How to ensure smooth data migration with QA

How to ensure smooth data migration with QA Transferring information is an elaborative and time-consuming initiative. Explore how to streamline this shift by applying comprehensive QA and software testing to your data migration strategy.

White paper

How to ensure smooth data migration with QA

2

Contents

Introduction

01. Data migration essentials

Main triggers. What drives data migration

Types. The point to knowing them

Challenges. The complexity of data transmitting

Approach 1 — “Big Bang” migration

Approach 2 — “Trickle” migration

Best practices. Getting the most of data transition

02. QA scenario for error-free data migration

3

5

6

7

8

9

10

11

14

Step 1. Analyze the project’s details

Step 2. Outline a strict QA plan

Step 3. Perform pre-migration testing

Step 4. Execute post-migration testing

Step 5. Carry out tests in production

A proven way to enhance your data migration strategy

03. An example of high-quality data transition

04. Why entrust data migration testing to a1qa?

Final note

15

16

17

18

19

20

22

25

27


3

With growing business expectations, the IT market’s demands make companies across any industry continue to reimagine IT strategies to enhance their software products and deliver the novel ones to win the competition.

Yes, in the informational era, data has turned

into a precious asset gathering greater attention. Ensuring secure and reliable data processing is becoming vital like never before. So, organizations are embarking on performing data migration to address the issues with outdated software and keep up with prompt tech market evolution.

At the first glance, it may seem like a simple copy-paste action. However, this is not the case. Data migration is a complicated initiative that takes a

long time and needs comprehensive insight into its basics — from drivers to the ultimate phase.

Why is data transfer so complex and how to perform it with confidence and ease?

Let’s get these questions sorted out in this white paper. Explore data transition essentials and a five-step QA scenario that will help provide a smooth shift along with total information consistency.

Introduction

Data migration essentials

01


5

01. Data migration essentials

The generation of an extensive array of data across businesses is often a common reason for such substantial figures. Clients’ information, details on the internal processes, metadata, and others — all these should be stored and handled reliably. While companies are optimizing the mechanisms of data processing, the volume of information is steadily growing.

In many cases, big data, IoT, and AI induce

transmitting data to more productive and secure solutions. Data migration is an inevitable process for organizations intending to overcome data quality and integrity issues while keeping up with the IT market pace.

Let’s take a closer look at the essentials that companies should consider before performing a data migration.

*Research study completed by Reports and Data on the “Global Data Migration Market”

$22.78billion is the forecasted size of a global data migration market by 2026.*

6


Main triggers. What drives data migration Data transmitting helps enable business growth while reducing expenses on storage maintenance, increasing security and efficiency of IT solutions, improving brand image and customer experience (CX), and others. Some of the data shift cases are as follows:

Improving security levels to enable total data privacy

and safety

Modernizing or replacing legacy software/hardware to ensure high efficiency and

usability

Merging systems to centralize databases and

reach compatibility

Enlarging storage capacities to cover a greater volume of

information


7

Types. The point to knowing them

In line with massively moving to an online environment and total availability of network systems, cloud migration is coming to the fore.

Being an affordable and cost-effective way of storing and handling data, cloud migration covers the following types of data transmitting: storage migration, database (DB) migration, data center migration, application migration, and others.

Considering the unique IT and software

infrastructures, the process of data transfer differs. Some business objectives require shifting from one on-premises platform to another (that is more productive), while others need to make a transition from physical storage to the cloud, and so on.

However, all kinds of data migration are risk-prone and rigorous. Why? Let’s get this straight.

45%of IT expenses will be associated with shifting to the cloud by 2024.*

*Smarter with Gartner article “Cloud Shift Impacts All IT Markets”


8

Challenges. The complexity of data transmitting

Taking part in a data migration journey often provokes issues of:

• Low level of data security

• The unmatched speed with transfer channel capacity

• Superficial risks assessment

• Unexpected delays

• Incomplete understanding of legacy data

• Absence of business involvement

• Poor data quality

• Lack of knowledge of a target system.

These and many other failures are mostly the

outcomes of a flawed data migration strategy and underestimated efforts.

How to avoid falling into such a trap? Obtain the comprehensive understanding of the data migration process and define shift approach beforehand to get confident that the transition will be error-free.

58%of ERP migration projects have timeline overruns.*

*2019 ERP Report by Panorama Consulting Solutions


9

Approach 1 — “Big Bang” migration

Idea

The process occurs at one time — both the legacy and new software are stopped during data shift.

Pros:

• Cost-effectiveness

• Less complicated than the iteration-based approach

• Less time-consuming than the phased strategy

Cons:

• High risk to fail

• Needs pausing

Steps to make

• Compose a well-tuned data migration strategy and plan to provide as minimal downtime as possible.

