As customer needs continue to evolve at an ever increasing pace, businesses are finding it challenging to deliver new digital products and feature releases to keep up with their requirements. This in turn is creating a pressure on software development teams to deliver at the pace required.
In this blog, we take a look at data abstraction, and how decoupling data from its underlying systems can simplify the software development process, speeding up the release of new products and features, and thus driving greater value, faster.
What is data abstraction and how does it accelerate value?
Data abstraction is the process of simplifying complex systems by hiding their underlying implementation details and therefore presenting only what’s considered essential information to users. With data abstraction, developers separate the details of how data is stored and accessed, instead providing interfaces that allow other parts of the system to access and manipulate the data in a simplified way without needing to understand the underlying implementation. This in turn simplifies and speeds up development cycles, accelerating value creation from digital products.
What are the advantages of data abstraction?
- Simplicity and organisation: data abstraction simplifies complex systems by hiding unnecessary details, thus promoting a separation of concerns and increased modularity. This in turn makes a system easier to understand and work with.
- Maintenance and flexibility: data abstraction allows for easier maintenance and modification of the underlying implementation as it is decoupled from the data layer. This enables replacement of, or changes to the underlying data storage to be made more easily.
- Security and performance: data abstraction can improve security by providing controlled access to data. In tandem, it can also help to optimise performance by centralising data access.
- Reusability: data abstraction can also accelerate value through its promotion of the reuse of code, which is achieved by providing standardised data interfaces.
What are the risks associated with data abstraction and how do I mitigate them?
- Short-term costs: it can be expensive to implement data abstraction, especially if you’re refactoring existing projects, which may require developer time and resources that are needed elsewhere. To mitigate this, make sure you have a clear business case for abstraction and have undertaken cost-benefit analysis up-front.
- Unintended complexity: over-abstracting your data can make your system overly complex and difficult to work with. There is always a risk of adding unnecessary abstraction layers, which could potentially decrease developer productivity or negatively impact performance by adding unnecessary overheads. It can be challenging to strike the right balance, and it’s worth spending time to find the level of complexity vs simplicity that works best for your organisation.
- Security risks: If not implemented correctly - with proper access controls - abstraction could accidentally expose sensitive data or open unforeseen security vulnerabilities. It’s important to take the time to ensure that you’ve configured your access controls in a way that will grant the people who need it the relevant permissions, in a way that is compliant with your security policies.
- Lack of buy-in from your developers: all of your developers need to understand and agree with the abstraction approach in order to make proper use of it. Good change management principles apply here, and you need to take stakeholders on the journey with you, starting with clear reasoning for implementation. As with anything new, forced abstraction could reduce buy-in and thus erode the gains you’re hoping to achieve.
5 potential use cases for data abstraction
Now that you understand the advantages that data abstraction can bring to your organisation, and have a handle on how you can mitigate the potential pitfalls. It’s time to consider how you might apply abstraction to accelerate value.
(1) Database integration:
By abstracting the underlying database, the application code doesn't need to change if the database needs to be upgraded or replaced. For example, swapping from MySQL to PostgreSQL would only require changes within the data access layer, not throughout the application code. This simplifies maintenance and reduces the risk of breaking existing features.
(2) Updating external services:
Abstraction provides a standardised interface to retrieve and manipulate data from external sources, regardless of the specific API or data format used. If an API changes, only the abstraction layer code needs updating rather than every part of the code calling that API. This decouples dependencies and reduces brittle code, making your code more resilient to changes and thus of greater value if you need to pivot for any reason.
(3) Harmonising different storage methods:
As an example, a user profile service may store some data in a SQL database for queries, some in a NoSQL database for scalability, as well as caching frequently accessed data. The abstraction layer would provide a uniform way to access data without knowing the specific storage in use. This accelerates value by increasing flexibility.
(4) Overcoming different data formats:
If data is received from multiple sources in different formats like JSON, XML, or even a custom format, abstraction presents the data in a uniform object structure. This simplifies consumption of data without requiring knowledge of the original representation, accelerating the speed at which data can be used for different purposes.
(5) Performance monitoring:
By centralising data access, monitoring tools can more easily pinpoint if a slow request is due to a database bottleneck as opposed to say, application logic. Tracing tools can also capture detailed metrics on data access without needing instrumentation throughout the codebase, making it easier to diagnose and triage performance issues.
Accelerating value for your business through data abstraction
If you take only one lesson from this blog then it should definitely be to “Start simple, and iterate over time. Rather than trying to over-engineer your data abstractions from the beginning.”
It’s important to ensure that your development team is collaborating in order to strike the right balance between abstraction and simplicity within any given project or system. Their ‘North Star’ should always be to abstract data in a way that makes your development cycles either and does not add unnecessary complexity. With that in mind, it’s better to start with a less complex approach and improve it gradually based on evolving needs and real-world testing, rather than trying to design the perfect abstraction layer up-front.
If you take only one lesson from this blog then it should definitely be to “start simple, and iterate over time. Rather than trying to over-engineer your data abstractions from the beginning.”
It’s important to ensure that your development team is collaborating in order to strike the right balance between abstraction and simplicity within any given project or system. Their ‘North Star’ should always be to abstract data in a way that makes your development cycles either and does not add unnecessary complexity. With that in mind, it’s better to start with a less complex approach and improve it gradually based on evolving needs and real-world testing, rather than trying to design the perfect abstraction layer up-front.
