision-making. The trouble with earlier data architectures lies in how they are used to store and analyze data.

To resolve this issue, Bill Inmon and Ralph Kimball originated a centralized repository concept called a data warehouse, which collates data from disparate sources and provides a unified view that organizations can use for analysis and reporting pursuits. In this architecture, data existed in a relational database and was arranged in an OLAP cube model. It allowed users to fire complex ad-hoc queries and emanate intelligent insights from their data.

Fast Forward to the Era of Big Data and Hadoop

The 2000s brought the wave of internet, social media and IoT devices, drastically increasing data volumes. Traditional data warehouses failed to abide massive amounts of data generated at high velocity.

Doug Cutting and Mike Cafarella, in 2005, brought the concept of Hadoop, which was designed to process big data across multiple clusters. Hadoop presented the idea of distributed architecture, meaning data can be stored and processed across innumerable nodes rather than being confined to a single location. The data is stored in the Hadoop Distributed File system and processed by the MapReduce programming model. The elastic and scalable architecture can oversee growing data volumes by adding as many nodes to the cluster as needed.

The Advent of Cloud and Data Lake Architectures

In the 2010s, the cloud computing paradigm profoundly influenced data architecture with the inception of Data Lakes. Unlike data warehouses, a data lake stores raw data in its original format – structured, semi-structured or unstructured until required. It empowers users to perform high-speed analysis on immense data by leveraging the scalability and flexibility of cloud storage. The architecture also facilitated the use of machine learning and predictive modeling for advanced analytics.

Real-Time Analytics: The Need of the Hour

Today, when data can depict an organization’s present and future, businesses are inclined to analyze vast amounts of historical and real-time or near real-time data to identify trends, gain insights and make their operations and processes agile. Modern enterprises are looking for a decentralized architecture that can turn non-technical users into analysts by equipping them with democratized data access and allowing them to predict future outcomes without depending on the IT department.

What is Modern Data Architecture?

The data challenges organizations face today revolve around the scale and speed of analysis that impact data discoverability, governance and reliability. The market is overflowing with tools that can resolve these issues, but what organizations really need is a data architecture that can deliver directional guidance, allow for weighing trade-offs, is domain-agnostic and, at the same time, doesn’t become obsolete.

Based on what organizations need today, modern data architecture refers to a contemporary approach that facilitates designing and implementing such data systems that can effectively address the challenges and opportunities presented by the ever-increasing volume, variety and velocity of data.

Importance of Modern Data Architecture

Every art form, whether it’s music, poetry, or film, has a structure that conveys a story that inspires people because of the effective use of structure. The music follows the following structure: intro, verse, pre-chorus, chorus, bridge and chorus. Likewise, data also requires a well-defined structure.

A modern data architecture represents data in a structure that is both a standard and a communication tool that helps organizations maintain data quality, security and integrity. It also fosters collaboration and trust. An effective data architecture enables organizations to:

  • Acquire a comprehensive view of their data ecosystem.
  • Pinpoint and resolve data-related issues quickly.
  • Standardize data storage and management practices.
  • Enhance data quality and integrity.
  • Ensure security and governance compliance with regulations and standards.
  • Optimize data usage while minimizing redundancies.

Types of Next-Gen Data Architectures

Data’s histrionic growth demands next-gen architectures to deliver consistent, secure and timely analysis. Opting for next-gen architectures should not be about throwing out the entire data regime and starting from scratch; it should be embraced as an evolution. There are two types of next-gen data architectures that can determine a successful path for organizations –

Data Fabric - Most organizations today struggle with complex and cluttered data storage and processing solutions. Between accession, ever-evolving business needs and organic growth, an enterprise could have multiple data warehouses, different analytics platforms for different departments and data transformation practices directed by short-term needs instead of a long-term strategy.

A data fabric architecture could be a convenient option for these enterprises. It can be considered as an overlay that creates a bridge between data, analytics and users. It unifies disparate data sources and applications and focuses on automating data integration, engineering and governance securely between data providers and data consumers. It doesn’t change where or how the data is stored and allows enterprise-wide users to access data without migrating it. While the enterprise data remains disseminated across multiple on-prem and cloud resources, the data fabric architecture makes it appear as if it is stored in a unified source to end users.

