In recent years, there has been a significant increase in the number of companies that have fallen for Artificial Intelligence (AI) and Machine Learning (ML) technologies. The reason is quite intuitive – from improving customer experience to transforming businesses completely, the benefits of integrating these advanced technologies are numerous. This is why it makes a lot of sense to ask the question - what is the next step in this game?
Well, it seems that the answer to this question might be Augmented Analytics. Augmented Analytics uses Artificial Intelligence and Machine Learning algorithms to provide better insights to human analysts who are making decisions for their enterprises. Combining these two technologies can give businesses a significant competitive advantage by increasing revenue, reducing costs and growing their customer base.
The augmented analytics market is expected to grow at a CAGR of over 51% during the forecast period (2019-2024). That should tell you everything about what it can do for you. It is an absolute must for businesses that are looking forward to leveraging pioneering technologies and bringing them to the next level.
So, let's take a look at how essentially it works:
Augmented Analytics uses Data Science tools such as Machine Learning or Artificial Intelligence to increase the efficiency of data processing in an organization. In simpler terms, instead of applying Data Science tools on its own, it uses these tools to empower human analysts. So, essentially, the applications of Augmented Analytics are not restricted to a particular domain or industry. The potential of this technology is immense and can be applied almost everywhere – from banking to insurance, healthcare or even e-commerce.
How does Augmented Analytics work?
Let's take a popular example of a bank that wants to understand which customers are likely to default on their loans. In order to do this, the bank uses a Data Science tool such as Machine Learning or Artificial Intelligence and analyzes a number of variables from customer data.
For example: age, location, profession, marital status, income levels etc. After analyzing this data, the tool gives an output based on probability. Now, if the bank wants to make decisions about which customers are likely to default, it can use these outputs.
So far, so good. But what if the bank wants to know how accurate its analysis is? There are two popular ways of understanding the accuracy of data analysis:
Test the Data Science tool's output against a new dataset that is not used for training. If the data analysis tool predicts correctly, then it means that its accuracy in predicting defaults is high.
Ask human analysts to predict which customers are likely to default and compare their decisions with those made by the Data Science tool. If the accuracy of both decision-making mechanisms is similar, then it means that the Data Science tool's analysis is accurate and can be used for making decisions to provide better customer service.
This is where Augmented Analytics plays an important role – instead of taking decisions based on outputs from a Data Science tool, it uses these outputs to empower human analysts. It gives them more insights about which customers are likely to default so that they can use this information to improve customer service and build long-lasting relationships with customers.
How does Augmented Analytics benefit businesses?
We touched upon the benefits of augmented analytics in the previous section, but it is worth noting these benefits once again:
Augmented Analytics improves the efficiency and effectiveness of data analysis – instead of using Data Science tools to make decisions, it uses these tools to empower human analysts with better insights.
Better customer service – By leveraging augmented analytics, businesses can not only increase their revenue but also improve customer service by providing personalised services to their customers.
Data analysis becomes faster – augmented analytics is a powerful tool that makes analysis faster and more accurate by using Data Science tools to empower human analysts with better insights about data.
Timely decisions – businesses can use augmented analytics to make timely decisions based on critical insights from data, without having to wait for a long time.
Data driven decisions – augmented analytics can provide business with better insights about data, allowing them to make informed decisions based on accurate insights. These decisions are not only efficient but also unbiased.
Businesses can use augmented analytics for various use cases such as predictive maintenance in manufacturing or assessment of risks in banking and insurance sectors etc. As a result, augmented analytics enables businesses to make a better connection with their customers and provide them better service.
So how does Augmented Analytics work?
Using the example of a bank that wants to know more about which customers are likely to default on their loans, augmented analytics works as follows:
The bank uses a Data Science tool such as Machine Learning to collect and analyse data such as age, location, profession etc. from customer databases.
The bank uses Augmented Analytics to get insights about the data collected by the Data Science tool and empower its human analysts with these insights before they make decisions about which customers are likely to default on their loans.
As a result, augmented analytics makes decisions about customer defaults more efficient as the bank's human analysts are now able to understand which customers are likely to default on their loans.
Augmented Analytics uses Machine Learning and other data science tools to process huge amounts of data from multiple sources such as internal databases, public databases etc. so that it can provide accurate and relevant insights for decision-making.
So if you're wondering, "What is Augmented Analytics and how does it work?", then now you know the answer. With Augmented Analytics , businesses can make decisions based on data driven insights from Data Science tools without having to wait for long hours. To know more about augmented analytics, read our blog posts or contact us . And don't forget to check out our Data Science tools .
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