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What’s up everyone, today we have the pleasure of sitting down with Michael Rumiantsau, Co-Founder and CEO at Narrative BI.

Summary: Michael’s on a mission to make data insights accessible and useful for everyone, not just experts, by leveraging AI to provide tailored, easy-to-understand insights that boost decision-making. This episode unpacks the future of Business Intelligence, automating insights with LLMs, and the importance of anomaly detection. Michael also discusses how proprietary data gives companies a competitive edge in the AI market by refining models and creating tailored solutions, while well-structured data sources enhance natural language query tools. Anomaly detection is crucial for quickly identifying issues and uncovering new opportunities, with tools like Narrative BI automating alerts for unusual patterns, reducing the need for constant monitoring, and enabling more strategic decisions. Michael explains how Narrative BI, an augmented analytics platform, not only presents data but also provides context, explains trends, and suggests actionable steps, helping marketers focus on significant changes and improve performance.

Jump to a Section

• Deciding When to Commit Fully to Your Startup
• The Future of Business Intelligence and Dashbaords
• AI’s Role in Democratizing Data for Knowledge Workers
• Enhancing Data Value with Semantic Layers
• ChatGPT’s Limitation with Data Analysis
• Importance of Anomaly Detection for Marketers
• The Attribution Dilemma in Marketing
• A Faster Way To Uncover Why a Key Metric is Down

About Michael

• Michael started his career as an electronics engineer and then a backend software engineer where he dived into web dev, db management and API integrations
• He later took on the challenge of being CTO at an IT startup called Flatlogic based in Belarus
• He then moved to San Francisco and founded a web and mobile dev consultancy which he ran alongside co-founding a natural language search startup called FriendlyData with a mission of democratizing access to data 
• He went through 500 Startups, a VC seed fund acceleration program
• FriendlyData was acquired by ServiceNow in less than 3 years and Michael went on to join the company in a central product role to help develop their Natural Query Language AI tool
• He’s also an investor at founders.ai, a startup platform for disruptive SaaS products
• His latest entrepreneurial endeavor is Narrative BI, a generative analytics platform that helps growth teams turn raw data into actionable narratives

Deciding When to Commit Fully to Your Startup Idea

Starting a business varies greatly depending on personal circumstances. Michael explains that while it might be easier for a young, single entrepreneur to take the plunge, it’s a different story for someone with a family. Despite these differences, one thing is clear: at some point, you must go all in. Without full commitment, building something substantial is unlikely.

Michael highlights the need to have “skin in the game.” This means demonstrating serious commitment, which can convince others to support you. Investors, for example, are more likely to back someone who has shown they are fully invested. For Michael, this commitment meant leaving a secure, high-paying job and investing his own money into his venture, Narrative BI.

Michael’s story shows the kind of dedication required. He left behind a seven-figure salary to pursue his startup. This kind of personal risk can be a powerful motivator and a strong signal to potential investors and team members. Making the transition from a stable job to a startup isn’t just a career move; it’s a significant life decision that requires careful thought and total commitment.

Key takeaway: Prioritize a clear plan to balance your personal life and startup demands. Before you dive fully into your business, outline how you will manage family responsibilities, financial stability, and your well-being. This preparation can make your transition smoother and demonstrate your commitment to potential investors and team members.

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Encouraging Entrepreneurial Spirit in Employees

Michael isn’t on his first entrepreneurial venture. He believes expecting startup employees to match a founder’s dedication is unrealistic. Founders often work around the clock due to their significant equity stakes, but employees with smaller shares shouldn’t be pressured to do the same.

Michael values his employees’ time and boundaries. He doesn’t track how many hours they work, focusing instead on their contributions. This approach creates a healthier work environment, where employees feel appreciated for their results, not just their hours.

