In Part 1 of this article, Matthew Small and I explored the power of being Unreasonable as a driving force for innovation. Now, in Part 2, we shift the focus to real value creation, breaking down how the value equation works through a practical example. This is about how value can be shown through combined efforts.

If you missed Part 1, I’ll include the link in the comments.

We have now decided to extend this series with a Part 3, which will dive into the next evolution of the example discussed here. In the next series due to be released next week, we will explore how data leaders can clearly demonstrate the distinct value their initiatives bring to the business.

It’s time to face a stark reality: data leadership isn’t just about technical excellence or building sophisticated models. If data leaders want to earn their seat at the C-suite table, they need to shift their focus from the intricacies of data to the strategic impact of data. And that impact is measured in one universal language: business value.

Data leaders must bridge the gap between data initiatives and tangible business outcomes. The truth is, it doesn’t matter how complex or ground-breaking your analytics are if the leadership team can’t see the direct value they bring to the organisation. This is where most data teams fall short, they’re not failing because they lack talent or technology, but because they can’t demonstrate value in a way that the C-suite understands.

To help data leaders apply this mindset in practice, they need a framework for calculating valuethat connects their initiatives directly to business outcomes. Here’s a simple equation to quantify the impact of data efforts:

Business Value in financial terms is often placed into a business case. To demonstrate, a base case is made up of 3 things:

• Financials – Profit and Loss (Income less operational expenditure (opex) and taxation), cashflow and balance sheet (capital expenditure (capex)).

• Time period, usually 5 years, to demonstrate the value of the investment and its return over time. Most companies want a Payback Period of less than 2 years.

• KPIs – Discounted cashflow (DCF), Return on Investment (ROI), Net Present Value (NPV), Payback Period (PBP) and Internal Rate of Return (IRR) which will all have targets to be met in your company, usually called hurdle rates.

The total investment required for the data initiative

Let’s assume you are asked to help drive analytics on a marketing campaign that will require an initial investment of $500,000 from your team. The high-level assumptions from the CMO state that the project is expected to generate the following cash inflows over five years:

• Year 1:$100,000

• Year 2:$150,000

• Year 3:$200,000

• Year 4:$250,000

• Year 5:$300,000

The company’s cost of capital (discount rate) is 10%.

We will calculate:

1. Discounted Cash Flow (DCF)
2. Return on Investment (ROI)
3. Net Present Value (NPV)
4. Payback Period (PBP)
5. Internal Rate of Return (IRR)

Discounted cash flow takes into account the time value of money, discounting future cash flows at the company’s discount rate of 10%.

The formula to calculate DCF is:

Where:

• C = Cash inflows in each year

• r= Discount rate (10%)

Let’s calculate the DCF for each year:

Total DCF: $90,909.09 + $123,966.94 + $150,262.96 + $170,770.50 + $186,267.65 = $722,177.14

Return on Investment is calculated as the ratio of the total profit to the initial investment.

Where:

• Total profit = Total cash inflows – Initial investment

Total cash inflows = $100,000 + $150,000 + $200,000 + $250,000 + $300,000 = $1,000,000

The ROI for this project is 100%.

NPV is the total discounted cash inflows minus the initial investment.

NPV = DCF – Initial Investment

From the DCF calculation, we know the total DCF is $722,177.14, and the initial investment is $500,000.

NPV = 722,177.14 – 500,000 = 222,177.14

The NPV is $222,177.14, meaning the project adds significant value.

The Payback Period is the time it takes to recover the initial investment.

We need to accumulate cash inflows until we reach $500,000.

• Year 1: $100,000

• Year 2: $150,000 (Total: $250,000)

• Year 3: $200,000 (Total: $450,000)

• Year 4: $50,000from Year 4’s $250,000 completes the recovery of the $500,000 initial investment.

So, the payback period occurs sometime during Year 4. We need to calculate how long it takes within Year 4.

The remaining $50,000 will be covered by:

Therefore, the Payback Period is 3.2 years.

IRR is the discount rate that makes the NPV of the cash flows equal to zero.

We use trial and error or a financial calculator to determine the IRR. Based on this example, the IRR turns out to be approximately 23.6%.

Summary of Results:

• Discounted Cash Flow (DCF):$722,177.14

• Return on Investment (ROI):100%

• Net Present Value (NPV):$222,177.14

• Payback Period (PBP):3.2 years

• Internal Rate of Return (IRR):23.6%

This business case indicates that the project is financially viable, with a high ROI, positive NPV, and an IRR significantly higher than the discount rate of 10%.  The only potential hurdle would be the payback period as most companies want to see a return within 2 years.

Recommendation would be to try to see if you can move income forward to improve payback times.

With the calculations in hand, data leaders can demonstrate something essential: trustworthiness. The C-suite doesn’t need data leaders to be abstract futurists. They need them to be business drivers who understand that every initiative must pass the litmus test of value creation.

Trust is built by showing that every dollar invested in data is expected to generate real returns, whether it’s through increased revenue, optimised operations, or enhanced customer engagement. By continuously showcasing how data initiatives tie back to key business metrics, be it profitability, efficiency, or customer growth, data leaders can build the trust and credibility they need to push the agenda forward.

Data leaders who succeed in building a strong relationship with the C-suite don’t talk about data they talk about value. The frameworks and tools are there; the real challenge is adopting a business-first mindset. By consistently communicating how data drives business growth and aligns with strategic goals, data leaders can transform themselves from data gatekeepers to strategic partners.

Now, more than ever, it’s time to lead with value and prove that data is more than just numbers, it’s the backbone of business success.