What is AI Analytics?

Imagine your software transforming from merely a tool into a strategic partner that can automatically alert your users to trends, provide explanations of data with a click, and help you ask the right questions of your data-sets - in addition to delivering data-led insights. This is the power of AI analytics solutions for independent software vendors (ISV). Today's users demand more than just functionality. They crave intelligent software that analyzes data, surfaces insights, and empowers them to act. Yet, traditional analytics often fall short, lacking depth, intuitiveness or guidance needed to truly engage and drive business value. This leaves your existing solution at risk of stagnation and user apathy. Today, many embedded analytics solutions are disrupting the status quo with unique and sophisticated capabilities that aim to streamline the process of data monitoring and analysis for the average user. For ISVs, it’s crucial to understand what this emergent field of AI analytics brings, and how it can benefit your users' experiences moving forward.

What is AI analytics?

AI analytics refers to the process of integrating artificial intelligence (AI) technologies, including machine learning (ML) algorithms and deep learning (DL) networks, into business intelligence (BI) solutions to transform how end-users embed, analyze and share their data and insights. The AI analytics approach enables businesses and organizations to better uncover insights, patterns, and trends that might not be visible through traditional, manual analytical methods. For instance, automation is typically integrated into features within analytics platforms to help your end-users continuously monitor performance, detect patterns in their operational data and alert them of significant statistical changes. Machine learning and natural language processing are also commonly leveraged to generate helpful answers to dashboard reports or results in a way that any analytics user, whether they are a line-of-business person or analyst, can use in their analysis workflow. Whatever the use case, AI analytics provides both experts and non-experts with ways to:

• Understand what happened
• Learn how it happened
• Evaluate why it happened
• Evaluate what can happen next

For more insight into what AI and its many related technologies look like in an analytics platform, we recommend watching our video AMA (Ask me Anything) below.

AI analytics vs augmented analytics: What’s the difference?

The topic of AI analytics goes by other terms, depending on the audience and industry. The most popular term within the BI industry of the last few years has been augmented analytics, which encompasses the suite of AI-powered technologies that have been implemented into business intelligence (BI) and analytics solutions over the past decade. An end-user who leverages analytics and BI tools with AI-powered features is called an augmented consumer, though this term is also mostly used within the analytics industry. For laymen, the terms ‘AI analytics’ and ‘AI-powered analytics’ have arisen in usage as ISVs continue to investigate their options for improving their software with better data capabilities.

What does AI analytics consist of?

A BI or data analytics solution that offers AI analytics is one that leverages artificial intelligence, machine learning, natural language, data mining, predictive analytics, and big data technologies within its feature-set to help enhance decision-making, automate processes, and provide deeper explanation and guidance around insights for users. Yellowfin is one such solution that incorporates several AI-powered technologies within its feature-set. In the next section, we cover three of the most commonly used technologies, and how they are represented in Yellowfin to provide an illustrated example.  

1. Automated business monitoring (ABM)

Automated business monitoring (ABM) is the use of analytics platforms to automatically analyze data and deliver relevant insights to users. This helps businesses identify potential problems early on, improve their efficiency, and gain new insights from their data. ABM often consists of multiple AI technologies, including machine learning and natural language processing, to run its analysis and produce easy-to-understand results for analytics users of all skill levels. Yellowfin has ABM implemented in its suite via its Signals feature, which continuously monitors and auto-alerts your users to significant statistical deviations or patterns to stay ahead of potential insights. Watch the video below to learn more about what Signals can bring to your software and end-user experience.

2. Machine learning (ML)

Machine learning (ML) is a subset of artificial intelligence (AI) enabling software to improve automatically through experience by identifying patterns in data. For independent software vendors, ML offers a way to enhance applications with predictive analytics, automation, and personalized user experiences without explicit programming for each task. Yellowfin employs the use of machine learning in several of its features, with Assisted Insights being the prime example. This feature combines machine-learning with automation and human insight to help end-users get to the ‘why’ behind a dataset faster, by automatically generating immediately usable answers. A user simply has to click on a data visualization or element in a Yellowfin dashboard report, select ‘Auto-Analyze’ and choose either 'Compare' or 'Explain'. Yellowfin will finds the data to analyze, runs it through a series of steps to find the most statistically relevant results, and present answers in an easy-to-understand, best-practice visualization and narrative. To learn about Assisted Insights, watch our walkthrough demo video below.

3. Natural language query (NLQ)

Natural language query (NLQ) is a technology that enables users to interact with data and systems using everyday language, allowing for intuitive search, analysis, and commands without requiring specific technical or query language knowledge, making complex data queries accessible and straightforward for a broad audience. It allows ISVs to enable their applications with the capability for users to interact with advanced analysis features using everyday language, facilitating their search through relevant data without needing specialized query syntax (jargon), enhancing the overall user experience and accessibility to complex data insights. Yellowfin Guided NLQ is a feature unique to Yellowfin that further simplifies the way analytics users ask questions of their data. Using our exclusive AI NLQ mode, this feature allows users to ask questions in natural language (non-jargon) of their data using free text, as they would in any typical search engine or generative AI tool, and have the tool proactively and automatically structure the question for their with the proper syntax, data filters, and dimensions, eliminating guesswork, providing accurate and relevant answers, and greatly improving accessibility to data insights for the average user, regardless of skill level. AI NLQ also has a Suggested Questions feature which lets any user generate a list of helpful prompts to start their data query journey, should they not know what to ask. Watch our walkthrough demo video below for an in-depth look into how AI NLQ works.

What are the top benefits of AI analytics?

By integrating AI into analytics, companies can leverage automation and machine learning to process vast amounts of data at unprecedented speed, improving operational efficiency, customer experiences, and strategic initiatives. With tech such as natural language and deep learning, your analytics can help guide your exploration and querying of data with easy to generate suggestions, explanations and comparisons that don't use technical jargon and can be understood by anyone. Overall, AI analytics technology is applicable across various industries, including finance, healthcare, retail, and more, offering significant competitive advantages for ISVs in multiple sectors by enabling data-driven decisions and personalized customer interactions. It represents a pivotal advancement in how data is utilized, transforming raw data into actionable intelligence.

Why is AI analytics important for ISVs?

AI analytics offers a powerful tool for independent software vendors by simplifying the analysis of vast amounts of data to uncover valuable insights. Essentially, by adopting a BI and analytics solution with AI capability, your product’s embedded analytics act as an intelligent system that can help users of all skill levels to identify trends, predict future demands, and gain guided decision-making processes with precision. This technology is particularly beneficial for ISVs as it enables you to better understand customer behaviors, optimize product features, and innovate more effectively. By leveraging AI analytics, ISVs can enhance their product offerings, tailor their solutions to meet specific customer needs, and ultimately gain a competitive edge in the market. For ISVs, integrating AI analytics into their operations means you can make informed decisions faster, streamline their development processes, and deliver superior value to your customers, all without requiring deep technical expertise in data analysis.