Are you planning a migration from your current ERP system to NetSuite? Congratulations – moving to a cloud-based ERP solution like NetSuite can transform your business by providing a single, integrated platform to run your entire company. However, before you embark on your NetSuite implementation journey, it’s critical to first focus on data cleansing and preparation. After all, your new ERP system will only be as good as the data you migrate into it.
In this post, we’ll cover data cleansing best practices and tips to ensure your legacy data is clean, accurate, and ready for a successful migration to NetSuite. We’ll discuss why data quality matters, outline a data migration roadmap, share common pitfalls to avoid, and provide a checklist to guide your data preparation efforts. By following these steps, you can set your NetSuite implementation up for success from the start.
The Importance of Clean Data for ERP Migration
Your data is one of your organization’s most valuable assets. It’s what powers your business processes, informs your decision making, and helps you serve your customers. When migrating to a new ERP system like NetSuite, it’s essential that the data coming from your legacy systems is clean, deduplicated, and standardized.
Consider what would happen if you moved poor quality data, filled with errors and inconsistencies, into NetSuite. Suddenly, your shiny new ERP would be plagued with inaccurate reports, process inefficiencies, and a lack of visibility. Imagine not being able to trust your financial data, or struggling to fulfill orders because of bad customer and inventory records.
Poor data quality can undermine your entire NetSuite investment and sabotage your potential ROI. In fact, poor data quality costs organizations an average of $12.9 million annually according to Gartner. IBM estimates that bad data costs the U.S. economy $3.1 trillion yearly.
On the flip side, clean data primes your ERP for peak performance. With high-quality data fueling your NetSuite environment, you can:
Make confident business decisions based on accurate, real-time information
Optimize operations and reduce manual rework thanks to streamlined, error-free processes
Improve the customer experience with the help of reliable customer data and efficient order fulfillment
Simplify reporting and analytics to easily understand business performance
Ensure compliance with consistent and verifiable data
Maximize user adoption and trust in the new system
In short, data quality can make or break your NetSuite migration. Investing the time and effort to clean your data beforehand will pay dividends in the long run. Clean data is the foundation upon which ERP success is built.
Data Migration Roadmap: Key Steps
A successful data migration doesn’t happen by chance. It requires careful planning, preparation, and execution. While every project is unique, most data migration efforts follow a similar path. Here’s a high-level overview of the key steps:
Assess
Start by comprehensively assessing your existing data landscape. This involves:
Inventorying data sources and systems
Determining data dependencies and intersections
Uncovering data quality issues like duplication, incompleteness, inconsistencies
Defining future state data requirements and KPIs
Design
Next, design your data migration strategy and plan, including:
Determining what data to migrate vs. archive vs. eliminate
Defining target data structures and taxonomy
Mapping source data to the NetSuite data model
Establishing data quality criteria and KPIs
Identifying data enrichment needs
Developing data validation and testing approach
Outlining migration timelines, resources, budget
Prepare
With the assessment and roadmap complete, begin data preparation work:
Extract data from source systems
Profile data to find anomalies and quality issues
Standardize data formats, structures, naming conventions
Enrich data as needed (e.g. append geocodes to customer addresses)
Match and merge duplicate records
Validate data against quality thresholds
Test and iterate until data meets established criteria
Gain business sign-off on data readiness
Migrate & Validate
Finally, load the clean, prepared data into your NetSuite environment:
Execute data migration according to plan
Validate data in NetSuite staging
Conduct User Acceptance Testing (UAT) to verify data quality, processes, reporting
Resolve any issues identified in testing
Perform mock Go-Live data migration
Cut over to production
By breaking down the data migration process into these discrete phases, you can reduce risk and maintain momentum. Investing appropriate time and attention in the Assess, Design and Prepare stages is crucial for data cleansing success.
Data Quality Dimensions
To properly clean your data for migration, it’s important to evaluate data quality from multiple angles. Data is considered high quality if it is:
Accurate – The data correctly reflects reality, with no errors.
Complete – All required data elements are populated, with no blank fields or missing records.
Consistent – Data values, formats and definitions are uniform across systems and business units. For example, Customer Name is always recorded as “Last, First.”
Timely – The data is up-to-date and available when needed.
Unique – There are no duplicate or redundant records.
Valid – Data values conform to defined business rules and parameters. For instance, all email addresses contain “@”.
When assessing your data quality, look for deficiencies across each of these dimensions. Establish measurable criteria to evaluate data quality, like “95% of address fields must be in a standardized format.” This will give you a framework to identify data issues and gauge progress as you clean and prep data for NetSuite.
Common Data Cleansing Activities
With your data quality assessment complete, it’s time to roll up your sleeves and clean your data. Here are some of the most common data cleansing tasks you’ll likely encounter:
Standardization
Is your data recorded in varying formats across systems? You’ll need to standardize data to ensure consistency and enable accurate NetSuite setup.
