Data Integration Architecture and Customer Experience – A performance viewpoint

Cover image for the data integration architecture by Yethi

Data is omnipresent. It is available across multiple applications, data warehouses, databases, and even the public Cloud in every organization. Data belongs to various groups within an organization and is commonly shared across teams and applications.

As an organized sports team has a clear division of responsibilities, each with a dedicated role to play and win; organizations should ensure all departments have specific roles and coordinated effort from functional units to get the most out of their resources and get things done right. To make sure everything is cooperating effectively, companies need to work towards improving their data integration architecture. This will help them keep track of what is going on and share information in real-time, which gives them better insight into how things are progressing and where there might be opportunities for improvement.

What is Data Integration Architecture?

Data integration architecture is the engine driving the business data ecosystem, where people can focus on generating customer value. Too often, users spend time searching for data rather than using it to create new products or find ways to increase sales. A Data Integration Platform supports critical functions of an enterprise by allowing users to consolidate data from multiple sources into a single platform, transforming information into actionable knowledge, and seamlessly sharing that data across the organization for business decision making.

Why is Data Integration Architecture Important?

It’s important to create a data integration architecture to help you integrate whole data and normalize it to support faster decision support and innovation. Your company depends on the analytics and insights gleaned from all sorts of data. Having a dependable data integration architecture in place is so important when supporting these business functions.

Creating a data integration architecture does not mean creating a framework that combines all of your enterprise’s information source into one system, like a giant database or big data analytics

There are more issues like storing, managing and analyzing complex and large data in banks and financial institutions. Through data analytics, organizations can solve these issues. Financial organizations have realized the importance of data analysis and are slowly adopting these changes to improve accuracy and efficiency. 

Typically, there are multiple databases in financial industries that store the data. The banking data is complex and spread across many systems. It is challenging to unify the data into a single data warehouse from multiple systems. Banking professionals use data integration architecture or data warehouses to simplify and standardize the way they collate the data and create a single database.

Instead, it means understanding how different systems and tools across your organization communicate to share accurate and relevant information across the company. Data integration architecture helps define how relevant information can be shared between internal departments and external business partners through compatible technologies – usually ensuring that companies avoid ineffective redundancies and achieve better functionality and streamlined teamwork across the board.

Factors to be Considered

As analysts pursuing business intelligence, you must know how challenging it can be to find the method of data integration that will most ensure access and availability and flexibility for analysis.

Consider the following:

  • How many different data sources do you need to integrate?
  • Your data set’s size and format.
  • Your source data’s reliability.

Data integration should be considered by companies to embark on achieving their goals, which may take a combination of different methods and tools to accomplish.

Types of Data Integration

As analysts, make sure to consider multiple types of data integration methods for your business. It’s crucial to find the method that best suits the insights you need as a business, as well as what you’ll be using your data for.

Data Consolidation

Data consolidation is a method of acquiring data from different sources and usually requires specialized software with a query interface to combine data from multiple sources into a single database.

Data Propagation

Data propagation is a method of integration that duplicates data stored in source data warehouses. This can be used to transfer data to local access databases based on propagation rules.

Data Federation

Federating data means connecting various pieces of information so they can be viewed centrally. Data federation is a technology that allows companies to link together data from multiple sources using a kind of ‘bridge.’

Data Integration Techniques

There are several data integration approaches to choose from, each with its own set of capabilities, functions, benefits, and limitations.

  1. Manual Data Integration: The process of locating information, accessing different interfaces directly, comparing, cross-referencing, and combining it yourself to get the insight you need is a manual data integration.
  2. Application-based integration: Application-based integration is the process of accommodating individual applications, each with their unique purpose, to work in conjunction.
  3. Middleware Data Integration: It serves as a “layer” between two dissimilar systems, allowing them to communicate. For example – The architecture in Finacle 10x is SOA, which has middleware that integrates with CRM to offer a 360-degree view of customers and learn about the customer experience.
  4. Uniform access integration: Uniform access integration is a type of integration that focuses on developing a uniform translation process that presents information obtained from multiple sources in the best way possible. It does this without having to move any information – data remains in its original location.

