Business Intelligence Before Artificial Intelligence: Going Slow to Go Fast

An Iterative Approach to Data Strategy

In the current tech environment, companies are racing to implement Artificial Intelligence features (whether users actually want them is a topic for another article), often overlooking a crucial step: establishing a solid Business Intelligence (BI)/Data Analytics foundation. This approach is misguided. Remember Moneyball? The Oakland A’s prioritized statistics over star players, winning 20 straight and changing the game and business of baseball in the process. That’s the key here: prioritize data analytics before pursuing AI. It’s about building a strong foundation before reaching for advanced technologies.

Nomenclature:

Business Intelligence (BI)/Data Analytics

The process of transforming raw data into actionable insights that drive strategic and tactical business decisions. This encompasses data collection, processing, analysis, and visualization. The terms “Business Intelligence” and “Data Analytics” are often used interchangeably in the industry, both referring to the practice of extracting meaningful insights from data for business decision-making. We use both terms throughout this article to reflect this common usage.

Artificial Intelligence (AI): A Note on Terminology and Landscape

In the context of this article, “AI” refers to advanced machine learning models and applications, including Large Language Models (LLMs), used for tasks such as natural language processing (NLP), computer vision, and complex decision-making. This usage reflects common industry terminology, but it’s valuable to understand its limitations and historical context.

Many “AI” technologies have been accessible for years. AWS Rekognition, for instance, has offered computer vision capabilities via API since 2016. While powerful, these tools are fundamentally statistical models excelling at pattern recognition and prediction. They lack the reasoning, understanding, and general intelligence that characterize human cognition—in other words, they’re not true artificial intelligence as envisioned by the field’s pioneers.

Our discussion of “AI” focuses on the practical application of these advanced models in business contexts.?

A Tale of Two Approaches

Consider two e-commerce companies aiming to boost customer retention:

Company A adopts an iterative data analytics approach. It begins by implementing basic sales metrics, gradually progressing to more complex analyses. Its initial focus is on understanding customer segments and purchase patterns. As the company matures, it leverages its foundation to implement targeted marketing campaigns. Eventually, they incorporate AI for real-time churn prediction, building upon its existing data infrastructure and insights.

Company B, enticed by the promise of AI, immediately pursues an AI-driven churn prediction model. However, without a proper data infrastructure, its data scientists find themselves burdened with data engineering tasks. The lack of clean, structured data and clear business metrics hinders its ability to train effective models. It struggles to connect AI outputs to tangible business outcomes, effectively building a sophisticated product without the operational insights to leverage it fully.

A year later, Company A has an effective AI system driving measurable improvements in customer retention. Their iterative approach allowed them to continuously deliver value while building towards more advanced capabilities. Company B, on the other hand, is still grappling with establishing basic BI capabilities. Their AI project is on hold as they backtrack to build the necessary data foundation, resulting in higher costs and delayed returns.

The Essential Role of Data Analytics

Data analytics serves as the basis of your data strategy. It’s not merely a stepping stone to AI, but a crucial component of a mature data ecosystem. Here’s why:

1. Data Preparation: BI processes clean, structure, and organize data, creating a solid foundation for any advanced analytics or AI application.
2. Business Understanding: Organizations gain deep insights into their operations, customers, and market dynamics. This understanding is crucial for identifying where AI can provide the most value.
3. Problem Identification: Helps pinpoint specific business problems or opportunities that AI might address, ensuring AI initiatives are aligned with business needs.
4. ROI Demonstration: BI projects often demonstrate quick wins and clear ROI, building organizational support and data literacy necessary for more advanced data initiatives.
5. Data Culture: Implementing BI fosters a data-driven culture, preparing the organization for the changes AI adoption will bring.

Business Intelligence in its Current Form

Modern data analytics tools often resemble AI applications when implemented properly. The insights provided by applying the right BI tool to a cleaned data set can offer powerful insights into your business.

Key features of modern BI tools include:

1. Data Integration: Seamlessly combine data from various sources, providing a holistic view of the business.
2. Advanced Analytics: Incorporate predictive analytics, allowing for forecasting and trend analysis without the complexity of full AI systems.
3. Interactive Visualizations: Enable real-time data exploration, uncovering insights that might otherwise require complex AI models.
4. Automated Reporting: Free up analysts to focus on more complex problems by automating routine analysis and reporting.
5. Democratized Data Access: Allow non-technical users to perform complex analyses, spreading data-driven decision-making throughout the organization.
6. Human-in-the-Loop Integration: Combine BI tools with human expertise to create a powerful synergy. This approach allows for nuanced data interpretation, mitigation of algorithmic biases, and increased trust in insights. By integrating human judgment with BI capabilities, organizations can achieve more accurate and contextually relevant analyses, often surpassing purely AI-driven solutions in complex decision-making scenarios.

These features enable organizations to forecast trends, detect anomalies, and create personalized experiences, often outpacing rushed AI implementations in terms of delivering business value. The human-in-the-loop approach further enhances these capabilities, ensuring that insights are not just data-driven, but also aligned with real-world complexities and business nuances.

Get Started with Business Intelligence

To optimize your data approach:

1. Audit existing data infrastructure and processes, identifying gaps and inefficiencies.
2. Assess your current data analytics capabilities against industry benchmarks.
3. Develop a roadmap that prioritizes data analytics improvements before AI initiatives.
4. Invest in data analytics tools that can scale with your needs and integrate with future AI systems.
5. Implement a comprehensive data governance strategy to ensure data quality and accessibility.

As you transition towards AI, focus on building a team with a mix of skills:

• Backend engineers skilled in AI API integration and scalable data systems.
• Data engineers capable of building hearty data pipelines for AI-ready data preparation.
• AI/ML engineers for custom model development and optimization.
• Product managers with a deep understanding of AI capabilities, limitations, and ethical considerations.
• Data analysts who can bridge the gap between raw data, BI insights, and AI applications.

The Road Ahead: Slow is Smooth, Smooth is Fast

While AI promises transformative potential, a strong data analytics foundation is the true cornerstone of sustainable, data-driven success. This isn’t about dragging your feet on innovation. Rather, it’s about building the right capabilities in the right order.

Start with the basics: implement solid BI practices, clean your data, and gain a deep understanding of your business through analytics. This approach delivers immediate value while preparing you for more advanced technologies. Remember, modern BI tools often rival AI in their ability to provide actionable insights and solve real business problems.

Your data strategy should evolve through continuous iteration. Begin with a data analytics MVP, learn from it, and build upon it. This method ensures ongoing value delivery while creating the infrastructure and culture necessary for successful AI adoption.

As you progress, focus on building a versatile team with a mix of skills – from backend engineers to data analysts who can bridge the gap between raw data and AI applications. Implement a comprehensive data governance strategy to ensure data quality and accessibility. If you’re looking to build your team, Gun.io can help you source world-class developers, data analysts, data scientists, and other technical professionals to support your data journey.

Ultimately, the goal isn’t to have the most advanced AI features, but to make better, data-driven decisions that drive business success. By prioritizing a strong business intelligence foundation, you’re not just preparing for AI—you’re creating an adaptable, data-driven organization capable of leveraging whatever technological advancements the future may bring.

In the race to become data-driven, remember: BI before AI. It’s about going slow to go fast, building a solid foundation that will support and accelerate your future AI initiatives.

If you’re unsure about your next steps or need expert guidance, don’t hesitate to reach out to Gun.io . Our team of technical leaders and founders have been through this journey before and can provide valuable insights, often highlighting directions you may not have considered. We’re here to help you navigate your data strategy and AI implementation effectively.