API integration and predictive analytics: work together for successful forecasting
Businesses that rely on data to make decisions can no longer afford to silo that data. In order to get the most accurate picture of what’s happening and to make the best predictions about what will happen next, businesses need to be able to integrate data from a variety of sources.
APIs (application programming interfaces) are one way to make data integration possible. An API allows different software applications to communicate with each other and share data. This means that businesses can use the data they already have, regardless of where it’s stored, to make better decisions.
When APIs are combined with predictive analytics, businesses can reap even more benefits. Predictive analytics uses historical data and mathematical models to predict future events. This means that businesses can not only make use of the data they already have, but they can also anticipate what might happen next.
This short guide will take a closer look at how API integration and predictive analytics work together to provide successful business forecasting.
We will also explore some of the benefits that businesses can experience when they use these two technologies together. Finally, we will provide tips on getting started with API integration and predictive analytics.
APIs defined
Business softwares need to be able to communicate with each other to transmit data. That’s where APIs come in.
An API (application programming interface) is a set of rules that govern how one software application can interact with another. When two applications have an API in common, they can exchange data with each other. This exchange of data can happen in real-time or near-real-time.
APIs are a common way for different software applications to share data. For example, if you run an eCommerce business that relies on a third-party fulfillment center to ship orders, you will likely need to use an API to connect your order management system with the fulfillment center’s system.
This way, when a customer places an order on your website, that information can be automatically sent to the fulfillment center so that their team can start packing and shipping the order. The API would then send tracking information back to your website so that you can provide it to the customer.
Predictive analytics defined
Predictive analytics is a branch of artificial intelligence that uses historical data and mathematical models to predict future events.
Predictive analytics can be used to anticipate customer behavior. For example, a business might use predictive analytics to identify customers who are at risk of churning (cancelling their subscription or service). The business can then take steps to prevent these customers from leaving.
How APIs and predictive analytics work together
So how do APIs and predictive analytics work together? Consider the following scenario experienced by enterprises every single day:
Step 1: Customers interact with a company in some way, creating data (e.g. they make a purchase, leave a review, etc.)
When a customer interacts with a company, data is created. This data can be in the form of a purchase, a review, a customer service interaction, or any number of other interactions.
Step 2: This data is stored in a database
This data is then stored in a database. The database can be on-premises or in the cloud. However, this data is useless until acted upon.
Step 3: An API is used to retrieve the data from the database
An API is used to retrieve the data from the database. The API acts as a bridge between the database and the application that needs to use the data.
The application could be a predictive analytics tool that uses the data to create models that predict future events.
Or, the application could be a decision-making tool that uses the data to help businesses make better decisions.
In either case, the data that is retrieved from the database via the API is used to create insights that would not be possible without it.
Step 4: The insights are used to improve business decisions
The insights that are generated from the data are then used to improve business decisions. For example, a retail business might use predictive analytics to forecast demand for a particular product. This forecast can then be used to inform inventory decisions.
The benefits of using APIs and predictive analytics together
Making use of APIs and analytics together can seem like a lot of work. However, the benefits of using both technologies together are numerous.
Some of the benefits of using APIs and predictive analytics together include:
Increased efficiency
By using an API to retrieve data from a database, businesses can save time that would otherwise be spent on manual data entry.
In addition, companies that have the power to make decisions based on predictive analytics are often able to make those decisions more quickly and efficiently.
Improved accuracy
When data is retrieved automatically via an API, there is less chance for human error. In addition, predictive analytics models are often more accurate than manual forecasting methods.
Better insights
As mentioned before, using an API in conjunction with predictive analytics can provide insights that would not be possible with either technology alone.
For example, a business might use predictive analytics to identify customers who are at risk of churning. However, if the data that is used to create the model is not up-to-date, the results will not be accurate. By using an API to retrieve data from a database in real time, businesses can be sure that the data used to create their predictive analytics models is always accurate.
Insight into the future
Perhaps one of the most valuable benefits of using predictive analytics is that it gives businesses insight into the future. This insight can be used to make inventory, marketing, and even product development decisions.
Businesses can create predictive analytics models that provide valuable insights into the future by using an API to retrieve data from a database. These insights can then be used to improve business decisions and help companies plan for the future.
How to get started with APIs and predictive analytics
If you’re interested in using APIs and predictive analytics to improve your business, there are a few things you need to do to get started.
Learn about API uses and capabilities
The first step is to learn about what APIs are and what they can do. When your team can understand the foundational concepts, you’ll be better able to use them.
Plan your implementation
The next step is to plan your implementation. You’ll need to decide which APIs you want to use and how you want to use them.
Some companies choose to use an API management platform, while others prefer to build their own solution. There is no right or wrong answer here—it all depends on your company’s needs.
Implement your solution
Once you’ve planned your implementation, it’s time to implement your solution. This will involve setting up the necessary infrastructure and connecting to your chosen APIs.
The process will be relatively simple if you’re using a management platform. However, if you’re building your own solution, you’ll need to put in a bit more work.
Monitor and evaluate your solution
The final step is to monitor and evaluate your solution. This will help you ensure that it’s working as intended and that you’re getting the most out of it.
You should also keep an eye on changes to the APIs you’re using. As new versions are released, you’ll need to make sure that your solution is still compatible.
Use BI to enhance API productivity
If you want to get the most out of your API investment, it’s important to use BI tools to enhance productivity. With the right tools, you can automate API tasks, visualize data, and track KPIs.
API management platforms usually come with built-in BI tools. However, if you’re using a different solution, you’ll need to choose a BI platform that integrates with your API solution.
There are many different BI platforms on the market, so it’s important to choose one that meets your specific needs.