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Why analytics makes for a successful API



Today’s APIs (less well known as Application Programming Interfaces) are business-level artifacts: the channel to scale your business, elements to improve innovation and agility, and even a representation of your brand. Analytics plays a critical role in ensuring the success of APIs by providing the necessary insights to assess its value, measure its success, and identify how to improve performance. 
 
API Analytics looks at APIs as a product - a key mindset to adopt. Thinking of the API like a product engenders related questions: is it the right product for its purpose? Is it functioning the way it should? Are people using it in the way it was designed and intended to be used? 
 
To address these questions, API Analytics goes beyond traditional trend and volume reports that only divulge “what happened” and answers more mission-critical questions such as “what is happening” and “why.” For instance, early detection of low API volumes, combined with visibility into whether the problem stems from IT issues or from confusion due to poor documentation, allows for more speedy and effective resolution before the problem becomes widespread. 
 
Insight into “what makes a good API product” varies based on the perspectives along an API value chain - including perspectives from those that design and operate the APIs, as well as from those application developers (better known as app developers) that use the APIs. 
 
The API Product Manager oversees an API product and needs to meet certain business goals. While the goals themselves may vary— from increased efficiency, to sparking innovation, or pure monetization - a common thread is that success hinges on making the API product appealing to the consumer. This approach raises questions about who is the end consumer, in other words who are the app developers who build the APIs into applications.   
 
While answers may start with the traditional, fixed reporting on volumes and trends that serve to identify popular APIs and heavy users, however these insights aren’t enough. Machine learning assists in the real-time detection of access patterns defined by the timing and ordering of API calls an application makes.     
 
Understanding “what is happening” and “why” helps the product manager get a holistic view of the business effectiveness of the API and take the appropriate proactive action. 
 
On the API Operations side, IT focuses on keeping APIs running and meeting service level agreements (SLAs). This focus requires real-time insights to troubleshoot performance and security issues, and also long-term insights for capacity planning.
 
Like with the product manager, IT operations can leverage machine learning to identify events and patterns that are undetectable by fixed reports or human inspection. However operations have specific need for speed: they must quickly identify issues, troubleshoot root causes, and apply a fix to minimise performance degradations or even outages. 
 
Exploring general perspectives along the API value chain identifies a number of desired insights: availability and performance monitoring, volumes and trends, and access patterns.  Instrumentation across the vantage points within the API call gathers the data needed to formulate these insights, including metrics: from the deployed application (that is, on the end-user mobile device, server, or cloud-based system), through the API management gateway that processes the API request and response, into the backend resources that serve the data and services.
 
The takeaway is to not confuse access to metrics and measures as sufficient to measure and evolve an API program. Rather success starts with identifying the questions and then designing the analytics to obtain the right data to answer them. 

Posted by Teresa Tung, Ph.D., Senior Manager, Accenture Technology Labs
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Tags: analytics, api, application programming interface, business, companies, google, google analytics, web apis

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