He also encourages side hustles. For Michael, these ventures aren’t distractions; they’re sources of valuable experience that can benefit the company. His small team of eight includes individuals with diverse entrepreneurial backgrounds, with many already engaged in other income-generating activities. Michael sees this diversity as an advantage, bringing fresh ideas and perspectives to the company. This is a refreshing perspective coming from a founder and not shared by everyone. Shopify CEO for example is well known for discouraging side hustles and expects unshared attention from his team.

Michael takes pride in his employees’ entrepreneurial efforts. If someone leaves to start their own company, he sees it as a success and supports them fully. By fostering an entrepreneurial spirit, he believes his team becomes more innovative and motivated.

Key takeaway: Seek out employers who encourage side hustles. Working for a company that values and supports your entrepreneurial efforts can lead to greater innovation and personal growth. This environment respects your time and contributions, fostering a healthier and more motivated work culture.

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The Future of Business Intelligence and Dashboards

BI is here to stay. Michael points out that despite its $30 billion market size and growing influence, BI tools are still primarily designed for data specialists. In even the most advanced tech companies, adoption rates hover around 20-25%, leaving a vast majority of knowledge workers without direct access to valuable data insights.

Michael sees a significant opportunity in democratizing BI. He believes every knowledge worker should access data insights, regardless of their technical background. This can be achieved through automated or AI-generated insights, making data more accessible to those who make critical business decisions but lack deep data expertise.

Discussing dashboards, Michael notes their static nature as a limitation. Traditional dashboards rely on predefined metrics and queries, which can miss the nuances of a constantly evolving business environment. The static approach often results in overlooked insights that could be pivotal.

Michael envisions a future where BI tools are dynamic, AI-powered, and user-friendly. This would allow real-time insights tailored to specific roles and individuals, enhancing decision-making processes across all organizational levels. By enabling a broader audience to harness the power of data, the potential impact of BI could be far greater than ever imagined.

Key takeaway: Advocate for integrating user-friendly, AI-powered BI tools in your organization. By pushing for tools that offer real-time, dynamic insights tailored to various roles, you can ensure that valuable data reaches every decision-maker, not just data specialists. This approach can significantly enhance your team’s ability to make informed decisions and stay agile in a rapidly changing business landscape.

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AI’s Role in Democratizing Data for Knowledge Workers

Michael acknowledges that while BI tools are a boon for data enthusiasts, their complexity often hinders wider adoption among knowledge workers. Even with advanced natural language query tools, users need to understand database structures, table names, and relationships. This level of data literacy is uncommon among marketers and executives, creating a significant barrier.

AI offers a promising solution to this challenge by proactively generating insights. Instead of waiting for users to ask specific questions, AI can analyze data trends and patterns to provide personalized insights tailored to individual roles or teams. This approach reduces the need for deep technical knowledge and makes data more accessible to everyone in the organization.

Michael highlights that modern AI-enabled solutions can process vast amounts of data and deliver relevant insights automatically. By personalizing these insights based on past behavior and preferences, AI can make BI tools more user-friendly and valuable to non-technical users. This proactive, personalized approach could drive higher adoption rates and make data-driven decision-making a standard practice across all levels of a company.

The evolution of large language models has made implementing natural language queries easier, but AI’s true potential lies in its ability to anticipate user needs and provide actionable insights without requiring specific queries. This shift towards AI-driven, personalized insights could revolutionize how knowledge workers interact with data, making BI tools indispensable in their daily workflows.

Simplifying Analytics for Marketers

Marketers often lack the technical skills to navigate complex BI tools. Michael emphasizes that while many marketers can benefit from analytics tools, they typically rely on data engineers or analysts to handle more intricate tasks. This dependency creates bottlenecks and delays, especially for basic data inquiries.

At ServiceNow, Michael witnessed these challenges firsthand. He learned that data teams typically handle three types of requests: simple queries, moderately complex aggregations, and deep-dive analyses. Simple queries, like average revenue per user, can be answered in minutes with some simple SQL. More complex tasks involving data aggregation with a few joins may take a few hours, while in-depth research can require weeks of preparation.

