Your Complete Guide to a Successful Data Migration

data migration cleansing

data migration cleansing

If you’re reading this guide, chances are you’ve already decided to initiate a data migration project and are looking for additional guidance. Cutting right to the chase, this guide will help you create a data migration strategy and follow a framework that will reduce your chances of making costly mistakes. It will also help you understand where and how you can use a data quality solution to optimize the process and make your data migration a success. Let’s get started!

Understanding the Challenges You May Have to Face

According to industry analysts, at any given time, around two-thirds of Fortune 1000 companies are engaged in data migration or data conversion projects. However, nearly 70 – 80% of these projects fail, translating into a loss of billions of dollars. Yes, the statistics may sound alarming, but if you really take into consideration the complex and time-consuming process of data migration, it’s easy to see how there is a higher failure ratio. Data migration is inherently a high-risk project. Moving your data from a legacy or mainframe environment to a new environment requires more than just technical expertise. You would need a foolproof data migration strategy, a plan, checklists, all while fixing data quality issues, hiring or retaining talent, ensuring team collaboration, and conducting trial and tests among many other tasks. In our experience working with 4,500+ clients, we’ve noticed several major challenges that eventually become the reasons for data migration failure. Here’s a quick heads-up on potential challenges you may face and how you can prepare for them.

    1. Neglecting the Importance of Data Quality:

Ample studies and research prove that poor data quality costs businesses millions of dollars, yet, businesses tend to overlook the importance of data quality. The result? Poor data becomes a bottleneck that prevents successful data migration. If data is being moved from a legacy system to a new system, chances are the system may even outright reject the data, especially if the newer system has stricter standards and quality governance.

You may have a data quality crisis if your data suffers from:

  • Inaccurate, invalid, or incomplete information
  • Typos, character, or punctuation errors
  • Duplicate entries
  • Incorrect formatting and casing errors (upper and lower case inconsistencies) and more.

If these issues are not resolved, your data migration process is bound to be a failure.

    1. Business Users and High-Level Executives Oblivious to Data Quality Issues

Data quality is seldom considered as a business problem especially since it involves data warehouses which are managed by the IT department. Business leaders of various departments are hardly aware of quality issues, causing a blind-fold approach to data migration. It’s important for business leaders need to be aware of their organization’s data collection process and the issues caused at this stage. Unless business leaders know and deal with these issues, any data migration project will be doomed.

    1. Lack of a Concrete Migration Plan that Binds Everything and Everyone Together

Most companies restrict their migration plan to technical requirements, budget, timeline, and costs. A robust migration plan focuses on processes, planning, resource management, workload management, change management, and much more. Moving data from one environment to another can take months of intensely focused work that takes into account:

      • Data auditing
      • Data cleanup
      • Data quality controls
      • Monitoring and managing business activities
      • Setting of expectations
      • Resolving conflicts

… and much more.

      1. Poor Execution

Data migration is not something that can be accomplished on a whim. You will need extensive expert guidance, attention, and consultation – not to mention combining several external tools and applications to make the migration a success. Most organizations overestimate their team’s capacity for an in-house, DIY data cleaning and data migration process. Ultimately, this results in costly expenses, loss of talent and failed projects. Because data migration is a one-time effort, it is always wise to work with experts than to attempt to task your IT team to the project. Here’s a quick breakdown of the costs you will incur if you get your team to even do basic data quality checks in-house. The cost multiplies exponentially if you get your team to also perform a data migration process.

 

      1. Poor Organizational Planning

Data migration will be a tough journey, hitting hard on your organization’s resources. Companies that fail to clearly define deliverables, timelines, job responsibilities, and expectations will find it challenging to succeed. At the end of the day, any activity within the organization is dependent on its people. While businesses focus on the technology part, they are likely to overlook the fixed and variable labor costs. Here is a simple breakdown:

Data Migration Project Cost Breakdown

This is why it’s important to create a structured task workflow so members are aware of their job role and are able to perform it without any confusion disrupting the process.