• Incorporate testing phase before transitioning to eliminate data quality issues.

• Ensure that migration tools operate correctly.

• Run a target system after confirming that the migration is successful, and all the information is safe and sound.

Verdict

• Regarding the necessity of pausing both environments, the “Big Bang” approach suits small organizations with not so extensive DBs.

• It doesn’t work for solutions that are to be available around the clock.


10

Approach 2 — “Trickle” migration

Idea The process is aligned with the Agile experience and is divided into sub-migration phases. Both the legacy and target systems are run during data transfer.

Pros:

• No downtime required

• Low risk to fail

Cons:

• Complicated execution

• High expenses

• Needs additional resources

Steps to make

• Prepare a robust plan for the entire transition process and specify the sequence of data to move.

• Onboard high-skilled personnel to continuously monitor the shift while maintaining the systems.

• Execute each iteration, as it is a separate project with its scope, outcomes, and deadlines.

Verdict

• It is a perfect match for mid-sized companies and large enterprises that have numerous arrays of data.

• This approach fits those who cannot allow long software pauses and have enough resources to accomplish this tough task.

https://www.a1qa.com/services/agile-software-testing/


11

Best practices. Getting the most of data transitionRegardless of the data transfer approach, there are some rules of thumb that enable seamless data shift.

01Back up data in advance

While executing data migration, the risks of data

loss or its severe damage are high. Surely you don’t

want your clients’ sensitive information to leak

affecting your reputation management, so it’s a

mission-critical task to back up resources and verify

data consistency in advance.

02Strictly follow the migration strategy

Transmitting information holds the potential for

unforeseeable issues that adversely impact on desired

business outcomes. A thorough plan helps get ready to

meet face-to-face with emergencies as well as define

risks beforehand to eliminate them on time.


12

Best practices. Getting the most of data transition

03Ensure the highest level of security

In many cases, poor data safety and privacy is a

common reason for customers’ outflow. The price

of a security breach reaches thousands or millions

of dollars if not considering operational downtime,

loss of customers’ trust, legal actions, and other

consequences that are not easy to overcome. So,

make sure that your team uses safe communication

channels and follows all security requirements.

04Incorporate QA and software testing practices

The lack of preliminary checks often causes infallible

data transition and problems with information

integrity that decrease customer experience. Well-

tuned quality assurance helps address these and

many others issues by preventing expensive bug

fixing after going live, boosting overall efficiency on

the project, executing data migration faster, and

astonishing end users with high-quality software.

QA scenario for error-free data migration

02


14

02. QA scenario for error-free data migration

In most cases, this also refers to the data migration process. Relying on QA assistance throughout the entire transition life cycle helps ensure high data integrity and consistency.

Implementing catch-all testing to the data transferring project requires a strict QA plan as well as carefully determined timelines, scope, and efforts needed. Preliminarily defining them helps

prevent business and operational issues and detect potential risks during data migration in a timely manner.

Let’s figure out the actions of a solid QA strategy and delve deep into each step of the successful data migration testing scenario.

74% of surveyed companies consider business growth as one of the QA objectives.*

*The World Quality Report 2020-2021


15

Step 1. Analyze the project’s details

Data migration testing requires profound groundwork. Before starting it from scratch, get ready to:

• Evaluate risks and potential failures as well as think over resolutions

• Define the migration testing scope, QA expenses, and deadlines

• Assess the tools and environment and select the ones that meet your project needs

• Indicate desired business and operational outcomes

• Identify data mapping regulations.

Moreover, the specialists choose an appropriate transfer strategy that is most suited to the company’s infrastructure and objectives. Recalling the approaches of transmitting data, the “Big Bang” fits projects with a small amount of data and a low risk of downtime, and the “Trickle” — for a great volume of information with no pausing required.

Using test infrastructure wisely contributes to greater savings.


16

Step 2. Outline a strict QA plan

Once the migration approach is selected, QA specialists embark on developing a QA strategy that should cover the ETL process of data transition. ETL goes for Extract data from the legacy software, Transform it within a defined layout, and Load information to the target solution.

To optimize the full test coverage, a QA team composes testing documentation embracing test cases before and after data transfer as well as

checks when going live. So, the QA process includes the following stages:

• Pre-migration testing

• Post-migration testing

• Testing in production.

Let’s get them sorted out and discover what and when should be tested.

80% of companies admit the need to empower teams with greater responsibility for quality.*



17

Step 3. Perform pre-migration testing

What to test

• Correct data storage in the current software

• Proper functioning and security of transition toolkit, environments

• Accurate operation of the target solution

• Corresponding mapping of data and business processes

Deliverables

Owing to a holistic QA approach with functional, security testing, and continuous data monitoring, a QA team detects issues in data housing and fixes them to provide information integrity and its readiness for the shift.