For example, if the Sales team enters U.S. state as “California” and “CA” in different systems, standardize on the ISO 2-letter abbreviation “CA.” If phone numbers are captured with parentheses and dashes in one system and dots in another, convert them all to a unified format like 123.456.7890.
Be sure to standardize naming conventions, date formats, units of measurement, address structures, category values and other key fields according to the taxonomy defined in your data migration design.
Deduplication
Duplicate data is one of the most pervasive data quality challenges organizations face. Without a single source of truth, customer and vendor records tend to multiply across disparate systems. Do you have the same customer listed five different ways because of spelling variations or separate entries for multiple locations? This can wreak havoc on business processes, reporting and the customer experience.
Deduplication involves identifying and merging redundant records to create a single Golden Record. Typically, this process entails:
Defining match criteria across data fields to identify potential duplicates. For example, match on email address, phone number and address to spot duplicate customer records.
Comparing records and assigning a match score based on the match criteria. Tools can automate this process at scale.
Setting a match threshold, like 90%, for records to be considered duplicates. Those that fall below the threshold are assumed to be unique records.
Merging duplicate records by retaining the most complete and accurate field values across each record. Some fields like email address must be unique, so if two records have different email addresses, one is selected as the master.
Creating a new Golden Record based on the merged values. The Golden Record represents the unique, complete and accurate version of the customer or entity.
Sounds straightforward enough, right? Dedupliction can actually be one of the most complex and time-consuming aspects of data cleansing, especially when records span multiple siloed source systems. The more data sources, the higher the probability of duplicates and variations.
Data profiling tools are indispensable for identifying potential duplicate records at scale based on your defined match criteria. However, some manual review and remediation is almost always required. Expect to devote significant time to deduplicating your data to ensure a clean, consolidated record is migrated to NetSuite.
Data Validation
Cleaning data is one thing, but how do you maintain pristine data quality through to Go Live? The answer is data validation. Validation ensures you’re not migrating data that violates defined quality rules.
Start by defining data validation rules for each core data object like customers, vendors, items, orders, etc. These rules should align with the data quality criteria established in your migration design phase. Some common examples include:
Customer email addresses are valid and unique
Vendor bank details are complete
Item prices are numeric, not negative
Order dates are not in the future
Then, establish checkpoints to programmatically validate data against these rules at key junctures:
Source data extraction
Data transformation and load to staging area
NetSuite data load
User Acceptance Testing (UAT)
By validating early and often, you can catch new errors introduced in the migration process. You can also ensure that data issues found and fixed in one area didn’t create unexpected problems downstream. If validation fails, take corrective action before proceeding to the next phase.
An automated data validation process will be immensely valuable, particularly for large, complex migrations. Data quality firewall tools can simplify rule definition while programmatically enforcing data standards. This real-time validation prevents bad data from infiltrating NetSuite post Go Live.
Data Enrichment
After cleansing your data, you might need to complement it with supplemental information to unlock more NetSuite features and capabilities. Enriching data helps maximize your NetSuite investment and drive additional business value.
Consider the following data enrichment scenarios:
Appending geographic information like longitude and latitude to customer addresses to enable NetSuite location-based sales insights
Adding NAICS or SIC industry codes to customer profiles for improved segmentation and reporting
Enhancing vendor records with diversity status and certifications to support supplier diversity tracking
Calculating the total lifetime value of each customer to inform sales strategies
Data enrichment can be accomplished by aggregating information that’s spread across disparate fields and systems. Data can also be acquired from third-party sources like D&B, data.com, and government databases. When purchasing data, be sure to map it to your data model and clean it to the same standards as your internal data.
Avoiding Data Migration Pitfalls
Even the best-laid data migration plans can encounter issues. Many NetSuite projects experience delays or surprises due to unforeseen data challenges. Here are some of the most common data migration mistakes and how to avoid them:
Underestimating Data Cleansing Effort
Data cleansing often ends up consuming more time and resources than anticipated. It’s an intensive process that requires deep data analysis, thoughtful planning and iterative cleansing and testing. When building your data migration plan, be realistic about the time required to profile data, standardize values, deduplicate records and validate quality. Expect data cleansing to take 30-50% of your total data migration hours.
Neglecting to Involve Business Users
Your data migration will only succeed if the clean data delivered to NetSuite supports your business processes and reporting needs. IT teams and implementation partners can’t define data quality rules in a vacuum. Engage subject matter experts from each business unit to outline data requirements and determine key data quality criteria. By involving end users throughout the process, you gain valuable insights while building buy-in for the new ERP system.
