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INSIGHT: Top 5 steps for building a great data science team

INSIGHT: Top 5 steps for building a great data science team

Ross Farrelly, Chief Data Scientist, Teradata A/NZ has identified five steps to help organisations build a great data science team.

The aim of collecting Big Data is to uncover key insights that can deliver business transformation and success.

But the trick is to find people capable of analysing the data effectively.

Ross Farrelly, Chief Data Scientist, Teradata A/NZ has identified five steps to help organisations build a great data science team:

1. Stop hunting unicorns

Businesses are unlikely to find a single person with the requisite development, mathematics, statistics and business domain expertise. Instead, they should assemble a best-of-breed team and empower them to work together.

Some of the key types of people that should be included in the team are: data engineers; project managers; machine learning experts; and data modellers. If possible it is advisable to fill some of the roles with people already in the organisation, delivering some team members with existing business and domain knowledge.

2. If you build it…

As well as a team of smart people, organisations need a solid data infrastructure. Having the infrastructure in place while you assemble a team means they can get started right away.

3. Have a compass

Analysing data indiscriminately is ineffective and costly. The challenge to the data science team should not be to simply ‘find something interesting’. Their efforts must be aligned to business goals. It is vital that the business provides a question or hypothesis for investigation.

In addition, data scientists often have favourite tools and techniques. Make sure they are not so tied to a particular toolset or algorithm that they lack the flexibility to work on the organisation’s mission instead of their own research interests.

It is also critical to avoid getting caught up in problems that are interesting but have little bearing on the organisation’s main goals and priorities.

4. Have a timetable

Unlike pure research where publication is the benchmark for success, business demands iteration and delivery. It is therefore crucial to populate the data science team with people that know how to get things done, and can track project efforts and deliver at a steady pace.

5. Learn to spot success

When there is clear collaboration among business analysts, data engineers and data scientists, then the data science operation is on the right path.

Data science is a big field and a cross-functional team is better prepared than an individual to handle real-world challenges and goals.

Hiring smart people who like learning and collaborating with others on interesting problems is the best way to create great data science teams.

By Ross Farrelly, Chief Data Scientist, Teradata A/NZ


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