Moreover, QA specialists perform trial migration iterations using test data to identify possible drawbacks and eliminate them on time while guaranteeing error-free and safe transition of real data.

59%of the companies follow a “shift-left” method (testing at the early SDLC stages).*

*The State of Testing Report 2021 by PractiTest


18

Step 4. Execute post-migration testing

What to test

• Data consistency in the target system and its equivalence with the input one

• Proper structure in a new solution

• System’s recovery after possible crash or malfunction

• Performance of a target system and/or its comparison with the legacy software

Deliverables

QA specialists enable high quality of new data storage as well as prevent data loss and system vulnerabilities by applying end-to-end, performance, and security testing.

What do you get? Increased revenue, compliance with the ever-changing market requirements, efficient and safe software, and many more.

83%of CIOs said their app security concerns have increased.*



19

Step 5. Carry out tests in production

What to test

• Data usability

• Consistency of user and business cases

• Smoke tests of new system’s features

• Compatibility with third-party solutions

Deliverables

Customers’ expectations are constantly rising, and people want to use IT products of higher quality and network connectivity. Incorporating QA to the go-live stage helps strengthen users’ loyalty as well as entice new ones.

By improving customer experience through implementing more secure and reliable data storing and processing solutions, companies enhance their reputation and stay one step ahead of the competitors.

Provide end users with impeccable customer experience in a new data storing solution.


20

A proven way to enhance your data migration strategy

Yes, test automation is the best practice to optimize QA processes while providing extra benefits such as minimizing human errors, reducing QA costs, speeding up testing time, and others. However, it brings real value only when applied wisely.

In terms of data migration, incorporating a test automation approach will help deal with a formidable amount of information, especially on the phased strategy. With that, automated testing

is mostly an escape solution when it needs to gradually move data to a new platform verifying how it reflects already added information. Thus, it helps accelerate time to market and deliver upscale and high-secure high-secure software at short notice.

Having discovered steps of successful data migration testing, let’s have a look at them in practice and move on with an example of smooth data transfer within QA performed for a telecom player.

Organizations cover 75% of functional or regression checks with test automation.*

*The State of Testing Report 2021 by PractiTest

https://www.a1qa.com/services/test-automation/

An example of high-quality data transition

03


22


Business need

An enterprise in telecom, providing cellular and local telephone service as well as broadband Internet and cable television access, turned to a1qa to enable flawless data transmitting and the high quality of new software. The company aimed to replace a standalone billing system that was divided into 25 branches with a single solution.

Project scope

Within the large size of the project and sophisticated logic of the solution, a1qa’s specialists built a testing strategy including functional testing of the current platform, test automation, load testing methodology audit, and continuous quality monitoring throughout the entire data shift process.

Considering that transition should not impact the end-user experience, QA consultants have

composed a migration testing strategy that presupposed Parallel Run and Dry Run approaches.

Owing to Parallel runs, the source and target billing systems were operating simultaneously using the same input data while QA engineers were verifying the correspondence of the output results. Before performing it on the production, they launched Dry runs that helped prepare users’ data for the migration.


23

Results

By assigning quality assurance function to a1qa, the client achieved the following outcomes:

• Enhanced quality of the delivered software

• Flawless data transmitting

• Accelerated time to market due to performing test automation

• Recommendations on eliminating performance bottlenecks.


2 2,000+100+years of the project duration

system tests were created bottlenecks detected during the audit of the load testing methodology

Why entrust data migration testing to a1qa?

04


25

a1qa delivers a1qa contributes to

04. Why entrust data migration testing to a1qa? While ensuring flawless data transfer, a1qa brings business and operational benefits.

• Accelerated time-to-market

• Improved customer experience

• Cut operational costs

• Enhanced brand image

• Increased project’s efficiency

• Compliance with security requirements

• Quality end product

• Reduced level of organizational stress


26

completed projects

18

800 1,500+

10+years in SQA business industries covered

clients from 39+ countries

04. Why entrust data migration testing to a1qa?


27

Final note Within the advent of the informational age, a transition to more efficient data handling solutions has become an across-the-board need. Involving substantial investments of time and efforts, data migration requires thorough preparations and a strict strategy.

Introducing robust QA and testing practices helps ensure faultless data transition. What’s more, the right QA strategy brings extra value of business growth and guarantees impeccable CX through confidence in data security and privacy.

Need professional QA support for your data migration project? Reach out to a1qa.

https://www.a1qa.com/contacts/

United States tel.: +1 720 207 5122

United Kingdom tel.: +44 208 816 7320

Follow us

a1qa.com [email protected]

https://twitter.com/a1qa_testing

https://www.linkedin.com/company/a1qa/

https://www.facebook.com/a1qa.software.testing/

https://www.a1qa.com/

Documents

How to ensure smooth data migration with QA