Natural Language Processing has been on the rise — from simple text to speech conversion to powering intelligent voice assistants, we have seen various applications of NLP. Voice assistants like Siri and Alexa have become widely popular among users. Today, NLP has become a core component of all search engines.
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NLP for Business
In the BI domain, NLP helps by improving the user experience in drawing insights from a huge collection of data. Business Intelligence tools are becoming increasingly popular and are being relied upon to understand data and NLP provides a simpler user interface and ease with BI tools. For example, businesses rely on IT experts for report creation and modification of existing reports, with NLP this task flow can be directly between the BI tool and its user.
How NLP works for BI Tools
Search engines make use of NLP to transform natural language into queries which fetches information from data warehouses. The same workflow is applied by BI tools. Users can speak to tools which then uses NLP to convert spoken words into SQL or any technical language queries that can fetch the required information from a database. What happens behind the scene is a complex flow of queries fetching data from different sources. NLP makes the process as simple as asking your virtual assistant app for the weather report. For example, Microsoft’s Power BI and the popular tool Tableau have already introduced these features.
The Challenges of Implementing NLP in BI
Some requirements must be met for NLP to work in a BI tool:
- Single Repository: The data applicable for business intelligence needs to be stored in the same repository
- Naming Conventions: The naming conventions of tables and databases has to reflect the natural spoken languages
- Data Model for NLP: A simpler data model that adapts to the assumptions of the NLP interface is required
Logical Data Architecture to the Rescue
Implementing the requirements to transform a traditional data warehouse to use NLP is a big challenge that needs to be overcome. Logical Data Architecture helps meet the requirements without modifying the traditional system but by adding on top an extra layer of Logical Data Warehouse. LDWs do not replace the traditional data warehouses and can be added along.
Logical Data Warehouse logically connects data that are distributed across various sources or databases thus satisfying the initial requirement. Also implementing a logical Layer is cheaper and less time consuming than physically bringing all data together into a single repository.
Logical Data Warehouses allows organisations to create logical views over the data with a separate semantic layer that uses business terminologies. LDWs also provide a centralized architecture for an organisation’s databases and facilitates the creation of universal policies that conform to the NLP interface’s assumptions.
Organisations have already started implementing NLP for a variety of tasks beyond just raw text or speech processing. Voice assistant apps and devices have become immensely popular with more and more popping up every now and then. In the coming years, we will see more apps and devices using NLP to take user interaction and experience to a whole new level.