What it is, why it matters, and best practices. This guide provides definitions, examples and practical advice to help you understand and practice modern AI analytics.
Augmented analytics refers to a type of analytics which uses artificial intelligence (AI) and machine learning (ML) to make it easier for business users to prepare, analyze and get insights from their data. It also helps data scientists by automating many of the tasks involved in developing, managing and deploying AI and ML models.
First, let's start with some definitions. Machine Learning (ML) is a type of AI which automates analytical model building by allowing software to learn from historical data, identify patterns, and make predictions and decisions with little to no human guidance. AutoML (automated machine learning) refers to the tools and processes which make it easier to build, train, deploy and serve custom ML models.
Data analytics and machine learning
ML models are the basis for most augmented analytics capabilities. They’re the source for insight recommendations and conversational, search-based analytics. They’re also the engine for cleaning and prepping the data, reducing errors and saving countless hours of manual work in curating tables, text, images and videos.
The best BI and data analytics platforms go a step further by integrating a robust AutoML capability into their solution. This allows you to build custom ML models and then explore interactive visualizations and dashboards with predictive insights based on the models you develop.
Human intuition is still required.
Most business problems can’t be solved purely by algorithms or machine learning. They require human interaction and perspective. Augmented analytics is a combination of human curiosity and artificial intelligence, making BI and data analytics accessible to more people by allowing them to ask questions and automatically generate insights from their data, through simple conversational and visual interactions. Ultimately, this brings deeper insights and data-driven decisions which improve a company’s performance.
Use this checklist when you’re evaluating data analytics platforms to make sure you get the most possible value from AI.
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.
See how natural language search makes it easy for a sales leader to compare sales and margin by country.
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.
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.
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.
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.
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.
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.
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.
Explainable AI (XAI) refers to a set of techniques and processes that help you understand the rationale behind the output of your ML or AutoML algorithm. With XAI, you can improve and debug your models, meet regulatory requirements, and have more trust in your AI models’ decisions and predictions.
See how to explore information and quickly gain insights.
Here are some of the biggest barriers organizations face in adopting augmented analytics.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.