How Data Integration improves performance and customer experience

Understanding your customer, their needs, and their purchasing preferences are essential parts of any successful business. With the amount of data about your customer available to you right at your fingertips, it’s becoming easier for any entrepreneur to build a successful customer-driven strategy. However, with most data now being stored digitally, the challenge today is to quickly assess and apply all this large amount of data with limited resources!

There is a lot of data that’s floating around for you to take into account. With so many numbers and figures to consider, it can sometimes become difficult to determine which information is helpful and which isn’t. Luckily, having a customer data integration tool can help you better understand your consumer base by providing valuable insight and ways to reach those consumers as well as manage those.

Conclusion

Data integration architecture is the process of combining data from different sources into a single system. This data is then structured to be used for a specific purpose, such as a marketing campaign or a manufacturing process. Data integration architecture uses tools and technology to combine data from multiple sources. This process can have several benefits, including improved performance and a better customer experience.

Risks Associated with Data Migration and How to Mitigate Them

Let’s begin with some numbers! According to IndustryARC, the global data migration market that emphasizes Cloud-based servers over on-premises ones is predicted to reach an estimation of $10.98B by early 2022. In addition to this stat,  the Cisco Global Cloud Index shows that  Cloud traffic is expected to reach 7680 Exabytes in North America alone! Similar enhancements in modern data management technology bring more efficiency and transparency, which will directly surge the adaptation of application and data migration in small-scale and large-scale enterprises.

Given the risks associated, the question “Is data migration really important?” isn’t unusual. And the answer must always be “Yes!” Delaying data migration while holding onto outdated IT infrastructure isn’t an option with increasing market intrusion from non-traditional competitors who can create more nimble and responsive approaches towards delivering unique products. Because monolithic application systems weren’t designed to quickly adapt to business dynamics, they have to be replaced. Failing which, may pose further risks of losing market share and retention.

Let’s understand data migration first

At its core, data migration is the process of transferring data from one location to another, from one application to another, or from one format to another. This crucial step towards improvising an outdated IT infrastructure is generally taken during the installation of new systems or upgrading legacy ones, which will share the same dataset without affecting live operations. In recent years, the majority of data migrations are executed for transferring actionable data from on-premises infrastructure to Cloud-based options, that too, while undertaking data migration testing.

Concerns with legacy systems

The primary focus of IT infrastructure has already shifted towards better performing, more efficient, cost-effective, and secure solutions. CEOs and IT admins are struggling to maintain or support legacy systems as common challenges in legacy designs are time-consuming to tackle while the technology is mostly unfamiliar to new-age IT personnel. Some of the key concerns of using legacy systems include:

  • Heavy Maintenance Costs: Legacy systems are now obsolete, primarily, because of higher maintenance and operational costs. Further, the poor performance of such legacy systems cannot support new business initiatives.
  • System Failures: With legacy IT infrastructure, system failures are a daily occurrence. Since the professionals who implemented such systems have retired, new-age IT admins lack the skills to maintain legacy systems.
  • The Inability of Processing Complex Data: Within legacy systems lies old technology and computer systems that are fundamentally unable to execute complex enterprise operations with enough speed or reliability.

The increasing challenges to using legacy systems in today’s tech-driven world has led to migrating to new-age systems to keep up with the trend. However, migration to new systems may come with a set of potential risks which the organization should be able to mitigate and yield the best outcome from the migration.

