What is augmented analytics?
According to Gartner, “Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.” It includes Natural Language Processing (NLP) and conversational analytics, enabling fewer technical experts such as citizen data scientists to interact with the data and insights to give recommendations for the business. It democratizes insights from analytics and AI to all business roles.”
Augmented analytics uncovers the most crucial insights in the business in a user’s context. It does this in less time, with less data science and ML skills.
What are the benefits of augmented analytics?
- Faster data preparation using AI/ML automation that replaces manual processes and reduces time to insights.
- Improved data literacy by democratizing AI across the data value chain and empowering less technical experts to interact with the data without specialized skillsets.
- It reduces human analytical bias by performing automated analysis across a large dataset.
- It democratizes analytics and makes data science and AI/ML model building accessible to business users in an organization.
What are the challenges of augmented analytics?
Dependence on legacy platforms acts as a barrier to adopt augmented analytics.
- The prevalent black box image of augmented analytics is not transparent in terms of decision-making.
- Lack of trust in the displayed insights unless a specific explanation is provided.
- Augmented Analytics has a lifecycle of its own and takes time to mature, improve its analysis and outcomes over a period.
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