Building a robust real-time data architecture is crucial for businesses aiming to leverage the power of instantaneous insights and informed decision-making.
This step-by-step guide will help you navigate the process of creating an effective real-time data architecture that facilitates seamless data processing and analysis.
Step 1: Assess Business Needs and Objectives
Identify the specific business needs and objectives that your real-time data architecture aims to fulfil.
Understand the critical use cases and requirements for real-time data processing in your organisation.
A tried and tested method we use to gather information is by conducting stakeholder interviews. Make sure you create trial run your user interview questions to ensure that you are asking the right questions.
We usually focus on stakeholder pains and gains as well as drawing inspiration from other data capability frameworks as well as ones we have developed over time.
Once you have a view of the problem you can develop a product strategy with a clear definition of value creation for stakeholders, some initial candidate high value use cases and a view of required platform capabilities.
Step 2: Evaluate Data Sources and Integration
With a defined goal in mind, you can search the organisation for pertinent data. Established enterprises, particularly those with a history of mergers and acquisitions, often boast intricate information frameworks.
Similarly, swiftly expanding businesses tend to accumulate various solutions as they transition from their startup phase.
Consequently, data sources within companies are spread across different business units, geographical locations, on-premises systems, and cloud platforms.
Sources typically involves a varied assortment of databases, messaging and integration platforms, third party vendors, and other technologies that house diverse data types, ranging from structured to unstructured.
You may expend considerable time tracking down data sources, comprehending their configurations, and establishing the data’s relevance to the project.
Furthermore, you can speed up this process by ensuring you have identified data stewards, product owners and data and integration stakeholders in your initial interview process.
As a result, you will now have the start of a realtime data catalog as well as an initial view of complexity in accessing and curating the data.
Finally, you can use this complexity to support initial prioritisation of use cases before moving on to the next step.
Step 3: Design a Scalable Real-time Data Arhitecture
Design a scalable and resilient data infrastructure capable of handling real-time data processing and storage.
Implement technologies such a data streaming platforms to accommodate the influx of real-time data.
There are many in-market options for streaming data platforms.
You may want to assess different options to determine the correct one for your enterprise. Here are some initial questions to think about when assessing different options.
- What do you use already?
- What features are a must have to meet stakeholder needs?
- What is your preference for managed service over in-house?
- Do you have any data sovereignty requirements?
- Do you have any privacy, ethics and governance requirements?
- Do you have any applicable mandatory cyber controls?
- What would an ideal operating model look like in run state?
- Do you have any interoperability requirements with existing observability and service management tools?
- Does the supplier culturally align to your organisation?
- Are they making a strategic investment in the product?
There are many different areas of assessment that may be relevant to choosing the right streaming data technology for your enterprise.
Now outline and communicate the result by developing an architecture with a clear preferred choice of technology based on what you consider important.
Step 4: Implement Data Processing and Analysis Tools
We suggest you develop a software and data engineering sprint plan based on all activities required to implement the real-time data streaming architecture. One way to think about the platform is in terms of it’s operating stack. You can use the below as a guide to identify what elements of the operating stack a relevant to you, and what activities are required to implement each element.
Step 5: Ensure Data Security and Compliance
Implement robust security protocols to safeguard sensitive real-time data from potential breaches and unauthorised access.
Ensure compliance with industry regulations and data protection laws to maintain data integrity and customer trust.
To ensure you have the right controls in place, use industry essential security practices to assess the relevant prevent, detect and remediate controls in place.
You may now also develop standard operating procedures and DevSecOps playbooks to ensure appropriate data management practices such as:
- Data integration automation and provisioning
- Data privacy assessment and metadata management
- Onboarding data producers and consumers
- Data cataloging and governance processes
- User access reviews
- Secure operations and support
Step 6: Establish Real-Time Monitoring and Maintenance
Set up monitoring mechanisms to track the performance and health of your real-time data architecture continuously.
Develop proactive maintenance procedures as part of the real-time data architecture to address any issues and ensure seamless operations without interruptions.
There are many observability tools that offer managed connectors to track and visualise core platform health.
In addition to data infrastructure health we encourage you to think about data product health metrics for each use case.
Consider developing use case specific dashboards for data consumers that provide insight into any interruption to a data stream that they subscribe to.
Providing this additional context to end users ensures they are making an informed decision based on data recency.
Step 7 : Promote adoption within the community
Encourage a data-driven culture within your organisation by promoting the utilisation of real-time insights for informed decision-making.
Provide training and resources to empower teams to leverage the capabilities of the real-time data architecture effectively. There are many training resources available for a wide variety of stakeholders.
Consider real-time data user experience patterns when surfacing data insights to promote usability and trust in the insights presented.
Conclusion
Developing a comprehensive real-time data architecture requires careful planning, seamless integration, and robust security measures.
By following these steps, you can establish a powerful data infrastructure that enables real-time data processing, analysis, and informed decision-making, giving your business a competitive edge in today’s dynamic market.
Lastly, we would love your feedback on this playbook, contact us for a chat.