Data fabric architecture works on the “active metadata” concept, meaning it identifies hidden patterns in various types of metadata using knowledge graphs, semantics, data mining and machine learning (ML) technology. These hidden insights direct and automate the data value chain.

Data Mesh - Several data management complexities originate because organizations still work on a decades-old tradition of heading data and communed architecture as projects. The tools and techniques they use to implement a particular solution must be established by a small team for a specific purpose. Over time, these narrow techniques complicate architectural design, obscure ownership and create cumbersome rules throughout the organization for access and leverage over data.

Data Mesh architecture addresses these issues structurally by establishing data as a product. This is how it works: internal data experts from different departments take ownership of their domain data and establish data workflow and delivery rules for end-users. For instance, the marketing team collates all the marketing data, and the financial team collates all the financial transactions and figures. The domain experts accumulate data and create data products, which are then shared with data scientists and analysts, who can blend them to use as per their needs. In contrast to the data fabric’s approach of centralization, data mesh works on the notion of decentralized architecture, where domain experts act in a segregated mode but in accord with the uniform standards of interoperability and governance.

Benefits of Modern Data Architecture

There are several benefits of having a modern data architecture

Interoperability: A modern data architecture collects data from various sources and standardizes data formats, APIs, data governance, metadata management, data virtualization and much more to deliver a common language. This could make data exchange between multiple systems easier. Using interoperability, organizations can segregate data and eliminate silos, fostering a more collaborative environment.

Distributed data governance: A modern data architecture disseminates data ownership and governance across domains. Each domain expert establishes data quality standards, privacy policies and regulatory compliance for every domain user. By doing this, organizations can efficaciously manage and govern their data across enterprise-wide teams.

Self-service culture: Modern architectures equip enterprises with democratized data access that empowers business professionals to execute queries and develop reports on their own without any help from IT departments. Users can generate insights to discover trends, prospects and abnormalities, which can help them enhance decision-making.

Reduced redundancy: A modern data architecture can standardize how data is stored and lower duplication, eliciting better data quality and holistic analyses. It can eliminate overlying data fields across diverse sources that can become a reason for inconsistencies, data inaccuracies and overlooked opportunities.

Real-time and near-real-time processing: There are many modern data architectures that underpin real-time or near-real-time data processing, allowing organizations to make decisions based on the most current information. This is notably beneficial in industries where timely insights are vital, such as finance, healthcare and e-commerce.

Elastic scaling: Many organizations are adopting computing data architectures that are deployment-unaware, meaning the capability to run applications on any environment such as cloud, on-premises, multi-cloud, etc. These architectures have a scalable infrastructure to handle heavy workloads. Also, they can facilitate dynamic resource allocation and auto-scaling to ensure consistent performance during fluctuating workloads.

Cost optimization: Due to growing data volumes, organizations are opting for the cloud and data lake architectures to leverage their scalable infrastructure and accommodate increasing volumes without spending on new hardware. Mainly, these architectures employ cloud-computing platforms that provide usage-based pricing models, so users pay only for the resources they use.

Key Steps for Creating a Modern Data Architecture

Work closely with every business unit to develop a robust data strategy – Understanding every business unit’s requirement and developing a modern data architecture tuned with the latest technology requires a tangible data strategy. A reliable data strategy gives a broad perspective of what companies want to achieve with their data and why. Majorly, it provides a framework for data management that acts as a roadmap to improve the synchronization of data processes, technologies and operations, effectively managing massive volumes of data.

Prioritize data governance – A well-managed data governance strategy helps secure an organization’s data assets and avoid probable ramifications such as financial penalties, brand damage and the loss of customer trust. Organizations should include data discovery and automated policy management in their data architecture. Data discovery enables organizations to create a valuable data catalog that provides an exhaustive inventory of all the data assets across the organization. A potent data catalog ensures that the data is accurate and comprehensive and can be stored and employed from a central location. AI-enabled automation for policy management can assist data breaches faster and can control intentional or unintentional unauthorized access to data. It enables data access to only authorized users and safeguards the centralized records while adhering to compliance laws.

Build an architecture that can adapt to changing business requirements – The foundation of modern architecture is strengthened by analyzing legacy systems and pinpointing dead-end solutions that can intercept the organization’s growth. The architecture should be agile enough to accommodate a cloud-based alternative that can perform functions that legacy systems could not due to the massive data volumes. Choose an extensible cloud-native platform that can be built on as operations grow.

