Augmented analytics is trending. You’ve read about it, you’ve heard about it, you may even be in the process of acquiring systems running it. But what exactly is it, and how can you recognize it? As the guys building augmented analytics, we’re here to dispel some of the hype.
On the highest level, augmented analytics is the machine learning processes geared at making data more accessible and actionable for both data scientists and business users.
If we step back to get a broad view of the evolution of data, augmented analytics are the next logical step. In a nutshell, first there was big data, then data science and BI were brought on to tackle big data. As big data got bigger and AI matured, ML-based techniques were used to make the analysis of big data more powerful, faster and accessible to everyone.
These are augmented analytics, and here’s how they do it.
Flipping the Analytics Pareto
Data drives the modern business world, but getting insights from data is traditionally an excruciating process, demanding unique expertise and ample resources. But even experienced data scientists who rely on traditional analytics, spend 80 percent of their time handling the data, and only 20 percent of their time on actually deriving insights that can propel their business forward. Cleansing, preparing, transfering the data through ETLs, building dashboards, setting thresholds — the sheer manipulation of data required to get it to “talk” is overwhelming.
Augmented analytics is set to flip that 80/20 ratio on its head, and then some. By relying on advanced machine learning, augmented analytics autonomously takes care of “back-end” data processes. Data from multiple sources is independently consolidated into one warehouse. Thresholds are set with no human in the loop. Correlations and root cause analysis are autonomously analyzed across metrics. This enables augmented analytics platforms to provide businesses the full value of data insights, without the associated time-consuming data handling.
Even Better Insights
Augmented analytics are the love child of Machine Learning (ML) and Neural Networks. Over the past decade, these two families of algorithms have matured to the point that they can derive deep insights from vast amounts of data in real time.
The benefits here are three-fold:
- Deep insights: algorithms that learn by themselves are infinitely more insightful than those which rely on more structured rules, because they adapt to the dynamic nature of data and can correlate between massive numbers of data metrics and sets. For example, an augmented analytics monitoring solution will “understand” that a spike in sales around Black Friday is seasonal, it will also “know” that if the spike isn’t steep enough it should alert the appropriate stakeholders, and it will also report on the causes of this anomaly—all this without having been explicitly programmed with thresholds, seasonal patterns etc.
- Vast amounts of data: traditionally, businesses monitor only ~5% of their business data. Their data insights can be only as wide as their dashboard allow. The scope of augmented analytics enables it to actively analyze all the data generated and processed by the business. Shining a light on the business’s “dark data” is a powerful way to leverage data assets that are traditionally non-actionable, but often conceal powerful intelligence.
- In real time: augmented analytics are geared at delivering insights in real time. By bypassing manual data manipulations, this technology can be used in extremely time-sensitive scenarios, when every second counts.
One of the greatest benefits of augmented analytics is making data accessible to everyone. Gone are the days when business users needed to rely on IT for reports or dashboards. Augmented analytics leverage Natural Language Processing and Explainable AI to enable users with no data science experience to analyze and query data. This democratizes data literacy, extending analysis from expert data scientists to other professionals, coined in this context by Gartner “citizen data scientists.”
In this vein, Gartner predicts that with the proliferation of augmented analytics, “by 2021, NLP and conversational analytics will boost analytics and BI adoption from 35% of employees to over 50%, including new classes of users, particularly front-office workers.” By the same year, “50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.”
Twice the Business Value
The ultimate goal of augmented analytics is to empower businesses to leverage more of their data to make better decisions, faster.
This new generation of analytics and BI platforms — characterized by AI/ML automation of the insight discovery, exploration, explanation, prediction and prescription processes — significantly reduces time-consuming data handling. When compared to visual-based manual exploration, the breadth and scope of augmented analytics also creates a qualitative edge by dramatically reducing the risk of missing important insights in the data, helping optimize resulting decisions and actions, and increasing the number of data-driven decision-makers within the business. Advanced BI platforms already offer these augmented capabilities, which are forecasted by Gartner to deliver twice the business value compared to traditional BI — as soon as next year.