Over the last century, data has become the lifeblood of every organization, from e-commerce giants to healthcare providers and governments. Effectively collecting and managing this data can provide organizations with valuable insights to aid in decision-making, yet it is a daunting task.
Despite the importance of data, CIOinsight claims that only 10% of organizations consider themselves good at managing data analytics. Recognizing this significant gap in data utilization, organizations are aggressively adopting modern data architectures to close the gap.
Data architectures are structured frameworks and systems that define how data is organized, integrated and accessed within an organization. Architecture defines the blueprint and establishes guiding principles for data and its flow through the data storage system.
This article discusses the evolution of data architecture, the underlying principles, and the benefits of adopting a modern data architecture to effectively manage an organization's data.
The Evolution of Data Architecture
Over the years, data architectures have evolved to accommodate growing business needs. A noteworthy shift discussed below is the transition of data architecture from logical warehouses to data fabrics.
1. Logical warehouse
Logical warehouses, also known as data warehouses, have been the foundation of data management for decades. These data warehouses are central repositories designed to store data from disparate sources such as transactional systems, application log files, or relational databases, etc., thereby providing a consolidated view of information.
Typically, a logical warehouse uses an extract, transform, and load (ETL) process to extract data from source systems, transform it to ensure consistency, and load it into the warehouse. Logical warehouses are used only for query and analysis and often contain large amounts of historical data.
Logical Warehouse Challenges
While logical warehouses serve their purpose, they face several challenges as data volumes increase. Some major limitations include the following:
- Data silos: Logical warehouses often lead to data silos, where different departments or teams maintain their own siled data sets, leading to inconsistencies and duplication.
- Performance: As data has to go through multiple processes and stages before it can be used for analysis, this greatly affects the performance of the data warehouse.
- Scalability: Implementing a data warehouse is complex and expensive due to hardware limitations. It also requires expertise in data modeling, ETL processes, and database management, making it more difficult to handle data explosions.
2. Data Lake
To address the challenges of data warehousing, the data lake architecture was introduced in 2010. While a data lake architecture is very similar to a data warehouse, the difference between the two is that a data lake also applies to semi-structured and unstructured data.
The ability of data lakes to store large amounts of data in natural or raw formats helps us:
- Use data from multiple sources regardless of data type (unstructured, semi-structured, structured);
- Create focused segments of data to meet the needs of specific use cases;
- Uncover new insights using advanced analytics and machine learning techniques without being limited by predefined patterns.
Data Lake Challenges
This open-format nature of data lakes makes them more popular than data warehouses. However, data lakes present challenges as data ingested without strict normalization leads to inconsistencies in the database. Additionally, data stored in data lakes requires extensive transformation and integration efforts before it can be used for complex and time-consuming analysis.
3. Data Grid
Data grid architecture is an emerging approach that provides an alternative to central data lake architecture. Created by Zhamak Dehghani in 2019, the data grid is a decentralized data architecture that organizes data by specific business domains.
Improve data quality and governance by introducing domain-oriented data ownership where teams responsible for each domain are accountable for their data and products.
Traditional data lakes often face scalability and performance challenges when handling large amounts of data. However, the data grid architecture addresses these scalability issues through its decentralized self-service data infrastructure.
Because each domain is autonomous in choosing the technologies and tools that best suit its needs, data grids allow teams to scale their data storage and processing systems independently.
4. Data structure
Data Fabric is an adaptive, flexible, and secure integrated data architecture. It is an architectural approach and technical framework that addresses data lake challenges by providing a single, integrated view of data across various data sources.
By addressing the technical complexities involved in data integration, transformation, and movement, data structures allow for faster and more efficient access to data so that anyone can use it.
Modern Data Architecture Principles
According to Dataversity, data architecture principles refer to a set of policies to govern the enterprise data framework and operational rules for collecting, integrating and managing data assets. These principles help us create consistent, reliable, and efficient data architectures that align with organizational goals and objectives.
To effectively leverage data as a competitive asset, here are a few common modern data architecture principles to follow:
- Data Quality (DQ): Data quality is essential to any data architecture; establishing data quality standards and processes ensures data accuracy, integrity, and reliability. Data quality principles guide us to implement data analysis, cleansing and validation techniques to immediately identify and correct data problems, thereby avoiding poor data quality. Efficiently managed and reliable data are critical to developing accurate models and reliable patterns to extract valuable insights.
- Data Governance (DG): The Experian Data Quality Report reveals that 78% of organizations worldwide suffer from poor data governance, which leads to mistrust in data and the insights derived from it. Data governance tells us that at any point in the data lifecycle, data consumers should know where the data is, its format, usage relationships, and any other relevant information related to the data to avoid data debt. Data governance is closely related to data quality, enabling data architecture to ensure data integrity and improve data quality.
- Strive for consistency: This data principle emphasizes ensuring consistency across all data structures, formats, and processes across the organization. Using standard terms and vocabularies ensures that data is defined consistently across systems, making it easier for developers and non-developers to collaborate on the same project. Consistency in data representation across organizations provides teams with a "single version of the truth," making data and data analysis readily understandable to a wide range of users within the company.
- Make data a shareable asset: Making data a shareable asset emphasizes that we view data as a valuable resource that can be shared and accessed across different systems. This principle aims to eliminate data silos and encourage the design of data architectures that facilitate the efficient sharing of data. Doing so ensures that all stakeholders have a comprehensive understanding of the company and data for collaboration and informed decision-making.
- Data Security and Privacy: Modern data architectures must ensure the confidentiality and integrity of data while protecting sensitive information from unauthorized access, destruction or misuse.
Characteristics of a Modern Data Architecture
Some characteristics of modern data architectures are discussed below:
- Automation: Modern data architectures use automated processes, tools, and techniques to optimize data-related tasks and operations. Unlike traditional systems, modern data architectures can build complex processes in hours or days using cloud-based tools.
- Adaptability: Modern data architectures must be flexible to respond to changing business needs. It must support multiple types of users, query operations and deployments, data processing engines, and pipelines.
- Scalability: Scalability is a key feature of modern data architectures, allowing us to scale up or down quickly and economically as business needs change.
- Cost-effective: A modern data architecture that leverages a scalable infrastructure allows us to easily accommodate future growth without over-purchasing hardware initially. Additionally, modern data architectures sometimes employ cloud computing platforms with a "pay-as-you-go model," whereby we only pay for the resources we actually use.
Benefits of a Modern Data Architecture
After discussing modern data architecture and its characteristics, the following discusses the benefits that modern data architecture brings to enterprises and organizations.
- Provides a holistic view of the company: Because data integration enables seamless integration of data from various sources and systems within an organization, data can be collected and stored consistently. This integration ensures that data at any time provides a comprehensive view of the company, a "single source of truth".
- Reduced Redundancy: By collecting and harmonizing disparate data and data sources, data integration reduces the overlap of data fields in an organization.
- Improved data quality: Modern data architectures incorporate techniques such as data cleansing and validation, data standardization, data quality monitoring, and remediation to ensure data reliability.
in conclusion
The increasing importance of data in organizations has driven the development of data architectures. From traditional data warehouses to modern approaches to data grids and data structures, these architectures address specific challenges and bring new opportunities.
By adopting a modern data architecture, organizations can benefit from improved data quality and comprehensive data insights to unlock the full potential of data and remain competitive in today's world.