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From Theory to Practice: 3 Applications of Augmented Analytics

From Theory to Practice: 3 Applications of Augmented Analytics
By Pete Reilly

Augmented analytics refers to the combination of machine learning and natural language generation (NLG) to automate insights.

In practice, this means augmented analytics can analyze an entire data warehouse to answer business questions quickly and thoroughly.

Machine learning algorithms parse through data, identifying connections and influences that drive the data. NLG produces easy-to-understand insights in plain language.

Together, machine learning and NLG effectively automate insight generation for business users, not just data analysts. Business users get their questions answered directly with deep insights that point them toward important actions, meaning that the applications of augmented analytics are diverse and can ripple through different departments.

Let’s discuss the big-picture impact of augmented analytics applications.

1. Answer complex “why” and “how” questions.

Data analysis is composed of many questions, from “what are sales by category and geography” to “compare costs by dealer from January to May.” 

Beneath these “what” questions are the larger questions of “how” and “why.” 

In terms of business outcomes, understanding why costs have increased or how a brand performed can get employees closer to actions with bottom-line impact. If businesses have objectives like acquiring more customers, understanding the “why” and “how” underneath current customer acquisition metrics will be more meaningful than a report of what those metrics are.

Ultimately, it’s understanding “why” that leads to data-driven action.

Augmented analytics solutions can answer these questions with the power of machine learning and NLG. Because machine learning algorithms can test every assumption in the data, they can identify which factors are truly driving the numbers on the surface.

For example, machine learning algorithms can determine which brands drive the most growth and influence sales value to the highest degree.

As such, machine learning can identify a tree of metric connections so that business people can better understand the nuances of performance and which focus areas can generate the largest impact.

NLG also plays a significant role by contextualizing various data insights into a cohesive narrative. It’s one thing to say that sales increased by 11%. It’s another to understand which brands contributed to this increase and how negative fluctuations in market share detracted from potential growth. Without the full narrative, it would be easy for a business user to see the 11% increase and miss the bigger picture.

2. Automate data analysis and insight generation.

Because machine learning handles the bulk of the analysis, and NLG generates insights in plain language, augmented analytics automates meaningful insights.

In practice, augmented analytics democratizes analytics for the business user.

Natural language processing allows users to type or ask questions in plain language, like the “why” and “how” questions mentioned above. With this interface, business users can leverage augmented analytics without help from a data analyst.

This is a critical application of augmented analytics because of its benefits to both technical and non-technical team members:

  • Business users can get the answers they need, quickly and independently. With valuable insights in hand, they can spend time setting data-driven strategy and executing work instead of waiting on routine reports or making decisions on gut feelings.

  • Data and analytics professionals don’t have to spend their time running repetitive analysis and can instead focus on more complex machine learning models and data security standards, for example.

As a whole, the organization operates more efficiently. Insights are produced in seconds, relieving bottlenecks. Employees have direct access to the unbiased, accurate information they need to succeed in their roles.

Businesses can quickly identify growth opportunities and avoid pitfalls.

A truly future-proof augmented analytics solution can elevate automation to proactive insight generation as well. In this instance, the machine constantly analyzes data to keep users informed about meaningful changes. Essentially, the solution works without users having to ask questions or otherwise trigger analysis because the AI understands what matters.

These applications are well beyond the scope of traditional business intelligence solutions.

3. Understand performance over time— and in the future.

Augmented analytics can leverage the power of machine learning to illustrate trends over time and forecast future performance.

Trend insights monitor performance to pinpoint fluctuations and anomalies in the data. Since augmented analytics centralizes data from disparate sources, trend analysis can be performed across the timespan of the data warehouse. Where gaps appear, a good augmented analytics solution should allow further analysis of null values to help the user understand why the data may be missing.

Forecasting, too, leverages an existing data warehouse to make predictions. By searching data with AI and machine learning algorithms, augmented analytics can measure the impact of key decisions before they’re made.

Machine learning can also support “what if” scenarios, where a user asks questions like “What if we raised the price of our product by 10% in secondary markets?” The algorithm helps the user simulate the scenario to understand impact on P&L. From this understanding, business people can zero in on the possibilities with the greatest ROI.

Ultimately, augmented analytics helps users make better decisions with insights. These three examples are only some of the ways businesses are benefiting from these solutions.

 

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