Michael believes the first two types of queries can and should be automated. Simple questions that currently require human intervention should be answerable through AI interfaces. For moderately complex queries, even tools like ChatGPT can generate SQL code. By automating these tasks, BI teams can focus on more advanced analyses, providing deeper insights and driving strategic decisions.

Automating Insights for Marketing Teams with LLMs

Michael points out that a big part of a marketer’s job is figuring out what makes their campaigns successful. Tools like Narrative BI help by sending insights directly to marketers via product-led email alerts. These insights often highlight unexpected but valuable information, allowing marketers to focus more on execution rather than spending time figuring out what questions to ask the data team.

He explains that understanding a company’s operations is crucial. Marketers need to know which campaigns perform best and where they can save money or redirect efforts. This often leads to a backlog of questions for BI teams, causing delays in getting actionable insights.

To tackle this, Michael’s team introduced LLM recommendations. This feature is a real breakthrough for marketing teams. With just a click, users receive tailored recommendations without needing to ask specific questions. The tool goes beyond just presenting data; it provides context, explains trends or anomalies, and suggests actionable next steps. This cuts down the time marketers spend analyzing data themselves.

Additionally, the tool’s conversational abilities make it even more user-friendly. Users can interact with the system, asking follow-up questions to get deeper insights or clarification on the recommendations. This interactive feature ensures that marketers can continuously refine their strategies based on real-time data insights, making the tool a crucial asset for any marketing team.

Michael believes that automating routine insights and recommendations allows marketing and growth teams to concentrate on more advanced, impactful tasks. This shift not only saves time for BI teams but also empowers marketers to make swift, data-driven decisions.

Key takeaway: Leverage AI to proactively provide insights to your marketing team. Implement tools that automatically deliver personalized, actionable recommendations, reducing the need for constant data team intervention. This allows your marketers to focus on strategic execution and make quicker, data-driven decisions, enhancing overall efficiency and effectiveness.

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Enhancing Data Value with Semantic Layers

Semantic layers are critical for building accurate natural language query tools. Michael emphasizes their importance, noting that without them, systems lack the accuracy needed for reliable use. Each business has its unique data structure, business rules, and definitions of key metrics like conversions. This variability makes semantic layers indispensable despite the significant time investment required to set them up.

Michael highlights that their approach seeks to avoid the complexities of semantic layers by leveraging popular, well-structured data sources. Tools like GA4, used by millions of websites, have a standardized structure that can be universally applied. Similarly, data from platforms like Facebook Ads and other widely-used tools benefit from this approach. By setting meaningful defaults, Michael’s team minimizes the need for extensive customization for each customer.

While they aim to simplify the process, Michael acknowledges the need for some level of customization. Users can introduce custom metrics, aligning with the semantic layer concept. This flexibility allows businesses to tailor the tool to their specific needs without the heavy lifting typically associated with semantic layers.

An interesting development in their tool is the ability to personalize the user experience based on engagement. Michael mentions large buttons for users to like or dislike particular insights. This feedback feeds into machine learning algorithms, fine-tuning future recommendations to be more relevant. This interactive feature ensures that the tool not only provides insights but also evolves based on user preferences, enhancing its overall utility.

Key takeaway: Integrate feedback mechanisms into your BI tools. Encourage users to like or dislike specific insights, feeding this feedback into machine learning algorithms. This approach personalizes and refines future recommendations, making your BI tools more user-friendly and tailored to your team’s needs, thereby increasing their overall effectiveness and adoption.

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Leveraging Proprietary Data for AI Advantage

Proprietary data offers a significant competitive edge in the AI landscape, much like a chef’s secret recipe creates a dish that stands out from all others. Leveraging unique data makes AI solutions more effective and unique, providing an advantage that others cannot easily replicate.

