Beyond Numbers: Find out how to Humanize Your Data & Evaluation

something strange with the most important post image?

What you really see there may be a variation of Hermann’s grid, which I generated with the assistance of Gemini. And to be exact, I based it on the various modifications of this grid created by Jacques Ninio. The classic Hermann Grid creates illusory grey spots at intersections because retinal cells misinterpret the brightness of peripheral stimuli. Jacques Ninio’s variations magnify how ‘easy’ it’s to control perception by visual grouping and using focus [1].

Returning to the opening query, if the reply is positive, then perhaps you’d have an interest to know that you just were deceived by a strong optical illusion referred to as the scintillating grid illusion. As you glance on the grid, you’ll likely see dark, phantom-like ‘ghost’ dots appear contained in the white circles on the intersections. These dots appear to ‘scintillate’ or sparkle, appearing and disappearing as your eyes move, however the strongest a part of the illusion is that whenever you attempt to look directly at certainly one of the black dots: it vanishes. Actually these dots appear only in your peripheral vision. This phenomenon is attributable to the way in which neurons in your eyes process high-contrast areas, essentially ‘tricking’ your brain into perceiving a dot that isn’t actually there.

These grids function powerful cautionary tales for data and evaluation. For my part, they illustrate thoroughly the gap between ‘raw data’ (the actual black and white lines) and ‘perceived data’ (the illusory black/ grey spots). They exhibit that the way in which information is visually presented can fundamentally alter human perception and even create false realities. Does that ring a bell? Consider data visualization: if a chart or graph is designed without considering the ‘bugs’ in our own perceptual systems, it could actually inadvertently mislead the audience, causing them to perceive false trends or correlations—‘grey spots’—that don’t actually exist.

Call for data humanization

‘Traditional’ approaches to data evaluation, business intelligence, and data science focus totally on the  attributes of knowledge—its volume, velocity, and variety. On this setup, metrics are treated as ends in themselves. The result? Critical insights remain buried in extensive spreadsheets or lengthy reports. ‘Data-driven’ decision-making, in turn, takes ages and sometimes proves ineffective [2]. Even with probably the most meticulous plans, comprehensive dashboards, and robust data sets, leaders, managers, and colleagues today still find themselves asking:

This particular ‘vanishing dot’ exemplifies that even perfect data cannot at all times eliminate the basic uncertainties of any complex endeavor if people cannot use it properly (on this case read it the other way up).

To flee the ‘data-rich, action-poor’ paradox, organizations need a brand new philosophy: data humanization.

This idea is greater than simply buying a brand new tool: it’s embracing a brand new way of pondering. The target is to rework data from a passive spreadsheet right into a compelling narrative that  stakeholders to motion. Implementation of ‘humanized’ approach, for my part, relies on 4 elements:

1. Some small fixes (for a start): Quite than launching one other complex corporate project, start with making just a few small fixes today.
2. The Artisan: Establishing ‘Data Artisan’ roles to shape and translate complex data.
3. The Story: Embedding ‘Data Storytelling’ as a core competency to make insights clear and actionable.
4. The Impact: Enforcing robust ethical governance and, critically, measuring the financial return on evaluation.

What does ‘humanized data’ mean?

Before discussing these elements intimately, it is important to determine a transparent understanding of what we mean by ‘humanized data.’

Humanized data is a strategic asset that translates  is occurring into  it matters. This context is what makes the info actionable. As a substitute of just tracking symptoms (Key Performance Indicators, the KPIs), teams can finally solve the root-cause problems.

The true power emerges when traditional KPIs and humanized insights are combined. They mutually enhance one another, making the trail forward clear and simple.

From metrics to meaning: examples

Table 1 illustrates the claim mentioned above. The left side shows easy comments based on raw KPIs, while the suitable side enriches the identical metrics with broadened, humanized insights. As this comparison shows, raw KPIs merely reveal symptoms, whereas humanized insights expose the foundation causes—reminiscent of customer motivations or process roadblocks. This resulting clarity is way more actionable, enabling teams to maneuver beyond just tracking metrics and start solving the core problems that stifle success.

Key advantages of humanized data:

Elements of knowledge humanization

Small fixes & quick wins on the trail to humanized data.

To streamline my weekly reporting process, which involves pulling data from multiple sources, reminiscent of an in depth KPI deck, I recently developed an agent. With a view to make sure the report provides greater than just an easy number update, I give the agent a further task, prompting it:

Regardless of the agent produces, I at all times review it and enrich with my very own qualitative insights gathered from business meetings. Occasionally, I feed this enhanced, final comment back into the model, allowing it to learn and improve its suggestions for the next week.

