Conversational systems that leverage natural language interactions are already considered a main driving force for a paradigm shift in business. According to Gartner, by 2021, “conversation first” will be adopted by the majority of data-driven enterprises as the most important new platform paradigm, superseding “cloud first, mobile first.”
Despite being “Mobile-First” and intuitive user interfaces including the widespread adoption of web and mobile interfaces, most enterprise software failed to cross the profound difference between people, viewpoints, feelings, etc. of user training. Developers still expect their business users to adapt to the applications they build, hoping that they would refine the experience as adoption increases. This is typically done through change requests or extensions/add-ons within the product, resulting in perpetual user training cycles.
Today, even a kid can start using Amazon Echo by just talking to it. These are early days of human interfaces but these applications will start to learn and adapt to user preferences soon. The more you use it the more it learns about you. Gartner predicts that by 2020, customers will manage 85% of their relationships with an enterprise without interacting with a human. With conversational experiences, this shift will be a rapid one.
From Digital First to Conversations First – Why Conversational Analytics Will Transform Businesses?
The main reason that AI-based conversational analytics systems will radically transform business ties back to the issue of data. Lack of data is not a problem most enterprises face today. In fact, it’s the exact opposite – most have an overabundance of data but lack the resources and technology to harness it. As a result, data, which could be useful, instead stays dormant. There are two main benefits for businesses that make conversations with data a possibility.
• They should enable early diagnosis and triage of data quality issues. Ideally, you should be notified the instant some number seems off.
• Tools should place demands on source systems and integration efforts to supply better data. Integrating with the tool should force data teams to reckon with a certain baseline of expected quality.
• They should provide users with specific descriptions of data errors when encountered during ETL. This is a tool-specific requirement, and it applies to both ETL and ELT-paradigm tools.
• There should be a framework for capturing all data quality errors. That is, data quality errors should be treated as a source of data itself, which means that:
Democratization of Data
Before voice-enabled technology, or even the internet, the task of buying paper napkins was already democratized. There were no special skills needed to drive or walk to the corner store or supermarket and buy a roll of napkins. In the business world however, accessing and getting back user-friendly insights from massive amounts of data traditionally required special skills. Data in the enterprise hasn’t been accessible to everyone in the organization. Normally people with coding skills, statisticians and analysts were the gatekeepers of data. AI-powered conversational analytics platforms can break down the barriers and provide the everyday business practitioner – from the CEO to a marketing manager – with a way to interact with data in an accessible system using natural language.
Imagine a CEO being able to ask an AI-powered advisor – “Give me my weekly market share update,” or a VP of marketing: “How did last week’s new direct marketing campaign perform?” or “What is the propensity for customers to respond to the campaign being launched over next few weeks?” and receive back a report or answer instantly based on all available data in the system. The power for anyone in the company to access and leverage data on-demand by voice in natural language has the potential to unlock barriers and scale adoption of insights.
Standardization of Data Reporting and Insights
A well codified AI conversational interface or app helps ensure consistency and accuracy of data reporting and insights enterprise wide. Because companies have people across the organization with varying technical capabilities and incentives to have data fit their own agenda or align with desired outcomes, data can often be misrepresented. As a result, inaccurate conclusions and erroneous decisions are taken by management who do not have the time or expertise necessarily to understand the flaws in the data.
When data is standardized in one system and accessible to all, a C-suite executive can easily ask the system for a market share report and for the factors that drive share performance, without having to wait for an analyst being constrained by analyst capacity. Insights are quickly surfaced, and ultimately voice assistants could also be set up to provide intelligent feedback (suggest actions based on the data nuggets pulled from the massive data sets).
With machine-learning and AI trained conversational platform systems gaining the ability to make sense of data and context, they are now being used increasingly across the board. They have found their place in a far bigger, organization-wide strategy which uses them for business process automation, cost-savings and empowering teams with additional and more granular insights. Besides giving you on-demand information, an intelligent enterprise conversational solution goes beyond the usual – it recommends insights that other business teams look for, or surfaces insights that you did not know from your conventional systems.
We believe the time for a “Conversation First” strategy has arrived. Entrepreneurs and users will rely on self-learning and personalized conversational edge devices to maximize impact at work, whether at home or office, ubiquitously.
Is your organization ready to make the shift?
About the Author
Santosh Tiwari
Director – Solutions
