The speed, flexibility, and creativity demands of today cannot be satisfied by the data architecture of yesterday. Agility is the secret to a successful upgrade and substantial potential rewards.
Organizations have had to act rapidly to implement new data technologies alongside old infrastructure in order to achieve market-driven innovations like tailored offers, instant alerts, and predictive maintenance during the past few years.
However, the complexity of data architectures has dramatically increased as a result of these technical advancements, such as data lakes, customer analytics platforms, and stream processing. It often significantly impairs an organization’s ability to deliver new capabilities continuously, maintain current infrastructures and guarantee the accuracy of Artificial Intelligence (AI) models.
Here are a few fundamental modifications firms are making to their data architecture plans to expedite the rollout of new capabilities and greatly streamline existing architectural strategies.
Processing data in real-time instead of batches
Real-time data communications and streaming capabilities are now much more affordable, opening the door for widespread adoption. A wide range of new business applications are made possible by these technologies. For example, insurance companies can use real-time behavioral data from smart devices to personalize rates; and manufacturers can foresee infrastructure problems based on real-time sensor data.
Data consumers, such as data marts and data-driven employees, can subscribe to “topics” with real-time streaming services, such as a subscription mechanism, so they can receive a continuous feed of the transactions they require. The “brain” of such services is often a shared data lake that stores all granular transactions.
Domain-based architecture from enterprise warehouse
To strike down the time it takes to launch new data products and services, many data architecture leaders have switched from a central business data lake to “domain-driven” architectures. In this method, “product owners” in each business domain (such as marketing, sales, manufacturing, and so on) are tasked with organizing their data sets in a way that makes them easily consumable for both users inside their domain and for downstream data consumers in other business domains, even though the data sets may still reside on the same physical platform. This strategy necessitates careful balancing to prevent fragmentation and inefficiency. Still, in exchange, it can cut the time required to build new data models into the lake in the beginning, frequently from months to just days. It can also be a simpler and more efficient option when mirroring a federated business structure or adhering to regulatory restrictions on data mobility.
Pre-integrated commercial solutions to modular, best-in-class platforms are all available
Companies frequently need to go well beyond the limitations of legacy data ecosystems from major solution suppliers in order to scale applications.
Today, many are moving toward a highly modular data architecture that makes use of best-of-breed and frequently open-source components, that can be upgraded without affecting other components of the data architecture.
The previously mentioned utility services provider is switching to this strategy in order to link cloud-based apps at scale and quickly deliver new, data-heavy digital services to millions of customers. For instance, it gives customers accurate daily views of their energy usage and real-time analytics insights comparing their consumption to that of their peers. The business established a separate data layer with elements from both open-source software and commercial databases. A proprietary enterprise service bus keeps data synchronized with the back-end systems, and micro services deployed in containers execute business logic on the data.
Decoupled data access transitions from point-to-point
While simultaneously providing quicker, more current access to popular data sets, data exposure via APIs can ensure that direct access to view and edit data is constrained and secure. As a result, the development of AI use cases is made more effective by facilitating data reuse throughout teams, expediting access, and enabling seamless communication among analytics teams.
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