How to turn raw data into a powerful business-winning opportunity for an Organization

1. Start with the business pain, not the data

Do not ask, “What can this data tell us?”

Ask instead: “What decisions are we making blindly?” or “Where are we bleeding cash or customers?”

Examples:

• Are customers churning after 3 months?
• Which branch underperforms and why?
• What are our top 3 product complaints?

2. Inventory your data exhaust

Most organizations already generate rich data that they ignore:

• CRM logs
• Sales receipts
• Website traffic
• Call center transcripts
• Audit trails
• Delivery time stamps

Map all existing data sources across departments. Label them: structured (e.g., Excel) vs unstructured (e.g., voice notes).

3. Appoint a Data Stewardship Cell

Create a Data Value Cell (not just “IT” or “MIS”).

• Cross-functional: strategy, operations, marketing, finance.
• Mandate: convert data into decisions that lead to money or impact.

A case in point: “What combination of channel, timing, and message gives us the highest conversion rate?”

4. Build decision-first dashboards

Do not make “data dashboards.” Make decision dashboards.
Each dashboard should answer:

• “Should I act now or wait?”
• “Where should I double down?”
• “What must I stop doing?”

Tool tip: Use tools like Power BI, Tableau, or Looker, but only after clarifying the decision logic.

5. Use AI/Analytics to create unfair advantages

Once patterns emerge, shift from descriptive to predictive and prescriptive analytics.

• Predict customer churn
• Optimize inventory
• Detect fraud
• Suggest next-best offers

Do not stop at insights. Embed them into workflows (automated SMS, alerts, approval gates).

6. Monetize or operationalize

Now use the insight to win:

• Revenue: Refine pricing, upsell smarter, reduce churn
• Cost: Prevent fraud, cut waste, optimize operations
• Speed: Cut turnaround time, automate decisions
• Risk: Spot red flags before the audit report does

7. Institutionalize the loop

Winning with data is not a project. It is a muscle.

Build quarterly rituals:

• Ask: “What are our top 5 blind spots?”
• Track: “What insight saved or made us money last quarter?”
• Reward: “Which team converted data into dollars?”

Common traps to avoid

• Data for vanity (pretty reports no one uses)
• Tech-first approach (buying systems before framing business questions)
• Siloed data (finance knows one truth, sales knows another)
• No accountability (everyone collects, no one acts)

From raw data to business win – summary

Raw data is not the asset. Data that leads to better decisions, faster, at scale, that’s the asset. Otherwise, you are just hoarding noise. Are you optimizing your data?

Case in point, a “Silent Churn” in retail banking

Most banks focus on active churn, customers closing accounts. But silent churn is worse.

Customers leave mentally (no more transactions) but stay on your books (no alert triggered).

• They stop saving.
• They stop borrowing.
• They stop logging into mobile banking.
• But the account still exists dormant, unprofitable.

Problem: The bank thinks it has 10,000 customers.

Reality: Only 4,500 are active. The rest are dead weight.

Step-by-Step: Turning raw data into action

1. Frame the decision question clearly

“How can we detect and win back customers who are silently churning before they go cold?”

This is not an IT problem. It is a profitability problem.

2. Inventory your data signals

You already collect the signals that reveal silent churn. Examples:

You do not need new systems. You need to listen to your data.

3. Define churn risk score (CRS)

Assign a score to each customer:

Example CRS formula:

• 0 if no debit in the last 60 days
• +1 if zero card usage
• +1 if balance dropped below minimum
• +2 if no logins in last 90 days
• +2 if inactive across all products
• +3 if recent complaint not resolved

High CRS = High risk of silent churn. Now you can see the ghosts.

4. Segment and prioritize

Now categorize customers:

5. Monetize the data insight

Use the churn model to:

• Reactivate accounts with dormant balances
• Offer microloans or tailored credit products
• Trigger win-back offers (free transfers, cashback)
• Route high-risk customers to Relationship Officers
• Close ghost accounts and free up operational costs

Result: Reactivating even 20% of dead customers boosts transaction volume and lowers your cost-to-income ratio.

6. Automate the loop

Build triggers into your core banking and CRM systems:

• Auto-email + SMS + in-app nudges for “Slipping” customers
• Flag “At Risk” customers to branch managers
• Incentivize Relationship Officers based on reactivations

Real ROI Example (Mid-tier East African Bank)

• Total customer base: 100,000
• Silent churn rate: 30%
• Reactivation conversion: 15%
• Monthly avg. income per active customer: UGX 18,000

New revenue = 4,500 reactivated customers × UGX 18,000 = UGX 81 million/month

This is before factoring cross-sell (loans, cards) or lifetime value.

What banks get wrong

The point in brief

In banking, data is not just the new oil it is the new oxygen. If you do not use it to detect invisible loss, you will die quietly, just like your customers.

Case example 2: “Hidden Downtime” in a Manufacturing plant

Every manufacturing plant tracks major breakdowns. But micro-downtime, short, frequent interruptions often go unreported.

• 2 minutes here.
• 5 minutes there.
• Equipment resets. Waiting for raw material. The line is idle due to a lack of technicians.

You think OEE (Overall Equipment Effectiveness) is 85%. Reality? It is below 60% you are losing production capacity and don’t even know it.

The strategic question

“Where are we silently losing production time, and how can we recover it to boost output without buying new machines?”

Step-by-step: turning raw data into value

1. Identify the invisible loss

Start by defining:

• Planned downtime: scheduled maintenance, shift change
• Unplanned downtime: machine failure
• Hidden downtime: minor stoppages, waiting, human delays

The last one is the silent killer. It never shows up on reports unless you hunt it down.

2. Capture granular operational data

You already have this, but are not using it.

Most stoppages are NOT mechanical. They are human, process, or coordination-related.

3. Create a real-time downtime tracker

Use simple tablets at each production line, or automate if possible.

Categories for micro-downtime:

• No operator available
• Machine reset required
• Material not delivered
• Quality issue hold
• Changeover delay

Insight: Even 7 minutes of micro-downtime per hour = 9% capacity loss per day.

4. Analyze patterns and root causes

After 2 weeks of tracking, visualize:

Root Cause: Lack of line synchronization and no escalation protocol.

5. Monetize the insight

Suppose plant capacity is 50,000 units/day.

You are losing 2,500 units due to hidden downtime.

Average margin per unit = UGX 800

Daily loss = UGX 2 million

Annual loss (260 workdays) = UGX 520 million

6. Redesign for action

Implement simple data-driven changes:

• Pre-stage raw materials by shift schedule
• Auto-alerts to technicians if the line stops >90 seconds
• Shift-based downtime scorecard by line and supervisor
• Changeover checklists with QR code tracking
• Incentivize operators for lowest downtime ratios

7. Automate and embed

• Use Power BI / Tableau/Python to show real-time line utilization
• Integrate downtime alerts into WhatsApp groups or radios
• Review weekly with plant managers: “What downtime was avoidable this week?”

Real-world payoff

Mid-size East African FMCG Plant

• Before: Reported OEE = 84%
• After 30-day downtime analysis: True OEE = 59%
• Changes implemented: Changeover SOPs, real-time dashboards
• Capacity gain: +5,000 units/day
• Additional monthly sales: UGX 120 million
• Cost? Mostly training + dashboards + discipline

What manufacturers get wrong

In manufacturing, you do not scale by buying more machines. You scale by removing invisible friction.

Data shows you exactly where to look, if you stop ignoring the whispers.

I remain, Mr. Strategy