How Does One Ensure a Successful Data Migration?

An organization’s business model, size, resources, ambitions, goals, revenue, etc. affect the scope of their migration process and creates unique challenges. Therefore, it would not be fair to impose a one-size-fits-all answer or success recipe. That said, over the years, we’ve gathered a key understanding of factors that impacts the success rate of a data migration process and we’ve had clients who successfully pulled off a major migration project because they addressed these factors and did it the right way.

Here’s what you should do to ensure you’re on the right track.

Creating a Plan and Answering Key Questions

Despite its challenges and problems, data migration can be successful if a robust plan is put in place. We’d say you must spend at least 3 months in simply creating a plan. The plan must effectively consider the following:

WHY you want to migrate and what goals do you want to achieve with the migration?

  • Is your data suitable enough to be migrated?
  • What type of data migration methodology will you follow? (more on this below)
  • Is your data quality up to the mark? How is data stored in source systems?
  • What are the possible risks involved in the process?
  • Who would you be choosing as your migration service provider?
  • The tools, consultants or experts you will be using for the job
  • The resources you have for the job and their respective expertise
  • Do you have a cost list – i.e the costs associated with each step of the process?
  • Will your employees be able to readily adopt and adapt to new processes and systems?
  • Would you be needing additional talent?
  • How much are you willing to give to the migration process?
  • What controls do you have in place to ensure that the process will not cause regular business activities to suffer?
  • What workflows and processes do you have in place to ensure everything is in sync?
  • Do you have a set of data migration policy documents?

It’s highly recommended to use a project or task management tool to create these plans, workflows, and processes and update their statuses as you go along.

Dealing With Scope Creep

In migration projects, it is not uncommon to deviate away from the scope. Known as, ‘scope creep,’ this is the phase when multiple change requests, adjustments and fixes begin affecting the original scope or intent of the project.

It also happens when more tasks and responsibilities are added or when new issues are discovered that were unexpected or undiscovered during the planning phase. It is therefore imperative that the initial business goals, planning, and evaluation of records in the old system are effectively taken into consideration to avoid a scope creep that can become a potential problem for the smooth execution of the project. It’s bad for your project and for everyone involved in your team.

Here’s how you can avoid scope creep from causing your project to fail:

      • Be Very Specific With Your Project Goals from the Start: It’s “cool” to add a new feature or a new tool, but if it’s not part of your project goals or if it doesn’t impact the migration in any way, it’s not required to be part of your project. Managing scope creep starts from ensuring that your team is on track with the project and goals are communicated and approved right from the start.
      • Document Requirements: Set requirements and clearly define the timelines, budgets, and responsibilities of your team members. The more thorough you are with requirements, the better your chances of avoiding new requirements creeping up on your initial scope.
      • Use Task Management Tools to Keep Everyone On Track: Your schedule, deliverables, tasks, goals should all be documented on a project or task management software to keep everyone on track. This also helps you identify any red flags that may potentially become a bottleneck and may need instant attention.
      • Implement a Change Management Plan: It’s not to say that you should entirely ignore any new issues – rather, you have to be smart about it and implement a change management plan to keep the changes in control. Make sure any change is analyzed thoroughly in terms of its impact on your business and your migration project. Additionally, you will have to go through the whole decision-maker/client/stakeholder approval process to ensure the change is approved and is in the pipeline to be executed along with other tasks.
      • Avoid Being Overwhelmed or Getting Into Panic Mode: New requirements, unexpected requirements, etc. may cause you to panic and become overwhelmed. Avoid letting it deviate from your initial goal. Focus on following the process and be realistic with your timeline. If a necessary change causes the timeline to extend, it’s fine – as long as you’re taking care of it and will not let it affect your project.

Whether you like it or not, scope creep will possibly happen but it’s necessary not to let it dictate your core process flow or project management plan. You will have to find a smart way to incorporate new changes with your existing plans.

