Augmented Analytics

Augmented analytics is the use of machine learning (ML) and natural language processing (NLP) to enhance data analytics, data sharing and business intelligence
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.

  • Machine learning
  • Natural-language generation
  • Automating insights

Machine Learning


Natural-Language Generation

When it comes to Gartner’s definition, there are three key components that businesses should understand:
Machine Learning is a field of artificial intelligence that “is based on algorithms that can learn from data without relying on rules-based programming,” according to this McKinsey article.
Put another way, machine learning programs are capable of adapting to different uses without being explicitly programmed to do so.
In practice, this means machines process a ton of data until they get really good at completing tasks — much in the same way people learn and become more proficient when they gain more experience in a subject or field.
“A dataset contains different photos of wine and beer, labeled as such. The machine processes this data, identifying patterns between all of the wine images and beer images. The machine builds an algorithm based on those patterns to identify which images are wine or beer. This algorithm can then be applied to different data sets, without labels. The algorithm is then tested again and again, and its accuracy improves over time.“
Over time, the machine gets better at identifying which images are wine and which images are beer, making fewer mistakes the longer it learns.
This rather low-stakes example has massive implications for the world of business. Machines analyze data by selecting and building algorithms that can process more data with a higher degree of accuracy than humans can.
In addition to classifying some of the world’s most important beverages, machine learning can apply statistical models to business data and identify trends that directly impact your bottom line.
But augmented analytics is more than just machine learning. Natural-language generation really takes this technology to the next level.

Natural-language generation (NLG) refers to the process that translates a machine’s findings into words and phrases that humans can understand.
Specifically, NLG focuses on the output of data analysis. When a system finds that sales are down in a certain category, NLG enables the system to tell you, directly: “Sales in Category A declined by 30 percent.”
NLG is a vital partner to machine learning because it enables the average, non-technical person to understand what’s occurring in your data.
It’s not just about communicating data trends effectively; it’s about transforming intangible algorithms into something human, so business users can internalize and apply the insights they’re receiving.
That said, the value of natural language isn’t solely limited to generating insights. Some augmented analytics platforms apply natural language to their search functions so business people can ask questions like “What were sales in 2018 by category?” and receive an answer in the form of a visualization.
Scatterplot Augmented Analytics

in other words, business users can phrase questions in the same way they’d address a colleague. The implications of natural language and NLG mean that augmented analytics solutions have the potential to facilitate a conversation between a user and the machine.
These platforms aren’t just telling you what your data means. They’re prompting you to ask for more information.
When we read “Sales in Category A declined by 30 percent,” we react with follow-up questions and hypotheses of our own. We may think “Why did sales in Category A decline?” or wonder “What were sales in Category A in 2018 compared to 2017?”
With natural language search, users can ask those follow-up questions directly (though the degree to which augmented analytics platforms can support “why” questions varies greatly). Plus, augmented analytics solutions can enable users to drill down into the specifics of their insights to, for example, gain more detailed information about each category.
Automating Insights
Data-driven insights determine business strategy.
The combination of machine learning and NLG allows businesses to automate the labor-intensive process of analyzing data and communicating important findings to business people.
These automated insights can then be leveraged to assess your performance and overall brand health, identify growth pockets and opportunities, and determine a holistic understanding of how your brands compare to the marketplace. All of these factors contribute to a solid business strategy.
Ultimately, this automation leads to insights that are driven by algorithms that would otherwise require a significant investment of time and energy from technical members of your team.
As such, augmented analytics democratizes data, so data scientists and analysts aren’t the only people on your team who can make sense of the results.
Augmented Analytics

That said, automation isn’t limited to routine reporting. Rather, automated insights have the capacity to lift enormous amounts of data and determine the root causes of your business’s trajectory.
It’s one thing to ask “What are sales for Category A?” Many business intelligence tools that leverage augmented analytics can easily and quickly answer this question. Questions like “Why are sales declining for Category A?” are much more complex, requiring more processing power and machine learning capabilities — capabilities that are on the forefront of modern advancements in data and analytics.
When the steps toward these “why” answers can be automated, business people can operate off of insights that truly get to the heart of their business. At the end of the day, insights that identify causes are more actionable because they point business users in the direction with the greatest possible impact.

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