Data Architecture
Adopt cloud-based data management and cloud-based data storage.
Add a data fabric architecture for seamless integration. Organisations are looking for flexible data management solutions as data continues to sprawl across disparate destinations—on-premises data centres, multiple clouds, and edge devices.
Add a data mesh architecture to simplify and focus your response to the changing data landscape. It will enable your organisation to respond quickly and cost effectively to the data changes that abound in 2024.
Adopt automation using generative AI and ML in data management.
Adopt low-code / no-code for data integration. Find the vendors or data specialists that have completed work similar to your challenge. Successful experienced operators are key.
Provide data governance, security, and privacy in an automated fashion. There is no time in 2024 to manually rediscover and develop data changes that other organisations have already found and adopted. These changes must be automated through generative AI and ML capabilities.
Data management guiding principles still apply in today’s data landscape, however, today change is very rapid and there is no room for a bottom-up approach to managing your data, rather you should build pervasive AI / generative AI / ML models that do the heavy lifting for you.
In 2023 and still today we talk about data gravity as a key challenging principle of today’s modern enterprise data platform, but increasingly anti-data gravity is seen as the key issue in 2024 and beyond.
Data gravity is well documented, essentially this is where the accumulation of data (operations and analytical) attracts more data and services into its business data mix, thereby increasing data complexity and potentially causing serious challenges for an organisation to maximise the value of their data, especially if you have adopted a cloud first approach for your infrastructure. To further complicate this scenario, consider the challenges if you are a global organisation as you manage across different time zones, regulatory and business structures.
Anti-gravity advocates that the data and expertise should stay local and the two modern data architectures when used correctly can help moderate these challenges are data fabric and data mesh architectures.
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Adopt cloud-based data management and cloud-based data storage.
-
Add a data fabric architecture for seamless integration. Organisations are looking for flexible data management solutions as data continues to sprawl across disparate destinations—on-premises data centres, multiple clouds, and edge devices.
-
Add a data mesh architecture to simplify and focus your response to the changing data landscape. It will enable your organisation to respond quickly and cost effectively to the data changes that abound in 2024
-
Adopt automation using generative AI and ML in data management.
-
Adopt low-code / no-code for data integration. Find the vendors or data specialists that have completed work similar to your challenge. Successful experienced operators are key.
-
Provide data governance, security, and privacy in an automated fashion. There is no time in 2024 to manually rediscover and develop data changes that other organisations have already found and adopted. These changes must be automated through generative AI and ML capabilities.
Data management guiding principles still apply in today’s data landscape, however, today change is very rapid and there is no room for a bottom-up approach to managing your data, rather you should build pervasive AI / generative AI / ML models that do the heavy lifting for you.
In 2023 and still today we talk about data gravity as a key challenging principle of today’s modern enterprise data platform, but increasingly anti-data gravity is seen as the key issue in 2024 and beyond.
Data gravity is well documented, essentially this is where the accumulation of data (operations and analytical) attracts more data and services into its business data mix, thereby increasing data complexity and potentially causing serious challenges for an organisation to maximise the value of their data, especially if you have adopted a cloud first approach for your infrastructure. To further complicate this scenario, consider the challenges if you are a global organisation as you manage across different time zones, regulatory and business structures.
Anti-gravity advocates that the data and expertise should stay local and the two modern data architectures when used correctly can help moderate these challenges are data fabric and data mesh architectures.
Today, data in motion is quite mature. In Australia, from the early 2000’s there was a significant investment made by organisations to modernise their interconnectivity between key operational applications that drive business functionally. Moving forward 20 years this integration type is pervasive and stable, and while there are some minor improvements that have been made during the digital transformation era such as API’s and event driven architecture, by and large compared to the investment required in the analysis of data, the need to improve data in motion is relatively small.
For data at rest however, which is both the management and access to the analytical data, as well as the connection of all the disparate data silos, be they located in the cloud or other countries for global organisation remains difficult at scale, for today’s modern enterprise. This is where the data mesh and data fabric architectures can be focused to successfully resolve these challenges.
While the technology advances of the past decade have addressed the volume of data and data processing compute requirements, they are still behind in addressing scale for other data driven dimensions. Changes in the data landscape, proliferation of data sources, diversity of data use cases, and the speed of response to change, to name a few. So what data architectures are available and how do they help tame the wilds of big data in 2024 and beyond.
Kiwibank engaged Novon to assess any risks in their proposed data and integration architecture. This included the preservation of their back-office finance and risk capabilities for integration, internal and regulatory reporting, whilst also allowing for additional business banking capabilities provided in real-time to their customers.
Novon conducted an architectural review of Kiwibank’ s current core applications. It reviewed Kiwibank’ s proposed data and integration architecture including the new real-time applications, [Thought Machine Vault (TM), Dynamic 365 and nCino]. Novon provided a detailed review of the data and integration landscape including improvements to the proposed solution, highlighting potential risks and issues, with recommended remediation actions and options.
Finally, Novon provided a data driven solution architecture and high-level design with the primary focus on the Risk, Regulatory & Finance applications including their pipelines for streamlining batch and real-time processes between business applications.