Potential risks of data migration

  • Lack of Transparency: Not allowing key stakeholders to input in the undergoing data migration process is the mistake often made by enterprises. At any stage, someone might need the system to remain operational or would care if the data is being migrated, therefore, it’s vital to maintain complete transparency on the process.
  • Lack of Expertise or Planning: The primary cause leading to unsuccessful data migration is lack of expertise. With modern systems getting complex with millions of data points, it’s essential to evaluate which data points must stay operational. As data migration is more about risk mitigation, any disruption may leave IT admins clueless.
  • Addressing Data Privacy with Proven Migration Plans: When an enterprise doesn’t assess how many people might receive access to the data in the migration process, potential data breaches can occur. Conducting any data migration always requires proven migration strategies that further raise the probability of its success.
  • Defective Target Systems: Projects and vendors must be managed parallelly while flipping the switch from legacy systems to new-gen infrastructure. Suppose an error occurs in either the source system or the target system, it may derail the migration process in the middle of transferring vital data, raising the risk for data corruption.
  • Trying to Execute Complex Data Conversion: Unnecessarily making the migration process complex without any tangible increase in outcomes must be avoided at all costs. Complex conversions add extra steps to the process that just makes it challenging to execute. Only undertaking essential migration steps will surely get it done fast.

Why is data migration more about risk mitigation?

As legacy systems are growing organically, the need to adapt to modern business applications are raising concerns with their data quality. There might be millions of data points that must be assessed before concluding which ones must stay operational for any enterprise-scale migration. Along with regulatory and analytical needs, the data must be Extracted, Transformed, and Loaded (ETL) to modern systems without disrupting major business applications. As datasets get complex, things are no longer so simple!

The importance of conducting data migration testing

Once the data has been Extracted, Transformed, and Loaded (ETL) into new-gen systems, what stops it from being deployed? The answer is Data Migration Testing! As enterprises are swiftly migrating their operations to the Cloud, ensuring the integrity of data is key to ensuring further business applications. Here’s how enterprises achieve it:

Data-level validation testing

With certain data migration testing tools, data-level validation testing ensures that the dataset has been efficiently migrated to the target system without any disruptions. With data-level validation testing, data will be verified at:

  • Level 1 (Row Counts): Verifies the number of records migrated from the legacy system to the target.
  • Level 2 (Data Verification): Verifies data accuracy from any selected portion of the total migrated database.
  • Level 3 (Entitlement Verification): Verifies the destination database setup for users and selected data samples.

Application-level validation testing

In contrast, the functionality of the sample application is validated with application-level validation testing. This ensures the smooth operation of the application with the new infrastructure using specific data migration testing tools.

Conclusion

If you are concerned about the risks associated with data migration, you’d be relieved to know that the benefits far outweigh the risks. The importance of expertise and planning is still evident in data migration and data security concerns. In addition to having an efficient and rock-solid data migration strategy, enterprises must also practice data migration testing. Data migration processes remain an activity with potential risks, successfully testing can drastically reduce the migration errors while optimizing future data migration processes.

What Is Migration Testing: How to Efficiently Conduct Testing While Migrating Data?

Technology is ever-evolving with new advancements making its presence every day. Organizations are constantly updating their legacy systems to the new ones to take advantage of these developments and align with the constantly changing end-user preferences. However, the major challenge lies in migrating to the new system without losing data. Data owned by organizations is an asset that provides critical insights to plan future endeavors, hence, organizations cannot afford to lose them.

Though data migration is a tedious process that requires enormous effort and time, it is a significant process that plays a crucial role in application redesign. Data migration cannot yield favorable results without a thorough testing process in place. Migration testing ensures that data integrity is maintained while upgrading, integrating, or transferring the system, while making data migration a success that affects your business positively.

Migration testing ensures the data migration has not resulted in any disruption and all functional and non-functional aspects of applications are retained even after migration. It is extremely important to undertake Data Migration Testing to find out the discrepancies that arise while migrating the data from the parent or legacy database to the new or destination database. Organizations must efficiently conduct testing while migrating data for smooth operations.

The need for migration testing

Testing of the system is critical even when the slightest change is made, to ensure the incorporated change doesn’t create any conflict in the current workflow or make any further unnecessary modifications. Hence, it becomes important to carry out end-to-end testing during the system migration process. Migration testing is essential as it:

  • Ensures continuity and consistency after the platform migration – imagine if your mortgage computation changed because the lender changed systems?
  • Ensures no data loss when you move to the new platform – imagine if your broker could no longer locate your retirement account after her company upgraded their systems?
  • It identifies any defects in the new application and ensures that it works perfectly without any issue.
  • When migrating from one system to another, migration testing ensures proper flow and working of the application, as it was before migration.
  • In addition to testing data retention and functionality checks, migration testing also checks if the application is optimized to the new workflows and environment.

