By Pete Reilly
Share this post
Augmented analytics refers to the combination of machine learning and natural language generation that automates the production of meaningful insights by business analytics software.
Coined by Gartner, the term “augmented analytics” has gained traction as business intelligence (BI) tools increasingly leverage AI to streamline tasks for the end user.
Businesses now have the choice to pursue augmented analytics in their BI tools, and there’s certainly incentive to do so.
Whether for building more efficiency into your data analysis process, equipping business people with tools that can answer their data-based questions in seconds, or edging ahead of your competition, adopting augmented analytics can be a strategically wise decision.
The simple, yet comprehensive, definition of augmented analytics
As mentioned above, augmented analytics automates tasks by invoking a combination of machine learning and natural language generation.
In layman’s terms:
- Machine learning uses algorithms to parse through your data and identify patterns, trends, and relationships. For example, machine learning can evaluate your data and determine the key drivers behind your hard numbers, so you can better understand what factors are contributing to and detracting from your brand health.
- Natural language generation (NLG) refers to the insights you receive when you ask a question or run a machine learning program. Machine learning operates in programming languages like R and Python, but NLG translates the algorithms’ findings into plain language. For example, NLG could tell you “Brand A was up 1.97 million units over last year, supported by a market volume increase of 8.6%.”
Together, NLP, AI/machine learning, and NLG automate the process of data analysis so that users are simply asking questions and receiving answers. This process can be accomplished in seconds, compared to the hours of labor that data scientists and/or data analysts would be required to perform.
That’s one of the amazing things about machines — they can evaluate data much faster and easier than a human. As a result, people can spend more time working on the truly subjective, interpretive aspects of data analysis, such as setting business strategy against the results of your automated analysis.
How augmented analytics impacts business
As BI tools continue adopting augmented analytics, it’s important to know what to look for in a solution. Plus, it never hurts to know the latest developments in your industry to ensure you’re staying ahead of your competition.
Specifically, we’ll walk through ways in which augmented analytics can be incorporated into these tools.
Ultimately, when someone performs data analysis, they’re trying to find the answer to a question.
This question could be simple and straightforward, like “What were sales last year by channel and region?”.
These kinds of questions seek out facts and hard numbers, and they’re usually the precursor to more advanced questions like, “Why did sales increase last quarter?” and “How can we grow market share next year?”.
BI tools that incorporate augmented analytics can automate these questions to varying degrees.
For example, a user can type a question into a search box and receive an answer in natural language, accompanied by a visualization and insights.
Some tools allow users to type their questions in natural language as well, just as they would write a query for a search engine like Google. This ability is called natural language processing (NLP), not to be confused with NLG— many augmented analytics solutions lack NLP features.
When looking for an augmented analytics solution, it’s important to consider whether your users will be able to ask questions using only keyword search or whether they would benefit from an experience afforded by natural language. This distinction is the difference between typing “sales + increase + Q3 2018” and “why did sales increase last quarter.”
Further, there are stark distinctions between tools that can address the “what” questions and those that can tackle “why” and “how” questions. The latter apply more advanced machine learning algorithms to determine the relationships between your data and provide nuanced, multi-tiered answers.
Visualizations of your data
Once a user asks a question, it’s important they receive an answer that makes sense. Most BI tools employ colorful visualizations so data can be presented in a digestible, friendly way. Where augmented analytics shines is the automatic generation of these visualizations and accompanying insights.
For context, many BI tools require technical users to build dashboards. These tools are designed for data scientists to present findings back to business people.
With augmented analytics, creating dashboards and visualizations is simple for the business user. Users don’t need to input data to create graphs, regional maps, scatter plots, and pivot tables; instead, an augmented analytics solution draws on data that’s relevant to a user’s natural language query and creates an intelligent visualization in seconds.
These visualizations will, of course, vary in their flexibility and interactivity.
For example, interactive visualizations allow the user to click on interesting data points and drill down into more information on that specific point. This kind of interface encourages follow-up questions so that business people can work through a question organically.
Additionally, flexible models enable users to tweak and adjust visualizations so that they’re presentation-worthy and tailored to your audience.
Advanced data insights
To accompany your visuals, augmented analytics is also uniquely capable of producing comprehensive insights into your data. Augmented analytics can analyze your data quickly, investigating metrics across every level and dimension to find drivers and the root cause of the answers you receive.
Insights can range from an overall summary of your brand health to a more in-depth diagnosis of how your most important metrics — like total sales — are impacted by other variables in your business, such as penetration, market share, regional sales performance, and sales by category.
Plus, the advantage of NLG is its ability to tell you, directly, what your data says about your business.
Leaders in augmented analytics have leveraged NLG to not only provide answers to your questions, but to also offer highlights related to your query— notable trends or other factors that are influencing the end result.
With full insight into your data, it’s much easier to understand and address the reasons behind your performance.
Augmented analytics solutions can only work as well as they’re implemented, which means businesses need to identify a platform that can accommodate the unique needs of their data, whether it lives on-prem and/or in the cloud.
It’s also important to consider a solution’s adaptability for an increasingly digital world, where data is a hot and volatile topic, and new technologies develop quickly.
With online channels gaining more importance and traction, businesses have the potential to acquire more data than they have before. This data influx is an opportunity to acquire more insights into consumers, but it also presents a challenge as companies must determine how to host and structure this data.
Plus, with developments like the GDPR, companies that leverage e-commerce and online communication channels must contend with changing legislation when they decide how to capture online data— meaning an agile solution is key.
As such, augmented analytics needs to anticipate the complex data structures that multichannel sales and marketing now require.
Data is one of your most valuable assets.
Augmented analytics has the power to help businesses make the most of such data, but not every solution that incorporates augmented analytics will be able to address your needs.
Though many different tools can invoke augmented analytics to answer questions, be mindful of differentiators we’ve discussed, such as:
- Whether the tool can accommodate natural language processing in addition to NLG.
- Whether the tool can answer “why” and “how” questions in addition to “what” questions.
- How flexible and interactive your data visualizations will be.
- What kinds of insights are generated and how in-depth and actionable these insights are.
- How the solution will be implemented to accommodate complex data sets.