How Augmented Analytics Is Changing the Business Landscape

Imagine you’re the owner of a growing online retail company, where understanding customer behavior is crucial for your marketing campaign’s success. You’re trying to sift through mountains of data to find patterns and insights that could inform your next big strategy. Lo and behold, you turn to a sophisticated tool that not only mines this data but also provides you with actionable insights through natural language processing—welcome to the realm of Augmented Analytics.

What is Augmented Analytics?

Augmented Analytics is an emerging approach that uses machine learning and artificial intelligence to assist with data preparation, insight generation, and insight explanation to amplify how humans explore and analyze data in analytics and business intelligence platforms.

Think of it as having a personal data scientist at your disposal, one that can help you filter out the noise and focus on what matters.

Transforming Decision-Making with Augmented Analytics

With Augmented Analytics, businesses have an ace up their sleeve for a variety of uses, such as:

• Personalization of Customer Experiences: It can analyze customer data and provide bespoke suggestions to enhance the user experience. This is akin to an e-commerce site recommending products based on browsing history.
• Advanced Forecasting: By leveraging historical data, it can predict trends and inform strategies for inventory management, sales, and more.
• Risk Management: Its predictive capabilities aid in identifying potential risks and taking pre-emptive measures.

The workflow of Augmented Analytics: A step-by-step illustration

Let’s break down the workings of Augmented Analytics into an easy-to-understand sequence:

1. Automate Data Preparation: Augmented Analytics tools preprocess the data automatically, detecting quality issues, normalizing it, and ensuring it’s ready for analysis.
2. Generate Insights: The tools apply various algorithms to uncover patterns and correlations that might not be immediately apparent to human analysts.
3. Explain Insights: What sets Augmented Analytics apart is its ability to offer explanations in a human-like manner, using natural language generation to describe intricate insights.
4. Act on Insights: Users can make informed decisions based on advanced analytics-produced insight without requiring a deep knowledge of data science.

Essential Augmented Analytics Tools

A number of platforms are leading the charge when it comes to implementing Augmented Analytics, including:

• Tableau with its Explain Data feature
• Qlik with cognitive insights
• Power BI blending AI within its business analytics

Affiliated Technologies

While Augmented Analytics is transforming data exploration, it shares the spotlight with related technologies such as data mining, natural language processing, and cognitive computing.

Advantages and Drawbacks of Augmented Analytics

Indeed, Augmented Analytics brings a fresh perspective to the table, but let’s consider the broader picture.

Advantages:

• Empowers decision-makers with less technical expertise.
• Increases the efficiency of analysis and reduces time to insight.
• Scales the expertise of data scientists across an organization.
• Improves data literacy by providing understandable insights.

Drawbacks:

• Still an evolving field which means it has a maturity curve to overcome.
• Relies on the quality and breadth of data.
• Initial set-up and integration can be complex and resource-intensive.
• Can sometimes lead to an oversimplification of complex data trends.