In the past, the biggest pain point facing enterprise software developers was writing and testing code. Thanks to the productivity and agent AI capabilities that have become widespread in the last few years, however, the coding part of the development task has become much easier.
But here’s an equally pressing challenge for software developers that AI has yet to solve: Accessing data and integrating it into applications. Indeed, access to data has undoubtedly emerged as a major obstacle to the development of modern businesses.
Additionally, better data integration methods have grown to be one of the most important steps organizations can take to improve the developer experience, add efficiency to application delivery and put new features in the hands of end users faster.
Here’s why – and tips for what businesses can do to ensure developers have access to the data they need, when they need it, how they need it.
Why software efficiency depends on data access
Almost all software applications need to import and process data of some kind. The data may include customer names and email addresses stored in the database. They can be documents that reside within the file system. It can be log data generated by other applications.
Without data sources like these, applications cannot access the information they need to be useful. So, the easier it is for developers to access the data they need and plug it into their applications, the easier they can build useful software.
In this context, “easy” access to data means three main capabilities:
- Finding the right data sources within enterprise IT quickly.
- Importing data sources into applications with minimal need to transform the data before it is useful.
- Less effort is spent on writing custom logic within applications for working with data or storing it.
The challenge of accessing business data
Unfortunately, enabling these capabilities within large enterprise environments is often not easy. Enterprise data structures are often very complex, making it a challenge for developers to find the data they need, let alone work with it effectively.
This is why business development teams often use practices such as creating and maintaining separate copies of the database. Rather than being able to connect multiple applications directly to data stored in a central repository, they create separate databases for each application – which is not an efficient approach.
So, again, how fast data access can be a major bottleneck in enterprise software development. Development teams have trouble dealing with data entry, conversion and maintenance work, which distracts them from what should be their main focus – writing and deploying software.
Modern data integration as the key to successful software development
Fortunately, it doesn’t have to be this way. By investing in modern data integration, businesses can remove obstacles that stand in the way of successful 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 company. It solves the problem of developers having to keep separate copies of the information their applications work with. Instead, applications can connect directly to the integrated data layer.
In addition, applications can also use the data transformation and maintenance features provided by the data layer, further reducing the required developer effort.
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.
For example, those who use SAP as their ERP can use SAP’s Business Data Cloud (BDC), which includes SAP business data management and the ability to access data pool platforms like Databricks. A data lake provides a central location to host all of an organization’s data, while SAP delivers turnkey capabilities to connect applications to data.
Integrating enterprise data management and data lake platforms is an important step that businesses should be taking today as they look for ways to make software development faster and more efficient. Dealing with the complexity of data may not sound as exciting as using AI models to generate code, but it’s just as important in a world where applications are only as good as the data they work with — and where development teams are only effective when they use an integrated layer of business data.