Michael explains that only a few organizations can afford to build foundational models like GPT-4 due to the immense computational resources required. Companies often need to raise hundreds of millions to create something groundbreaking. However, building basic AI applications or Small Language Models (SLMs) has never been easier, thanks to open-source models and accessible APIs from providers like OpenAI.

The challenge lies in differentiating these AI applications in a crowded market. With many startups using the same underlying tech, the unique factor becomes proprietary data. This data is crucial for refining models and enhancing AI capabilities. 

Michael gives practical examples of how his team uses proprietary data for benchmarking and prompt injection. By benchmarking, they can provide insights based on how customers in specific segments perform. For prompt injection, they tailor prompts for specific customers or market segments, leveraging unique data to offer more relevant and precise outcomes.

Moreover, proprietary data is essential for outperforming generic AI systems in specific domains such as voice recognition, image recognition, or natural language processing. Michael points out that domain-specific proprietary data allows companies to excel in vertical markets. This specialization provides a significant advantage, as it involves data that is not accessible to other players.

Key takeaway: Proprietary data is the key differentiator in the AI market. Leveraging unique data for refining models, benchmarking, and creating tailored solutions allows companies to stand out and excel in specific domains. This focus on proprietary data is crucial for staying competitive and future-proofing their AI initiatives.

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ChatGPT’s Limitation with Data Analysis

When asked about using AI, specifically tools like ChatGPT, for data analysis, Michael emphasized the limitations and challenges. AI systems, especially those designed to generate natural language responses, often struggle with accuracy in data management and analysis. A significant issue is that these systems can hallucinate, producing answers even without reliable data. This tendency to provide confident but incorrect responses creates a trust barrier, making it risky to rely on such tools for critical data analysis tasks.

Michael elaborates on how his team at Narrative BI addresses this issue. They do not use AI for generating insights directly. Instead, they rely on proprietary machine learning technology. This approach ensures more reliable and accurate data analysis. While AI can be incredibly effective for tasks like summarization and natural language processing, its role is more about enhancing the presentation and accessibility of insights rather than generating them from scratch.

For example, Michael uses AI for summarizing large sets of data, such as CSV files or unstructured text. This application is where AI excels, providing concise summaries that are easy to digest. However, for the actual analysis and generation of insights, Narrative BI implements rigorous post-processing steps. This additional layer of verification ensures that the data and insights provided to their clients are accurate and trustworthy.

By focusing on the strengths and limitations of AI, Michael’s team has developed a differentiated approach. They harness the power of AI for what it does best—processing and summarizing information—while relying on more reliable methods for critical data analysis. This strategy not only enhances the quality of their insights but also builds trust with their users, ensuring that the information they receive is both useful and dependable.

Key takeaway: Use AI to summarize and present data, but rely on more reliable methods for critical analysis. By combining AI’s strengths in processing and summarizing information with robust verification processes, you can ensure your insights are accurate and trustworthy, enhancing the quality and reliability of your data-driven decisions.

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Importance of Anomaly Detection for Marketers

Anomaly detection is like spotting a UFO in the night sky; it’s about catching the unexpected and unusual events that can dramatically impact performance.

Michael shared compelling reasons why anomaly detection is crucial for marketers. He recalled numerous instances where this feature saved the day for his clients. For example, one customer noticed a sudden drop in conversions. Upon investigation, they discovered a broken form on a landing page. Thanks to Narrative BI’s alert, they quickly addressed the issue, preventing further loss in conversions.

Anomaly detection is not just about catching problems. Michael explains that it can also uncover valuable opportunities. For instance, tracking how pages are indexed on Google through the Google Search Console can reveal unexpected drops in indexing, prompting timely action. Similarly, a sudden spike in CPC for advertisements can indicate issues that need immediate attention.

