Augmented AnalyticsAugmented Analytics

Augmented Analytics: Artificial Intelligence and Machine Learning — terms that resonate not only with tech enthusiasts but also increasingly within the business world. Today, data is generated in unprecedented volumes and diversity, influencing almost every aspect of life, especially in business contexts. The concept of “Augmented Analytics” is gaining focus, promising to democratize data analysis by moving away from complex tools to an intuitive, AI-supported experience.

What You Need to Know:

  • What is Augmented Analytics? An advanced form of data analysis that uses artificial intelligence and machine learning to make analytics more accessible and efficient.
  • The Benefits: From faster, data-driven decisions to minimizing human errors — Augmented Analytics can support organizations in various ways.
  • Challenges and Solutions: Despite clear benefits, challenges such as data quality and the balance between human and artificial intelligence need to be mastered.

Easy-to-Understand Example of Augmented Analytics

Augmented Analytics is an emerging paradigm that harnesses the power of artificial intelligence (AI) and machine learning (ML) to simplify data analysis. These technologies transform decision-making processes across businesses of all sizes by effectively utilizing complex data volumes. Imagine an e-commerce company noticing a high cart abandonment rate when customers view shipping costs. Augmented Analytics can quickly identify this trend and suggest solutions like free shipping for orders exceeding a certain value to reduce abandonments and increase sales.

With Augmented Analytics, specific trends can be swiftly pinpointed. The tools automatically scan existing data for patterns indicating such abandonments. For instance, they might detect a spike in abandonment rates as soon as shipping costs are displayed in the cart.

Upon identifying this critical point in the purchasing process, Augmented Analytics allows the company to act quickly and with informed strategies. The analysis might show that customers are willing to accept higher total costs if no additional shipping fees are applied.

Based on these insights, the company decides to implement a strategy where customers receive free shipping if they reach a certain minimum order value. This threshold is set to appeal to customers and be economically viable for the company.

The outcome of this measure is doubly positive: the abandonment rate significantly decreases as the main reason for abandonment — shipping costs — is eliminated. Moreover, average order values increase as customers add more products to reach the free shipping threshold. Overall, this leads to increased sales and improved customer satisfaction.

Mechanisms of Augmented Analytics

The technology behind Augmented Analytics combines machine learning, natural language processing, data visualization, and AI to offer a comprehensive solution:

  • Machine Learning: Dives deep into data to identify patterns and provide valuable insights.
  • Natural Language Processing: Adds a human touch by extracting meaningful information from various sources.
  • Data Visualization: Creates user-friendly dashboards that make complex data tangible and understandable.

Benefits of Using Augmented Analytics

Implementing Augmented Analytics offers numerous advantages:

  • Enlightening Data-Driven Decisions: Tools highlight crucial data trends, guiding organizations to make more informed decisions.
  • Minimizing Human Errors: AI-driven data tools play a significant role in reducing errors.
  • Promoting Collaborative Insights: By providing a common platform for data analysis, these tools strengthen collaboration within an organization.
  • Time and Resource Savings: Integrating AI tools simplifies data queries and management.
  • Efficient Data Management: Ensures compliance with data protection regulations while effectively managing large data volumes.

Challenges in Implementing Augmented Analytics

Introducing these technologies is not without challenges:

  • Clearing Misconceptions: It is crucial to educate employees about the purpose and benefits of AI tools.
  • Balancing AI and Human Involvement: Human intervention is essential for providing context and critical thinking.
  • Ensuring Data Quality: The quality of data is fundamental, as poor data can lead to unreliable insights.

About DASCA – Data Science Council of America and 4IR

This paper was published by the Data Science Council of America (DASCA), a leading organization dedicated to advancing and standardizing the data science profession. DASCA is renowned for its innovative certification programs and accreditation services, aimed at training a new generation of data experts proficient in Big Data technologies.

DASCA plays a central role in the Fourth Industrial Revolution (4IR) by providing essential knowledge and programs that support critical decisions in companies and nations. Their work standardizes the education and training level in the rapidly evolving field of data science, making them an indispensable partner for professionals and institutions. In an era where sound data analysis is crucial, DASCA sets standards that globally shape practices in data science and Big Data technologies.

Conclusion on Augmented Analytics

Augmented Analytics utilizes machine learning to simplify data generation and interpretation. These tools not only aid in analysis and decision-making but also reduce the workload through automated analytic systems. The future foresees even stronger integration of these technologies into everyday business processes, where data-driven decisions will become the norm.


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