The impact of natural language processing (NLP) on the productivity and decision quality within an organization cannot be overstated. As simplified search analytics expands into all corners of the enterprise, the business can expect business users to embrace advanced analytics and, in so doing, to become more of an asset to the organization.
The Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics, published on October 31, 2018, includes the following strategic assumptions:
- By 2021, conversational analytics and natural language processing (NLP) will boost analytics and BI adoption from 32% of employees to over 50% of an organization’s employees, to include new classes of users particularly in front offices.
- By 2020, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.
Consumers and business users alike have used natural language processing to search for and find information and, in the case of business users, there is an increasing expectation that search analytics will be simple and as easy to use as the search mechanisms provided by Google and other social networking companies and search engines.
So, what is search analytics today? It is a process that allows a business user with average technical skills to leverage sophisticated algorithms and techniques in a simple environment. With natural language-processing-based search capability, users do not need to scroll through menus and navigation. They can simply enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.
An enterprise that commits to these types of advanced data analytics tools can enjoy the benefits of a shared understanding of data and goals, improved decision-making, fact-based analysis that avoids guesswork and allows for refined planning and forecasting at every level of the organization. Data Scientists and professional analysts can focus on strategic issues and analytical projects that require 100% accuracy while business users enjoy the benefits of increased accuracy, improved productivity, and ease of data sharing and collaboration.
Users ask a simple question and get a simple answer. For example, a business user might create the following search using natural language: ‘how did John Smith’s product sales in 2017 compare to his product sales in 2016?’
Conversational analytics and natural language processing (NLP) will advance the knowledge and skill of every business user and educate each user in the importance of metrics and analysis, so every business user will become a crucial business asset.
By simplifying search analytics, businesses will simplify and improve planning, forecasting, and capitalize on competitive opportunities and human capital and resources will be optimized.
Original Post: Why is Natural Language Processing Important to Enterprise Analytics?
