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.
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?
As a bleeding-edge technology, the benefits of augmented analytics are still emerging. Nonetheless, the leading vendors in analytics are already offering practical applications that have significant real-world use:
“Building approaches to automatically extract smarter insights out of data is a crucial step in data science evolution," says Olga Tataryntseva, Data Scientist at ELEKS. "Augmented analytics, as soon as it is ready for real-life adoption, will be able to generate conclusions and observations out of your data with minimal efforts and resources."
Nevertheless, there are still multiple tasks, requiring a deep understanding of the business context, objectives and constraints, which even robust automation methods won't be able to handle effectively. Using augmented data analytics at the beginning of the problem-solving process can be highly recommended, but afterwards, the real work of a data science expert is going to start.
It is important to note that even when augmented with machine learning and AI, data analytics can never improve on poor data. Data that is fundamentally incorrect, collected in a biased manner or otherwise unreliable will not be improved by augmented analytics. Good data sources, through data collection and practising data hygiene, is, therefore, the first step.
Next, enterprises should always involve expert oversight when it comes to data analysis, even if augmented by AI and machine learning. Don’t have in-house data scientists? Consider bringing on board a vendor with data analytics experience.
At ELEKS we know how advanced data analytics can benefit enterprise data insight. Contact us to accelerate your enterprise data analytics.
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