This document discusses best practices for developing data-driven products. It advises that data scientists should focus on writing batch processes and machine learning, while engineers build production frameworks. For a data science team to succeed, it requires competent management, cross-functional collaboration between data scientists, engineers, product managers, and investment in infrastructure. When hiring data scientists, companies should select for intelligence, practical skills, and communication over degrees or past employers. Successful data-driven products often have an elegant key insight, can be proven quickly, and use a batch processing framework.

1. Big Data
little big results
Some thoughts on
the management of
the development of
data-driven products
- Steven Stegman, linkedin.com/in/stegman

2. What do “Data Scientists” do?
• Write batch processes.
– Many features that involve data are much easier to build
using batch processes.
– Batch processes are easier and faster to iterate on.
– This is changing (engineers are using Hadoop now)
• Play with machine learning.
– The details here usually don’t matter much:
strategy and insights trump algorithms 95% of the time.
“We’re not building a cathedral, we’re building a website.”

3. What don’t Data Scientists do?
And what does that mean for you?
• Don’t write production code.
– Engineers need to build frameworks to put stuff on site, so…
– 99 data scientists with no engineers == useless.
• Don’t have expert product skills.
– Everyone thinks their ideas are great and will work, but…
– Product ideation/strategy is a separate skill, and…
– Most data scientists aren’t good at this.
• Don’t want to “just do what they’re told”.
– Most have a strong independent streak, but…
– The Montessori model (“do what you want”)
will fail 95% of the time
due to agency costs and lack of product skills.

4. Setting them up for success
• It’s a team effort (limited by its weakest link)
– No design, engineering, ops, and product resources  failure.
– No competent management  failure.
Resourcing, prioritization, mentoring, supervision.
– No investment in infrastructure  failure
• Hire for the future, not the past
– Degrees, former employers do not predict future success.
– Select for intelligence, practicality, product intuition, motivation,
and communication.
– Skills can be learned, but you can’t fix stupid (or lazy*).
• Culture: sense of urgency.
“adapt, improvise, overcome”.

5. Many data-driven products…
• …have a “crux”:
a key insight needed to solve the problem…
in an elegant manner
– Have you solved the crux?
• Yes? Then build it!
• No? Then keep thinking while you do something else.
• …can be proof-of-concept-ed quickly.
– If it takes more than a week, it’s not a proof-of-concept
– Will this idea work or not? Be honest.
• …use batch-compute  store  retrieve.
– This is a framework you need to invest in.

6. Big Data
little big results
- Steven Stegman, linkedin.com/in/stegman