Data analytics consulting: what to expect and how to choose

Why companies bring in outside help

Expertise is the bottleneck almost everywhere. Among EU enterprises that considered AI but don't use it, lack of relevant expertise is the most cited barrier ^[1], and in Türkiye the figure is 72.3% ^[2]. Hiring a full data team is slow and expensive, and for many mid-sized companies it isn't yet justified. Consulting fills the gap: senior expertise for a defined problem, for a defined time.

What a good engagement looks like

1. It starts with a decision, not a tool. Which decision should be faster or better: ordering, pricing, which customers to retain, what management sees on Monday?
2. It checks the data first. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data ^[3]. A consultant who doesn't look at your data before promising results is guessing.
3. It defines success up front. Forecast error, hours saved on reporting, retention rate: written down before the work starts.
4. It delivers something working early. A fixed-scope pilot of a few weeks beats an open-ended project.
5. It plans the handover. Documentation, training and a clear owner, so the result outlives the engagement.

Engagement models compared

Seven questions to ask before you hire

1. What decision will this improve, and how will we measure it?
2. What will you need from our data, and what happens if it isn't good enough?
3. When will we see the first working result?
4. Who exactly will do the work?
5. What do we own at the end: code, models, documentation?
6. How will personal data be handled?
7. How do we end the engagement if it isn't working?

Red flags

• Results or percentages promised before anyone has seen your data.
• A pitch that lists technologies instead of outcomes.
• No clear owner or handover plan.
• Long lock-in with no practical way out.

Be especially careful with agentic AI projects: Gartner predicts over 40% will be canceled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls ^[4].

Working with Klarvo

At Klarvo Analytics I turn companies' data into decisions and products: corporate trainings, in-house AI assistants, demand forecasting, customer analytics, reporting automation, custom dashboards and fractional data science leadership. I hold an MSc in Physics and have 7+ years in machine learning, much of it for international clients. As co-founder and Chief AI Officer of Causality Engine I built its analytics engine from scratch, and at BilgeAdam I've trained around 140 people, including teams from Türkçimento, TUSAŞ and TEI.

• I reply to every message within 24 hours.
• You can end an engagement with 30 days' notice.
• Invoicing goes through my company registered in Türkiye.