The Importance Of Data Engineering in Finance

May 31, 2022  |  Mark Hillary

Data engineering is an often forgotten discipline outside hardcore IT teams. Many modern managers believe that any business problem will already have a software solution that is developed and ready to go. This is not always the case – especially in areas of business that are changing rapidly, such as finance.

The finance industry is constantly evolving and offering new services and products to customers. Many traditional banking brands find that their ability to innovate and create new products is largely defined by how quickly their customer platform can evolve.

Often the problem is not the front end – it is the data itself. New products and services require new data and how this is stored and connected to all the existing data and processes is the real challenge. So data engineering is an important discipline because smart data design can then create a more flexible business.

Information and data engineering really has four main phases:

  1. Strategic business planning: the business plan and future projections for the business need to be considered so the engineers can build a data model that can be expanded for future requirements.
  2. Data modeling: the goals and objectives in the previous step can be used to form the basis for a data model that supports the business.
  3. Process modeling: this is where the process flow can be defined – how the systems use the data that is defined in the previous step. Knowledge of the financial products is also essential here as processes supporting those products need to be designed using the data that has already been modeled.
  4. Systems design and implementation: this is where the data and process models are turned into a final product that can be delivered to the company requiring the system.

When you consider new information systems in this way it becomes clear that using a new technology system is not as simple as just buying a new spreadsheet. The processes that support the products inside your business are bespoke and this also applies to the data model.

A finance company needs to think carefully about the entire data engineering process because this creates a foundation for both innovation and supporting ongoing products.

Think about a simple example.

A bank wants to create the ability to send two payment cards to a customer – not just one. Some customers may find it difficult to visit shops, so they request an extra card for a carer to use. How does this change the underlying data model and processes? Is it just a clone of the existing card or are new security protocols required to manage an additional bank card?

Finance companies that want the ability to create new products quickly need to think carefully about how their data is organized and who is organizing it. Do they have a strategic vision with one eye on the future or are they just fighting fires day after day and trying to stop the system from collapsing?

An understanding of this strategic approach to data engineering is important for any company – particularly in industries such as finance – that wants to evolve gradually, but also innovate rapidly.

Banking is an industry that has always been filled with data. We have already written why Big Data in banking is set to explode. Moreover, the COVID-19 coronavirus pandemic has offered data analysts some great opportunities to study Big Data trends across the world. Read more about Big Data in a time of pandemic.

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