Data is the ultimate driver of excellent decisions; and why not? It helps identify the problems and challenges, highlights opportunities, and helps see changes, so we can work towards the desired goals.

As data becomes more complex with every passing moment, managing it and extracting valuable insights using traditional BI systems is not feasible. Addressing this challenge is Augmented Analytics.

In this article, we will discuss Augmented Analytics and how it is transforming business intelligence. Further, we will also discuss use cases that benefit technology.

Augmented Analytics

Gartner, the technology research and consulting company, came up with the term Augmented Analytics. Augmented Analytics can be defined as the next step in analytics’ evolution. The technology helps data scientists and business users to use artificial intelligence (AI) and machine learning (ML) to find and visualize information from unstructured data.

Data scientists can utilize augmented analytics to examine data without prejudice or previous beliefs about how variables in the data are related. It eliminates the requirement for specialist expertise in the creation and management of advanced analytics models. It allows data scientists and developers who must integrate ML/AI into applications to provide data science and machine learning content. Data scientists with advanced skills get more chances to dedicate themselves to creative work and develop the most relevant models.

How Augmented Analytics is transforming Business Intelligence (BI)

Using powerful AI and ML algorithms, Augmented Analytics helps businesses reduce their reliance on manual processes and/or data scientists by automating the insight-generating process. It also reduces errors and inconsistencies because of human interventions while generating insights. It is essential for making decisions and providing a clear image of the situation, revolutionizing how consumers engage with data, consume it, and turn insights into action.

Augmented Analytics is transforming three key stages of Business Intelligence which are now conducted manually and are prone to human error.

Data Preparation

Using advanced analytics, it is possible for modern Business Intelligence systems to precisely analyze large amounts of data. However, data purification, which comes before the analysis, is still a very tedious process that requires data scientists. They need to manually create metadata beforehand, and also ensure data profiling, modeling, quality, and data manipulation. The risk of human errors rises much before the analysis.

With Augmented Analytics’ feature of data preparation, businesses can enjoy data preparation automation and self-service. Machine learning can recognize information and offer optimal techniques for data cleaning, profiling, and modification. This speeds up the data preparation process and boosts data scientists’ productivity.

Discovering Patterns In Data

Using modern business intelligence and analytics systems, it is easy for business users to find data and explore its relationships and patterns. However, there is a limitation – users may miss understanding the underlying data trends and deformities that can affect the business. The problem becomes more clear when the size and complexity of the data increases.

Users continue to focus on data exploration based on their own biases and previous experiences. Alternatively, they’ll have to manually investigate all of the permutations and combinations. This procedure is obviously time-consuming, and there’s a good potential that consumers will overlook crucial information.

With augmented data discovery, several powerful algorithms are used to identify data outliers and correlations. It adds relationships to data automatically and thus reduces the likelihood of missing critical insights.

Operationalizing Insights From Data​

Most of the Modern BI platforms offer advanced interactive and visualizations dashboards. However, not all businesses can decipher what is genuinely significant in this data. Natural Language Generation (NLG) is used by Augmented Analytics platforms to notify users about some of the most significant observations in the data that they must know.

Benefits of Augmented Analytics

As Gartner has predicted, data science automation tasks have overtaken the tasks performed by data scientists in terms of the huge amount of advanced analysis produced.

Considering the enormous volumes of data being produced today, accessing and deriving valuable information from it is becoming extremely difficult for traditional data scientists. Consequently, businesses are missing out on some valuable insights and information.

Augmented Analytics speeds up the time-consuming process of data exploration while also detecting false or irrelevant findings. It decreases the chance of missing crucial insights that might be gleaned from data by using a variety of algorithms at the same time and displaying actionable discoveries to consumers. It also optimizes the decisions and activities that follow.

Gartner also predicted that citizen data scientists are expected to grow five times faster compared to professional data scientists. Data insights will be made available to a larger group of business users thanks to concerted efforts by Augmented Analytics and citizen data scientists.

Essential Elements for Augmented Analytics Tools

Machine learning is used in augmented analytics to automate many tasks in the data value chain, such as data preparation, discovery, and sharing of insights with business users, operational personnel, and citizen data scientists. For augmented data discovery, machine learning and augmented data science, and augmented data preparation, several components of an Augmented Analytics tool come together.

• Natural language processing: Citizen data scientists and BI stand to gain the most from technology’s linguistic capabilities. Users can communicate with the system using natural language via text and even voice instructions, thanks to NLP.
• Natural language generation: This allows BI solutions with Augmented Analytics abilities to interpret results interactively, allowing organizations to easily understand the data’s intricacies.
• Recommendation: To avoid confirmation bias and enhance the performance of the BI tool, an Augmented Analytics system should suggest
  • The best graphics for specific data
  • How to enhance data to analyze and interpret it better
  • How to prepare data for business use

• Generation of insights: Augmented Analytics systems must be able to deliver insights that are free of bias and produce outcomes that are consistent with the hypothesis. These algorithms must be able to explain the data, identify significant performance drivers, and extract the factors affecting the conclusion. Outliers that may behave differently than expected must also be identified by the tools.
• Prediction: Augmented Analytics tools have to provide forecasts and trends, and statistical outliers and clusters, with ease. These procedures include the application of algorithms to train prediction models based on a variety of business characteristics, such as attrition, churn, and consumer behavior.

Integrating artificial intelligence and natural language processing with Augmented Analytics is a fundamental component of improving the user experience across the data analytics value stream. It makes processes such as insight discovery, data ingestion, data correlation comprehension, and user interaction become more streamlined and efficient.

Final thoughts

With innumerable IoT devices development, data is growing at an extraordinary pace, and people are leaving new digital footprints every second. With data being gained and processed in a variety of sophisticated ways, strong analytical tools powered by AI are critical for unlocking data’s full potential.

To make sense of a sizeable amount of data and easily disseminate their discoveries throughout the entire business, businesses would need an Augmented Analytics platform.

By adopting Augmented Analytics, businesses can automate various critical components of insight generation, improve data governance and accuracy, increase the productivity of data scientists, and lower costs.

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