This easy example demonstrates certainly one of the powerful techniques I’ll be sharing on this paragraph. All of them have three common characteristics: they’re practical, require hardly any capital investment, and eat just just a few minutes out of your weekly schedule. You may begin these at either a team or individual level, applying them on to your personal work.

Listed here are eight easy ways to start.

The Artisan

Humanizing data is the important thing to creating complex information accessible. By adding context, raw data is transformed into consumable insights, empowering business analysts without requiring them to grow to be programming or statistics experts.

This transformation requires elevating the role of knowledge analyst into that of a ‘Data Artisan.’

The Data Artisan must learn the best way to act as an ‘architect of context.’ This effectively becomes a hybrid role that mixes deep business knowledge with technical skills to construct sophisticated data workflows. Their primary function is to make data ‘tell its story,’ enabling and driving strategic decisions.

The Data Artisans should fulfil these functions:

• Ingest and integrate: They master ‘the art’ of mixing traditional structured data with unstructured context from sources reminiscent of social media or sensors. Something that machines still can’t do – seek unexpected patterns, associate facts (or assumptions) which have theoretically no obvious, clear linkage, that otherwise might have been read by an AI assistant.
• Seek patterns over perfection: They shift the analytical goal from ‘pixel-perfect’ accuracy to identifying meaningful, predictive patterns inside large data volumes. Sometimes, a daring hypothesis that’s later not confirmed can bring more value than spotless data. Sometimes, having something that answers our query with 80% accuracy tomorrow is price greater than 99.9% accuracy, but in three weeks.
• Insight at the purpose of decision: Artisans help to decentralize powerful analytical tools, making them accessible to empower decision-makers. They advocate for utilizing easy dashboard creation tools, reminiscent of Looker or Datawrapper, even in the event that they are fed with static data. The goal is just not the flawless UX or beautiful design. The goal is to facilitate faster decision-making. If the insights ‘click’, it’s at all times easy (or at the least easier) to search out time and resources to make sure proper data uploads or a friendly interface.
• Reuse analytical IP: Create robust, reusable data objects and analytic workflows. Optimize your work. Create Agents to handle repetitive tasks, but give them ‘freedom’ to identify something beyond the fundamental algorithm.

The principal goal of this role is to democratize complex analytics. The Data Artisan absorbs the burden of complexity by creating reusable IP and accessible platforms. This, in turn, enables non-specialists across the organization to make informed, rapid decisions and fosters true organizational agility [3].

The Story

Data storytelling is the first conversion mechanism that translates technical insights into persuasive, human motion. If insights are the currency of the insight-centric organization, storytelling is the transaction system.

Every compelling data story must intentionally acknowledge and integrate three foundational elements:

Selecting a narrative framework is a critical, strategic decision that hinges on the communication’s primary goal. This selection becomes paramount when the audience consists of executive stakeholders. Executives operate under intense time pressure and are focused on strategy, risk, and ROI. A knowledge story built for a technical team—perhaps a deep, exploratory dive—will fail to resonate.

The framework should be tailored to the goal. If the goal is to secure funding for a brand new platform, a persuasive structure like AIDA (Attention, Interest, Desire, Motion) is crucial for constructing a compelling business case. If the goal is to report on an operational bottleneck and propose an answer, the logical, problem-centric SCQA (Situation, Complication, Query, Answer) framework will more effectively exhibit due diligence and result in a transparent suggestion. The framework serves because the vehicle for insight, and for an executive audience, that vehicle should be fast, clear, and directly targeted at a choice.

Strategic data storytelling frameworks examples

For executives, effective data storytelling is a strategic translation, not an information dump. Leaders don’t need raw data; they need insights. They require data to be presented clearly and concisely in order that they can quickly grasp implications, discover critical trends, and communicate those findings to other stakeholders. A powerful narrative structure—one which moves from a transparent problem to a viable solution—prevents priceless insights from being lost in a poorly presented argument. This ability to translate data into strategy is what elevates data professionals from mere statisticians to true strategic partners able to influencing high-level business direction.

Principles of high-impact data visualization

Data visualization is the bridge between complex datasets and human understanding (and a subject of a lot of my articles). To be effective, the selection of a chart or graph must align with the message. For instance, line charts are best for showing trends over time, bar graphs for making clear comparisons, and scatter plots for revealing relationships between variables.

