Data has become critical for every enterprise like never before. It played a key role during the onset of the pandemic in contact tracing and later when employees were returning to work. Last year, when businesses were trying to recover, a TCS survey indicated that robust AI-based analytics set apart the leaders from the followers. Today, as more and more people are dependent on digital for their needs and enterprises seek to better serve their customers and expand their services, copious data is generated. Enterprises were always challenged in overcoming departmental silos to source data and glean valuable insights. Now they must also deal with tapping into data that is outside of their enterprise and critical for their success.
Cloud being the unifying digital fabric is invaluable as it integrates data across the ecosystem onto a cloud-based data platform to derive faster and meaningful insights for competitive advantage. A data integration service such as Google Cloud Data Fusion enables enterprises to achieve this vision.
Challenges in data integration
Some of the key challenges that enterprises face in data integration are long lead times and effort given data resides in disparate systems inside the enterprise. External ecosystem data comes from various sources. What’s more, the nature and structure of data can vary as well. Data integration, therefore, is complex; it takes considerable effort to integrate data and manage the same on an ongoing basis. Implementing and managing data integration solutions require licensing third-party tools, upfront infrastructure planning and provisioning, integration with other systems, specialized technical skills, ongoing maintenance, and extensive upgrades. All these drive up the total cost of ownership. Other challenges include scalability, extendibility, flexibility, ease of use and the ability to capture the metadata (business, operational and technical).
Data integration solutions can help address these challenges as well as take care of the end-to-end processes involved in quickly building and managing data pipelines. These include data cleaning, preparing, blending, transferring, and transforming data without the additional burden of managing the underlying infrastructure. The solution should enable a wide range of use cases, such as establishing data warehouses, data migration as part of legacy retirement, data consolidation, master data management to ensure high-quality data, and data consistency across cloud and on-prem infrastructure. A fully managed, cloud-native, enterprise data integration service is essential.
Advantages of a cloud-native data integration service
Scalability, reliability, security, and privacy without the responsibility of managing any of these processes
Self-service through a graphical interface, enabling point-and-click data integration for ease of use by non-technical users
Flexibility and portability to build standardized data integration solutions across hybrid and multi-cloud environments, if the underlying technology is open source
Built-in connectors to a variety of modern and legacy systems, along with personalized options for internal library of customer connections and transformations, that can be validated, shared, and reused across the organization to generate value
Real-time data integration from transactional and operational databases, and batch integration to run high-volume data pipelines spanning file systems, object stores, databases, SaaS, and legacy systems
Other aspects to consider include provisions for collection and aggregation of all business, operational and technical metadata for data lineage identification and root cause, provenance analysis for data operations. A flexible pricing model based on usage, use cases and data integration needs, helps to drive faster realization of business benefits and return on investment.
Accelerated business value
A cloud-native enterprise data integration service holds the rich promise of enabling accelerated decision making as well as achieving value from data such as data monetization and new revenue models. The fully managed service model and the standard connectors reduce the total cost of ownership by following an iterative approach and making it easier to implement and maintain. Transparent and integrated customer data offers insights into current state customer engagement, which gives scope to improve customer experience and therefore brand equity.
Managing data integration services
A trusted global system integrator and managed service provider can bring in significant value to the table in the data modernization journey of an enterprise. An understanding of various on-premise pipeline and integration platforms and the ability to identify suitable patterns required to accelerate the cloud journey are key assets to expect from a managed service provider. Likewise, building the ETL (extract, transform and load) /ELT (extract, load and transform) pipelines requires very careful planning, execution, and experience, which may not be possible for internal IT teams.
Finally, the contextual knowledge of an enterprise’s volume of data, integrations, transformations required, and the foresight of its vision to choose the platforms and services that can make the enterprise future-ready is also an important criteria when seeking a managed service provider.
Our expertise in Google Cloud Data Fusion, a market-leading cloud-native data integration solution, can enable you to take advantage of its capabilities to build and manage data pipelines, and build scalable data integration solutions as part of enterprise-wide data modernization efforts.
For more information please write to BusinessAndTechnologyServices.Marketing@TCS.COM.