Studies forecast that there will be an incredible 42.2 % annual growth in enterprise data volume by 2022.

The rapid pace of digitalization happening across industries aided by an increasingly digital-savvy consumer mindset and events like the COVID 19 pandemic has brought out an unprecedented volume of data for enterprises to manage. From hyper-converged digital experiences on the consumer side to multi-disciplinary analytical and computational workloads on the operational side, businesses are dealing with more data than they could have ever planned for. But thanks to the growth of cloud computing and SaaS services, businesses of all sizes can extract the best insights from their data pool.

But the real challenge is setting up a data architecture that supports the easy channeling of enterprise data to different systems for further insight generation and decision making.

In the past, organizations found it easy to build a data warehouse architecture on the cloud to handle sufficiently large volumes of their data. The focus of the architecture was predominantly to make data available on-demand and without missing critical levels of quality and integrity. But fast-forward to today, data availability and quality are not the only parameters that enterprises need to consider for sustained success from their data initiatives. For delivering on experiences of today, enterprises have to deal with the exponential scale of growth of data generation sources, increased diversity of data facets from within each source, and multi-disciplinary analytical and computational systems requesting different dimensions of data generated. Even as that happens, enterprise analytics systems need access to the data in real-time and on-demand.

In simple terms, the mere adoption of data architecture for the cloud is fast becoming an obsolete design for handling enterprise data in the digital economy.

Just by achieving scale, it may be possible for businesses to sustain continuous operations, but they will definitely lose competitiveness from their data initiatives as their legacy cloud data architecture will stall ongoing innovation or deliver sub-optimal results.

So, how can enterprises build a data architecture that can accommodate such dynamic needs?

The solution is to create a cloud-based data architecture that is designed from the ground up based on expected business outcomes for the enterprise. Such an architecture has the entire end-to-end data management infrastructure comprising data storage, purpose-built data services, data transfer, and movement policies, and governance models tailored to business outcomes.

Let us have a look at the key business outcomes that modern-day enterprise data architecture should be designed to facilitate:

Customer Experience

From self-service options to personalized services, today’s customers demand a wide range of amenities and convenience from every digital channel they interact with. For businesses, this requires a heavy-duty digital backbone that can handle diverse requests for personalized services. Take for example an online digital streaming platform. It needs to offer personalized content recommendations to millions of subscribers by analyzing their past interaction history. Every facet from the genre they are interested in to their favorite actors or language preferences needs analysis. Hence their backend digital architecture needs to be supportive of such a wide array of digital interactions be it from a storage point of view or from a performance point of view since millions of subscribers may be simultaneously using the service.

Scalable Performance

Events like the pandemic saw the digital adoption pace accelerate by leaps and bounds in a matter of days. Such dynamic and unexpected scaling of digital services can be made possible only if the underlying data architecture has room for scaling at this pace. It must also support diversifying its information retrieval and exchange mechanism to include a vast pool of diverse data streams coming in from all quarters. Both performance and storage may need to be scaled on-demand and the data architecture needs to be ready for the unexpected at any time.

Innovation Support

With new technologies being launched every so often, businesses are in a race to integrate the latest stuff in the most impactful way before the competition does. Hence the enterprise data architecture that powers the digital backbone of the organization needs to have supportive services, accommodative policies, and the necessary intelligence infrastructure to help quickly build innovations on top of it. For example, an AI-powered textual and voice recognition service from a cloud provider can help enterprises build a custom chatbot service on top of it quickly to deal with their customer queries.

Cost Optimization

When the underlying data architecture is optimized and empowers autonomous scaling and storage management options for large data volumes, the business stands to benefit from a cost perspective. Also, when more smart data services are offered as part of the data architecture by leveraging an intelligent cloud service like AWS, businesses need less effort to set up high-end analytical and computational workloads. Their employees can be freed up for higher-value tasks while mundane activities can be automated by the cloud itself. This allows better resource utilization and lower running costs significantly.

Preparing for sustainable business growth in the era of digital-first operational models requires more than just innovative technologies. The foundation required for making progress is a solid enterprise data architecture that can accommodate market dynamics over a period irrespective of what technology or innovative services are created on top of it. Get in touch with us to know more.