Accelerate AI app development with natural language query and popular language support

By | 4 minute read | September 21, 2020

As many organizations begin their Journey to AI or seek to strengthen their AI initiatives, a key consideration is ease of use. Each rung of The AI Ladder™ — Collect, Organize, Analyze, Infuse and Modernize — must integrate seamlessly. Each rung must also prioritize ease of use.

The Collect rung of The AI Ladder comprises hybrid data management solutions that help set a strong data foundation for AI to leverage for insight. Databases should be designed such that they are not only powered by AI, but built for AI as well. Being powered by AI means that AI is infused directly into the database to provide more powerful, efficient querying whereas being built for AI indicates that the database empowers AI developers to develop AI initiatives more effectively.

This concept is explored in greater detail in the eBook, “Db2 – The AI Database” which takes a deeper look at eight technologies that help make a database both powered by and built for AI.

Encouraging more users to embrace AI

To increase the likelihood that organizations will adopt and use AI more widely, databases can incorporate capabilities like natural language querying and support for multiple popular languages to help everyone from business users to data scientists and developers perform their jobs more effectively.

Business users

At first it may seem like AI is too complex for most business users to reliably leverage its benefits – the coding required to establish queries may take undue time for developers to create and business users may not have the skills to run queries at all. However, businesses can make AI much simpler to use by incorporating natural language search features.

A web-based interface similar to search engines like Yahoo or Google is used so that anyone can ask a question of the data in the same way they might ask a person. For example, “how often did it snow in 1991?” The end result is that the user receives an easy-to-understand visualization or summary answering their question without needing to know how to code either for SQL or AI.

Behind the scenes, when a question is asked a SQL query is generated to return relevant results based on all available data sources. Machine learning technology makes this possible, having crawled and indexed the data to better understand context, synonyms and syntax so that it can translate the natural language question into the SQL query.

Natural language querying empowers more people across the organization to access data-driven insights to guide decision making. When business users no longer need to learn SQL, nor do they need to burden developers for answers to their questions, time to insight is faster. And when business users can ask questions framed in the language of their areas of expertise, querying is enriched — a significant improvement over options provided by pre-populated dashboards — leading to unexpected insights.

Data scientists and developers

Data scientists can use natural language querying to perform initial queries on unfamiliar or large data sets to reveal what the data sets contain and what routes of deeper investigation may be beneficial. In some cases, developers can also use REST APIs to add natural language querying to applications. Yet, for the most part, data scientists and developers will be using advanced languages and libraries to uncover and leverage the complex insights not possible through natural language querying alone.

Databases should have popular language, data format and library support including Python, JSON, GO, Ruby, PHP, Java, Node.js, Sequelize and Jupyter Notebooks. Again, the benefits are numerous:

  • Existing employees can use the same skills to which they are accustomed
  • A large candidate pool with the necessary skills will be available to choose from when making hiring decisions.
  • Data can be housed in the enterprise’s database and accessed natively without the need for custom back-end code – reducing opportunities for error
  • Code examples can be consulted when assistance is needed from peers, particularly for industry-specific use cases

Ultimately, this means data scientists and developers can spend more time incorporating AI and machine learning into business processes rather than troubleshooting database issues.

Putting easy-to-use AI in your organization

Being built for AI refers not only to a database having the underlying capabilities, but to making AI usable across the organization without extra hassle. This is possible with natural language querying and support for popular languages which are both key features of IBM Hybrid Data Management solutions, and can help accelerate your Journey to AI. One example is IBM Db2 which can be deployed on-premises, in the cloud, or as part of IBM Cloud Pak for Data, which is built on the Red Hat® OpenShift® Container Platform.

To learn more about technologies IBM uses to deliver data management that’s both powered by AI and built for AI read our eBook, Db2 – The AI Database. It has more information on natural language querying, language support, and six additional features positioned to help you succeed on the Journey to AI.









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