The Analytics of an API Ecosystem

Analytics and KPIs are a central element of the modern business landscape and play an increasingly important role in defining business strategies, evaluating results and predicting future trends.

The term analytics refers to the collection, processing, analysis and presentation of data. In other words, they enable the transformation of large amounts of raw data into information by using complex algorithms, which can identify patterns and correlations between data.

“The goal is to turn data into information and information into insight.” ~ Carly Fiorina

Proper analytics produce insights that allow for a detailed understanding of business processes, relationships and factors that influence the success of the company. Let’s break down what some of those insights can be:

The insights of analytics

Access to analytics provides numerous advantages, including:

• Better understanding of users: they allow for the analysis of data relating to user behaviour, preferences, and satisfaction, enabling companies to offer personalized products and services and improve the end-user experience.
• Better operational efficiency: they enable the identification of inefficiencies in business processes and the optimisation of operations, reducing costs and increasing productivity.
• Prediction of trends: they allow for the analysis of historical data and prediction of future trends, enabling companies to anticipate user needs and adapt to changes.
• Better, quicker enterprise decisions: they enable decisions based on what has happened in the past without relying on mere hypotheses.

Beware of data

It is important to note that data accuracy is crucial to ensure the reliability of analyses and predictions. For this reason, companies must invest in data quality, including identifying errors, managing exceptions and normalising data. A good analytics system must be able to handle large amounts of data from different sources, integrating them and creating a single analysis environment. Wrong raw data would lead to incorrect predictions.

“Garbage in, garbage out.” ~ George Fuechsel

In addition, it is essential to have a team of experts who can interpret data correctly and translate it into useful information for the company. Working together, they can create advanced analytical models to predict future trends and make more informed decisions.

ApiShare and the analytics of an API Ecosystem

Any API Management Ecosystem must of course be able to track its APIs, but it must also collect data on their usage and their performance. When we talk about performance, however, we don’t just mean the technical performance of the software and infrastructure behind an API, but also about its performance as a product. (Read more about why APIs should be treated as products here.)

That’s what ApiShare does, as it collects both quantitative and qualitative information about many different aspects and entities that comprise an API Management Ecosystem.

The main entities for which data is collected are APIs, Applications, the Subscriptions that link these last two, Organizations and Groups, and their Users. For all of these entities, it is not only important to collect numbers, but also the details which make up those numbers. Let’s see what kind of data is collected:

APIs

• The number of API searches made on a given time frame, which can give administrators and executives an idea of an API’s demand and discoverability.
• The amount of API concepts created (the concept is the first state of an API’s lifecycle in ApiShare). This represents the backlog of APIs that are going to be developed.
• The percentage of conversion of API concepts to fully fledged APIs. This reveals how efficient and accurate the API Program is.
• The number of APIs published in each environment, which measures the effective size of the API ecosystem.
• The rate of reuse for each API within ApiShare. This indicator can provide quite a few insights from the actual demand of an API to its discoverability, and, most importantly, its success.
• …and much more.

Applications

• The number of Application searches made within a given time frame.
• Details showing which APIs each Application is subscribed to, which allows you to grasp how much an efficient API Management can facilitate API reuse.

Subscriptions

For this entity, which is inextricably linked to the previous two (see how), ApiShare collects data relating to their state for each environment (DEV, TEST, PROD…). The data can be broken down as the number of subscriptions requested, accepted or rejected over a given time frame.

Organizations, Groups, and their Users

• The total number of Organizations and Groups, as well as the total number of User accounts created and deleted. This can give an idea of the size and complexity of an API Management Ecosystem.
• The number and identity of Organizations and Groups that were created or deleted on a given time frame. This can provide insights regarding the evolution of the ecosystem and its stakeholders.

• A count, with related details, of distinct users who have performed at least one action within ApiShare on a given time frame. This metric shows how effectively ApiShare is being used over a given timeframe.

Conclusions

Thanks to the quantity and quality of data collected, it is possible to understand how effectively ApiShare is being used and where improvements can be made to optimise processes. Not only that, but past data can be compared with current data to understand how your API Program evolves over time.