Is it ARTIFICIAL intelligence or AUGMENTED intelligence?

The truth? It depends on the design's purpose. An organization’s purpose is informed by its values and profit motivation. Artificial intelligence aims to create autonomous systems that can perform tasks without human intervention, while augmented intelligence seeks to enhance human capabilities by providing AI-powered tools and assistance.

When driving data projects, you will encounter business stakeholder challenges that often go unspoken. This is not always because people hold back but because they don't fully know how to vocalize their constraints.

If they can't directly address their requirement, chances are we can't either. To hear others' speech, we start by asking questions from different perspectives.

The transition from traditional spreadsheets to sophisticated data management and analysis algorithms represents a significant evolution that has revolutionized how businesses process and leverage information. Algorithms have reshaped the landscape of data-driven decision-making. Facebook's filter bubble is an early example of a machine learning system individualizing the user experience based on user patterns.

As we continue to drive data projects, familiar challenges begin to present themselves. By observing, we can become better diagnosticians of systemic issues. Learn what to avoid and how to navigate them better.

Until an organization is willing to invest in its data capabilities, aligning data resources to answer complex business questions will be like riding a bicycle to chase a Formula One racer and never catching up. Scoping project opportunities well is about building enough trust to eventually scale resources. While a single project manager can accomplish some initiatives, most data projects require multi-disciplinary resources to execute.

Book Excerpt: While a fully funded budget that supports data as a service is an integral part of a data transformation’s financial picture, few are fully staffed or funded. Three-quarters of executives confirm their organization now has some form of data strategy (however rudimentary), but a paltry 16% say they have the skills and capabilities necessary to deliver it.[1] Even though the average staffing budget is growing yearly, finding the skills and capabilities to execute data projects is becoming harder and harder.