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What Is Next for Augmented Analytics? (2021)

Publication date: 6 April 2021

Data analytics used to be incredibly challenging for businesses to set up and run. To be fair, it is quite a lengthy process – from integrating various data sources and preparing them for analysis to actually analyzing the data and communicating the insights across the company. Recently, Business Intelligence (BI) tools advanced to the next level and started incorporating augmented analytics, making data analysis a breeze and enabling faster, real-time decision-making.

Augmented analytics can be defined as an approach that makes use of disruptive technologies such as machine learning and artificial intelligence to automate data preparation, insight discovery, and intelligence sharing. In other words, augmented analytics is a combination of analytics and artificial intelligence (AI).

It entails implementing AI into conventional analytics, often in the context of machine learning (ML) and natural language processing (NLP). Augmented analytics differs from conventional analytics or BI methods in that ML algorithms are constantly learning and improving outcomes behind the scenes.

Here are the main benefits of adopting augmented analytics to deal with data.

Democratization of Data

Thanks to augmented analytics, data becomes available to everyone. With augmented analytics solutions, companies won’t need data scientists or IT. Instead, these solutions come pre-built with models, algorithms, and user-friendly interfaces, making them simple to understand for all types of users.

Faster Decision-Making

Augmented analytics will suggest the datasets to use in tests, notify users when datasets are modified, and offer new datasets if users aren’t getting the desired results. With only one click, all users can get detailed forecasts and projections dependent on historical evidence.

Automated Recommendations

Augmented analytics platforms have advanced to the extent that they can provide automated recommendations. These platforms usually feature NLP, allowing users who lack technical knowledge to easily ask questions from source data by using simple business terminology. Natural language generation (NLG) then automates translating complex data into text with intelligent recommendations, thereby accelerating analytic insights.

The software will locate and query the appropriate data, resulting in easy-to-understand results using visualization tools or natural language production. Automated data enrichment and visualization recommendations will allow any user to uncover unseen patterns and predict trends to optimize the time it takes to go from data to insights to decisions.

Becoming Data-Driven…

To keep up with their competitors, companies have to be able to adjust to change and predict possible outcomes rapidly. Data and analytics have become the core of any task, ranging from understanding sales trends to segmenting customers based on their online behaviors and predicting how much inventory to hold. Companies that can understand and manipulate their data will gain access to specific insights that offer distinct advantages

Traditional analytics solutions can take your business only so far. As structured and unstructured data increases, it is in every business’s interest to make more intelligent decisions than to operate on anecdotal evidence and limited data. Augmented analytics is still in its early growth phase, but companies can already start reaping its benefits now by teaming up with BI partners that use advanced tools.

At HICO-Group, we offer a holistic approach to customer solutions for business intelligence. From KPI identification and planning to building future-proof data solutions, we implement state-of-the-art BI concepts customized for your company that meet your business and market needs. We are masters in working with KPIs. Let’s chat.


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