Focusing on Data Quality

A lot has been said about data quality, but how do you go about practically implementing it? Here are things you can do:

      • Question the reliability & quality of your data: Issues with data quality may seem so deceptively simple that you may end up neglecting it completely. Problems like typos, spelling mistakes, invalid or incomplete addresses are not noticed until they become the cause of flawed reports and analytics. In the case of data migration, poor data quality can result in complete failure. Don’t assume your data is perfect by looking at it superficially. Ask your teams to create data quality reports and identify commonly-faced issues.
  • Do they have complete physical addresses?
  • Do they have the right unique identifiers?
  • Is the data duplicated?
  • Is the data suffering from the lack of standardization (upper and lower case issues)
  • Are phone numbers valid
  • Are physical addresses validated with the government database?
  • Is redundant data removed?
  • Is there a data governance policy in place?

Examples of common data issues would be:

Invest in a Data Cleaning and Matching Tool

      • Data migration will require you to invest in multiple tools to fix existing issues. One of these being a data cleaning and matching tool. Why? Because this tool will help you:
      • Match and consolidate data from disparate sources. All that data you have siloed away will need to be merged and consolidated into a single source of truth before you attempt to move them into the new system.
      • Remove duplicates and ensure consistency of data.
      • Profile data and lets you discover errors plaguing your data including typos, case issues, format issues, etc.
      • Validates addresses against government databases
      • Helps create a master record which you can eventually use for data migration

Regardless of structure, type, or format, source data intended for migration should be validated in terms of the following key attributes:

      • Relevance: Is it relevant to its intended purpose?
      • Accuracy: Is it correct and objective, and can it be validated?
      • Integrity: Does it have a coherent, logical structure?
      • Consistency: Is it consistent and easy to understand?
      • Completeness: Does it provide all the information required?
      • Validity: Is it within acceptable parameters for the business?
      • Timeliness: Is it up to date and available whenever required?
      • Accessibility: Can it be easily accessed and exported to the target application?
      • Compliance: Does it comply with regulatory standards?

The biggest and most drastic mistake you would make would be to migrate your data untreated.

Choosing a Migration Strategy

There are multiple ways to build a data migration strategy. Your organization’s specific business needs and requirements will establish the most appropriate strategy. However, most migration strategies are bundled into two categories – “Big Bang” and “Trickle.”

Big Bang Data Migration

This is a common strategy, but one that is performed under immense pressure. The company’s resource is shut down for a limited timeframe, a period within which the data goes through the ETL process (Extract, Transform, Load) and transitions into the new database.

There is a limited time-frame. Teams are under pressure to pull off the transition without any failure and must be achieved to avoid maximum business loss.

While this is an easy, do-it-at-once strategy, it will require the process to be executed flawlessly. You could try emulating the process through a simulation before the actual event.

The Trickle Migration

This approach is less intensive and is a gradual process where the migration is done in phases. During this implementation, the old and new systems run in parallel, eliminating the pressure of downtimes and operational interruptions. Real-time processes are not affected and data can be migrated smoothly.

Compared to the big bang approach, this method is more complex and requires consistent effort, however, because it is low-risk, it’s more preferable.

The use of both of these approaches depends on your business size, requirements, resources and most importantly on your timeline. If data migration is an immediate, top-priority need, you may want to use the big bang approach, provided you are aware of the risks involved.

Whichever approach you use though, make sure that you:

Have Data Controls in Place: Fix data quality, create a backup of your fresh data. You cannot afford to lose data in case the operation goes wrong.

Don’t Deviate From the Plan: Stick to your plan even in cases of unexpected or ad-hoc situations.

Always Test Your Processes: What you don’t test, you won’t know. During the planning and design phases, test your data to make sure you achieve the outcome you need.

For more information on how you can implement a comprehensive data migration project, feel free to get in touch with us by clicking here.

Fahad is a Product Marketing Manager at Data Ladder. Combining his background in marketing and experience in sales and content, Fahad aims to deliver consistent, quality messaging across all platforms to influence product sales.

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