What Are Augmented Analytics? And How to Get Started

Learn more about augmented analytics—data analysis assisted by AI, ML, and NLP to help make data generation, preparation, recommendations, and analysis more efficient.

Best Practices
July 20, 2023
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Darshil Gandhi
Principal Product Marketing Manager, Amplitude
Augmented analytics blog illustration with line shapes

There’s a way to make your job easier—and it’s likely that your competitors are already doing it.

The secret is augmented analytics.

And, it’s used by one out of three businesses today, according to Gartner.

Augmented analytics can automate and improve all kinds of business tasks, from segmenting customers to forecasting sales, to driving better business results. Here’s everything you need to know to leverage these tools to drive better business results.

What are augmented analytics?

Augmented analytics combines data generation, preparation, and analysis assisted by artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to help make data analysis and recommendations more efficient and effective. This type of approach to analytics helps users find insights and identify patterns in their data quicker. It also helps predict future trends so you can make data-driven decisions about your organization.

For example, say you’re analyzing customer churn risk and find that there’s a seasonal risk of customers leaving when they have to adjust their budgets for the next fiscal year. You could reach out proactively to minimize that churn by offering a discount and trying to improve their customer experience.

How augmented analytics works

Augmented analytics provides extra insight into an organization’s data, whether that’s customer behavior, data from A/B testing, or sales activity over a certain period of time.

The technologies used within augmented analytics include machine learning, natural language processing, data visualization tools, and artificial intelligence:

  • Machine learning analyzes data and identifies patterns to make predictions or insights about the data. 
  • Natural language processing tools read through customer feedback, like reviews and surveys, to search for words that indicate trends in customer sentiment.
  • Data visualization tools present the insights in an easy-to-understand dashboard format.

While AI and ML are frequently used interchangeably, they’re different concepts. Machine learning is a type of artificial intelligence, so while all ML is AI, all AI is not ML.

Challenges and benefits of augmented analytics

If you’re not taking advantage of augmented analytics yet, then you’re missing out on features that can help your organization in numerous ways.

  • Enabling faster, data-driven decisions: Augmented analytics tools can easily shed light on data trends that need to be addressed. If you spot that customers abandon their carts when they reach the shipping fee information, you might try offering free shipping when they reach a certain threshold of spending.
  • Reducing human error: Have you ever entered a lot of numbers into a spreadsheet, then realized you put in the wrong data? Using AI data tools can help reduce the risk of human error, which can lead to unreliable insights.
  • Saving time and money: With AI, your team won’t have to spend a lot of time manually searching through data. AI and ML tools also make it easier for people to access data when they need information—meaning they won’t have to wait on your data analysts to compile a report.
  • Managing data storage efficiently: Augmented analytics tools can both manage your data volume and secure customer information in compliance with GDPR and other data privacy laws.
  • Increasing data visibility: Tools offering augmented analytics make data more accessible to everyone—you don’t need deep knowledge or data analysis skills to find insights. But they also help analysts and data scientists by making their jobs easier, as they can perform data preparation for them and free up time to work on other tasks.

However, these types of analytics offerings do come with challenges, which you’ll want to consider before implementation.

  • Addressing people’s misconceptions: Many still think AI and ML will take over their jobs—when it’s really there to augment their work and make their jobs easier. Educate your employees about the uses and benefits of these tools to achieve buy-in.
  • Finding a balance between AI and humans: AI and ML can handle mundane tasks that can be automated, but they can’t do everything. Humans still need to step in to provide context and critical thinking. Make a plan to determine how your employees and augmented data analytics softwares can best work together.
  • Using poor-quality data: You’ve probably heard the phrase, “garbage in, garbage out.” If you have poor quality raw data and don’t have a solid data governance policy in place, you’ll feed the AI bad data, which can lead to bad decisions. Make sure to clean your data so that you have the best quality possible.

Use cases for augmented analytics

Augmented analytics are used across industries and departments to help drive change in customer relationships, business processes, decision-making, and more.

  • Marketing: Marketers can gain deep insight into customer preferences, buying behavior, and the effectiveness of marketing campaigns.
  • Fraud detection: Augmented analytics can detect patterns in large data sets so organizations can act quickly.
  • Maintenance: AI can analyze data from machines and equipment to spot potential issues and alert maintenance teams.
  • Sales: Your sales teams can use AI to look at trends throughout the year, or to take a deeper dive into the sales pipeline to see where potential leads stop engaging with your team.
  • Supply chain: You can use AI to analyze your supply chain and spot ways to improve delivery times or to ship products more efficiently.

Augmented analytics best practices

To adopt augmented analytics successfully, make sure to consider these best practices as you create your strategy and select your platform.

  • Choose the analytics tools that are best for your business: Research your available options and work with all internal stakeholders to choose a platform offering augmented analytics that will help align with your business goals, and one that has all the functionality that you need. If you’re working with a limited budget, try some tools that offer free options, like Amplitude.
  • Show success with one project first: Let everyone see that your first AI project is successful before implementing it throughout the organization. It’s easier to get buy-in when you can provide people with concrete results.
  • Collaborate with other teams: Talk to other teams throughout the process to see where the tools are providing the best results. Collaborate with those teams to find out why and how you can tweak the AI algorithms so everyone can benefit from those successes.

Augment your data analysis with Amplitude

If you’re looking for an out-of-the-box analytics solution to help your data analysis process go as smoothly as possible, look no further. Amplitude can help you explore and gain valuable insights from all types of business data, from customer segmentation to A/B testing.

Sign up for free today to start exploring our platform and see how our capabilities can help your organization use augmented analytics to make better business decisions.

About the Author
Image of Darshil Gandhi
Darshil Gandhi
Principal Product Marketing Manager, Amplitude
Darshil Gandhi is a Principal Product Marketing Manager at Amplitude. He leads global technical marketing and collaborates with product and go-to-market teams on strategy, positioning, messaging, campaigns, and enablement. He was previously a solutions consulting team principal, and has helped dozens of Amplitude customers turn their data into actionable insights. He enjoys showcasing the power of Amplitude's platform to connect digital bets into valuable business outcomes. Darshil graduated from Dartmouth College with a Masters in Engineering Management.

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