In this initial stage, the predominant tools are Excel spreadsheets and basic, often free, visualization tools. These allow for monitoring a few key metrics within the company, primarily focused on customer acquisition and sales.

This stage features numerous dashboards across various departments, leading to a costly search for consistent data truths. With multiple 'truth centers' and varied metric definitions, departments calculate data differently, escalating the cost of uncovering factual data.

We set up and manage your data warehouse, connect data sources, create forms for data input, and prepare data for visualization using top-tier data engineering tools

Here, a manager receives not just suggestions, but a portfolio of modeled decisions showcasing different impacts, timelines, budgets, and primarily, the calculated outcomes. The focus is on rapid and effective decision-making. It's crucial to generate innovative ideas from forecasts and existing data, searching for unique solutions tailored to specific goals and datasets. This stage also starts documenting all decisions for later evaluation of their impacts.

As stakeholders acquire their dashboards, they gain timely, informative insights, yet decision-making still requires additional data collection and analysis. The focus now turns to forecasting, aiming to manage not only current data but also future projections to strategize short-term and long-term objectives. This stage emphasizes a close integration of business and data models across all departments.

In this culminating stage, the business operates within a fully integrated AI ecosystem. This system not only automates decisions but also learns from outcomes to refine its algorithms continuously. The AI seamlessly collaborates with human teams, enhancing both strategic and operational decision-making across the organization.