One of the latest developments for business intelligence tools is the rise of augmented analytics. We’ll cover the basics of augmented analytics, a combination of machine learning and natural language generation, in this guide.
When we talk about augmented analytics, we’d be remiss if we didn’t begin with its origin.
In 2017, Gartner published a report that introduced the concept of augmented analytics, defining it as “an approach that automates insights using machine learning and natural-language generation.”
Gartner further distinguishes augmented analytics as “the next wave of disruption in the data and analytics market” that “data and analytics leaders should plan to adopt.”
It’s clear that Gartner holds augmented analytics in high esteem, so let’s talk about why it’s so important and what it actually means for business analytics.
What Does Augmented Analytics Refer To?
In the world of business intelligence, augmented analytics solutions have been gaining traction. In practical terms, augmented analytics is designed to facilitate more growth and help generate more revenue.
For example, this quote from the AnswerRocket CPG Analytics guide discusses how augmented analytics solutions can impact the consumer packaged goods industry:
“With the right solution, you should be able to investigate your sales pipeline to track your leads, selling stages, average time to close, and more. On the marketing side, you need the ability to perform churn analytics to find changes in purchase tendencies and save at-risk customers. “
All of these tasks can be accomplished with augmented analytics solutions, but how exactly do these solutions work?
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.
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.
Which brings us to the third crucial aspect of augmented analytics: 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.
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.
How is Augmented Analytics Unique?
Augmented analytics platforms crunch data much faster than a person could, without the same degree of human bias.
To contrast against a typical business intelligence model, data analysts usually approach data by testing their theories and hypotheses; in doing so, they’re operating on a premise of knowledge. Data analysts are, of course, knowledgeable, but people will always be limited in some capacity by their viewpoints.
It’s much more challenging to draw a comprehensive, unbiased and completely accurate conclusion without awareness of every factor that could be influencing the results.
This means many businesses are potentially functioning with limited views into their data landscapes, which leaves money on the table.
Augmented analytics, on the other hand, invoke the power of machine learning to process more data at a much faster rate than humans can (compare seconds to weeks). As we discussed before, machine learning works with minimal human interference, which means these algorithms aren’t predisposed to the same bias.
Coupled with NLG, the insights business users receive are truly the most comprehensive look into your data — packaged in a way that’s easy to understand.
As such, augmented analytics empowers business users to act on the insights they receive, freeing up data scientists to focus on much more complex queries.
How Businesses Can Take Advantage of Augmented Analytics
Augmented analytics can benefit businesses in a myriad of ways, so let’s talk specifics:
- Deeper data analysis. Augmented analytics do the heavy lifting for you. By analyzing exhaustive data combinations, augmented analytics can pinpoint which factors are truly influencing your output.
- Faster results. Augmented analytics allow business users to get answers to their questions directly, in a matter of seconds. No longer will they have to wait for data analysts to fill the gap.
- Better use of resources. When your data analysts aren’t bogged down with questions, they have more time to focus on deeper research that machines can’t yet support, providing more bang for your buck.
- Actionable insights. Ultimately, augmented analytics simplify the data analysis process so you can gain important insights into your data that can inform your business strategies.
These benefits are the foundation of a solid business strategy that addresses the ever-changing needs of consumers and shifts in the market.
Plus, they amplify efficiency. Without a long, drawn-out process to get business questions answered, your team can work with more up-to-date and relevant data. Quick, agile analytics are increasingly driving revenue. They can play a key role in edging out competition and, ultimately, propelling your business forward.
About the Author
Prior to AnswerRocket, Pete founded and led Retality, a firm focused on helping companies conceive, build and bring new technologies to market. Before founding Retality, Pete was a founding team member and SVP/GM of BlueCube Software, where he led the Workforce Management business unit before the company was sold to RedPrairie. BlueCube was a spin-out from Radiant Systems, where Pete spent eight years driving the development and market introduction of new products at the company. Pete got his start at Accenture working with Global 2000 organizations. Pete has a B.S. in Computer Science/Economics from Union College in Schenectady, NY.