Path to AI: Why Investing in Business Intelligence is Key to Data Maturity and Growth

Open AI’s chatGPT is hyping up AI and Data Science, and all of a sudden, I feel smarter just being in the field! Or should I be worried I don’t know a whole lot about Large Language models? Perhaps no one does, and we are all learning. Anyway, as we progress at a breakneck speed, it is important we bring along everyone for the ride.

One of my passions is exploring ways to make Data and AI accessible for companies of all sizes. Every company has data, and deriving insights from it can be a powerful driver of revenue and value. In one of my previous articles, I laid out a roadmap for building a data infrastructure to leverage the power of your data. In this article and others, I’d love to dive into the individual stages of the process and share some insights I have learned along the way.

As you build out a company’s tech towards AI, each phase generates varying returns on investment (ROIs), making it a constantly evolving process. One very exciting and tangible phase is when companies turn their data in spreadsheets into automated visual reports. This is Business Intelligence (BI) in its simplest form.

What is Business Intelligence?

Business Intelligence combines business analytics, data visualization, and data mining to help organizations derive actionable insights from their data. A comprehensive view of your organization’s data enables you to drive change, eliminate inefficiencies, and adapt quickly to market or supply changes.

Modern BI solutions prioritize flexible self-service analysis, govern data on trusted platforms, empower business users, and speed up insight. Modern BI solutions provide the historical context of your business so you can start recognizing patterns and derivations.

Data Maturity

Data maturity is the ability to manage and leverage data to drive business value. It involves your company’s ability to collect, process, store, analyze, and interpret data effectively and use it to make data-driven decisions.

Different phases of data and analytics maturity include Chaotic (data is disorganized, misunderstood, and inefficiently used), Reactive (data is used primarily to react to business problems as they arise), proactive (leveraging data strategically to drive decision-making), and Integrated (use advanced analytics and data science to uncover insights and drive innovation). I’ll expand on these phases and their impact in upcoming articles.

Why invest in BI?

Building Business Intelligence and Data Visualization competence is a major step toward data maturity. Visual reports are easy to understand and provide a consistent and trustable view of the business that executives appreciate. Often, a good reporting automation and BI infrastructure gives a significant ROI for the business and helps prove data proficiency in a very tangible way.

Here are some reasons why investing in a solid BI roadmap is key for data maturity and growth:

• Easy to understand: A good BI infrastructure provides insights in an easily understandable manner. This enables decision-makers to quickly identify trends and patterns.
• Flashy: A good BI infrastructure is visually appealing and engaging to users. This increases user engagement, leading to better insights and outcomes.
• Tangible: As data evolves into advanced analytics, modeling, and AI, data becomes more abstract and becomes more of a black box to executives. However, BI is much more accessible and transparent, making it a crowd favorite in the world of analytics.
• Historical Context: A good BI infrastructure provides users with a historical context of the business and helps users understand trends and patterns over time. This allows for better decision-making across all departments.
• Data Literacy and Adoption: BI being easy to use and understand, it is typically an easy sell for data teams and is a great tool to improve data literacy across the company. This initial momentum can be used to promote data-driven decision-making.
• Build Trust: A good BI infrastructure helps build trust by providing accurate and reliable data for better decision-making. This confidence and familiarity with data often lead to further investments in the data teams to build other advanced analytics capabilities.
• Easily Scalable: A good BI infrastructure can be easily scaled to meet the needs of the organization as it grows. This can be achieved through cloud-based technologies that enable easy expansion and scalability.
• Cross-Functional and break silos: BI is like a universal language. It enables cross-functional collaboration. Visual reports provide context about different departments and enable stakeholders from various teams to understand each other's business.
• ROI — A good BI infrastructure can provide a high return on investment through the identification of patterns, trends, and seasonalities that can lead to improved decision-making.
• Springboard for Advanced Analytics: A good BI infrastructure can provide a foundation for more advanced data science initiatives, such as predictive analytics and machine learning.

Investing in a good BI infrastructure is like having a personal assistant who helps you make sense of your data and provides you with valuable insights. It’s like having a map to navigate through the complex terrain of your business. With intuitive dashboards and visualizations, decision-makers can quickly identify trends and patterns, making it easy to understand what’s happening in the business. These advantages typically lead to further investments in the data teams to build out more advanced capabilities.