Kairos MBA Masterclass·18:23

Gaps in the Ecosystem: Silos, the Pandemic Planning Case, the Cockpit Analogy, and the Five-Step Solution

Anand Menon Analytics trainer - heads strategy, special projects and international business at Wakefit; ex Flipkart (kids fashion P&L) and Sephora India; IIM Kozhikode

▶LMS lesson

The short version

Business teams ask vague questions and ignore how data is captured; data teams analyse for perfection, not decisions. Pitfalls: gut decisions despite models, deleted dashboards someone relied on, days stretching into weeks, two sources of truth. Principle: 'every team is a data science team, and every team is a business team' (0:00-0:07).
Case 2 (lockdown, CEO wants impact plus a six-month plan): the business team spreads the shortfall and pushes what sells but misses seasonality; the data team builds a sales equation and trend lines but misses actionability. Cockpit: one checks fuel and improvises, the other plans the perfect route and is derailed by weather - you need both (0:08-0:17).
CXOs want: understand the customer (transactional plus survey data), find gaps, optimise revenue and profit. The five-step solution: terminologies, analysis process, P&L and metrics, first principles, presentation (0:14-0:21).

At a glance, three clicks deep

Skim here first: the closed row is the glance, open is the study card with the key points and timestamps, and the ↓ link drops to that concept's full write-up below.

01Silos, and 'every team is a data team and a business team'Silos cause the failures;›

Silos cause the failures; everyone owns both data and outcome.

Pitfalls and principle (0:00-0:07)

↓ Full write-up of this concept

02Business-team vs data-team approach, and the cockpit analogyBoth approaches miss something;›

Both approaches miss something; combine them.

Case 2 (0:08-0:17)

CXO expectations and 5 steps (0:14-0:21)

↓ Full write-up of this concept

The concepts in full

Silos, and 'every team is a data team and a business team'

The data team deleted the dashboard nobody used. One team was running the business off it.

Business teams hand over broad questions without caring how data is captured; data teams optimise for perfection rather than a decision. Named pitfalls: gut-based decisions despite models, dashboards deleted as unused, simple requests stretching from days to months, two sources of truth, detailed dashboards nobody opens. The fix is mindset, not tooling: every function handles its own data and owns business outcomes. The challenge is too much data, not complex methods.

Why it matters

Diagnoses why analytics fails in most companies before any technique is taught.

Business-team vs data-team approach, and the cockpit analogy

One pilot checks the fuel and improvises; the other plans the perfect route and hits weather. Fly with both.

Lockdown case: the business team measures lost sales against the annual operating plan, spreads the shortfall over remaining months, pushes categories that work (skincare and wellness up, makeup down), tracks monthly - but misses seasonality and sets unrealistic Q4 targets. The data team builds a sales equation (traffic x conversion x pricing), uses historical analogues (a city flood) and projects trend lines - but misses actionability by category, product or budget. CXOs expect three things: understand the customer (transactional data for existing customers, surveys for non-customers), identify gaps and opportunities, optimise revenue and profit. The course's five-step answer: terminologies, analysis process, P&L and metrics, first principles, presentation.

Why it matters

The balanced stance the rest of the module teaches.

Tools referenced

Tool	Coverage	Moment	Context
Microsoft Excel	mentioned		Spreadsheets and dashboards, generic

Action items

Resources mentioned

Resources

docLesson page on the platform

Extraction notes

This page was built from an auto-generated transcript, which garbles product and people's names. Those were corrected silently in everything above and logged here for transparency. The warnings flag claims that were true on the recording day but change fast.

Transcript corrections applied

The transcript says	The trainer actually means
test case / SQ / SQL	SKU
GMB	GMV
average selling point	average selling price (ASP)
1 0 1 / 2 0 1	101 / 201

True on recording day — verify before relying

Lockdown-era case and '2023' comparisons date the analytics recording to 2023-24; the methods are evergreen