Develop an architecture that can support historical and real-time streaming data processing – A modern data architecture should be built with diverse data processing and analytics techniques to support batch processing, stream processing, and real-time analytics. Organizations should adopt tools capable of addressing high-volume historical and live data streams from numerous transactional applications, devices and sensors across the Internet of Things.

Data architecture should enable self-serve data analytics – Modern data architecture should empower business users and analysts with self-service capabilities to access, analyze and visualize data. Organizations should adopt user-friendly tools for data discovery, data preparation and interactive dashboards to facilitate data exploration and insights generation. Providing democratized data access to business users empowers them to make intelligent decisions instantly.

Principles of Modern Data Architecture

There are five essential principles of modern data architecture:

Regard Data as a Strategic Asset: Data can open doors to new opportunities and pinpoint oddities to create value and make strategic decisions. Therefore, it is critical to provide democratized data access to make data accessible to business units across the organization, bridging the gap between data and non-data experts.

Use Multiple Interfaces to Consume Data: Today, every organization desires to build a data-driven culture, but storing data in one place isn’t helping. While developing a modern data architecture, organizations must use multiple data storage platforms such as data warehouses, data lakes and data marts to meet scalability requirements. The aim is to make data move freely among different data structures so that users from all business units can consume it as a shared asset.

Enable Security and Access Controls: Implementing robust access control policies to limit unauthorized data access is integral to modern data architectures. Data lineage tracking can help trace the steps of the complete data lifecycle, including its origins, movements and transformations.

Ensure Everyone Speaks the Same Language: Every modern data architecture has a universal semantic layer to help organizations establish a common terminology by standardizing business logic, metrics and calculations. Data cataloging can help organizations consistently understand their data assets, making it easily discoverable to users who need it.

Eliminate Data Copies and Movement: Every time organizations move their data from one interface to another, there is an impact on cost, accuracy and time. Cloud data platforms and distributed file systems promise a multi-structured, multi-workload environment for the parallel processing of massive datasets. These platforms can handle growing data volumes as they can scale linearly. Modern enterprise architectures eliminate the need for unnecessary data movement — reducing cost, up-to-date information, hence optimizing overall data agility.

What are the Risks of Bad Data Architecture Design?

The risks of having a bad data architecture design include:

  • Organizations with inadequate data architecture may end up having inaccurate, incomplete, inconsistent or outdated data that can lead to unreliable insights, incorrect decisions, customer dissatisfaction, compliance issues and wasted resources.
  • Insufficient or inaccessible data access to business users leads to missed opportunities, delayed processes, reduced productivity and loss of trust.
  • Inadequate data security measures can provide unauthorized users or malicious access, resulting in reputational damage, legal liability, financial losses and regulatory penalties.
  • Poor data architecture can present slow or inefficient data processing that can directly impact user experience, business outcomes and operational efficiency.

Data Architecture Vs. Data Modeling

Data modeling and data architecture complete each other. Data modeling concentrates on the elements of specific data assets, while data architecture focuses more on creating a data framework that could provide a global view of an organization’s data for usage and management objectives. Data modeling creates a graphical representation of data entities, their attributes and how they relate.

How Does Kyvos Support a Modern Data Architecture Platform?

Kyvos is a GenAI-driven, high-speed data analytics platform that offers limitless scalability while accelerating analytics on billion-scale datasets on modern data platforms. The cloud-native platform uses AI-powered smart aggregation technology that modernizes advanced analytics while reducing the time and cost to extract insights. It leverages the flexibility of cloud platforms to build massively scalable data models, eliminating the limitations of traditional OLAP or in-memory solutions to meet the growing analytical needs of an enterprise.

The platform’s universal semantic layer consolidates as much data as needed in a single data model, eliminating the need to create multiple data models. The layer sits between the enterprise data storage system and the BI tool to enable granular-level access control through row- and column-level security at the group and user levels. The three-tiered architecture ensures data protection at multiple levels.

The GenAI capabilities of Kyvos simplify key business calculations, deliver summarized insights and initiate interactive, conversational data exploration. This generates a pathway to streamlined data processing, reducing dependence on specialized skills and enabling swift and easily comprehensible analytics.

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