The challenges in the IT industry are never-ending and when it comes to building data architecture, it’s really challenging! Because speed, flexibility, and power are crucial abilities of data structure, it’s hard and complex to meet next-level technological challenges. The focus on market-driven innovations can make the organizations seamlessly deliver their service and maintain their data in a structured way. The success of any organization in the present AI-driven world depends on building a competitive edge which is possible when data architecture becomes a game-changer by pushing innovation to the next level.
Rapid deployment of new data technologies along with legacy infrastructure has enabled organizations to bring market-driven innovations such as enhanced user experiences, personalized data maintenance, secured data access across teams at an organization level, and developing agility to meet their business standards and client needs in their very own ways.
As the dependency on data lakes, customer analytics platforms, stream processing, etc., has increased, the complexity of data structures has risen thus curtailing:
- Organization’s ability to deliver new capabilities.
- Maintaining the existing data infrastructure.
- Integrity of applied AI algorithms.
Today’s market dynamics never allow organizations to slacken on the aspects mentioned above. Being the successors – Amazon and Google have exceptional technology innovations in AI that have reformed most of the traditional business models. Also, cloud providers offer cutting-edge serverless data platforms for instant deployment to attain greater agility.
Analytics users have the most demanding automated tool adoption of new models which is quick and effective. APIs dependency has exposed data from their systems and integrated insights into front-end applications in the fastest way possible. However, the impact of the pandemic accelerated the need to build a data structure and the only need required is speed and flexibility.
The greatest advantage of achieving speed and flexibility is – competitive advantage.
To make a difference while building data architecture where speed and flexibility seem promising, the organizations must make 6 shifts.
Six shafts to align for building a powerful data architecture:
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Disruptive driver of a new data architecture – cloud platforms
To scale AI tools and build capabilities that turn the clock inwards, the cloud is the disruptive driver to build a new data architecture. The global players – Amazon (AWS), Google (Google Cloud), and Microsoft (Azure) have changed the game of organizations as their cloud platforms have extensibility and flexibility for organizations of all sizes to run data infrastructure, applications, and platforms at scale.
Serverless data platforms such as Amazon S3 and Google BigQuery have enabled organizations to build data-centric applications where installing and configuring solutions or handling workloads have constantly fallen apart.
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Adapt real-time data dynamics
As companies so far are enabling the use of real-time data capabilities, there’s a significant decline in costs thus allowing firms to adapt real-time messaging and streaming across core functional areas. Real-time data processing is enabling these companies to make a shift in their business applications which gives an understanding of real-time behaviors. In the manufacturing sectors, real-time data helps to identify issues quickly and resolve them in a considerable time.
Most of the streaming processing and analytics solutions are including advanced analytics with integration capabilities. When integrated, they combine historic data to compare patterns, analyze direct messaging in real time, give insights about behavioral data, and much more.
The real-time data platforms are helping organizations to smoothly run their ERPs and CRMs.
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Highly modular architectures scale cloud-based applications
Scaling of applications needs a push for the legacy data systems as these are provided by vendors in most cases. A shift toward high modular data architecture where open-source components can be replaced with new technologies without affecting other parts is much crucial.
Integration between tools and platforms across various layers is only possible when data pipelines and API-based interfaces are established. These will speed up the time to market and reduce the rise of new problems in existing applications. This shift and scaling help deliver data-heavy services or solutions to millions of customers. From the daily view of customer needs and consumptions to real-time insights, modular data architectures scale the business to new heights.
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Decoupled data access to build efficient AI use cases
Using APIs to expose data ensures security and limited access while offering access to the up-to-date common data sets. This enables data reusability across teams and seamless collaboration to develop AI use cases efficiently. This is widely observed in pharmaceutical companies where internal data marketplace is shared for all the employees through APIs. Sharing data simplified and standardized access to the data assets. Not only in the pharma industry but the use of APIs to build efficient AI use cases also creates an innovative strategy for companies to thrive and survive in the marketplace.
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Domain-based architecture is an effective choice
The adopted data architectures have been divided within organizations based on their domains. The leading industries have shifted from using a central data lake to a domain-based architecture. This is because of the customization it provides and perfectly syncs for improving the time to market new products and services. The domain data architectures will have data sets on a single physical platform and are divided to product owners to extract data they needed at convenience. Even users can consume the data efficiently without any hassles. However, this approach requires careful planning and balance.
In sales and marketing, the use of domain-based architecture lines up customer data to data scientists through digital channels. This can be done in three ways:
- Making data infrastructure as a platform to create own data assets
- Integrate distributed data sets with data virtualization techniques
- End-to-end access through data cataloging tools
For every company to switch towards domain-based architecture, hackathons and forums are the available options to promote and adapt to create compelling products or services.
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Extensible data schemas achieve greater flexibility
In a more rigid way, organizations are using predefined data models that are driven by vendors. But these data models only serve a specific purpose and are built in highly normalized schemas with data tables and elements to reduce redundancy. Widely used in regulatory-focused use cases, it’s a must-go long cycle for organizations to incorporate new data into the existing.
Greater flexibility and a powerful competitive edge are built when advanced analytics become core of every organization.
Schema-light approaches have denormalized data sets with fewer data tables and these organize data to accelerate performance wherever they are used. The range of benefits in schema-light approaches include:
- Digging deeper into exploring agile data
- Achieving greater flexibility in storing structured as well as unstructured data
- Reducing complexity
Either to adapt to changing information models or maintain standard business information models, data vault 2.0 techniques, graph databases, technology services, and JavaScript Object Notation are applied.
As the evolution of data technologies is quick, there are potential practices that everyone must adapt. The crucial practices are:
- Apply a test-and-learn mindset to build architecture as part of agile practices and apply them in the application development process.
- Create data tribes where developers, data engineers, and data modelers work together for building a data architecture. These people must feel accountable to support the development of highly curated data sets.
- Invest in DataOps which helps in accelerating the design, development, and deployment of new components into data architecture.
- Create a data culture for people so that they can apply new data services in their roles. Ensure that the data strategy syncs with business goals and reinforces business teams to achieve goals in more flexible ways.
When considering data analytics, AI is more connected to everyday operations in every industry. So, it’s a clear perspective to create a data-centric enterprise with leaders who embrace the change and make their company agile and resilient by creating potential competitive edges.
