Once upon a time, the largest pain point facing most enterprise software developers was writing and testing code. Thanks to the generative and agentic AI capabilities that have become widespread over the past few years, however, the coding part of development work has become substantially easier.
But here’s an equally pressing challenge for software developers that AI hasn’t solved: Accessing data and integrating it into applications. Indeed, data access has arguably emerged as the greatest hurdle to modern enterprise development.
By extension, better approaches to data integration have grown into one of most important steps organizations can take to improve the developer experience, add efficiency to application delivery and place new features in the hands of end-users more quickly.
Here’s why – along with tips on what businesses can do to ensure that developers have access to the data they need, when they need, in the form they need.
Why software development efficiency hinges on data access
Virtually every software application needs to import and process data of some sort. The data could be customer names and email addresses stored in a database. It could be documents living within a file system. It could be log data produced by other applications.
Without data sources like these, applications can’t access the information they need to be useful. Thus, the easier it is for developers to access the data they need and connect it to their applications, the more efficiently they can build useful software.
In this context, “easy” access to data means three main capabilities:
- Finding relevant data sources within an enterprise’s IT estate quickly.
- Importing data sources into applications with a minimal need to transform the data before it’s useful.
- Minimum effort spent on writing custom logic within applications for working with data or maintaining it.
The challenge of enterprise data access
Unfortunately, enabling these abilities within large enterprise environments is often anything but easy. Enterprise data architectures tend to be extremely complex, making it challenging for developers simply to find the data they need, let alone work with it in an efficient way.
This is why enterprise development teams often resort to practices like creating and maintaining discrete copies of databases. Rather than being able to connect multiple apps directly to data stored in a central repository, they build different databases for each app – hardly an efficient approach.
Hence, too, how data access can quickly become the biggest bottleneck in enterprise software development. Development teams become bogged down dealing with data importing, transformation and maintenance work, which distracts them from what should be their core focus – writing and deploying software.
Modern data integration as the key to efficient software development
Fortunately, it doesn’t have to be this way. By investing in modern data integration, businesses can clear the hurdles standing in the way of efficient software development.
Modern data integration means creating a unified data layer that can address the needs of developers (and other stakeholders to boot) across the entire enterprise. It solves the problem of developers having to maintain discrete copies of the information their apps work with. Instead, the apps can connect directly to the unified data layer.
What’s more, apps can also take advantage of data transformation and maintenance features provided by the data layer, further reducing the effort required of developers.
What makes this approach even more attractive is that building a unified data layer is not all that complicated, especially for businesses that already have modern enterprise resource planning (ERP) systems in place.
For example, those that use SAP as their ERP can take advantage of SAP’s Business Data Cloud (BDC), which combines SAP’s enterprise data management and access capabilities with data lake platforms such as Databricks. The data lake provides a central place for hosting the entirety of the organization’s data, while SAP delivers turnkey capabilities for connecting apps to the data.
Combining enterprise data management with data lake platforms is the crucial step that enterprises should be taking today as they look for ways to make software development even faster and more scalable. Dealing with data complexities may not feel as exciting as leveraging AI models to generate code, but it’s equally important in a world where applications are only as good as the data they work with – and where development teams are only efficient if they can leverage an integrated enterprise data layer.
