Navigating the Future of AI at Yellowfin: Innovation with Care

In this blog, Yellowfin GM Chance Coble discusses the integration of artificial intelligence into our embedded analytics suite, including how we balance innovative features with a responsible approach to the rapidly-evolving fields of AI technologies.

At Yellowfin, our philosophy toward artificial intelligence (AI) in business intelligence (BI) solutions is grounded in a deep commitment to innovation and responsibility.

As technology continues to rapidly evolve, we are eager to harness AI's potential in our embedded analytics platform to create simpler, more intuitive ways for your business and end-users to achieve their analytics objectives. However, with great technological power comes the responsibility to proceed thoughtfully and with balance, which guides our journey into AI analytics solutions, ensuring that we deliver our customers real value without compromising on trust and security.

The benefits of AI in Yellowfin BI

The integration of AI into business intelligence solutions is often referred to as augmented analytics or AI analytics. The transformative power of leveraging AI in reporting processes is significantly revolutionizing how organizations access, analyze, and interpret their data. From uncovering trends to automating insights, the possibilities are endless. Yellowfin has integrated AI into many of our exclusive features for years, including automation in Signals, machine learning (ML) in Assisted Insights, and natural language query in Guided NLQ, and our users have unlocked several benefits to aid in their data discovery and decision-making processes.

Here are some general examples of the benefits well-integrated AI can bring to your analytics process and solution:

Faster data preparation

AI-assisted data preparation significantly accelerates the data integration process. By leveraging advanced algorithms, AI-powered BI solutions can automatically detect data schemas and identify optimal join conditions. This automation extends to repetitive data transformations and integrations, streamlining workflows and minimizing manual effort. Furthermore, analytics solutions that do use AI for the data preparation process allows your users to proactively generate and leverage recommendations for enhancing data quality and enriching datasets.

Before embarking on data preparation, AI analytics tools can also automate the profiling, tagging, and annotation of data sources, ensuring data cleanliness and reliability for subsequent analysis. This means a big reduction in time and resources traditionally required for manual data prep.

Increased time-to-insight

By employing sophisticated machine learning (ML) algorithms, AI analytics solutions can automate the identification of hidden patterns within your data. Your analysts are no longer burdened with manually testing numerous data combinations, as AI-driven BI systems can automatically detect correlations, clusters, outliers, and significant segments. The most statistically significant findings are then presented through intuitive and insightful data visualizations, optimized for further analysis and action by data analysts.

Certain analytics vendors also leverage AI technologies, such as natural language, to make the process of querying data and discovering relevant insights far easier than traditional methods. For example, Yellowfin leverages natural language query with its Guided NLQ feature to automatically generate suggestions and filters for a user trying to ask a question about their data, ensuring they form the right type of question to get the type of answer they seek (an example is shared below).

Instant market differentiation

Even if you already offer robust reporting capability within your software application, there's no guarantee of your users engaging with these features or finding value.

Traditional reporting often presents a significant barrier to entry, as users are typically expected to independently navigate dashboards and reports, manually sifting through data to uncover critical insights. The effectiveness of this approach is inherently limited by individual skill levels, knowledge, and available time. AI analytics transforms this process by automating the heavy lifting of data exploration. It eliminates the time-consuming manual search through vast and complex datasets, proactively alerting users to anomalies and significant changes that might otherwise go unnoticed. This automation optimizes analytical workflows, allowing users to focus on higher-value tasks and gain deeper insights, and find value in your analytics module faster.

While this is a great selling point for AI analytics, like any powerful tool, AI must be adopted with care. Yellowfin recognizes the need to align AI capabilities with your analytics and business intelligence goals, data compliance and data governance requirements, and industry-specific data privacy standards, in order to enhance your end-users’ decision-making potential.

The challenges of AI in business intelligence

While the excitement surrounding AI is palpable, ensuring AI integration enhances - not jeopardizes - the integrity of your data and decision-making process is critical. 

