Augmented Analytics: What it is, why it matters, and best practices

Explore this in-depth guide to AI analytics strategies,examples, and technologies to learn how artificial intelligence in data analytics can help your organization.

What is augmented analytics?

Augmented analytics uses artificial intelligence (AI) and machine learning to enhance analytics across all phases of the data lifecycle — from the way data is prepared, to how analysis is performed and insights are delivered. A combination of data science and artificial intelligence, augmented analytics makes data analytics accessible for more people to get value from data, allowing them to ask questions and automatically generate insights in an easy, conversational manner.

An example of augmented analytics in action

Let’s say a sales leader wants to gather insights around the cost of sales and the performance of her team. Augmented analytics capabilities found in business intelligence software can help her more efficiently gain the insights she needs.

Analyze Regional Performance

See how natural language search makes it easy for a sales leader to compare sales and margin by country.

Compare Sales Rep Performance

See how a sales leader uses search-based analytics to easily evaluate performance for individual sales reps.

In both cases, the business person uses natural language processing to type a question into their BI and analytics platform. Data science and artificial intelligence immediately go to work, considering both structured and unstructured data, as well the search terms, to display the most relevant results, including visual representations. The user then has the opportunity to explore angles of that data they’ve never considered before to help make the best business decisions.

What are the benefits of augmented analytics?

The most effective augmented analytics combines the best aspects of machine intelligence and human curiosity to help users get faster insights, consider data from unique angles, increase productivity and help users of all skill levels make better decisions based on AI analytics. So if you’re evaluating data science vs data analytics, keep augmented analytics in mind.

  1. Delivers value faster: When data science and artificial intelligence come together, the result is faster data preparation, speedy visualization, accelerated insights and higher productivity. On the data prep side, algorithms replace manual processes, and automatically recommend associations between different data sources, as well as suggestions for cleaning up data. When it comes to data discovery, a quick drag-and- drop auto-generates bar charts, maps, KPI objects and other visualizations based on the data you choose.

  2. Uncovers hidden possibilities: With prior BI tools, users would have needed an idea, or a hypothesis around the kinds of insights they wanted to uncover. But with AI analytics, the algorithms do the work, providing contextual suggestions that uncover insights users never thought they needed. By surfacing relationships, correlations and outliers, data science and artificial intelligence help users build confidence as they’re guided through the process of making their own discoveries.

  3. Increases trust: Every time a user interacts with data, they provide clues to machine learning algorithms about their role, skill set, business context and intent. Over time, algorithms provide more relevant and accurate suggestions and interactions based on these clues, increasing user trust in data. And, because people play a role in the analytical process, rather than simply accepting insights that come from a black box, that trust grows even stronger, facilitating buy-in and wider adoption of analytics in the organization.

  4. Increases data literacy: As businesses continue to collect massive amounts of data, it’s important that everyone, regardless of analytics skills, has the opportunity to gain value from that data. AI analytics can promote data literacy by automatically surfacing insights, making recommendations, and empowering all users to confidently take action on their data. Because users can easily search for insights using natural language, and visualize insights with very little effort, creating a data literate workforce becomes far more accessible.

Key capabilities of augmented analytics

From automation and data discovery to contextual insight suggestions and conversational analytics, augmented analytics enhance business intelligence processes in many valuable ways:

  • Task automation: AI can help you get to insights faster by automating routine tasks related to data preparation, analysis and visualization. For instance, when a user wants to gain insights, machine learning helps to clean and prepare data, find patterns and relationships, auto-generate code, suggest insights and create visualizations.

  • Context-aware insight suggestions: When analytics takes into account user intent and behaviors, the insights generated are context-aware and highly relevant. Based on the questions users ask, the machine points them toward new ways of looking at data, and hidden insights they might have never seen otherwise.

  • Conversational analytics: Conversational analytics provides a quick, easy way for users of any skill level to uncover insights simply by asking questions and getting answers in natural language. As a user types or speaks, related data fields are displayed which suggest and validate what the user wants to uncover. Each time a question is asked, algorithms present relevant charts, graphs and information to help users gain insights faster.

Key challenges of augmented analytics

Here are some of the biggest barriers organizations face in adopting augmented analytics.

  1. Accuracy and trust: Ensure the insights your tools generate are accurate and trustworthy. This means using comprehensive data that is free of errors and updating models as your data changes.

  2. Relevancy: Users don’t have time to filter out irrelevant information. Test out your data to ensure that searches yield relevant results. If not, users will stop using the tools because they won’t provide value.

  3. Training data quality: If you don’t have the right data to train your analytical models, your insights won’t be worth much. Maintain and update models to keep insights quality high.

  4. Performance and scalability: Depending on your platform and capabilities, augmented analytics could take a lot of computing power. Also, keep in mind that the volume of data significantly affects response time.

  5. Data bias: Bias is typically caused by incomplete data sets and lack of context. Be sure you have context built in so algorithms can analyze all of your data and provide more objective results.

Augmented analytics best practices

Set your AI analytics initiatives up for success with these best practices.

  • Start small and align KPIs: Your data doesn’t have to be perfect to get started with data science and artificial intelligence. Instead, choose a use case that is aligned with your KPIs and has high business value. Once you see success, celebrate it and move on to larger projects.

  • Collaborate to build trust: One of the biggest sources of mistrust in AI is lack of transparency. Avoid the black box by inviting workers from across the organization to be a part of your analytics initiatives so they can build trust through insights.

  • Encourage a data-driven culture: As more people in your organization begin to use analytics, you should make sure they have the strategies and training they need to get the most from your company’s valuable data assets.

Learn more about augmented analytics

Frequently Asked Questions

What is augmented analytics?

Augmented analytics uses AI and machine learning to enhance human curiosity, making it easier for business users to prepare, analyze and visualize their data. Users gain insights faster by exploring their data using conversational language, while algorithms provide contextual suggestions for relevant insights.

How can businesses benefit from augmented analytics?

Because AI analytics automatically suggests insights based on natural language, users can get what they need faster, speeding up time to value. And, when it’s easy to search and visualize insights, more people can access analytics, increasing data literacy across the organization.

How does augmented analytics work?

AI automates repetitive tasks like preparing data, recognizing patterns, generating code and creating visualizations. Augmented analytics also automatically provides suggestions for insights that users may not have considered by analyzing user behaviors and intent. And with the help of conversational analytics, users can quickly gain insights by getting answers to their data questions in natural language.

What are best practices in augmented analytics?

Rather than waiting for your data to be perfect, you can get started with artificial intelligence in data analytics now. Choose small, high-value projects that support your business KPIs, and celebrate wins to demonstrate value. Collaborate with co-workers across business functions to promote transparency and build trust through insights. And give workers the tools and training they need to be successful with artificial intelligence data analytics.

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A best-in-class, self-service business intelligence architecture is just one way Qlik Sense® sets the benchmark for next-generation data analytics technologies. Our one-of-a-kind associative analytics engine and sophisticated AI empowers people at all skill levels to freely explore data, make bigger discoveries and uncover bolder insights that can’t be obtained using other analytics tools. With Qlik, you can support nearly any use case and massively scale users and data, empowering everyone in your organization to make better decisions every day.