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Where’s the Money?

Data, AI, Business Analysis, and Results

I’ve been working in the data space since 2004 (which reveals my rather advanced age).
In my first role in the field, I was at Pelephone. There were just two of us handling data in the marketing department: myself, a young analyst starting my first position, and my mentor, Ilan Barant, who taught me all the secrets and fundamentals of building proper data infrastructure.

I could give an entire talk about how the data discipline has evolved since then.
At the time I got a lot of critical feedback on how much emphasis  I was placing on data. In their words,  “ you can’t analyze every single data and action”

Today, that is exactly what is happening! The data field has  developed so enormously that it encompasses a vast range of disciplines.

It’s no longer just “analysts” like back in the day. Now we have data operations, information architecture, data engineering, pipeline management, product data management, data science, AI, machine learning, generative AI, marketing analytics, product analytics, business analysis, SOC analysis, fraud and anomaly detection, analytical tools, statistical tools, visualization tools  and much more.

All said,  I’ve always been drawn to the data used in service of increasing revenue or profitability to achieve real business outcomes.

Sometimes, complex models are required to predict churn, customer value, or campaign performance.
But in many cases, the greatest and most meaningful value from data lies in simply reflecting the true state of the business.

That means analyzing business processes, describing them with metrics, tracking business activity, and continuously improving performance through measurement and iteration.

This discipline is called Business Analysis.


It’s not as flashy as machine learning or GenAI, but it offers enormous business value.
(That’s not to downplay the tremendous value that ML and especially GenAI can bring to organizations.)

On the foundation of this approach , though not exclusively , I developed Growth & Data Driven Execution(GDDE).

The approach encompasses many additional business, managerial, operational, organizational, procedural, and technological components ,  but one thing at a time…

Back to the Numbers

The first step toward good business analysis is to quantify 100% of the organization.
This is called a budget, a business budget, not a financial one.

(Over time, this developed into a field within finance departments, known as FP&A :Financial Planning & Analysis — but its heart remains within the business unit. It requires full understanding of the business, the customers, marketing activity, trends, etc.)

The budget can include indicators such as:

  • Revenue trends
  • Expense trends
  • Customer value
  • Number of active customers
  • Marketing channel performance
  • Customer acquisition cost
  • Customer behavior and value over time
    … and more.

The goal is to describe 100% of the organization using this kind of framework.

Let me give a simple example:


Let’s say I define a customer value metric for year one in a gaming company.
And let’s say that after extensive product work, I manage to increase this value by 10%.

Does this mean I met my targets?

At first glance, you might assume that this would increase revenues by 10%. But in reality, you need to understand the full picture in order to assess the actual impact on the bottom line.

  • Is that 10% spread evenly over the entire customer lifetime?
  • What percentage of my customers are in their first year?
  • Do I have a long-tail customer base, where most users are veterans?
  • Is this improvement limited to new users on the latest version of the game, and will it take time to trickle into overall revenue?
  • Did it shorten the customer’s lifecycle, thus making this a short-term impact?
  • Did it increase acquisition costs, thereby canceling out the profitability gain?

Business analysis of organizational activity means breaking it down into measurable, trackable indicators and applying systematic, planned, and consistent actions to influence those indicators.

In my view, this is the core of data’s business value — the most powerful connection between data and the organization. And this is where the greatest potential for business impact lies.