AWS Startups Blog
A Startup’s Guide to AWS Services Series 5: Analytics and Automation – Superhighways to Scale
As a founder, getting up and running on AWS, even knowing where to start can seem overwhelming. What services do you need? How do you build with best practices in mind? This series is your guide to getting started on AWS, from account setup and security, to choosing an operational model and database selection. Come explore the AWS cloud environment with us.
Advancing for scale and maturity
Everything’s going well. The product is out there, and customers are using it. But suddenly, you find yourself on unfamiliar ground: you need to keep building and deploying updated versions of the product in response to customer needs. And you need analytics flowing in continuously to identify what those needs may be.
This is a crucial intersection for startups, when time and resources are often stretched as far as they can go, and various branches of the infrastructure need to communicate. The Startup Guide to AWS Services free video series can help startup founders and developers bridge the gap, obtaining the tools they need without having to build them from scratch. Automation—using machine learning—can put startups on a growth superhighway.
In Advancing for Scale and Maturity, AWS Startup Solutions Architect Zoish Pithawala describes a two-pronged approach for startups scaling an application. “At this stage, it’s essential to review the useful data your application is collecting and apply those insights to improve the experience for customers.” To stay nimble for continuous adaptation, you need detailed analytics, allowing you to anticipate customers’ needs. Then, Pithawala explains, you need automation and integration to reduce the workload for the staff. Taken together, these twin engines—analytics and automation—keep information flowing in and incorporating into a product that is effective and up to date.
AWS Artificial Intelligence and Machine Learning Services (AI/ML) are powerful tools, which put machine learning into the hands of every developer, allowing them to add intelligence to any application without needing ML skills. Your individual use case will help identify which service to implement for your product, such as personalization or fraud detection.
Pithawala calls AWS Analytics “the fastest way for me to turn my rows of data into answers.” You can implement a data strategy and make data-driven decisions throughout your business. Meanwhile, the AWS Application Integration suite can be applied to microservices, distributed systems, and serverless applications to allow for communication between decoupled components. This can help with API management, messaging, and workflows, among other tasks.
Technical founders may already be proficient in these various fields. If not, Pithawala recommends working with specialists. For ML solutions, for example, startups can enlist the aid of data scientists to help them get started.
Data gathered can be an invaluable resource, as long as it is analyzed in a timely manner so you can act on it. Then, automation helps implement the changes needed in ways that are manageable for your busy staff. With these two service solutions together, AWS makes advanced technologies accessible to all, while keeping your information flowing and up to date.