Beyond identifying problems, anomaly detection can highlight positive trends. Michael shared how he uses Narrative BI to monitor traffic patterns, especially from sources like GA4 and Google Search Console. If there’s a sudden surge in traffic from an unexpected source, it often signals a mention in a popular blog post or a viral social media share. These insights allow marketers to engage with the new audience, leveraging the opportunity for greater visibility and impact.

The real power of anomaly detection lies in its ability to provide actionable insights promptly. It ensures that marketers are always in the loop, ready to tackle issues or seize opportunities as they arise. This proactive approach is essential in maintaining effective marketing strategies and optimizing performance.

Key takeaway: Implement anomaly detection to catch both issues and opportunities in real-time. Use it not only to identify problems like broken links or sudden drops in performance but also to uncover positive trends, such as unexpected traffic spikes. This proactive monitoring allows you to promptly address issues and capitalize on new opportunities, ensuring your marketing strategies stay effective and optimized.

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Focusing on Growth as a Vertical

When asked why Narrative BI initially focused on growth and marketing data, Michael explained the strategic choice behind it. Specializing in one vertical, rather than spreading efforts across multiple areas, allowed them to create a more refined and impactful product. This lesson came from past experiences, where attempting to build a generic product proved less effective.

Michael highlighted that marketing data is particularly dynamic and closely tied to business outcomes. Key metrics like conversion rates, revenue, and new users frequently change, and these changes often have significant implications for the business. By focusing on these critical metrics, Narrative BI can clearly demonstrate the potential return on investment, making it easier for users to understand and appreciate the tool’s value.

Narrative BI’s approach is to share meaningful insights whenever something noteworthy occurs. This proactive alert system is especially useful for marketers who need to stay on top of fluctuating data. When a sudden change in a key metric occurs, it can indicate important shifts in the business environment. By providing timely notifications, Narrative BI helps marketers respond quickly and effectively.

Although they started with marketing and growth data, Michael shared that Narrative BI aims to become a comprehensive analytics tool for various departments. They have already added integrations with CRM systems and are exploring product analytics. Future plans include expanding to operational and accounting use cases, making Narrative BI a versatile tool for all knowledge workers.

Key takeaway: Specialize in a single vertical when developing a new product. Focusing on one area, such as marketing data, allows you to create a more refined and impactful tool. This approach not only makes it easier to demonstrate ROI to users but also provides meaningful insights and timely alerts, helping you quickly respond to critical changes. Expanding gradually from a strong foundation ensures broader and more effective applications across different departments.

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The Attribution Dilemma in Marketing

Attribution is a persistent challenge for marketers, one that Michael, CEO of Narrative BI, knows all too well. When asked about the role of BI tools in addressing this issue, he acknowledged the complexity and frustration it brings, both in his role and for the broader marketing community. Attribution involves tracking and understanding the source of each new signup or conversion, a task fraught with manual effort and prone to inaccuracies.

Michael explained that their internal approach involves manually pinning points on every new signup to determine its originating channel. They cross-reference this data with their customer systems, such as Intercom, and their own internal database. While effective to some extent, this process is cumbersome and far from foolproof. He also highlighted the risks associated with diving too deeply into attribution, particularly concerning compliance and privacy regulations. For a startup already navigating numerous risks, adding another layer of complexity around attribution isn’t always feasible.

Narrative BI addresses attribution at a surface level by providing impact event insights. This means they can show which specific campaign or action led to a spike in signups or other key metrics, but not at an individual signup level. This approach helps marketers understand the broader impact of their efforts without delving into the granular details that can become overwhelming and risky.

Michael emphasized that while Narrative BI seeks to solve many marketing challenges, they recognize their limitations. Attribution, with its inherent complexities and regulatory concerns, is not a problem they can fully resolve. Instead, they focus on areas where they can make a significant impact, providing valuable insights and actionable data that marketers can use to inform their strategies.

Key takeaway: Streamline your approach to marketing reporting by focusing on impactful incremental events rather than granular touch points. This method allows you to identify which campaigns or actions drive significant results without getting bogged down by the complexities and risks of detailed attribution tracking. This strategy provides valuable insights while maintaining compliance and reducing manual effort.

