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How Will Augmented Analytics Evolve?

Over the last year, my colleague Rich Clayton and I have been noodling around the idea of an Augmented Analytics maturity model. By every measure, organizations have begun to adopt AI- and ML-powered capabilities in everyday business intelligence and analytics. In fact, I believe that augmented capabilities will propel many firms to break through the 35 percent adoption barrier by turning BI outside-in—making it work the way you do rather than making you work the way the BI tool works.

The fundamentals of augmented analytics are pretty straightforward. Gartner's definition from its IT Glossary is as follows:

"Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment."
Source: Gartner.com

A big part of augmented is also Natural Language Query/Processing (NLP/NLQ) and Natural Language Generation (NLG) that creates dynamic narratives to describe what's happening in your data.

AI Means Applied and Invisible

In order for augmented analytics to progress, it has to become part of the fabric of your data and analytics regimen. That means it's not something you invoke, but something that just happens. That means the system will offer you recommendations, make the complex easy, suggest options you hadn't thought of. For many, that's the dream; for others, it's too much of a "black box"—they need to know what's happening at every stage. These divergent opinions mean that adoption (and acceptance) will take time to mature. And it needs to be applied automatically and invisible to users.

From Data to Insight to Action

Analytic workflows start when the first bit of data is captured to when action is taken to achieve your desired outcome. That's a pervasive flow that incorporates everything from data management through business applications. Augmented analytics plays a key role in this entire stream.

So, how will augmented analytics evolve? In our opinion, it will follow a typical Capabilities Maturity Model (CMM) six-stage progression:

Augmented analytics evolution

Source: Oracle Corporation


Level 0 – Artisan: Everything is hand-crafted, much like it has been for decades.

Level 1 – Self Service: Data management is still largely manual, but the human interaction with data will be enabled with Natural Language Query, one-click statistics for easy forecasting, outliers, etc. and recommended visualizations based on type of data.

Level 2 – Deeper Insights: Early stages of augmented data management appear (recommended sources, joins, crowdsourced suggestions, smart cataloging), and augmented discovery uncovers insights that do the heavy lifting, so you don't have to.

Level 3 - Data Foundation: The second wave of augmented data management begins, with corrections, and enrichments. Analytic automation hits its stride with narratives at every level—a visualization, a canvas, a data set.

Level 4 – Collective Intelligence: The system becomes instrumented and learns patterns of metrics and key performance indications (KPIs) that alert when conditions need attention. These are both business KPIs and system KPIs. Insights become pervasive, business intent goes from an idea to a reality, and outcomes are predicted, actions are recommended—but humans still take the action.

Level 5 – Autonomous Action: Everything truly becomes data-driven, with next best actions executed based on predictions, insights, and intent. The system is the engine of change.

The analogy to this is autonomous vehicles. Smart capabilities gradually extend to assist the driver, expanding over time, with the eventual goal that the "driver" is the machine. While we are a long way from "driverless" completely controlling data and analytics, there are situations where it already exists:

  • Real-time offers that are served up automatically to buyers online, all driven by rules and algorithms.
  • Programmatic trading in investments, where algorithms trigger buy and sell orders to public markets.
  • Self-tuning, self-managing databases, as in Oracle Autonomous Data Warehouse, that automates many activities.

Like any maturity progression, it's not always a strict "step by step". You may adopt capabilities in a different order or choose to bypass some capabilities all-together. So, no worries if this doesn't reflect your current journey or future plan.

What do you think?

I'd love to hear your ideas about Augmented Analytics maturity. Do you agree? Have a difference of opinion? We'll continue to refine this over time, with your input along the way.

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Comments ( 1 )
  • jimjiang Monday, October 28, 2019
    Good point.
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