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Why should you know about Augmented Analytics in 2021?

Augmented Analytics

It is no news that we are in the age of great possibilities with the current pace of technological advancement. In other words, technology is a crucial component of our day-to-day life. Leading brands such as Facebook, Amazon, eBay, Shopify, etc., spend millions yearly on business marketing, data mining, and machine learning systems. All these are to ensure they remain ahead of their competitors by understanding customer behavior, finding what’s in vogue, and avoiding possible irregularities. In summary, thriving industries respond accordingly to customer behavior insights.

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In 2017, Gartner introduced the concept of augmented analytics. It was defined as “an approach that automates insights using ML” and helps decision-makers within organizations have better tools to make better decisions. In that report, it was also included the usage of Natural Language Generation as a key component of the augmented analytics.

The role of modern AI technologies in businesses is beyond imagining. They automate discoveries and surface the most important business insights to help suggest its optimization. Besides, all this data processing is done in a fraction of time when compared to manual processing. The ease of automation possibility and swift data analysis is one of the key advantages of these technologies.

Now, let’s look into what Augmented Analytics is all about, its importance, and its features.

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What is Augmented Analytics?

It results from using AI and machine learning technologies within an organization in data preparation, analysis, and interpretation to get profitable insights. Augmented analytics, in other words, involves many aspects of data science, AI machine learning, development, and data management. As stated before, the Augmented Analytics approach is the product and service-oriented data science that focuses on human language processing and machine learning.

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Through machine learning and AI algorithms, augmented analytics can even take away a data scientist’s burden by automating insights generation processes. Hence, an Augmented Analytics system can automatically go through data cleaning, analysis, and modify insights to actionable data that is easy to grasp.

MAG — the three essential features to know

To better understand how the Augmented Analytics engine operates, it is vital to be familiar with its features. 

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  • Machine Learning: It is the use of algorithms to discover patterns, trends, and data relationships. Note, the system is not designed to follow pre-programmed rules/principles; instead, it learns dynamically from the available data.
  • Insight Automation: Augmented analytics saves a company’s data scientist headaches by automating its results.
  • Natural Human Language Generation: It refers to creating words, phrases that humans can understand from the algorithms’ insights just automated. It focuses on the output of data analytics. A clear example of that is Toggle.AI. It crunches huge amounts of financial data to automatically develop actionable insights that traders and investors alike can use to decide where to put their money to work. It does not replace at all their main roles, but instead, this tool empowers them to make better-informed decisions.

Augmented Analytics Stages

Augmented Analytics Stages: Data Collection, Data Cleaning, Analysis, and Insights Generation
Augmented Analytics Stages
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Benefits of Augmented Analytics:

Below are the highlights of the four services of Augmented Analytics:

  • Actionable Insights:
    • It guides business campaign activities (audience targeting).
    • It helps prevent anomalies.
    • Generates great Insight to help categorize customers based on tastes.
    • It helps to satisfy customers better.
    • It helps improve customer experience. 
    • It reduces analytical bias — neutrality.
    • It helps in suggesting the right product to customers.
  • Faster results:
    • It could crunch data much faster than any person. It allows having a deeper data analysis.
    • Accurate analysis in no time compared to selective research by data scientists.
  • Improved data literacy within your organization:
    • It can foster a data-led culture while enhancing data literacy.
    • It reduces the analytical bias.
    • It enables citizen data science a quick runup in bringing their value to the company.
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To read more on Data Science and its benefit in business, click here.

#DataScience #AugmentedAnalytics #AugmentedAnalyticsStages #AugmentedAnalyticsFeatures #AAS

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