The Art and Science of Conversing With Data - Conversational Analytics

published on 18 November 2022
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Conversational Analytics Defined

Businesses get a large amount of organized and unstructured data. The volume of data is increasing by the second as they receive new updates from various sources.

With conversational analytics and AI technologies, businesses can better navigate through all of their data, pull the necessary data sets from numerous sources, and make the data accessible via type searches. Conversational analytics use AI to extract information from human speech, both textual and spoken.  It is built on the natural language processing (NLP) notion of computers interpreting human language, allowing computers to be asked questions that can be replied.

What Is Natural Language Processing? (NLP)

Natural Language Processing (NLP) is a discipline of computer science that studies how computers communicate with human (natural) languages.  In NLP, computers intelligently analyze, comprehend, and deduce the meaning of human language. Developers can use NLP to organize and classify information in order to complete tasks. For a long time, NLP algorithms had valuable capabilities for assessing language for meaning.

How can it be utilized?

Non-technical enterprise users can utilize NLP to ask query-complex data for response and interpretations of findings. As a result, businesses are now focused with the challenge of programming computers to handle and analyze massive volumes of natural language data.

Conversational analytics aids organizations by utilizing artificial intelligence (AI). The AI locates data in such a manner that users can extract the appropriate data sets from numerous sources and make them accessible by voice or type searches. The advancements in the field of conversational analytics will make analytics more accessible to new user segments such as administrators, salesmen, and creative individuals.

How does Conversational Analytics work?

To begin, companies will require the data that they’ll like to get answers from. After that, the unstructured data is matched with metadata. Once all the data is in the same format, the query-complex questions can be understood and answered.  

Why is Conversational Analytics important for businesses?

Since the absence of data itself is no longer a concern it is one of the reasons why conversational analytics will transform organizations.  Businesses now have a lot of data, and have worked hard to put dashboards, graphs, and many other visualization tools in place. 

These data can be used to add the most value to the company, managers, and decision-makers. Data should not be considered a worthless resource for the company. It must be relevant, clear, updated, personalizable, and easily available. Conversational analytics, makes data more accessible and customizable.

What is the pain point? and How can Anania add value?

Conversational analytics will help professionals in conversing with data.Employees now can utilize the graphical interface to view statistics, graphs, and other data visuals in order to make sound decisions based on data. Senior management, in many instances, don't have the time to utilize a graphical user interface to acquire reports; instead, others prepare reports for them. However when not in the office , it is often hard to derive analytics. In this instance, information can be obtained by sending out an email and waiting for the analytics. Moreover,  employees within the organization work in various departments, having different responsibilities, and available analytics may not match their objective or coincide with intended goals.All of these factors create certain hurdles between employees and data, and can be time consuming to obtain the proper data at the right time, which can lead to incorrect conclusions.

The Benefits of Incorporating Conversational Analytics

Increased Accuracy

By reducing the number of human touchpoints in preparing records and viewing data, machines are designed to select desired records, mix and assemble them for the user. Which can  minimize human mistakes when creating reports.

Empowering Professionals

Users do not need to learn SQL to use NLP capabilities, making the act of searching easy. Now deriving the  best insights are dependent on asking the right questions in the right way.

Time Efficient

With conversational analytics, users do not have to learn how to run SQL queries to derive insights. The only input needed from them is a well formulated question and the analytics will provide the answer in the matter of seconds.

Data Oriented Employees

The more people in a company that understand how to gather insights based on data, the greater the company will benefit from a data-driven subculture. It will help employees to make evidence based decisions rather than speculating, observation, or hypotheses to make conclusions. Subcultures like this can be created in any sector and while many industries can benefit , it will be more beneficial to the healthcare, manufacturing, banking, retail, and logistics industries.

Maximized Accessibility 

Conversational AI interfaces are mobile, meaning they are available on all devices and do not need separate software, enabling consumers to acquire insights whenever they need them.

How to start today?

Using natural language, Anania provides a quick and easy approach to ask the right questions and uncover insights. It creates appropriate responses, graphical statistics, and contextual insights that increase learning with each query. Insights are extremely relevant and increasingly wiser over time, thanks to powerful business intelligence and machine learning. Learn more on

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