Beyond choosing the suitable chart type, the intentional use of color and text is critical. Color mustn’t be decorative; it must be used purposefully to focus on a very powerful information, enabling the audience to understand the important thing takeaway more quickly. Text must be minimal, used only to make clear points that the visual cannot make by itself.

Finally, all visualization carries an ethical mandate. Data integrity should be maintained. Visualizations mustn’t ever intentionally misrepresent the facts, as an illustration, by utilizing misleading scales or inappropriate color contrasts.

The impact

The core idea: Prove data’s value to get support

To get executives to fund ‘data humanization’ (making data clear and simple to make use of), you could prove its financial value. The most effective solution to do that is by showing its Return on Investment (ROI).

Find out how to Prove the Value: A 2-Step ROI Plan

The ROI calculation is a straightforward comparison:

A confusing dashboard that gets ignored doesn’t just cost nothing; its ROI is since it wastes money and time. A transparent, humanized dashboard is an investment that makes teams smarter and faster.

Step 1: Find the of bad data

First, measure the cost of your existing, ‘non-humanized’ reports. This baseline is greater than just an analyst’s salary. Include the hidden costs of confusion:

• Time to insight: What number of hours do managers waste trying to know the complex report?
• Translation labor: What number of hours do analysts spend re-explaining findings or making simpler PowerPoint versions?
• Insight adoption: What number of key decisions are based on the report? (If it’s zero, the report is worthless.)

This total is the high price you might be currently paying for confusion.

Step 2: Measure the from humanized data

When you launch your recent, clear dashboard, measure the return against that baseline. The gains are twofold:

1. Efficiency gains (saving money):
   • The manager’s Time to insight might drop from one hour to 5 minutes.
   • The analyst’s Translation labor (re-explaining) all but disappears.
2. Value gains (earning money):
   • That is the true prize. Track the made the info was finally clear.
   • Example: A marketing team shifts its budget 10 days sooner, or a sales team spots a brand new opportunity, generating measurable recent revenue.

A straightforward example

• Before (bad data): A ten-tab data dump spreadsheet costs the corporate $10,000 a month in wasted manager time and analyst support.
• After (humanized data): A brand new, one-page dashboard costs $1,500 to construct.
• The return (Month 1): It saves $8,000 in recovered time helps a sales team generate $20,000 in recent value.

The underside line: Humanizing data isn’t a ‘nice-to-have’ design selection. It’s a high-return business strategy that converts organizational waste into decisive motion [7].

Conclusions

Ultimately, the journey from raw data to real-world impact is fraught with perceptual traps, very like the illusory dots of the Hermann Grid. As we’ve seen, numbers alone are usually not self-evident; they’re passive spreadsheets and abstract KPIs that always leave us ‘data-rich but action-poor.’

Breaking this cycle requires a strategic and cultural shift to data humanization. This transformation is just not a couple of recent piece of software but a couple of recent way of pondering—one which empowers Data Artisans to search out context, embeds Data Storytelling as a core competency, and relentlessly proves its Impact through a transparent ROI.

By embracing these principles, we move beyond the ‘ghosts’ within the grid—the false correlations and missed opportunities—to see the human reality underneath. That is how we finally close the gap between evaluation and motion, transforming data from an easy report of right into a compelling catalyst for .

Sources

[1] Ninio, J. and Stevens, K. A. (2000) Variations on the Hermann grid: an extinction illusion. Perception, 29, 1209-1217.

[2] Data Storytelling 101: Find out how to Tell a Powerful Story with Data – StoryIQ, 2025, https://storyiq.com/data-storytelling/

[2] Humanizing Big Data – DLT Solutions, https://www.dlt.com/sites/default/files/sr/brand/dlt/PDFs/Humanizing-Big-Data.pdf

[3] Gouranga Jha, Frameworks for Storytelling with Data, Medium, https://medium.com/@post.gourang/frameworks-for-storytelling-with-data-5bfeb1fbc37b

[4] Michal Szudejko, Turning Insights into Actionable Outcomes, https://towardsdatascience.com/turning-insights-into-actionable-outcomes-f7b2a638fa52

[5] Michal Szudejko, Find out how to Use Color in Data Visualizations, https://towardsdatascience.com/how-to-use-color-in-data-visualizations-37b9752b182d

[6] Michal Szudejko, How To not Mislead with Data-Driven Story, https://towardsdatascience.com/how-not-to-mislead-with-your-data-driven-story

[7] ROI-Driven Business Cases & Realized Value – Instrumental, https://instrumental.com/build-better-handbook/roi-business-cases-realized-value-technology-investments

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