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Finding Actionable Insights Given the Practical Limitations of Attributions

Attribution in marketing can feel like a political debate. On one side, some believe tracking every touchpoint is unnecessary. They argue that building intricate multi-touch attribution models to assign revenue numbers to numerous interactions before a free trial is a waste of resources. On the other side, some C-level executives demand detailed attribution to justify marketing budgets. These executives want to know the ROI of paid ads, content, and other marketing efforts. This often forces marketing teams to create models, even if they’re imperfect, to show a rough idea of what’s driving revenue.

Michael acknowledges the complexity of attribution, especially as companies grow and diversify their marketing efforts. He notes that each company has a unique customer journey, making it difficult to standardize attribution models. Different channels and even different campaigns within the same company can have varying methods of counting conversions. This inconsistency makes it challenging to create a one-size-fits-all approach to attribution.

At Narrative BI, Michael’s team addresses this by identifying specific campaigns or events that lead to traffic or conversion changes. However, they do not track attribution at an individual level. This approach provides insights into what works and what doesn’t without getting bogged down in the minutiae of every customer interaction. Michael believes this method balances the need for actionable insights with the practical limitations of attribution modeling.

As his company grows and adds more funding, Michael anticipates the need to prove ROI for various campaigns. However, he remains cautious about investing heavily in multi-touch attribution models. Instead, he focuses on overall trends and key events that drive results, ensuring that their efforts align with broader business goals without overcomplicating the process.

Key takeaway: Attribution is a complex and often imperfect process. Marketers should focus on identifying key campaigns and events that drive significant changes in traffic and conversions. Balancing the need for actionable insights with practical limitations can help ensure marketing efforts are both effective and efficient.

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A Faster Way To Uncover Why a Key Metric is Down

When faced with a sudden drop in key metrics like free trials, traffic, or MQLs, marketers often scramble to find the root cause. Michael explained how Narrative BI can streamline this process, making it less daunting and more efficient. Instead of spending half a day digging through data, users can leverage Narrative BI to quickly pinpoint issues and understand their origins.

At its core, Narrative BI doesn’t just highlight that a metric is down; it dives deeper. For example, if conversions drop, the tool shows which channels are driving conversions and which are underperforming. This initial layer of insight already provides a clearer picture, allowing marketers to start addressing the problem more effectively.

For those needing more detailed explanations, Narrative BI offers LLM recommendations. This feature uses AI to identify underlying reasons behind the metric changes and provide actionable suggestions. Whether it’s seasonality, a technical glitch, or a change in user behavior, the tool helps surface these insights, reducing the guesswork for marketing teams.

Another powerful feature is GPT Insights. By integrating OpenAI’s technology, Narrative BI can summarize complex metrics and explain potential anomalies or correlations. This not only saves time but also ensures that marketers have a comprehensive understanding of what’s happening and why. It’s like having a data analyst on hand to provide clarity and direction.

Michael emphasizes that these tools are designed to answer the pressing questions that arise in marketing. By using Narrative BI, marketers can quickly respond to concerns from leadership, back their strategies with data, and focus on driving results rather than getting bogged down in data analysis.

How to Stop Wasting Hours Monitoring Dashboards and Get Alerts When it Matters

Michael emphasizes the goal of Narrative BI: enabling marketers to react to data when it’s truly necessary. Instead of spending countless hours poring over Google Analytics or Search Console dashboards, marketers can leverage automation to focus on significant changes and anomalies. This approach shifts the focus from proactive monitoring to reactive, action-oriented responses.

The traditional method of constantly checking dashboards for unusual patterns is not only time-consuming but often ineffective. Michael points out that many important shifts and trends are missed because they don’t always reflect in everyday static dashboards. By using Narrative BI, marketers can receive alerts only when significant deviations occur, similar to a car’s fuel light that signals when it’s time to refuel. This reduces the mental load on marketers, allowing them to concentrate on strategic initiatives rather than routine data checks.

