Simplicity, Stories, and Sustainability in the Age of Analytics
“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”
In today’s data-driven world, we often find ourselves dazzled by the sophistication of technologies and overwhelmed by the sheer volume of data at our disposal. From AI and machine learning to data lakes and advanced analytics, these tools promise transformative insights. However, as we advance technologically, it becomes crucial to strengthen our foundations: governance, infrastructure, data literacy, and most importantly, our understanding of people and the unintended consequences of our actions. At its core, data is about people, and the path to sustainable, impactful innovation lies in simplicity, meaningful narratives, and thoughtful consideration of broader effects.
Simplicity Is Best
Complexity often feels inevitable in the world of analytics, but simplicity should be our guiding principle. Simplicity is not about oversimplifying or cutting corners—it’s about clarity, focus, and efficiency. Here are some key strategies for keeping it simple:
Start with where you are: Assess your current position and work backwards from your desired outcome. Ask, “What is the end goal? Where are we now? How do we get there? Who needs to be involved, and when?” Clarity in these questions ensures a targeted and efficient approach.
Embrace ‘good enough’ data: Striving for perfect data can be prohibitively time-consuming and costly. Instead, celebrate data that is “good enough” to answer your questions and drive action.
Let the question decide the data: Your research question should dictate the type of data you need. Quantitative data reveals patterns and trends, while qualitative data provides context and meaning. Both are essential tools, but misapplying them can lead to unintended consequences.
Use stories to connect: People remember stories more than statistics. A narrative is the story you tell others, and identity is the story you tell yourself. Crafting a compelling story helps translate data into insights that resonate and drive action.
Build on existing culture: Culture and trust are foundational. Effective data systems should complement and support an organization’s existing culture, rather than disrupt it. Sustainable innovation grows from aligning with people’s needs and values.
Data Is About People
Data may seem abstract, but its impact is always human. This principle should guide how we design, interpret, and apply analytics. Here’s how to keep people at the center:
Bridge the gap between data and meaning: People are often abstracted out of analyses. Ask, “What does this analysis mean for the individuals or communities involved?” Collaboration and early involvement ensure relevance and buy-in.
Balance quantitative and qualitative data: Both types of data are equally valuable. Quantitative data highlights patterns, while qualitative data explains why those patterns exist. Together, they offer a complete picture.
Value the research question over the answer: The question shapes the path forward. When you define a strong question, the appropriate methods, data, and analysis will follow naturally. This approach minimizes wasted effort and maximizes impact.
Practice humility: Certainty can be dangerous. Acknowledge trade-offs, opportunity costs, and risks. Consider second- and third-order consequences to ensure a holistic view. More thinking, less doing often leads to better outcomes.
Trust anecdotes when they clash with data: If data and anecdotes disagree, it’s often a sign that you’re measuring the wrong thing. People’s lived experiences can reveal gaps in data collection or analysis, prompting more accurate and meaningful insights.
Sustainability and Ecological Consequences
While analytics offers immense potential, it’s important to recognize its environmental and societal costs. For instance, interacting with a large language model (LLM) can consume 10 times more energy than a standard keyword search, with some generative AI queries using 30 times the energy of a traditional Google search. According to experts, meeting the future energy demands of AI could require adding the equivalent of another Michigan-sized state to the power grid by 2030.
These numbers highlight the need to connect the dots between digital actions and their invisible real-world costs. Sustainability must be a cornerstone of analytics practices. Here are some considerations:
Understand energy implications: Organizations need to be aware of the energy-intensive nature of AI and other advanced analytics. Choosing efficient methods and optimizing processes can mitigate environmental impact.
Promote responsible innovation: Innovate with a focus on long-term benefits rather than short-term gains. This means balancing technological progress with ecological stewardship and social responsibility.
Raise awareness: Educate stakeholders about the broader implications of analytics. Transparency about costs—both monetary and environmental—fosters informed decision-making and accountability.
Sustainable, long-term innovation doesn’t emerge from one-off breakthroughs; it’s built through consistent, incremental improvements. Whether it’s refining data governance practices, fostering data literacy, or strengthening trust within an organization, small daily actions create a robust foundation for growth.
Simplicity, stories, and sustainability are not just ideals; they are practical strategies for navigating the complexities of analytics. By keeping these principles in mind, we can unlock the full potential of data while ensuring it serves people, supports culture, and respects the planet.
