5 Steps for Implementing a Modern Data Architecture
There are five foundational shifts that your organization can make to enable rapid deployment of new capabilities and simplify existing architectural approaches.
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Current market dynamics don’t allow for slowdowns. Digital disrupters have made use of innovations in AI, serverless data platforms, and seamless analytics that have completely upended traditional business models. The current market challenges presented by the Covid-19 pandemic have only exacerbated the need for fast, flexible service offerings. To remain competitive and relevant, businesses today have to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance.
However, as businesses strive to implement the latest in data technology—from stream processing to analytics and data lakes—many find that their data architecture is becoming bogged down with large amounts of data that their legacy programs can’t efficiently govern or properly utilize.
There are five foundational shifts that your organization can make to enable rapid deployment of new capabilities and simplify existing architectural approaches. Some of these shifts can be implemented while leaving your core technology stack intact, and others require careful re-architecting of existing infrastructure.
1. Shift to Cloud-Based Platforms
Cloud has been the most disruptive force in driving a radically different data architecture approach. It offers companies a way to rapidly scale tools and capabilities for competitive advantage. Cloud is a great leveler in that it allows organizations of all sizes to source, deploy, and run data infrastructure platforms and applications at scale.
Serverless Data Platforms- these platforms allow organizations to build and operate data-centric applications with unlimited scalability and reduce overhead by removing the hassle of configuring and managing workloads on site. The easy accessibility of these technologies mean that solutions can be deployed in minutes instead of weeks, and overall operational overhead is decreased.
Containerized Data Solutions – Kubernetes enable companies to decouple and automate the deployment of additional data storage systems and compute power. This helps ensure that platforms with more complicated setups can still scale to meet demand as needed.
2. Move From Batch to Real-Time Processing
Real-time data streaming capabilities power some of the most cutting-edge business applications of today. Data from factory floor sensors helps manufacturers predict maintenance issues; insurers can individualize rates based on real-time behavioral data from smart devices, and customers can track their food delivery from the restaurant to their doorstep with to-the-second accuracy. The lowered price of data and compute power thanks to the cloud has brought the cost of real-time data messaging and streaming down significantly, making this technology available to organizations of all sizes.
Real-time streaming functions allow data consumers to subscribe to “topics” so they receive a constant feed of transactions relevant to their needs. This is commonly stored in a data lake that retains all the granular details for in-depth analysis and forecasting.
Messaging Platforms – modern messaging platforms provide scalable and fault-tolerant publish/subscribe services that can process and store millions of messages every second. This allows for real-time support and bypasses existing batch-based solutions, resulting in lower costs and a lighter footprint than legacy messaging queues.
Streaming Analytics Solutions – these systems allow for direct analysis of messages in real-time and compares historical data to current messages to establish trends and generate predictions and recommendations.
3. Upgrade From COTS (Commercial Off the Shelf) to Custom Solutions
When they reach a certain scale, organizations may find themselves bumping up against the boundaries of their COTS (commercial off-the-shelf) solutions. To address this, many are moving to customized, highly modular data architectures that utilize best-of-breed and often open-source components that can be upgraded as needed without harming other aspects of the architecture.
API-based Interfaces – when implemented in your data pipeline, these interfaces shield different, independent teams from the complexity of layers not related to them, which speeds the time to market and reduces the chance of human error. They also allow for easier component replacement as requirements change.
Analytics Workbenches – these services enable end-to-end communication between modular components, such as databases and services
4. Decoupled Data Access
API’s can help you decouple data access and ensure that direct access to view and modify it is limited and secure. This offers faster access to common data sets and allows data to be reused among teams, which enables seamless collaboration and increased efficiency.
API Gateway – this allows you to create and publish data-centric API’s, empowering you to control access, implement usage policies, and measure performance.
Data Buffer – many organizations find it necessary to have a data platform that buffers transactions outside of your core system. This could be a data lake, warehouse, or other data storage architecture that exists for each team’s expected workloads.
5. Shift to Domain-Based Data Architecture
Instead of housing all enterprise data in one location, many organizations are transitioning to domain-based architectures that shift the ownership of data sets to the teams that use them (i.e. marketing, sales, etc.) This allows each business domain to organize their data in a way that is more consumable for their users. This approach can be very effective when adhering to varying regulatory or mobility restrictions.
Data Infrastructure-as-a-Platform – these services provide common tools and capabilities for storage and management and empower data producers to implement their data requirements quickly without the hassle of building their own platform.
Data Cataloging Tools – these tools allow for the search and exploration of data without requiring full access. The data catalog also typically provides metadata definitions and a simplified interface to access data assets from anywhere.
How to Implement Your Modern Data Architecture
Data and technology leaders need to be able to quickly evaluate and deploy new technologies in order to keep up with the pace of modern data innovations. There are a few practices that will help foster a data-forward organization and prepare you to keep up with the latest technologies and best practices:
- Remember that failure is a learning opportunity. Be willing to experiment with different components and concepts to quickly identify what works, and more importantly—what definitely doesn’t. The agile practice of “test-and-learn” will help your organization develop a minimum viable product that can be tested to determine value before implementation.
- Invest in DataOps. DataOps is enhanced DevOps for data, and it will help accelerate the design, development, and deployment of new components so that teams can rapidly implement changes and update solutions where needed.
- Create a data-positive culture. Teach employees how data services can be used to enhance their efforts and make their job easier. Ensure that your data strategy ties to greater business goals to retain the support of your C-suite.
As data, analytics, and AI become more embedded in the day-to-day operations of most businesses, it’s clear that a completely different data architecture is needed to create and nurture the data-centric enterprise. Leaders who approach a modern data architecture will ensure that their organization remains agile, resilient, and competitive in today’s ever-changing market.
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