Whether you’re pursuing a digital transformation strategy or are simply upgrading a core tool (e.g., CRM, ERP, etc.), you’re probably facing a substantial data migration project. These projects often have challenges related to data quality, inconsistent data formats, or incompatible data structures. In many cases, there’s no API available to assist in the transition. Manually transferring data between systems is time-consuming (and expensive), tedious, and prone to error. 

Fortunately, any data migration project can be considerably improved with the help of Robotic Process Automation (RPA). 

RPA’s digital workers or “bots” are ideal for uninteresting, rules-based work. This article illuminates five of the most common issues these projects face and explains how RPA can make the project as pain-free as possible – identifying some quick wins to jump start process optimization and automation in your organization. 

1. Fix Poor Data Quality 

Most companies have messy data in their systems – the inevitable result of manual data entry processes and previous system changes. These data sets may have duplicate entries, missing data points, and inconsistencies in how data is entered (e.g., Road, Rd, or Rd.). These issues are particularly challenging to manage during a data migration project. 

To address poor data quality, your team could analyze the data manually using SQL, Microsoft Excel, Power BI, or other pivot and analysis tools. The challenge, of course, is that the setup for these tools can be time-consuming (depending on data volume and complexity), and frequently alternating from one program to another introduces yet another element of risk into the project. 

A better option is to employ RPA to aid with your data cleanup before your data migration begins. For example, bots can be instructed to read each relevant file, produce a detailed report on the data inconsistencies for your team to analyze, and summarize the entire process in a completion report. Bots can also be programmed to report duplicates across several tables, reports, or files and to highlight which pieces should be removed or merged with the primary record. 

By leveraging RPA, you straightforward data cleansing in the hands of a digital worker that can produce faster and more accurate results than a manual cleanup project. At the same time, your team focuses on the “eyes-on” analysis work. This human-in-the-loop approach to automating data cleanup improves the quality of the work and accelerates the time to complete these activities, both of which are crucial to your project’s success. 

2. Reconcile Incompatible Data Formats 

 
Short dates, long dates with timestamps, U.S. versus European date formats, four decimal places versus three…the list of possible ways to enter the same information is surprisingly long. However, having all those different expressions in your data set can be a major headache when migrating to a new system. For example, in some systems, if you enter an invalid pattern, the software simply refuses to let you proceed. You’ll need to convert all your data to a standard format before you can continue. 

Like other data quality concerns, the best time to resolve incompatible data formats is before you migrate systems. Sure, you could have a team of people performing the work manually, but why not let RPA and a team of digital workers do the tedious work instead? 

Digital workers are programmed to identify variations in how data is entered and then standardize the entire volume according to the predefined rules of the new system or database. Automating this work saves time and frees up your team to focus on the parts of the migration project that require human attention. 

3. Eliminate Concatenation Issues 

Another common problem that data migration projects face is concatenation issues. Every system has its own requirements for linking key fields of data together, and the time it would take to manually update the data to meet each standard quickly becomes unmanageable. Here are a couple examples of concatenation challenges: 

  • A client’s first and last name are stored in separate fields in your current data, but the new system needs them combined into a single field. 
  • You have an address field today that contains the customer’s house number, street name, unit number, city, state, and zip code all in a single field. In the new system, each value has its own field. 

Once again, digital workers solve each of these scenarios (and many more). To solve the address issue, the bot can be programmed to verify it on the USPS website, conform it to the USPS standard, and then separate each section into individual fields to match the format the new system requires. 

Bots are a perfect tool for either introducing or breaking concatenated data as required for a data migration project. 

4. Navigate One-To-Many or Many-to-One Data Transformations 

When moving from an older system to a newer one, there may be a new data structure that requires your data be split into different tables. For example, the customer profiles in your current system may live in a single table with multiple rows – one row for each location’s address. However, in the new system, this information is separated. Each customer appears in a “customer name” table, and the locations are in a separate “customer locations” table, and they’re joined by a persistent data key. 

Ensuring each record maps correctly to the new system can be an extremely tedious process. Preparing a massive upload file – or worse, manually copying and pasting the data into the new system – would be time-consuming and introduce significant data quality concerns. Instead, RPA’s digital workers can be programmed once to convert all existing data to the new system’s data structure and upload it quickly and without error. 

5. Import the Data Seamlessly 

 
Finally, beyond helping you prepare your data for the move, RPA can also complete the data migration to the new system. Unless your current systems have APIs available, RPA is the fastest way to ensure your data is transferred quickly and accurately. The automation can be designed to extract data from the existing system, convert it into the proper format like in the many examples above, and import it into the new system without a member of your team lifting a finger. 

Plus, if there’s a bridge period where users have access to both the current and new systems, the automation can be run as many times as necessary to ensure all the data is synced across both systems. 

RPA Shortcuts Data Migration Challenges 

Data migration projects are simply a byproduct of keeping your organization at pace with ever-improving technology. While necessary, they’re often fraught with challenges due to poor data quality or data formats inconsistent with the new system being introduced. With RPA, bots take over the critical yet mind-numbing tasks of cleaning up and standardizing your current data and uploading it into the new system. 

Automation all but eliminates human error and speeds up your overall data transfer process, freeing up your team to focus on the engaging and high-value activities that depend upon human judgment and problem-solving.  

In short, RPA’s capability and flexibility, and the time it creates for your team, makes it an ideal solution for data migration projects. Before you begin another such project, consider implementing RPA! You’ll be surprised the other types of projects and processes where RPA can help in your organization now.