Data transformation is a vital part of a successful data migration strategy, but doing it correctly requires a thorough understanding of the process and what it comprises.
There are numerous misunderstandings and assumptions about this topic, yet when done right, it can be a powerful business facilitator. In a nutshell, data transformation is required for any major business shift.
Why Migrate Your Data?
Data migration is a broad phrase that can refer to a variety of things, depending on your goals. For example, moving applications to a new infrastructure may be all that is required to migrate data to the cloud. In this instance, you may simply be shifting computation from on-premises data centres to cloud-based infrastructure.
Perhaps your primary goal is to transfer costs from capital expenditures (CapEx) to operating expenses (OpEx). As a result, when discussing cloud migration, it’s critical to first clarify the business goals that are driving the move. When data matters – and it always should – the value of data transformation becomes apparent.
Data transformation organises data, making it easier for machines and humans to use and comprehend. This well-organised, formatted and validated data improves data quality substantially and makes it easy for the systems and platforms you use to ingest it without problems. However, there are several common misconceptions about data transformation and how it fits into your data migration strategy that need to be dispelled.
“Lift and shift” will not suffice
A successful data migration involves a substantial amount of time, effort and finances. If done correctly, it won’t simply be a “lift and shift” of historical data; the data will have been right-sized, cleansed and loaded with 99.5% accuracy or better. However, entropy seeps in, as it does in so many other areas of life. Data quality will deteriorate, expenses will rise, business processes will fail, and the value intended via these new system investments will deteriorate if they do not receive ongoing care.
Kicking the can down the road isn’t a viable option
It may be tempting to ignore data quality until after the migration, but this mindset will cost you more in the long run. Fixing data quality after the fact takes as much as ten times the money and work that it does to include it into the data transformation process from the start.
Your new application will manage the data quality
Not so fast. Most applications do not prohibit you from entering bad data into them. Simply filling out a few mandatory fields won’t guarantee that you have a good data set for all the purposes for which the data will be used by the organisation. You won’t be able to avoid making errors just because you’re using software. As a result, you’ll need a solid data management approach.
How Data Transformation Helps Data Migration
In other cases, data transformation as part of your entire data migration process may not be essential. It all boils down to fully grasping the reasons behind the migration to begin with. If it’s just about saving money, a transformation may not be necessary; if you have additional key performance indicators (KPIs) to reach, though, you won’t be able to do so until data transformation is included in the process.
These KPIs could include minimising total costs for new capital construction, optimising parts management for maintenance and repair operations, or reducing time to new product introduction. It could also refer to larger investments that corporations are making, such as cost reduction through mergers and acquisitions or divestiture.
Here’s a useful metaphor: Attempting to integrate and move data from one source to another without data transformation is like jamming a square peg into a round hole. Data transformation smooths out the rough edges, enabling what was once a square peg to fit snugly into its proper hole.
Getting Data Transformation Right
Migration is fundamentally a human-centered activity – not solely a technical one. You’re gathering professionals from various lines of business and collaborating with the technical team. It’s from this combination that you learn how the business operates.
To allow line-of-business owners – who are frequent data contributors and are often non-technical – to participate in the data migration, something more than “extract, transform, load” (ETL) is needed. ETL is very technical and is intended for technical people only, excluding 90% of the people involved in data migration.
Visibility is crucial. If you have a tool that engages all the stakeholders in a migration process, you can also assign tasks to each of them. You can check to see if all the stakeholders have completed their duties. And you can combine all of it into a single version of the truth about where you are in the process at any one time.
Time for Transformation
Data transformation is now a requirement for major business transformation; the two go hand in hand. Make sure you know what you want to achieve with your data migration and don’t believe the misconceptions that surround it. You’ll set the stage for having a well-defined process that serves as a primary business enabler by following the best practices noted above.
By Rex Ahlstrom, CTO, EVP Innovation & Growth, Syniti.