If API is the king of Fintech, data is the queen. Almost every Fintech company is or wants to become data-driven. Almost every Fintech company has huge amounts of it flowing in every minute.
But the arrival of data is just the beginning of its journey in your system. To be ready for your users’ queries, it has to go through several stages. Data transformation will be one of the first, and to make data integrated and accessible, you need to get it right.
Learn what happens at the data transformation stage, how it fits into a larger data processing context, and how to fine-tune it to your needs.
What is data transformation, and why do Fintechs need it?
API protocols to use
Data transformation is all about converting, scrubbing, and structuring data from the source format to the one your system or application supports. Usually, it’s a part of the data integration process, where you combine data from different sources to provide users with a full view of it.
Companies gather and transform data for the benefit of their clients but also use it to propel their own growth. Whether insights on your sales activity for the last ten years or the analysis of financial advisors’ preferences with your wealth management product, data can let you know it all and use the knowledge to your advantage. But to make it so, you must first process what you’ve reaped.
Data transformation stages, step by step
You can tackle data transformation in two ways: manually or using OLAP databases (such as Google BigQuery or AWS RedShift). Whichever you choose, the stages will be as you see them on the scheme below.
Usually, companies do things manually when they need to customize data (for instance, name the product differently in the data for internal use). Otherwise, leaving the work to data warehouses will do just fine: one option is to load data to the Bronze database and transform it into the Silver database using SQL.
Who’s involved in data transformation?
Typically, this process is the task of data engineers or developers. Data engineer is a trendy position now, and salaries can be pretty impressive in the USA.
But we developers can perform data transformation, too. Usually, they are the ones to do the job working on a product, when the process is a part of business logic. For most projects, we just get this information from another system and don't analyze it. We store data at the appropriate place, and functionality uses it. Data transformation can be a part of our bidaily, biweekly, or monthly routine.
Data engineers and devs can work closely with DevOps and data analysts. While data analysts are the information users, DevOps help build a data processing system.
The challenges of data transformation and integration
- Difficulties with the data sources. Your team might face problems with data providers and different types of files or APIs from where you retrieve the data. For instance, figuring out if the data is secure can be challenging.
- Preparing the correct schema for the result database. Most often, you’ve got a year of data on the plate. Even if the data type is similar, its amount is growing, and its structure may change after some of the releases.
- Fixing batching. Mostly it’s about creating an efficient pipeline for the data since the team will have to run daily or monthly jobs, some in a sequence and some simultaneously. Also, you’ll need to choose suitable tools.
The good news is that after you establish the process, everything works. So, you just bring beer and watch the wheels spin. Let’s get a few steps closer to that sizzling success.
Get a team that can create an efficient data pipeline for your business.
Data integration: ETL, ELT, or both?
As I mentioned above, data transformation is a part of a larger process called data integration. This process encompasses three stages:
- Extracting data from the sources,
- Transforming the data to make it compatible with your system,
- Loading the data to its destination.
The stages may not necessarily flow in this order: data processing can come as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) combinations you can see below.
ETL is a classic approach where you collect data across your sources, unify it, and load it into a database or a data warehouse. In ELT, after you extract raw data, you preload it to its destination, and only then, using its functionality, clean and format data.
In most cases, now you have to be flexible about processing your data, so mixing ETL and ELT is a frequent case. It would be wrong to say that one approach is better than the other—unless there’s a concrete case. With some data, it’s difficult to do the whole transformation inside the warehouse. Just go for what’s in line with your business logic and looks less problematic.
Transform data efficiently with these techniques
If you want to get just the data you need, quickly, and in a simple and efficient manner, there are a few steps you can take. Below are several data transformation techniques that I use to clean data and structure it before storing it.
Remember that not all techniques work with all types of data, and sometimes you’ll need to apply more than one technique.
#1 Revising
The aim of revising is to make sure that the data fits the users’ needs and is structured and presented correctly. Also, it can help you increase overall performance. You can achieve this in several ways:
- Eliminate redundancies through dataset normalization. On the technical side, it requires skilled reverse engineering.
- Clean (scrub, cleanse, name it what you like) data of incorrectly formatted, corrupted, or in any other way damaged files.
- Convert data into formats compatible with your system.
- Remove duplicates of the duplicated data.
- Validate data and eliminate incomplete records.
#2 Manipulation
Manipulation helps you make your data more custom. You can create new values or structure the data for machine learning (ML). For instance, internally, you can have another name for the product, and by using manipulation, you help the system identify the data related to that product under another name. Here’s what you can achieve by manipulating data:
- Derive data using cross-column calculation.
- Aggregate values through summarizing data.
- Pivot columns to rows and the other way round.
- Improve search performance by organizing data: sorting, ordering, and indexing it.
- Compare dissimilar numbers by creating a consistent scale. To get there, your team will need to scale, normalize, and standardize the relevant data.
- Form number arrays for ML apps by converting non-numerical data through vectorization.
#3 Separation
Separation prepares the basis for granular analysis. In this technique, you break down data values into parts.
#4 Coupling or integrating data
You can puzzle together records from different tables or other sources to get a complete picture of a specific activity, product, etc.
#5 Data aggregation
While number four helps get a full view of a particular subject, data aggregation is more about getting an essence from the raw data, like statistics. For instance, you can gather prices on one security from different sources.
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On the final note
Squeezing all the nuances of data transformation and integration into one article would make you scroll for ages, so I tried to pack in only key points. But I do hope to share new materials soon, so stay tuned.
By the way, you can get weekly updates from my teammates and our community of Fintech execs by signing up here. Feel free to schedule a quick call if you’re looking for an optimal way to tackle your current challenge in software engineering.
Author: Nazar Salo
Editor: Svietoslava Myloradovych