Big data has brought waves of disruption to industries, which has been compounding complexities in technology adoption. Disruptions from digital transformation have further accelerated obsolescence of existing data management architectures of enterprises. This has increased the burden on IT teams in migrating to big data platforms.
There has been a significant surge in the adoption of Big Data solutions within the enterprise IT landscape. This is mainly driven by the disruptive innovation, competitive advantage and monetization brought by the data, analytics and insights to businesses over the past couple of years. Results obtained from big data analysis turn into valuable inputs for other systems and applications. Organizations need to identify applications needed to be integrated with big data solutions, so as to leverage existing processes and applications while benefiting from the synergy.
The Cloud and The Modern Data Architecture
A growing number of organizations are considering the following approaches for building a modern data architecture:
Analysis of new data streams for business benefits, which include social media, streams, sensors, etc.
Initiating momentous proof of concepts for big data with tangible ROI, or moving from the pilot stage to production stage.
Augmentation of existing data architectures via big data technologies to realize agility, flexibility and scalability in data processing.
Leverage of big data technologies as the foundation of new and emerging use cases.
Devising plans for handling varied workloads without linear increase in costs.
Moving away from traditional software licensing and maintenance models.
Placing greater focus on core business use cases and off load aspects like software/hardware procurement & maintenance.
Reducing the time-to-market to leverage early adopter advantages.
Cloud computing promises acceleration of the above in more than a few ways. The cloud computing giants – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) – continue to see significant evolution, from being providers of IaaS to becoming solution providers of PaaS and SaaS. The organizations initially cloud computing platforms for compute, network and storage services, to offload hardware provisioning and maintenance. Their objective was inclined toward achieving elasticity to scale up and scale down their processes based on crests and troughs in data volumes.
Cloud providers continue to leap forward with continuous innovation, providing greater value offerings. The highest evolution of CSPs is their ability to offer SaaS. Mapping the developments of big data analytics, leading cloud providers are providing end-to-end services, so as to acquire, visualize, ingest, mine, process, and deliver data to end users. This complements organizations to take a huge leap, focusing on core business objectives and leveraging cloud solutions for the whole data management lifecycle.
Prospects of Big Data Implementation Seem Bright Despite Lag in Business Adoption
Key aspects continue to hold back the adoption of big data technologies, which include technological complexities, the imbalance between analytic teams and big data adoption strategies, and lack of collaborative and informed analytic culture. Despite this, prospects seem to be bullish for big data technologies, riding on the shoulders of cloud computing, which enables businesses to implement big data with reduced commitment of resources.
The future will see organizations banking on data harnessing as an asset and leveraging it to their benefit. Success of businesses will be directly proportional to their ability to mine assets of data for improving their processes. Saying that cloud computing plays a pivotal role in facilitating data acquisition and expedition is not an overstatement. Adoption of cloud computing will continue to grow as a key enabler of data implementation in the near future.