More and more businesses are adopting SaaS because it saves costs, offers scalability, and allows flexible access. This trend shows no signs of slowing down, and Gartner expects SaaS to grow 16.8% in 2023.
There are quite a few new players like Workday and Salesforce that have built their solutions from scratch in the cloud and have never provided licensed software. This has prompted traditional ERP vendors like Oracle and SAP to offer their SaaS solutions, even as they continue to market their legacy on-premise software. They all promise similar benefits and require customers to adapt their business processes to the software rather than tailoring it to customers’ needs.
However, SaaS implementation is not always straightforward, particularly when it comes to data migration. In this blog, we explore why data transfer can be the most challenging part of the process and why it places the greatest demands on the customer during SaaS data migration.
Why Can Data Migration Be Tricky for SaaS Apps?
In the age of SaaS applications, customers now expect faster implementation of new ERP apps, typically taking less than a year, compared to the traditional multi-year programs common for on-premise solutions.
SaaS adoption indeed allows less customisation than their on-premise predecessors. The development element of the implementation is, therefore, significantly lower, but other workflows have yet to be affected by the switch from on-premise to cloud-based applications. An important step that falls into this category is the data migration element of the program.
Although the ability to customise the application's functionality is reduced, SaaS data migration still offers numerous ways to configure and manage data within the application during implementation.
This process becomes even more complex when dealing with legacy systems that are either custom-built or contain commercial off-the-shelf (COTS) software. As a result, data migration requirements are unique for each new project, even if the source and target systems are the same as previous projects handled by the implementers.
The SaaS providers and their system integration (SI) partners promise ever-faster and cheaper implementations. However, it's only when customers who need to provide data in a certain way struggle to meet the suggested deadlines that they realize the timelines might suit the SI but not necessarily them.
Main Challenges of SaaS Data Migration
To align migration timelines with the shorter program plans proposed to customers, we need to change our data migration strategy. SI can quickly adapt code from previous projects to load data into the target system. However, the main effort is to extract, transform, and validate the data before loading it.
Usually, the customer is given this task, and SI gives them templates for the files they need to make. However, many customers don't have experience implementing a new ERP system. As a result, their staff may not know what is required. If the data provided to the SI is not good enough, this can lead to time being wasted loading data, much of which is rejected. This results in the project being tested with partially loaded and inconsistent data. This, in turn, affects testing and may prompt further rounds of testing to ensure that the data is in the proper condition for evaluating the fresh application and business procedures.
How to Solve SaaS Data Migration Challenges
We need a structured and repeatable data migration strategy. It should enable the SI to support the customer effectively by providing the required data without taking unnecessary risks. Besides, it should be quick to implement so we can start validating and enhancing the data in the legacy system early on in the project. That way, the data is ready for testing when we need to run it.
This SaaS data migration strategy must also support collaboration between the customer’s business people and the SI’s consultants in identifying, transforming, and validating data to ensure they can support the business in the target system.
This means using a toolkit that can process data quickly and reliably with minimal development effort. It should also have the data validation functions necessary to detect data problems. This will free up more resources to support the customer's Subject Matter Experts (SMEs) and improve their understanding of the data, increasing the efficiency of the SaaS data migration process.
The chosen data migration toolkit should also align with the project's methodology and provide an environment that fosters collaboration between the software vendor's functional experts and the business SMEs when analyzing and specifying the extraction, transformation, and validation processes.
Using spreadsheets or printed documents exchanged via email can lead to misunderstandings and confusion, causing errors, rework, and delays in data processing. For a smoother workflow, it's important to avoid such practices.
Can ETL Tool Tackle All the Obstacles in the Process?
Many programmers prefer to use one of the commercially available ETL tools instead of developing their applications from scratch or trying to pull together bits and pieces from previous projects to address data migration challenges. However, conventional ETL tools are designed for a range of requirements, only one of which is data migration, and therefore, requires a significant upfront effort to build an infrastructure that can begin processing data from the legacy environments.
They also provide no support for analysing or specifying the requirements for extracting, transforming, and validating data, so they contribute nothing to this part of the workstream. This means that the project may be forced to resort to spreadsheets or Word documents to specify the requirements, with the problems described above being almost inevitable.
Typically, when a data migration strategy involves using an ETL tool, the logical relationships between data elements need to be manually coded in the migration software. It’s necessary to do this to avoid inconsistencies when the data reaches the target systems. If this is not done, the data may be loaded incompletely. In such cases, the migration software must not only manage data loading into a clean environment but also add data to a partially loaded one or, even worse, update data that has already been loaded.
Why Choose Hopp Over ETLs?
Hopp software is custom-built for this task and provides an environment for capturing and recording the requirements for transforming and validating the data in a structured form. The data then can be used to generate code to automatically execute the specification. This reduces the chance of bugs in the software and thus contributes to the speed and quality of processing.
Hopp Tech’s data migration strategy splits the work into two steps, with a logical link to the target system in between. This means that the second step isn't affected by the source of the legacy data. It can be reused and includes validation to prevent data from being rejected during the loading stage. That way, the source data can be processed and validated multiple times without loading it into a test environment. This speeds up data clean-up and improves data quality for the rest of the project.
Hopp data migration software also has the infrastructure to execute the generated code without any pre-built intervention. This enables data from the legacy systems to be processed at an early stage of the project, providing as much time as possible for data cleansing and correction to be carried out.
Controlling the execution of the code and checking the results, whether positive or negative, is done via a browser-based Portal that all members of the migration team and the company's SMEs have access to. The Portal also enables the business to maintain all necessary translation tables, avoiding potential errors due to miscommunication or multiple versions of this critical data.
The software processes data as complete logical units. It even takes into account the relationships between logical units so that you can decide whether or not to load data for a logical unit if an error is found in a part of the unit. This alleviates the problem of incomplete or inconsistent data in the target systems and simplifies the design of the data migration system.
The key benefits of using Hopp software can be summarised as follows:
- Improved collaboration between technical implementers and business users;
- Fewer resources, but more functional than technical;
- Better data quality;
- More reliable data migration;
- An end-to-end solution for complex data migrations;
- Faster start of data processing.
In a Nutshell
SaaS data migrations are challenging processes that require a robust solution. Hopp software is an end-to-end solution for complex migrations that aims to streamline the transformation phase and reduce risks, time, and costs. It has a powerful and user-friendly interface, logical business objects, autogenerated code, track & trace capabilities, and an iterative process that consistently applies mapping, rules, and validation. The software uses an agile data migration strategy that separates source and target maps and ensures 100% reusability while improving quality and saving time and costs.