February 4, 2021

Augmented Analytics – combining artificial intelligence (AI) and analytics is one of the latest innovative developments in the data analytics industry. For businesses, data analysis has progressed beyond simply hiring data scientists, to incorporating smart technology that provides clear insights that directly influence decision making, thanks to AI.

Augmented Analytics or AI-driven analytics, supports organisations in determining hard-to-find patterns in large data sets and reveals patterns and actionable insights. It utilises a combination of analytics, machine learning and natural language generation to automate data management processes and help with more complicated elements of analytics.

A study by Gartner suggests that by the end of 2024, 75% of businesses will rely on AI and generate a forecasted 5x increase in streaming data and analytics services. The potential of AI will enable businesses to enhance their internal data-driven decision making while enabling all members to have easier access to the data. AI can save data scientists, analysts and other data professionals considerable amounts of time spent on repetitive manual tasks.


The progression in the AI industry plays an important role in making businesses more efficient and capable with the support of automation. With the support of ML algorithms, AI can automatically measure data and reveal hidden patterns and insights that can be applied to the decision-making process. AI automates the report generation process and enables data to be easier to understand by applying Natural Language Generation. Using Natural Language technology means AI enables all members of the business to discover the information and extract important insights from data efficiently.

While traditional BI utilised rule-based systems to generate static analytical reports augment analytics uses AI techniques to automate data analysis and visualisation. Machine Learning uses the data to determine trends, patterns and relationships between different data sets. It can apply past events to make the necessary changes.

Augmented analytics can apply user queries to create answers in the text and visual formats. This process of data generation is automated and allows non-technical users to understand data and detect insights.
Business Intelligence can support better business decisions and improve ROI by simply gathering and processing information. An efficient BI system collects important data from various sources and generates actionable insights. Augmented analytics will improve BI and support businesses in several ways:


Data analysts generally spend a lot of time extracting and cleaning up data. Augmented analytics eliminates the time spent on these processes by automating time-consuming tasks and generating valuable insights that can be applied for analysis.


Once data is ready for processing, augmented analytics can automatically generate insights. Using ML algorithms, it can automate processes and generate insights that generally take much longer to be completed by data scientists.


Augmented analytics will make it simpler for users to make queries and communicate with data sources. With the support of NLG, it can convert natural language into machine language and then generate useful insights in a much simpler language. This allows businesses to ask questions regarding their data and get answers in real-time.


The ability to query data makes data much more accessible for everyone in an organisation to use analytics products. Businesses no longer necessarily require data scientists or technical professionals to use BI tools and understand their data.

The level of complexity and scale of data now being generated and used by businesses has reached a level that is simply not manageable by humans. Organisations are embracing the development of AI in analytics to manage data and improve overall processes. Augmented analytics is enabling this movement and applying it with BI platforms is allowing businesses to interpret data quicker and as a result, enhance their operation and make their data teams more effective.

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