Data is the lifeblood of modern enterprises. From gaining necessary insights into customer behavior and streamlining organizational processes to making effective business decisions, data is now a corporate asset that allows enterprises to increase their revenue and profits.
However, data, in its raw form, is only as good as the person analyzing it. If you’re collecting data for informed decision making, the necessary first step is to formally define the data architecture that aligns with your organization’s commercial strategy.
If you’re already considering formalizing data architecture for your organization, here’s a short overview that’ll help you build a well-defined, future-ready data architecture.
The Importance of Data Architecture
With robust data architecture in place, everyone in your organization gets access to relevant data, based on which they can make informed decisions. Data architecture serves as a necessary infrastructure to support data warehousing, offer opportunities to integrate disparate data sources efficiently, and create an environment that promotes collaboration.
On the contrary, without formal data architecture, you’ll most likely have to deal with uncertain data quality and inconsistent reports. Poor data quality will further lead to an ecosystem that will struggle to foster integration, collaboration, and data governance. These inefficiencies eventually translate into poor customer service, ineffective marketing campaigns, bad decision making, and missed opportunities. Surprisingly, in most cases, your organization won’t even realize these “hidden” costs until it starts reflecting on the accounts.
Also read: Best Data Warehouse Software & Tools 2021
What is Data Architecture?
Data architecture is the foundation of any data strategy. It is essentially a process of standardizing how organizations collect, store, manage, utilize, secure, and integrate data with different applications and data repositories. Think of it like a master plan consisting of guidelines and standards that define how the information should flow within an organization and how you can control it.
Well-defined data architecture leads to an environment that:
- Ensures superior data quality
- Allows integrating data from disparate sources
- Provides a “single source of truth” about the company
- Enables teams to discover new insights and make data-driven decisions
- Ensures a reliable system in place to secure data.
A Shift from Primitive to Modern Data Architecture
From the definition, data architecture may seem like a technical process. Although this was the case earlier, modern data architecture aims to close the gap between business goals and technology. Therefore, instead of flowing from data sources, good data architecture starts with consumers, prioritizing their unique cultural and contextual requirements.
Modern data architecture also demands an upgraded technology stack. Traditional databases and data processing technologies cannot handle the large volume, variety, and velocity of data generated in this digital age.
Instead of a primitive data architecture relying on static warehouses and on-premise platforms, what you need is a Big Data Architecture that leverages emerging technologies like serverless platforms, AI, and ML to handle large and complex data efficiently. Modern data architecture, therefore, comprises new concepts and components to make the architecture more adaptable and future-focused for data-centric enterprises.
McKinsey highlighted six major changes that companies are making to their data architecture blueprints.
- From on-premise to cloud-based data platforms (serverless data platforms and containerized data solutions)
- From batch to real-time data processing (messaging platforms, streaming processing and analytics solutions, and alerting platforms)
- From pre-integrated commercial solutions to modular, best-of-breed platforms (data pipeline and API-based interfaces, and analytics workbenches)
- From point-to-point to decoupled data access (API management platform and data platform)
- From an enterprise warehouse to domain-based architecture (data infrastructure as a platform, data virtualization techniques, and data cataloging tools)
- From rigid data models toward flexible, extensible data schemas (data point modeling, NoSQL graph databases)
The Components of a Modern Big Data Architecture
While there are several data architecture models consisting of a range of components, fundamentally, the most straightforward data architecture model comprises three core layers.
- The storage layer collects all the data generated from different sources, be it internal or external.
- The processing layer is where this data gets processed in batches, real time, or hybrid.
- The third is a consumption layer that enables an organization to utilize the processed data through data queries, AI and ML apps, and analytics engines. It is the final visualization layer that facilitates data-driven decision-making.
Most modern data architecture layers utilize all or some of the following components:
- Data sources are fundamental to any data architecture. Sources can be anything from relational databases such as data warehouses, email, mobile devices, ERP, CRM, and more, consisting of structured and unstructured data in different formats. It can also include real-time data sources such as IoT devices.
- Data storage is where the ingestion of data in real-time or batches gets correctly formatted for analytics use. Relational databases are designated to structured data, while unstructured data stays in non-relational databases (NoSQL), data lakes, or warehouses.
- Batch processing: Large data sets generally require long-running batch jobs where the data gets filtered, merged, and prepared for analysis. This process reads, processes, and writes data output into new files. This operation happens through batch processing applications and frameworks.
- The real-time data ingestion component focuses on collecting real-time data and enables a smooth transition to stream processing. A big data architecture designed for real-time data sources must include a mechanism to capture real-time messages. In many cases, the solution may also require a message capture store for buffering, scale-out processing, reliable delivery, and other queuing requirements.
- Stream processing: The captured real-time data is then aggregated and filtered for analytics purposes and written to an output sink. Stream processing platforms or other managed stream processing solutions such as Azure Stream Analytics, Apache Storm, and Spark Streaming carry out this phase.
- Analytical data storage: After processing, the data is served in a structured format for analytics tools and business intelligence (BI) platforms. Data can also be served to the NoSQL low-latency technologies or HBase and Interactive Hive.
- Analysis and reporting: Most big data solutions extract actionable insights from data through the means of analytics and reporting. For users to analyze data, the data architecture may include data modeling layers such as OLAP cube. The component may also support self-service BI to create comprehensive models and visualizations.
- Orchestration: Big data solutions often require repeated data processing to ensure synchronization between data operations, such as the movement between real-time ingestion and stream processing or data transformation for visualization. Orchestration systems automate these workflows and processes.
Data Architecture Best Practices
Building a modern data architecture requires careful consideration of all the data technologies utilized to efficiently meet your enterprise’s commercial strategy. The following best practices can help you build a robust, future-ready data architecture.
Trial and Error
Getting accustomed to a complex data environment may take time. You can gradually construct your ideal framework by experimentation with different concepts and components. Testing will let your architecture ideas demonstrate their value before expanding further and give you better agility.
Data as a Shared Asset
Good data architecture eliminates departmental silos and makes data accessible to everyone in the company. It fosters collaboration between business and IT users to achieve shared goals and outcomes. When data is viewed as a shared asset, the end result is improved organizational efficiency.
Automated processes allow you to process seamless data flow with real-time trigger and anomaly detection mechanisms. Further, AI and ML enhance the elasticity of data architecture by improving the architecture’s learning capabilities.
Security and Governance
Security requires data classification according to sensitivity and significance and fabrication of flexible but unyielding access control. Data architecture also needs to be compliant with data privacy laws and regulations. For example, adhering to laws such as General Data Protection Regulation (GDPR) or California Consumer Privacy Act (CCPA) requires data encryption before ingestion and anonymization of Personally Identifiable Information (PII).
Prevent Data Copies and Movements
Every single time the data gets displaced, there will be an impact on variables such as cost, time, and accuracy. Optimize data agility by encouraging collaboration on the same data entries or incorporating distributed file systems that eliminate additional data movement and ensure a single source of truth for all users.
Becoming Future Ready
In this era of big data, analytics, and AI, it is critical for data-centric enterprises to build a robust data architecture that aligns with business processes, scales with business growth, and evolves with new technological concepts and components. While the path towards building future-ready data architecture is challenging, following the best practices and establishing the right framework can help you come up with a well-defined architecture to propel your organization forward.