Booster Analytics

A data strategy is a comprehensive plan that defines how an organization will collect, manage, use, and govern data to achieve its business objectives. It’s not just about technology — it’s a business capability that aligns data initiatives with corporate goals.

In my dual role as a CTO and an academic working closely with industry, I see a recurring pattern: medium-sized enterprises want to “do AI,” but they get the data strategy wrong, right from the start. Not because they lack ambition or talent, but because they underestimate what a real AI data strategy involves.

The result? Disconnected pilots, unusable data, and a growing pile of “AI initiatives” that never leave the lab.

Let me explain what I’ve observed and how I’ve approached AI data strategy in a way that actually works for companies without billion-dollar budgets or hundreds of engineers.

1. Mistake: Starting with the Model Instead of the Problem

One of the most common missteps is jumping straight to AI without a clearly defined business problem. Companies hire data scientists or launch AI proof-of-concepts, but no one can articulate what value success would bring or how it connects to the bottom line.

What I do instead:

We always begin with business questions:

– What decisions are currently made on gut instinct?

– Where are we losing time, money, or customers due to slow insights?

– What KPIs are we trying to improve, and how will we measure AI’s contribution?

This practice grounds the data strategy in purpose, not hype.

2. Mistake: Assuming Existing Data Is “AI-Ready”

Many executives assume that just because data exists in the organization, it’s ready to be used for AI. In reality, much of that data is fragmented, unstructured, inconsistent, or simply irrelevant.

What I do instead:

I lead a thorough data audit — not just of what’s available but also of what’s useful. We profile datasets, identify gaps, and assess lineage. I bring academic rigor here: we evaluate validity, sampling bias, and the long-term sustainability of the data.

In practice, this means rejecting “data swamps” and focusing on curated, governed sources that AI systems can rely on.

3. Mistake: Over-Governing or Under-Governing Data

Some firms impose rigid, top-down governance structures copied from larger enterprises. Others treat governance as an afterthought, letting each team handle data however they want.

What I do instead:

I advocate for contextual governance — lightweight but enforceable.

We assign data ownership, set clear access policies, and automate quality checks. It’s not bureaucracy — it’s risk mitigation and efficiency. And it scales without breaking operations.

4. Mistake: Building Tech Infrastructure Without Strategic Direction

Medium enterprises sometimes overinvest in tools — data lakes, visualization platforms, AI toolkits — without a roadmap for integration or use. This is the “build it and they will come” fallacy.

What I do instead:

We take a “just enough” architecture approach.

– Centralize what must be shared (e.g., customer data).

– Decentralize what’s domain-specific (e.g., operational logs).

– Use open and composable tools wherever possible.

The goal is not technical elegance — it’s business agility with a future-proof stack.

5. Mistake: Isolating AI Teams from the Business

Another mistake I often see is hiring a small AI team and isolating them from operational units. They become a disconnected R&D lab, working on projects no one understands or uses.

What I do instead:

We embed AI into existing workflows. That means:

– ML outputs are integrated into CRM dashboards, supply chain tools, or service portals.

– Model feedback loops are aligned with business processes.

– Explainability is a non-negotiable, especially for adoption at the executive level.

I’ve learned that even the best models fail if the business doesn’t trust or use them. Thus, I stressed to my students the importance of stakeholder management.

6. Mistake: Underinvesting in Talent That Can Bridge Domains

Too often, companies look for unicorn hires or overload a single data scientist with unrealistic expectations. Or they leave it all to IT.

What I do instead:

I build cross-functional teams. We combine:

– A strong data engineer (for pipelines and quality)

– A practical ML specialist (for prototyping and production)

– Business analysts who speak both business and data

A fractional advisor or academic consultant, when needed, to bring in an advanced perspective. You can also contact us at the university too.

Talent bridging is more valuable than talent stacking.

7. Mistake: Treating AI Strategy as a One-Off Project

AI isn’t a project — it’s a capability. Many medium-sized firms still treat it as a fixed-scope initiative with a launch date and a sunset.

What I do instead:

We treat AI as a living system. I use an evolving roadmap with three phases:

– Foundational (3 — 6 months): Clean up data, automate basic reporting, pilot use cases

– Operational (6 — 12 months): Deploy predictive models, integrate into decision-making

– Transformational (12 — 24 months): Intelligent products, autonomous workflows, NLP-based insights

Each phase is paired with measurable business outcomes and feedback mechanisms.

Parting Thoughts

When medium-sized enterprises fail at AI, it’s rarely due to a lack of vision. It’s usually because they misunderstood what building an AI-ready business really entails. They confuse buying tools with creating value, and they focus on models before mastering their data.

As both a CTO and an academic, I see the opportunity and the pitfalls more clearly now. AI isn’t about replicating what Amazon or Google does. It’s about designing for your size, your context, and your strategic priorities.

The companies that win in this space aren’t the ones with the flashiest tools — they’re the ones with the clearest purpose, the cleanest data, and the tightest integration between tech and business.