Skipping Data Profiling
Do you know what’s lurking in your legacy data? Data profiling gives you a comprehensive view of your data quality, structure and content so you can identify issues early. Profiling examines data values and formats across each field, highlighting anomalies, inconsistencies and integrity concerns. Profiling results then shape your data cleansing approach. Skipping this crucial step could leave you scrambling to rectify major data problems late in the game.
Overlooking Non-Standard Data Locations
Sure, most of your data probably resides in major source systems like your old ERP or CRM. But are you accounting for data tucked away in other nooks and crannies? From Access databases to Excel spreadsheets to employee desktops, data has a way of proliferating throughout the organization. Examine reporting outputs to uncover critical data that might live outside your standard systems. You don’t want to omit key data sources from your migration.
Failing to Plan for Data Growth
Your data will continue to grow and change after the initial NetSuite migration. Build processes to maintain data quality on an ongoing basis, or you’ll soon find yourself in the same position as before. Configure data validation rules in NetSuite where possible, and consider a data quality tool to automate quality monitoring. Train users on data entry standards, and periodically spot check data quality. With proactive data governance, you can ensure your ERP data remains clean over time.
Neglecting NetSuite Data Migration Automation
For huge data volumes, relying on manual data migration methods is a recipe for disaster. Hand-keying data into Excel templates is time-consuming, introduces human error, and makes it nearly impossible to implement and test data quality controls. Whenever possible, automate the transfer of data from source systems to staging to NetSuite. Take advantage of data loaders, data quality and integration tools to streamline the process while enforcing established data formatting and validation rules.
Treating Data Migration As a One-Time Event
Data migration is not a one-and-done endeavor. Rather, it’s a phased approach in which data passes through multiple iterations of extraction, cleansing, transformation, and loading. Data is migrated to a staging area for initial testing, then to a NetSuite sandbox for UAT. Even after Go Live, data migration continues with each new module or integration. Approaching data migration as an ongoing process with defined data quality checkpoints helps reduce risk.
NetSuite Data Migration Checklist
Ready to tackle your data cleansing project? Use this checklist to ensure you address all the key steps and considerations for ERP data migration success:
Assess Data Landscape
□ Inventory data sources & systems
□ Define future state data requirements
□ Map source data to target data model
□ Establish data quality KPIs
□ Identify critical data issues via profiling
□ Determine data to migrate vs. archive vs. purge
Plan Migration Approach
□ Design data migration and testing strategy
□ Establish data cleansing standards and validation rules
□ Set data quality thresholds
□ Identify roles and responsibilities
□ Estimate data migration timeline, effort, resources
□ Develop data migration and cut-over plan
Prepare Data
□ Extract data from source systems
□ Profile and analyze extracted data
□ Standardize data formats, names, structures
□ Correct erroneous data values
□ Eliminate duplicate records
□ Enrich data as needed
□ Validate data against quality thresholds
Test & Approve Data
□ Load clean data to staging area
□ Test data in NetSuite sandbox
□ Perform UAT focused on data quality
□ Resolve data issues identified in testing
□ Confirm that data supports end-to-end business process flows
□ Obtain business approval on migrated data
Migrate & Validate Data
□ Perform mock data migration
□ Execute production data migration and validation
□ Conduct smoke tests to ensure data accuracy
□ Reconcile migrated data to source systems
□ Resolve any data issues
□ Get final business sign off to launch
Maintain Data Quality
□ Develop data standards and procedures
□ Document data entry, validation, and update processes
□ Train users on data policies and NetSuite data management
□ Implement automated data quality controls in NetSuite
□ Monitor data quality KPIs
□ Perform regular data audits
□ Create a data governance framework to manage data long-term
Ensuring Clean Data for NetSuite Success
We’ve covered a lot of ground on data cleansing best practices for NetSuite migration. While it may seem daunting, taking a thorough, strategic approach to data quality will pay off when it’s time to push the Go Live button. By delivering accurate, complete, and trusted data to your new ERP, you can accelerate user adoption, streamline operations and make confident decisions.
Remember, data migration is not an IT-only endeavor. Designing and deploying an effective data quality plan requires partnership between technical, business and executive teams. By defining clear data requirements and collaborating cross-functionally to clean and govern your data, you can avoid common migration pratfalls and accelerate time to value.
Data migration doesn’t need to be a showstopper for NetSuite success. With proper preparation, tools and determination, you can efficiently clean your legacy data while maintaining your project timeline and budget. The key is to make data quality a priority from the start.
If you’re struggling to build data quality momentum, seeking an external perspective could help. Tap into the expertise of a NetSuite implementation partner who has guided numerous clients through the data migration maze. They can jump start your data cleansing efforts while helping you navigate around obstacles.
With the wealth of data migration resources, tools and expertise at your disposal, there’s no need to let dirty data derail your ERP goals. Follow the best practices and steps outlined here to clean your data and pave the way for a smooth transition to NetSuite. Your future data-driven organization will thank you.
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