Gartner predicts that ‘… augmented analytics will be ubiquitous, but only 10% of users will use it to its full potential.’ One of the primary reasons that augmented analytics is not adopted and leveraged to its full capacity is that the business chooses a solution that is not easy enough for team members to adopt – one that is restrictive, inaccessible or requires sophisticated skills.
‘If the user sees the value, and can easily leverage the new tools, without learning complex systems or techniques, your data democratization initiative is much more likely to succeed.’
In order to achieve data democratization and improve data literacy, the enterprise must understand its requirements, the current and desired business processes and its ability to provide and encourage augmented analytics solutions across the organization and to encourage the use of these solutions with cultural transition.
This article discusses the need for easy-to-use tools that will encourage business users to adopt augmented analytics and to embed fact-based, data-centric information into day-to-day decisions and recommendations.
Natural Language Processing Search Analytics (NLP) – is a crucial component of search analytics and smart data discovery today. NLP search allows business users to create complex searches without endless clicks and complex navigation and commands. Using this type of search analytics, users can access and view clear, concise answers and analysis quickly and easily. Advanced analytics with Natural Language Processing (NLP) provides a familiar Google-type interface where a user can compose and enter a question using common human language. 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.
Clickless Analytics – uses natural language processing (NLP) and machine learning to delivery augmented analytics with auto-suggestions and recommendations that help the user to choose the right data visualization and the right predictive analytics algorithms to best illustrate and reveal results for the type and volume of data they have selected. Users can simply enter the query in natural language and let the system do the rest. No advanced training is required. The Clickless Analytics approach is designed to support a self-serve environment that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI).
If the organization wishes to achieve expected results from data democratization, it must provide an augmented analytics solution with Clickless Analytics – one that is easy to use and will allow business users to leverage their professional skills and knowledge to find and use data and to gain insight, solve problems and share and collaborate. Most business users do not want to be a data scientist, and they will feel pressed to adopt analytics. But if the user sees the value, and can easily leverage the new tools, without learning complex systems or techniques, your data democratization initiative is much more likely to succeed.
‘In order to achieve data democratization, the enterprise must provide easy-to-use tools that will encourage business users to adopt augmented analytics and to embed fact-based, data-centric information into day-to-day decisions and recommendations.’
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