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What is augmented analytics?

The simplest augmented analytics definition? Augmented analytics are analytics that are “augmented” with artificial intelligence (AI) technologies, including machine learning and natural language processing (NLP). Machine learning automates complex analytics processes – like data preparation and insight generation. And NLP lets any user, even untrained business users, ask questions of their data and get answers in an easy, conversational way.


The term “augmented analytics” was coined by Gartner in 2017 and is now widely thought to be the future of business intelligence (BI) and data analytics – including predictive analytics.

Why are augmented analytics important?

Mining the Big Data opportunity


Data represents the biggest opportunity in the modern economy. With it, businesses can know what to produce when, who to market to, how to evolve, and so much more. But the volume of data today is too massive for humans to interpret on their own – or without bias – and the requirement for immediate answers is simply impossible to meet. Enabling technologies like AI and machine learning are needed to uncover meaningful insights in a sea of Big Data. This is one of the reasons why augmented analytics are so important: they combine data science and artificial intelligence to help companies analyse massive data sets in real time.


Reducing reliance on data scientists


The analytics process is a series of manual, time-consuming steps so complicated that usually only data scientists can perform them. These professional analysts need to:

  1. Collect data from multiple sources
  2. Prepare it for analysis
  3. Perform the analysis
  4. Find meaningful insights
  5. Visualise findings
  6. Share findings in a compelling way
  7. Create an action plan

The problem is, there is a major shortage of data scientists worldwide – and hiring them is expensive. While augmented analytics don’t replace these professionals, they can reduce your reliance on them by automating processes like data collection, preparation, cleansing, and analysis.


In addition to freeing up data scientists’ time for more important tasks, like interpreting results, augmented analytics can enhance the value these analysts bring to your organisation. AI- and machine learning-powered analytics help them make connections they otherwise may have missed – and find deeper insights in less time. These technologies can also empower employees in other analytics roles – from business analysts to citizen data scientists – improving their insights and helping them do the work formerly only performed by expert data scientists.

By 2025, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organisations.


Democratizing analytics for untrained users


Another reason augmented analytics are so important is that they let untrained “information explorers” in on the game. By automating complex analytics processes and allowing users to query data simply by asking questions, employees without data science skills can leverage advanced analytics. Machine learning can guide these information explorers by recommending which question they should ask next – and suggesting where to dig deeper. 


With augmented analytics, answers to queries come in the form of ready-made data visualisations, like charts, graphs, and maps – so users don’t have to create them themselves. These visualisations can be investigated with simple commands, strung together into data stories, and easily shared with other teams and leadership – no PhD required.

The evolution of analytics

Analytics and business intelligence have come a long way in recent years – evolving from sophisticated tools for data and analytics professionals to machine learning analytics that anyone can use.


Traditional analytics

  • Driven by IT
  • Limited user autonomy
  • Sophisticated tools for data and analytics professionals
  • Focus on reporting at scale

Self-service analytics

  • Driven by the business
  • More user autonomy
  • User-friendly interface
  • Focus on user-driven insights

Augmented analytics

  • Driven by AI and machine learning
  • True user autonomy
  • AI tools and guided processes
  • Focus on fast, deep, previously hidden insights

Use cases for augmented analytics

Augmented analytics have the power to revolutionise business processes – but what does this look like in the real world? Here are a few examples of use cases for augmented analytics in finance, sales and marketing, logistics, human resources, and accounts receivable.


Augmented analytics in finance
A business analyst can use augmented analytics to easily forecast and control travel and entertainment (T&E) expenses across different lines of business.


Augmented analytics in accounts receivable
Collections managers can use machine learning in augmented analytics to predict late payments, determine the right collections strategy, and stay on top of cash flow.


Augmented analytics in sales and marketing
Sales and marketing teams are empowered with better customer profiles – and rapid identification of cross and up-sell opportunities – using augmented analytics.


Augmented analytics in manufacturing
An analyst for a steel manufacturer can use augmented analytics to predict, monitor, and control spend across different factories throughout Europe.


Augmented analytics in HR
HR managers can predict employee churn, understand reasons why, and take corrective action to retain top performers – all with AI analytics.


Discover cloud analytics solutions

See augmented analytics features in action and how businesses are using them.

Analytics glossary and related terms

Augmented intelligence is the practice of augmenting human intelligence with artificial intelligence (AI). Rather than the science fiction portrayal of machines replacing humans, augmented intelligence focuses on AI’s assistive role in helping people learn, make decisions, and innovate.

Conversational analytics are analytics that use conversational AI technologies – namely natural language processing (NLP) and natural language generation (NLG) – to give machines the ability to understand human speech, process text or voice queries, and provide answers in a conversational way.

Natural language processing is a branch of conversational AI that allows computers to understand written or spoken human language. In the context of augmented analytics, NLP lets users query data by asking questions in a natural way, either by typing them or speaking them aloud.

Natural language generation is a branch of conversational AI that allows computers to transform data into written or spoken human language. In the context of augmented analytics, NLG answers user queries by generating phrases that describe, summarize, or explain results.

Advanced analytics is a type of data science that uses sophisticated techniques and tools – including Big Data and predictive analytics – to forecast future events, behaviours, and trends. Augmented analytics enhances these already advanced capabilities with AI, machine learning, and natural language processing – automating complex predictive modelling tasks and making it easier for all types of users to uncover forward-looking insights.

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