Using data and personal analytics to solve problems faster

Executive overview

Most people attack problems before defining them — skipping the data-gathering that makes solutions obvious. Greg Hall's framework, drawn from Lean Six Sigma and personal productivity practice, starts with a well-formed problem statement backed by quantified data.

A problem well defined is a problem half solved. Collecting the right data upfront — about your work and your time capacity — removes ambiguity and surfaces answers that would otherwise take weeks to find.

The core insight: quantifying both your workload and your available time forces a reckoning that planning alone never does.

Why problem definition is the hard work

• Most people skip straight to solving; the real leverage is in defining the problem
• A good problem statement requires quantifiable, objective data — not impressions
• The more data brought to a problem, the clearer the solution becomes
• Spending more time upfront gathering data feels slow but accelerates everything after
• Greg's GE mentor drilled this: do the homework before diving in

Personal time tracking: how to start

• Note the time and activity at the start of your day; record each switch as it happens
• Do not categorise in real time — that's the most common reason people quit tracking
• At end of day, dump entries into a spreadsheet and categorise them then
• After two to three days you have useful data; after three weeks, statistically significant data
• A side benefit: logging a switch before making it creates a small speed bump — a moment of intentionality

Quantifying work on your plate

• Getting tasks out of your head onto paper (David Allen's "mind sweep") is step one
• Step two is putting a number to each item: what is the deliverable, and how long will it take?
• Defining the deliverable concretely — document, deck, verbal recommendation, website — is what makes estimation possible
• With tasks quantified, a total emerges (e.g. 163 hours of work in mind)
• That number, compared against available capacity, makes the problem visible

Quantifying capacity

• Block out your calendar and count realistic working hours — not aspirational ones
• Distinguish protected, uninterrupted time from reactive, interrupted time
• Many roles have 30+ hours a week that are effectively reactive; plan around that reality, not against it
• Once capacity is a number, the gap between workload and time becomes undeniable

Closing the gap: adjusting scope

• "Fix the time, flex the scope" (J.D. Meyer, Getting Results the Agile Way) — decide the time budget first, then size the output to fit
• Options for closing the gap: renegotiate expectations, recruit help, reduce the elegance of the solution
• Reducing elegance is not failure — a simpler deliverable that gets used beats a polished one that doesn't
• Scope creep and conversational tangents are easier to resist when a fixed time has been committed upfront
• Alignment between goals and capacity removes the background hum of stress

Ongoing adjustment

• A plan made in one sitting degrades as real work unfolds; constant small corrections are necessary
• Analogy: a plane is off course 90% of the time but reaches its destination through continuous adjustment
• Weeds pulled early are easy; a backlog of deferred decisions is hard
• Reviewing and pruning daily keeps the system from requiring a full reset

The peace-of-mind payoff

• When work time is planned realistically, unplanned time (family, downtime) becomes genuinely present
• Unclear internal expectations are the root of most personal stress — the same dynamic that causes interpersonal conflict
• Quantifying the gap upfront produces a "sad face" moment early; carrying false hope produces it late and repeatedly
• Invisible queues (no wait-time estimate) generate more frustration than visible ones — planning works the same way
• The goal is peace-of-mind productivity: capacity and commitments in alignment

Iterating toward the goal

• Building something imperfect and learning from it is faster than waiting to build it right
• Greg's shift: in analytics, highly automated elegant workflows often went unused; simpler iterations got traction
• The minimum viable approach applies to personal projects as much as products
• Embrace the "ugly" versions along the way as the path to the eventual vision