Data Migration Testing: Strategy & Best Practices

Data Migration testing strategy & best practices

As the world is witnessing a huge transformation at the technological front, organizations are constantly upgrading their legacy systems to keep up with the trend. Though updating to new systems is the need of time, a major challenge lies in migrating data without losing it. Hence, it becomes important to plan out an efficient data migration strategy to ensure that migration happens without any data loss.

Testing of migration is as important as migrating data; failing to do so, organizations may face issues of discrepancies causing expected results, which can affect the organization adversely. Furthermore, to carry out efficient migration testing a well-defined strategy is required, without which the organization can be left financially drained of resources after setting up more processes than they need. They may even find that their commercial success is negatively influenced by not exploiting their data to the fullest.

What is Data Migration? Why Do Organizations Undertake Data Migration?

The process of moving data from one system to another, preferably from a legacy system to a new one, is known as data migration. However, the process is not as straightforward as it may seem because it involves a change in storage and database or application. The data migration process involves three defined steps extracting data, transforming data, and loading data. When the data is extracted from the sources, it must go through a series of cleansing to eliminate errors and inaccuracies to qualify the data for efficient analysis and load them to the targeted destinations.

Organizations perform data migration for varied reasons; first, as a part of their system revamping plan, the other possible reason could be during the upgrade in databases, while another possibility could be when creating a new data warehouse or merging data from acquisitions. But it’s most common when teams are deploying other systems alongside their existing applications for integration purposes.

Why is Data Migration Strategy Important?

A comprehensive data migration strategy comes in handy when performing large-scale operations that need to preserve business continuity simultaneously. Organizations perform data migration to improve performance and competitiveness. When organizations carefully control the data migration process, they can prevent delays caused by missing deadlines or exceeding budgets, while improperly managing the process can leave a lot of migration projects dead in their tracks. In planning and strategizing the work, teams must ensure that they put their best foot forward with undivided focus on one project.

Data Migration Strategies

There are several approaches to developing a data migration plan, however, the two major data migration strategies include “big bang” or “trickle.”

  • ·         ‘Big Bang’ Data Migration

Organizations follow big bang data migration to ensure that the data is moved from the legacy systems to the target destination and the full transfer is done in a limited time. As the data migration process goes through the three inevitable steps of extraction, transformation and loading, the active system may experience a little downtime towards transitioning to the new database. The data migration process has some challenges like validation implementation failure, lack of data analysis scope, and inability to validate specification to name a few. But companies implement this strategy as the entire process of data migration takes less time to complete even with many challenges

  • ·         ‘Tickle’ Migration

Tickle migration is conducted in phases to avoid downtime or operational interruptions. In addition, migration staging is conducted continuously to support migration, even during peak operations.

Key Components in Data Migration Strategies

Moving sensitive or important data isn’t a simple task as it involves a lot of aspects that would need consideration. Hence, it is not a good idea to begin the process without having a plan on how this should be done. One must consider the key components of data migration strategies based on the critical factors mentioned below.

  • Knowledge of data — It is critical to have adequate knowledge of the source data to find the solution to issues that may arise unexpectedly. Hence, consider doing a thorough audit of the source data before migration.
  • Data cleansing — Between source data extraction to data transformation, there is a critical step of data cleaning, which focuses on identifying the issues of source data and resolving them.  The data cleaning can be done using software tools and third-party resources. 
  • Data quality maintenance and protection — The quality of data may subside over a period. It is critical to maintain and protect the data quality to ensure the reliability of the data migration process.   
  • Data tracking and reporting — It is critical to ensure data integrity by data tracking and reporting. Use the right tool and automate the function wherever needed.  

Although we can follow many ways to move data, it is important to have adequate knowledge about the best practices to ensure that the process of data transfer is done systematically and seamlessly.

  • ·         Solid Planning

Good planning is half work done. Decide the systems that will need to be migrated and plan how they will affect the business. When migrating data from one system to another, always ask yourself if your changes can be made without affecting or hindering other systems already being used by the business. Solid planning will help in carrying out the entire process with utmost ease.

  • ·         Action Steps

It’s time to give your migration process a ticking clock and a detailed, step-by-step plan– including the plan of execution – what, who, why, and deadlines – to ensure your migration is successful and time-bound.

  • ·         Crosscheck

Decide what technology to use for the migration and how it will fit into the larger IT ecosystem. Make sure you have a plan in place for decommissioning old systems.

  • ·         High-Quality Conversion Process

Ensure you map out the technical details related to how you plan to move data. Then, put processes in place to ensure that your data stays organized and of high quality.

  • ·         Build & Test

Here, you will implement the software logic that performs the conversion of data. Test the script in a mirror sandbox environment instead of running it against your production database.

  • ·         Execute

You’ll need to verify that data migration processes are safe, reliable, and fit for use in your business before implementing them.

How to Make Your Data Migration Go Smoothly?

Transferring sensitive data is a complex yet delicate process. However, here are some best practices to follow to ensure a successful migration.

  • ·         A Thorough Migration Plan

It would help if you had a good idea about how much data to move, from where it will come, and an idea of how you’re going to implement its move into your target server or location. Your plan should outline each necessary step and who will be responsible for them, physical aspects such as technical or compatibility issues, downtime expected for your system, and the source data and migration tools if they are going to be used. Last but not least is protecting your data’s integrity. Backups may prove exceptionally helpful in preserving your original data.

  • ·         Examine your Data

Before you proceed, take a close look at the data that you’re going to be migrating. In particular, identify and weed out data that is outdated and no longer important. Separating it from your migration will help streamline your process and set a clean slate for your team after the migration is complete. If there are pieces of information that require security controls due to the nature of its regulatory information, make sure you take these details into account.

  • ·         Put Migration Policies in Place

A data migration policy ensures that your data is on the right path after it’s been migrated. It also organizes and gives control over who will handle it and how they will do it, along with adequately protecting your company’s sensitive data.

  • ·         Automatic Retention Policy

Once you’ve successfully migrated, you must take the time to ensure that everything is placed where it belongs and remains safe and secured. It’s essential to keep all your systems in working order by setting up automatic retention policies to prevent data leakage. Also, make sure that outdated data has been validated and permissions are granted accordingly. Finally, just ensure that old legacy systems will back up automatically in the event of any technical difficulties – but make sure to double-check them before they’re put on standby!

Conclusion

As technology continues to change, businesses must continue to evolve as well. As a result, companies must create a plan for their data and understand data migration in today’s business world. Data migration can be challenging, but a company can migrate its data with minimal downtime and stress with a proper strategy and a few best practices.

At Yethi, we have the expertise of handling complex financial data migration, with pre and post-migration testing along with regular audits. We offer the most efficient end-to-end testing service. Our test automation platform, Tenjin, can test large data migration easily and efficiently while reducing time and cost significantly.

Data Integration Architecture and Customer Experience – A performance viewpoint

Data Integration

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.