How to Build a Successful Analytics Team in 5 Steps?

by July 8, 2020


To ensure success in Data Science projects, the need is to fill it with a Talented Team!

Managing data science projects is not a piece of cake! It involves brainstorming, discussing and fine-tuning strategies that arise from stakeholders to better understand the manpower requirements that correspond to an organizations’ complexity as the amounts of data use increases. Here is how an enterprise can formulate successful analytics teams the backbone of data science projects in 5 scalable steps-

• Recognize Complementary Skills- BUILD a team from different academic backgrounds.

• Foster Team Collaboration- HIRE specialists whose strengths complement one another.

• De-Crystalize Hierarchy- TRUST in no one-size-fits-all approach.

• Formulate a Reporting Structure- ACKNOWLEDGE the change of reporting structure.

• Device Success Parameters- ENSURE availability of targeted success metrics.


BUILD a team from different backgrounds

Data scientists and Analytics professionals are hired from a variety of academic backgrounds including physics, statistics, mathematics and so on. With the availability of drag and drop data science tools, citizen data scientists from these diverse backgrounds are working alongside technical data scientists. What matters is a quest to adapt to the dynamic changes in technologies, with a logical mind and first-rate critical thinking skills. You never know marine biologist expertise may prove extremely valuable, who can translate domain knowledge about how dolphin pods behave in the wild can be surprisingly useful for modelling robots.


HIRE specialists whose strengths complement with each other

It is important to hire professionals whose skills and strengths complement each other, rather than building isolated teams who expertise in a particular domain. A big picture brings a banquet of professionals with a different set of expertise. Someone who can articulate stories with data, a visualization wizard working on interactive dashboards, and a business analyst who can communicate effectively with the stakeholders.


TRUST in no one-size-fits-all approach 

Different enterprises have unique data science project requirements. While hiring, this factor must be kept into account. The focus on decentralized teams assure that different data science projects are meet with equitable resources excelling in subject-matter understanding and process expertise as required. The result will be a winning combination where decentralized teams bring agility, business-specific expertise, flexibility, and customization to the workflows.


ACKNOWLEDGE the change of reporting structure

The reporting structure of data science professionals has changed, consider the past when analytics programs were spin-offs of existing analytics capabilities and consequently reported directly to departments like IT, business intelligence or finance. However, as data and analytics become more vital to how the organization executes work and makes decisions, this reporting structure has changed


ENSURE availability of targeted success metrics

Like any other program or team, organizations need to ensure they have targeted measures to monitor the success of their analytics and data programs. This not only helps the organizations identify improvement opportunities and manage performance, it additionally ensures that the long-term goals of a data-driven, decision-making culture are met.