The talk will focus on digital transformation key components, business agility and innovation, business innovation and three facets of digital transformation, five principles of business agility and the role of data engineering in digital transformation.
This talk entails —
* A typical batch data pipeline
* Use case for real-time ingestion
* A typical real-time data pipe
* Optimizing a batch pipe versus migrating to real-time
Industrialization of analytics can be achieved by implementing DataOps to get the data ready followed by institutionalizing MLOps at scale and finally applying AIOps in post-production environment to run the business. Out of all 3 dimensions mentioned here, DataOps continues to play foundational and the most crucial role to apply analytics in scale for a business context. To realize maximum value of DataOps we need to monitor and control a set of metrics aligning each phase of Data lifecycle. We would like to demonstrate our learning after working with a set of client business problems and solving the same using DataOps at its core.
The talk would focus on
What does Data Engineering mean in the gambit of Digital Transformation?
Business First Approach to Data Engineering
ROI of Data Engineering : How can DE enable business agility and drive innovation using a use case
Data and analytics leaders seek more efficiencies in the data management assets across the organizations, figuring out approaches to unlock better business insights for their organizations and making the data assets a differentiator from the competition. Organizations are looking at the decentralized, low point of failure approaches, data product-based approach to minimize the risks and better, quicker ROI
Data fabric is a design concept that serves as an integrated layer (fabric) of data and connecting processes. The fabric presents an enterprise-wide coverage of data across applications that are not constrained by any single platform or tool restrictions.
Data Mesh is a socio-technical concept that addresses the common failure modes of the traditional centralized data warehouses or lake architecture, with a shift from the centralized paradigm to distributed architecture considering domains as a first-class concern, applying platform thinking to create a self-service data infrastructure, treating data as a product, and implementing open standardization to enable an ecosystem of interoperable distributed data products.
Which approach best suits building a future-proofed data platform?
How organizations could use these concepts to transform the legacy data ecosystems.
How mature are the tools/technologies in the market that enables these concepts?
Competing in the real-time economy requires instant insights – and you can’t get them with manual data integration. Many businesses are now implementing data modernization, a strategy that enables them to take advantage of the latest innovations in the cloud without disrupting business-critical legacy processes and applications. Forward-thinking businesses want to leverage today’s most advanced analytics platforms as well as affordable, scalable cloud services, and modernizing their legacy systems is essential for doing that. For example, providing a 360-degree view of customers to the front-line support team requires real-time data replication from the various source systems. And traditional batch-oriented integration approaches won’t meet those needs. Qlik Data Integration offers an automated data fabric that delivers reliable, analytics-ready data in near-real time with a low-code approach.
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