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Companies are increasingly moving to an API-first approach in which they start any AI or digital project by focusing on the API they will use to integrate the data required.
In this blog, we look at the benefits of this approach and seven steps your company can follow to become API-first.
One of the clearest trends in corporate governance over the past decade is the move towards an API-first approach. This means companies are thinking about APIs (application programming interfaces) that bring in internal and external data before they try to build an app or buy in a third-party product.
Most new digital projects can gain valuable insights from a broad range of different data sets, including news, legal and compliance information, company and financial data, patents and intellectual property, and more. Using a third-party data provider with a flexible API allows access to multiple data sets through a single platform, enabling seamless integration into products and services for the benefit of a company and its users. This also streamlines the number of providers a company must source from and adds value by allowing different internal teams to use the same core of data for their own ends.
Postman’s latest State of the API survey, involving more than 40,000 software developers and API professionals, revealed a 3% increase in companies adopting an API-first approach in the last year. Leading organizations like the German telecoms firm Deutsche Telekom, multinational technology giant Cisco, and the US Department of the Air Force are among many organizations to have spoken publicly about adopting an API-first strategy, highlighting the growing influence of this approach across a range of industries.
MORE: How to use text-based financial data APIs
An API-first approach helps companies to successfully implement AI initiatives and other digital transformation projects. In another blog in our Harnessing Data for AI Innovation series, we explored the main reasons why 80% of companies’ AI projects end in failure (according to the Harvard Business Review). API-first companies are better able to overcome two of the main drivers of AI failures:
AI projects often bring in data from various sources, using a confusing mix of bulk data deliveries and individual APIs. Moreover, each data set may be structured (or unstructured) in a different way and require significant work to clean the data and ensure it can be used in the company’s chosen analytics software. But an API-first model carefully considers how data will be ingested into technology via an API before moving forward with an AI initiative. It aims to make data available to different teams as seamlessly as possible.
Companies often fail to apply AI towards their overall strategy. Projects should have clear objectives and attempt to solve a relevant and achievable problem. Author Bernard Marr studied failed AI projects and wrote in Forbes that “one thing they have in common is they are all caused by a lack of adequate planning”. By contrast, an API-first approach focuses a company’s attention on what parts of the business would benefit from an AI initiative, then bringing in an API which can best support those specific applications.
The State of the API survey also revealed other benefits companies have realized from adopting an API-first approach. Three-quarters of respondents (at least) said that API-first companies are more likely to:
Ultimately, an API-first approach can improve a company’s bottom line. Research by PwC found that “Australia’s global top performers are almost twice as likely than other Australian companies to take cloud native and API-first approaches”. Moreover, these profits should be more sustainable in the long-term, because focusing on the API is likely to make companies more resilient against future changes and evolutions in AI tools.
By adopting an API-first strategy, companies can streamline access to data and support better integration into digital projects, which are crucial tools in overcoming the challenges that can lead to AI project failures. This approach enhances a company’s AI strategies; its sustainable profit-making; and its ability to bring new products to market faster.
MORE: Exploring credible data for AI with LexisNexis
How should companies successfully embed an API-first approach which is capable of successfully implementing AI and big data projects? Here are seven things to think about:
Decide whether you need to build your own internal APIs to access external data, or if you can use an external API from a trusted provider to bring in multiple datasets. Most companies find the latter approach is more efficient and effective.
Identify the functions and objectives within your company that could benefit from AI and big data, and develop a strategy around governance, usage, and accountability for the API used to onboard data that supports those use cases.
Engage leadership across different functions to ensure buy-in to an API-first approach and prevent silos from developing. Provide training to employees on the opportunities, limitations, and best practices for making API calls and using the data.
Use providers of data and technology that allow you to test and vet their API against your core business needs.
Consider the quality of the data which the API is bringing into your AI tools and ensure that it comes from original sources and complies with regulations and ethical standards around data collection and publisher approvals.
Decide on the best format – structured or unstructured – for data delivery via the API based on both your company’s and users’ needs.
Cloud-based technology systems can raise the risk of a security breach or data breach. Ensure that your API provider has put in place safeguards to ensure compliance with relevant regulations.
MORE: Fueling data-driven innovation across your enterprise
Our API solution, Nexis® Data+, enables you to integrate our enriched data into your existing tools and platforms. This provides an outstanding foundation for carrying out analysis and AI initiatives and supports an API-first approach to your projects and products.
Nexis® Data+ offers direct access to our extensive data universe, encompassing news, legal, company, financial, biographical sources, ESG ratings, academic journals, compliance data, and more. Delivered through a single API, Nexis® Data+ can support a wide range of AI initiatives, including event modeling & prediction with pre-trained AI models; Natural Language Processing; generative AI; large language models; risk modeling; pattern discovery; predictive analytics; descriptive analytics; statistical analysis; and more!
Download our free ebook, Harnessing Data for AI Innovation, to learn more about the how your company can exploit AI’s opportunities and manage its risks with high-quality data.