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.
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.