During times of market turbulence, such as the COVID-19 pandemic, unexpected dynamics can create fluctuations that traditional dashboards may not capture accurately. Narrative BI addresses this by highlighting these anomalies, ensuring that marketers are aware of and can act on these critical changes in real-time. This capability is crucial for maintaining agility and responsiveness in an unpredictable market environment.

Michael explains that their platform’s ability to identify and alert users to unusual patterns means that marketers can spend less time sifting through data and more time implementing strategies based on these insights. This shift not only improves efficiency but also enhances the overall impact of marketing efforts, aligning actions with real-time data rather than historical trends.

Key takeaway: Narrative BI simplifies the process of identifying and understanding significant fluctuations in key metrics. By providing detailed insights, AI-driven recommendations, and comprehensive summaries, the tool empowers marketers to address issues swiftly and effectively, enhancing overall efficiency and strategic decision-making. This also reduces the need for constant dashboard monitoring in place for automating alerts for unusual patterns.

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The Importance of Separating your Personal Identity from your Company’s Success

Michael addresses a fundamental challenge for founders: the struggle to separate personal identity from their company’s success. He highlights the binary nature of startups—either you succeed spectacularly or face failure. This dichotomy, particularly in venture-backed environments, can be predatory, urging founders to “go big or go home.” Such a mindset often leads to stress and dissatisfaction.

Michael shares his strategy to combat this. He maintains strict boundaries, reserving weekends for personal activities like hiking. This separation ensures he doesn’t burn out and retains a sense of personal fulfillment beyond his work. Emphasizing the journey over the destination is crucial in an industry where positive outcomes, such as IPOs or lucrative acquisitions, are statistically rare.

Finding joy in daily tasks is another aspect Michael focuses on. Instead of fixating on the unlikely billion-dollar valuation, he derives satisfaction from helping customers, delivering new features, and building product integrations. This approach not only makes his workday enjoyable but also aligns his daily activities with the broader mission of his company.

Michael advises other founders to adopt a similar mindset. By finding purpose in the journey and setting clear boundaries, it’s possible to navigate the turbulent startup world without losing one’s sense of self or happiness. This philosophy helps maintain balance and fosters a more sustainable and fulfilling career in the high-stakes startup environment.

Key takeaway: Prioritize the journey over the destination. By setting boundaries and finding daily satisfaction in your work, you can maintain personal happiness and fulfillment, even in the unpredictable startup world.

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Episode Recap

This episode is an extensive review of the future of Business Intelligence, AI’s role is democratizing data for marketers, automating insights with LLMs, the importance of anomaly detection, and most importantly how to stop wasting hours monitoring dashboards and get alerts when it matters. 

The future of BI is all about making data insights available and useful for everyone, not just the experts. And AI is essential for making data more accessible. It can provide tailored insights that are easy to understand and act on, which boosts decision-making across the board.

Proprietary data is a major advantage in the AI market. Companies that can refine models and create tailored solutions using their unique data will stand out. This focus on proprietary data helps companies stay competitive and future-proof their AI initiatives. Additionally, using well-structured data sources enhances the effectiveness of natural language query tools, making them more user-friendly.

Anomaly detection is crucial for marketers. By staying alert to unexpected changes, marketers can quickly identify and fix issues while discovering new opportunities. This proactive approach keeps performance on track and helps leverage trends for better results. Narrative BI’s automated alerts for unusual patterns help marketers focus on significant changes, reducing the need for constant monitoring and enabling more strategic decisions.

Michael and his team have built Narrative BI, an augmented analytics platform for marketers that generates data insights in natural language. Unlike a dashboard that does a good job presenting data; Narrative BI also provides context, explains trends or anomalies, and suggests actionable next steps.

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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