In the world of business, much is made of singleness and unity. It's not hard to see why. Supply chains are more sprawling than ever, and even the smallest of brands now sits atop mind-boggling cairns of data.
For these reasons, more and more managers want that mythical-sounding "single source of truth". This is a place where all data is stored and unified. And while it sounds mythical, it's in fact achievable with the right tools.
Left to your own devices, however, it's far from a simple process. Historically, data has a tendency to exist in various source formats and be spread across different devices and pieces of software. Getting these to speak to each other can be like trying to speak 10 languages at once.
This problem becomes especially acute when systems are being integrated – systems that were, after all, designed to stand alone.
So, how can it be done without giving you a headache and your business grinding to a temporary halt? Before we answer that question, we need to look at data transformation – what it means and, more specifically, what it means for system integration.
What is data transformation in the context of system integration?
Whatever kind of business you work for, data transformation in system integration means roughly the same thing. It's the process of converting, cleaning and structuring data – data that originates in different source formats.
The aim is to produce a single, unified, compatible format. The nature of this format will depend on the system your company is getting ready to use.
Done right, system integrations built on data transformation ensure compatibility and allow disparate systems to speak to one another. By this, we mean that ERPs, CRMs and databases – to take just three common examples – can now effectively exchange and understand data from one another.
Along the way, the data itself improves in quality. Duplicates are removed, inaccuracies corrected. This all pushes businesses towards the goal of data-backed decision-making.
What does data transformation in system integration involve?
The exact processes used to transform data during a system integration procedure vary. However, they tend to fall into the following five categories: format conversion, data cleansing, normalisation, filtering and aggregation.
1. Format conversion
Format conversion involves changing data formats to match the new system's requirements. Common conversions include XML to JSON and CSV to SQL.
2. Data cleansing
Next, data is cleaned. This involves correcting inaccuracies and handling missing values. The data is then validated ahead of the new system launch.
3. Data normalisation
Data values are then normalised. Let's say that different data sources use different values to assign "male" and "female" to customers. Normalising data means making sure all values conform to a single standard. This allows the new system to interpret data consistently.
4. Filtering
Specific columns and rows are filtered, and unnecessary information is discarded. This leaves IT professionals with only the data that's required by the target system.
5. Aggregation
Now comes the exciting bit, as diverse data sources are aggregated, creating a single, unified view – that "single source of truth".
What are the challenges of data transformation in system integration?
The biggest challenge of data transformation in system integration is, well, data transformation in system integration. Different systems store data in different formats. It's difficult to normalise and merge these silos of data, unless you use a specialised software tool.
Legacy systems present their own problems. They tend to lack modern APIs and data structures. This can create bottlenecks when attempting to unify a variety of systems.
Then there's the question of compliance – a subject that no data handler can ignore. When data is moved between systems, security vulnerabilities are created. This is one key reason you should work with specialist data transformation tools.
These challenges are real but far from unavoidable. Your main priority as a business leader is to invest in data transformation tools that are up to the challenge. It's an investment that will primarily be felt in the increased confidence of your decision-making.
An example: the value of data transformation in warehousing
