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Kosta Kolev
CEO at Calliper | Sequoia Arc '22 | Follow for insights on startups, analytics & product management
Kosta Kolev is a seasoned entrepreneur with over a decade of experience in initiating, expanding, and sometimes encountering setbacks with startups in Europe and Asia.
His brainchild, Calliper, was established to consolidate his vast array of industry expertise and facilitate other entrepreneurs in discovering Product Market Fit promptly, with a vision to assist teams in utilizing data effectively to enhance their impactful endeavors.
Among his notable career milestones are organizing the pioneering hackathon onboard an aircraft, overseeing a hardware accelerator for MIT while aiding in the establishment of an innovation lab in Hong Kong, incubating Atfarm and MachineMax under BCG Digital Ventures, and engaging with over 30 startups as a proficient product coach and consultant.
Kosta conducted Product & Growth workshops for distinguished entities such as Antler, EF, and Imperial University, in addition to conducting 100+ interviews with Founders and Investors for the creation of the Investor Relations Field Guide.
His educational background includes pursuing Master's Degrees in Mechanical and Manufacturing Engineering from the University of Warwick, and Engineering from The Hong Kong University of Science and Technology.
In the realm of organizations, Kosta has held pivotal roles including Co-Founder at Calliper, advisor at Antler, product coach/startup advisor at Sabia Studios, Founder in Residence at Moonfire Ventures, advisor at Alena, Entrepreneur In Residence at WeWork Labs, Head of Product at Kantan, Venture Architect at BCG Digital Ventures, Co-Founder at Hack Horizon, Program Director at MIT, among others.
Kosta is always enthusiastic about engaging with and supporting fellow founders, inviting connections at [email protected].
Highlights
Are you data-driven or data-informed?
Let’s unpack what both mean.
📊 Data-Driven:
→ Make decisions based solely on data, even when other factors like your experience and intuition contradict it
→ Assumes data provides a complete understanding of a situation or problem
→ Assumes that reality lacks nuance
For example, this approach may work well when A/B-testing prices and features on your pricing page.
📊 + 👤 Data-Informed:
→ Blend human intuition with data to make decisions that are aware- and experience-based
→ The idea is that data augments, not replaces. This allows for the nuances of reality to seep into decision-making
→ Data is vital, but it’s one of several inputs considered in decision-making alongside experience, strategy, and culture
For example, this approach is preferable when refining a product’s UX.
Which approach should you strive for?
I think being data-informed is generally the best of both worlds, but it depends on the situation.
If you opt for being data-driven, you get:
→ Less human bias and more objectivity
→ Less responsibility placed on leadership
→ Greater company-wide transparency
Data-driven approaches overlook broader context and force unquestioning faith in data.
They fail to account for the nuanced nature of reality, which isn't always clear-cut.
Sometimes it’s hard to rely solely on data for solutions to complex, multi-faceted problems.
What do you think?
📊 Dashboards are everywhere, but many are useless. Here are the biggest dashboard pitfalls 👇🏻
🤯 Too Complicated
It fails to clearly convey information to your team by over-complicating it to the point of confusion.
📢 Low signal/noise ratio
It has a small amount of useful data (signal) versus not useful data (noise). The most common dashboard mistake we see is metric overload. Too many metrics dilute the focus on what directly affects your team's performance.
👀 Cluttered and unclear
It's difficult to understand the key takeaways at a glance, straining your eyes. This increases attention deficit and disengages your number-shy team members.
📉 Lack of benchmarks
Viewing data without historical context gives no crucial reference point. This makes it hard to understand whether a metric is improving or worsening. An absence of benchmarking also hinders setting and tracking goals.
🔎 Limited investigation
It doesn’t allow users to manipulate the data to drill down and explore different perspectives at a deeper level. This is problematic when you need specific answers to specific questions. Lack of interactivity through menus, splits, and filters limits usefulness.
📅 Outdated and inaccurate
If you're using spreadsheets that are updated monthly by different teams, you're likely operating in the dark and susceptible to human errors. An outdated, inaccurate dashboard isn’t ideal if you need to make decisions quickly.
🔗 Not shareable
What use is your data when it's siloed? Data is best used cross-functionally. Having a dashboard that isn't shareable hinders team alignment around goals, keeps people uninformed, and forces you to share ugly screenshots in Slack.
Have I missed any major pitfalls?