Selecting the right solution, then, requires an understanding of the current challenges in integrating AI with business intelligence.

In the race to incorporate AI analytics and augmented analytics features into business workflows, unintended consequences can arise, such as inaccurate insights, data vulnerabilities, and an over-reliance on automated decisions. 

Data quality and bias

AI algorithms are only as good as the data they are trained on. Data quality issues, such as inaccuracies, inconsistencies, and missing values, can significantly impact the accuracy and reliability of any AI-driven insights you generate. Inherent biases within historical data can also be amplified by AI models. Before you can adopt an AI analytics solution, you need to address these challenges with robust data governance practices, including data cleaning, validation, and ongoing monitoring for bias. If you’re unsure about where to start with your data quality initiatives, it’s best to learn whether the AI solution vendor you are assessing can also provide assistance with your general integration of embedded analytics into your existing software solution.

Integration and scalability

Integrating AI capabilities into your existing application can be complex and challenging. Technical hurdles, such as compatibility issues and data silos, can hinder seamless integration and limit the full potential of your chosen AI-driven solution. Additionally, scaling AI models to handle large volumes of data and support real-time analysis can be computationally expensive and resource-intensive. Overcoming these challenges requires robust infrastructure, skilled personnel, and a well-defined AI strategy that considers the specific needs and resources of the organization. Most importantly, it requires selecting the right BI vendor that offers full support for your journey into leveraging sophisticated AI solutions.

User adoption

Implementing AI-powered BI solutions requires significant organizational change, including adjustments to workflows, skill development, and cultural shifts. Resistance to change from your employees and your product users can hinder the successful adoption of AI technologies. It is important to implement change management strategies to address concerns, build buy-in, and ensure smooth integration of AI into your existing business processes (enterprise) and product user experience (product owners). This may involve providing comprehensive training, clear communication, and demonstrating the tangible benefits of your adopted AI-driven tools.

How Yellowfin is approaching AI analytics solutions

At Yellowfin, we take these risks seriously. That’s why we refuse to release any AI-driven augmented analytics feature that doesn’t meet our rigorous standards for accuracy, security, and reliability. Our goal is to ensure that every AI capability we introduce empowers our users, while maintaining the highest standards of trust and transparency.

With this caution in mind, you will see we’re excited to share that Yellowfin is actively expanding its AI capabilities to make your data experience even more seamless and intuitive. You will soon see AI enhancing the experience of our already powerful feature Guided NLQ with AI-enabled NLQ that provides your users with instant, actionable insights from more intuitive interrogations. 

You also will see AI-driven enhancements to our Assisted Insights feature, bringing more power to quickly finding the insights in the data you need to know. And we have already released new AI-powered chatbot assistants, in the form of Ask Yellowfin and Code Assistant as part of our recent Yellowfin 9.14 product release, that allow users to quickly get help on how to use Yellowfin and that even write code in a variety of programming languages to accelerate our API usage for developer integration.

Conclusion

The offering of AI tools in Yellowfin is about balance. While we’re excited about the potential of these new features, we also understand the importance of adopting AI responsibly. That’s why we’re investing not just in developing AI-driven tools, but also in ensuring they are trustworthy, secure, and transparent. 

Our mission is to empower you with the most cutting-edge tools without compromising on security, accuracy, or reliability. As we continue to expand our AI capabilities, we’ll remain committed to providing robust support and best practices to help you navigate this rapidly evolving landscape.

About the author

Chance Coble is the General Manager (GM) of Yellowfin BI and former President of Blacklight Solutions, an applied analytics consulting company. As a founding member of Yellowfin North America, Chance helped spearhead the introduction of Yellowfin into the US market through his tenure at Blacklight Solutions, with decades of experience in the data analytics industry. Chance has extensive experience in consulting with Fortune 500 companies and international organizations, helping them solve complex business problems through data-driven analytics for over 15 years. Coble holds a Bachelor of Science in Computer Science from The University of Texas at Austin and a Master of Science in Biomedical Informatics from The University of Texas at Houston.