Data Migration Testing Approach

Organizations should incorporate the following strategic approach for data migration testing for best results:

  • Form a specialized team for data migration testing: It is of paramount importance to have a team that possesses the required skill set and experience to carry out data migration testing.
  • Risk and Error Analysis: It is essential to ensure that data migration testing should be smooth and does not disrupt the current business. Data migration testing should focus on high-risk scenarios to validate and mitigate risks.
  • Scope of Migration Testing: Organizations should decide the scope of data migration testing- what is to be tested.
  • Select the Data Migration Testing Tool: An appropriate Data migration Testing Tool should be selected to achieve minimum discrepancies and anomalies.
  • Identify the Test Environment: The test environment should be set up according to the technical aspects of the source and target data system.
  • Migration Test Document: In the end, it is important to prepare a migration test document that states the testing tool, testing methods, schedule of testing, etc.

Data migration testing has assumed a lot of significance in the current scenario, where the availability of the right data at the right time governs the success of any organization. Organizations are investing a huge amount of resources in migration testing to avoid any mishaps later on. Efficient and effective data migration testing combines a systematic approach, prescient risk minimization techniques, and holistic test coverage.

Understanding data migration testing

Efficient data migration testing includes the following two levels of validation testing:

  • Data Level Validation Testing
  • Application-Level Validation Testing

Data Level Validation Testing

When data is moved from several databases to one common database, data level validation testing is done to ensure that there are no discrepancies. It ensures that the complete data is migrated to the new system and no loss was incurred during the migration process. Data Level Validation Testing can be further classified as follows:

Level 1: Counting the number of rows

In his stage, we find out the total number of records that would be moved or migrated.

Level 2: Verification of data

A sample is selected from the migrated data and checked thoroughly to verify the accuracy of the data.

Level 3: Verification of Entitlement

At this stage, the new database is verified for the users as well as samples of data.

Application-Level Validation Testing

Application-Level Validation Testing checks whether the migrated applications function efficiently in the new database or not; a sample application, that was migrated, is tested for its functionality. Following validations are carried out:

  • Verification of sample data by logging into the new application after migration
  • Verification of the status of accounts- whether it is locked or unlocked by logging into the legacy system post-migration
  • Verification of access to legacy systems during migration despite user blockage
  • Verification of instant restoration of user access to the old system in case of failure of migration
  • Verification of denial of access to legacy systems at the time of migration
  • Validation of login credentials for migrated applications

Data Migration Testing Tools

Data Migration Testing Tools can be classified into three categories as follows:

  • On-premise Tools
  • Open-Source Tools
  • Cloud-based Tools

On-premise Tools: When organizations want to transfer data from one server to another or from one database to another within the enterprise, they use on-premise data migration testing tools. In this scenario, it just involves changing the database or integrating databases.

Open-Source Tools: Open-source data migration tools are easily accessible by people. They are free to use. Organizations can use these data migration testing tools if the project is small and the data to be tested is less. Knowledge of coding skills is required if you use open-source tools.

Cloud-based Tools: Cloud-based data migration tools are the latest developments in data migration and testing. These tools enable the organizations to transfer the data to Cloud, which is the need of the hour. Hence, Cloud-based data migration tools are widely used as they are secure, flexible, and cost-effective.

Yethi’s data migration testing

Yethi is a well-known name in the BFSI industry for offering specialized QA services. We understand the importance of testing during data migration process, hence, we assure seamless data migration testing under budget and time constraints. At Yethi, we have the experience and expertise to handle testing of different scales and complexities of the data migration process. With our extensive technical and functional audits, you can be assured of having a perfectly working system, even after the migration process.