Cloud adoption is growing by the day. But enterprises will not reap the expected benefits from the move unless they get data integration right.

Data analytics help enterprises understand customers better, improve efficiencies, and unlock opportunities. But most enterprises have large volumes of untapped data. According to a Salesforce report, 67% of business leaders feel they miss opportunities such as optimal pricing because of such untapped data.

The main reason for untapped data is organic growth. Data comes from multiple and often disparate and inaccessible sources. It requires seamless data integration to pull data from disparate sources, transform it, and load it into target systems.  Such integration establishes a single source of truth for analytics and reporting.

Here are the best practices for seamless data integration in the cloud.

1. Understand the Data

Enterprise data invariably come from various sources.

All data is not equal. A large chunk of the data may not add value, even if collected and analysed. Some sources may have been indispensable in the past but may no longer be necessary.

Evaluate the relevance and criticality of different data sources. Classify data based on importance to the business, sensitivity, and regulatory requirements.

For instance, financial records, customer databases, and intellectual property count as critical data. Improper handling of such data leads to big-time disruption and losses. On the other hand, general correspondence and advertising collateral may be non-essential data.

2. Make the Data Discoverable

One of two enterprises suffers from “dark data,” or unknown or unused data. Dark data may contain valuable customer information, financial records and data logs. Examples include raw survey data, CCTV footage, or email archives.

Dark data exists due to poorly connected legacy systems or ineffective metadata management.

As solutions to integrate data from such sources,

• Conduct a comprehensive data inventory and create a catalogue that contains all the enterprise data assets. Clear-cut documentation ensures transparency.
• Define clear data mapping rules to ensure consistency and accuracy during data transformation.
• Spread awareness and educate the workforce on the importance of data management. Train them in metadata and the dangers of shadow IT.

3. Tackle Data Silos

Much critical data resides in inaccessible silos, set up either by design or as an offshoot of ad-hoc, organic growth. For instance, the marketing team may host customer data in a separate shadow CRM.

But not all silos are bad. Some sensitive data, such as trade secrets would still need to remain in inaccessible on-premises silos.

CIOs need to make the trade-off between keeping sensitive data secret and expanding the pool of data available for analytics.

They need to tackle shadow IT head-on and connect non-sensitive data trapped in silos or inaccessible shadow sources.

4. Ensure Data Integrity

Maintaining the integrity of the data is critical to ensure the success of data integration. The means to do so include:

• Implementing robust network security measures, such as a zero-trust approach and encryption.
• Having robust access control systems in place to make sure only authorised users can access the data.
• Adopting blockchain. Blockchain ensures verifiable data history, to ensure data quality, accuracy, and integrity.
• Set up robust data governance measures. Make sure the data governance strategy enforces standardised data formats and definitions.
• Implement data quality checks and lineage tracking to ensure accuracy and compliance.

5. Decide Between ETL or ELT

Transforming the data involves profiling, cleansing, and validating the data. This can take place either before transfer to the cloud or after transfer. Both approaches have their pros and cons.

ETL or Extract-Transform-Load involves processing the data before moving it to the cloud. ELT or Extract-Load-Transform processes the data after storing it in the cloud.

ELT is more cost-effective in the cloud environment and preferred for high-volume transfers. It leverages the scalability of cloud data warehouses to handle transformations.

 But ETL is the better choice for sensitive data. Performing the transformations before loading offers better control and security. Pre-cloud transformations allow data masking, anonymisation, and encryption.

6. Make a Trade-off Between Performance and Update Frequency

Frequent updates ensure the cloud databases have the latest data always. Several applications today survive on such real-time updates with zero latency. But frequent updates also degrade the performance of downstream applications and analytics. Seamless data integration depends on making a trade-off between performance and frequency.

The most suitable integration processes for frequent updates include:

• API integrations. API integration ensures seamless interaction between different systems and applications. API tools offer scalability, time and cost savings, security compliance, and cross-platform compatibility.
• Change data capture (CDC). CDC enables increment loading. On updates, only what has changed since the previous update gets transferred. It is far more efficient and cost-effective than transferring complete datasets every time.
• Stream processing. Data streams involve continuous data flows from social media feeds, sensors, financial markets, and so on. Here, the data processing and analysis is instant as the data flows in, based on defined rules or logic.
• Message queues. Message queues serve as temporary storage buffers, and enable asynchronous communication between different systems. 

7. Optimise the Process

The performance of the data integration process depends on the data volume.

Integrating large datasets is time-consuming and also costs bandwidth. The following approaches can reduce the time, cost, and complexity of the integration process:

• Compression. Protocols such as HTTP/HTTPS or FTP/SFTP reduce the size of data packets and speed up transfers.
• Data virtualisation. Virtualisation creates a virtual layer over disparate data sources. It offers a unified view of the data over these sources without physical replication or transfers. The unified view delivers simplicity. It does away with having to grapple with different formats, protocols, and security requirements.
• Serverless computing. Serverless options allow data integration without managing the underlying infrastructure. The platform scales automatically based on data volumes and processing needs.
• Microservices. Microservices architecture breaks down the integration process into smaller, independent modules. Each module becomes responsible for a specific task, such as connectivity and validation. Such an approach improves flexibility and especially suits high-volume frequent data transfers.

8. Monitor the Process Continuously

Data integration processes are not set in stone. Configuration changes may result in flaws and inefficiencies, necessitating fine-tuning. The data quality may change over time due to contamination of some source. Changes in the business may necessitate changes in data sources and compliance.

Data managers must monitor data quality and integration processes to identify and pre-empt issues. They need to:

• Set up monitoring and management tools to track integration performance. These tools maintain data quality and identify errors. Monitor the data integration pipelines to ensure smooth data ingestion and resolve any issues fast.
• Optimise the integration processes for performance and efficiency from time to time.
• Make changes to adapt to changed data sources and business requirements, as needed.

9. Select Appropriate Tools

The choice of data integration tools is a critical yet often underestimated component of cloud data integration.

Decide the repository. For unstructured data such as text documents or images, data lake architecture with NoSQL databases works best. Data lakes store data in its raw format and ingest data from all sources without being picky over data quality or schema differences. A data warehouse such as Snowflake or BigQuery is more suited for storing structured, analytic-ready data in a more organised way.

Cloud-based data integration platforms such as Informatica work best to handle a variety of formats. These platforms take care of the end-to-end integration process. It integrates data from multiple sources, regardless of format or location. It also processes the all-important data cleansing, transformation, and loading processes.

Informatica Cloud Data Integration solution has become popular due to its flexibility and ease of use. The platform supports microservices and serverless computing. It also offers preconfigured templates, out-of-the-box mappings, and mass ingestion capabilities. Intuitive wizards enable automation and make complex data integration tasks seamless.