Preparing and analysing data is a remorselessly repetitive task, ripe for automation. Data analysis is a natural home for machine learning and AI algorithms that can automatically prepare, analyse and interpret data. The result is augmented analytics, a fast and accessible way to draw data insights.

The problem with data analytics

Vast pools of data are one of the signs of our times but turning large amounts of data into meaningful insight has so far remained a challenge. Data science experts commonly spend a large proportion of their time – up to 80% – managing and preparing data. But what if this routine work is automated? What if machine learning algorithms can prepare, analyse and interpret data automatically? Enter augmented analytics.

Understanding augmented analytics

Augmented analytics uses a mix of machine learning and artificial intelligence algorithms to automate the data analysis process. An augmented analytics platform can automatically discover data, prepare data, and analyse data with minimal human intervention.

It is important to distinguish the new wave of augmented analytics from existing systems that aid data analysis. Yes, numerous solutions exist that can “support” data analysis by providing visual aids and by making analytical tasks easier. Instead, augmented analytics automate difficult tasks that ordinarily still require data scientists:

Pattern recognition. Just because data exists does not mean that analysing that data will deliver actionable insights. Pattern recognition involves sorting through batches of data to find the data sets that carry useful information and to eliminate data that is merely noise. Augmented analytics can automatically detect strong data signals.

Insight generation. Significant facts do not automatically equate to insight. For example, knowing that regional sales have increased is useful, but knowing why this occurred can make all the difference. Machine learning algorithms help automate the process of understanding what exactly it is your data is telling you about your organisation.

Augmented analytics takes the legwork out of data manipulation and analysis. Instead, your data scientists can focus on interpreting insights and turning these insights into actions. But how will this benefit enterprises in the real world?