read more – copyright by www.cio.com
Not everything that glitters is gold
Business analytics continues to be a hot segment in the enterprise software market and a core component of digital transformation for every organization. But there are many specific advances that are at differing points along the continuum of market readiness for actual use.
It is critical that technology leaders recognize the difference between mature trends that can be applied to real-world business scenarios today versus those that are still taking shape but make for awe-inspiring vendor demos. These trends fall into categories ranked from least to most mature in the market: artificial intelligence (AI), natural language processing (NLP), and embedded analytics.
Artificial Augments Actual Human Intelligence
The hype and excitement surrounding AI, which encompasses machine learning (ML) and deep learning, has surpassed that of big data in today’s market. The notion of AI completely replacing and automating manual analytical tasks done by humans today is far from application to most real-world use cases. In fact, full automation of analytical workflows should not even be considered the final goal — now or in the future. The term assistive intelligence is a more appropriate phrase for the AI acronym, and is far more palatable for analysts who view automation as a threat. This concept of assistive intelligence, where analyst or business user skills are augmented by embedded advanced analytic capabilities and machine learning algorithms, is being adopted by a growing number of organizations in the market today. The utility of these types of smart capabilities has proven useful in assisting with data preparation and integration, as well as analytical processes such as the detection of patterns, correlations, outliers and anomalies in data.
Natural Interactions Improve Accessibility of Analytics
Natural Language Processing (NLP) and Natural Language Generation (NLG) are often used interchangeably but serve completely different purposes. While both enable natural interactions with analytics platforms, NLP can be thought of as the question-asking part of the equation, whereas NLG is used to render findings and insights in natural language to the user. Of the two, NLP is more recognizable in the mainstream market as natural language interfaces increasingly become more commonplace in our personal lives through Siri, Cortana, Alexa, Google Home, etc. Analytics vendors are adding NLP functionality into their product offerings to capitalize on this consumer trend and reach a broader range of business users who may find a natural language interface less intimidating than traditional means of analysis. It is inevitable that NLP will become a widely used core component of an analytics platform but it is not currently being utilized across a broad enough range of users or use cases to be considered mainstream in today’s market […]
read more – copyright by www.cio.com
Not everything that glitters is gold
Business analytics continues to be a hot segment in the enterprise software market and a core component of digital transformation for every organization. But there are many specific advances that are at differing points along the continuum of market readiness for actual use.
It is critical that technology leaders recognize the difference between mature trends that can be applied to real-world business scenarios today versus those that are still taking shape but make for awe-inspiring vendor demos. These trends fall into categories ranked from least to most mature in the market: artificial intelligence (AI), natural language processing (NLP), and embedded analytics.
Artificial Augments Actual Human Intelligence
The hype and excitement surrounding AI, which encompasses machine learning (ML) and deep learning, has surpassed that of big data in today’s market. The notion of AI completely replacing and automating manual analytical tasks done by humans today is far from application to most real-world use cases. In fact, full automation of analytical workflows should not even be considered the final goal — now or in the future. The term assistive intelligence is a more appropriate phrase for the AI acronym, and is far more palatable for analysts who view automation as a threat. This concept of assistive intelligence, where analyst or business user skills are augmented by embedded advanced analytic capabilities and machine learning algorithms, is being adopted by a growing number of organizations in the market today. The utility of these types of smart capabilities has proven useful in assisting with data preparation and integration, as well as analytical processes such as the detection of patterns, correlations, outliers and anomalies in data.
Natural Interactions Improve Accessibility of Analytics
Natural Language Processing (NLP) and Natural Language Generation (NLG) are often used interchangeably but serve completely different purposes. While both enable natural interactions with analytics platforms, NLP can be thought of as the question-asking part of the equation, whereas NLG is used to render findings and insights in natural language to the user. Of the two, NLP is more recognizable in the mainstream market as natural language interfaces increasingly become more commonplace in our personal lives through Siri, Cortana, Alexa, Google Home, etc. Analytics vendors are adding NLP functionality into their product offerings to capitalize on this consumer trend and reach a broader range of business users who may find a natural language interface less intimidating than traditional means of analysis. It is inevitable that NLP will become a widely used core component of an analytics platform but it is not currently being utilized across a broad enough range of users or use cases to be considered mainstream in today’s market […]
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