3 Tips to Building an Effective Data Science Team

3 Tips to Building Effective Data Science Team - The Center of Applied Data Science
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What is the secret recipe to form an effective data science team?

It has become an open secret that for organizations to stay relevant in the digital era, they need to focus on developing their own data science capabilities. This involves owning the whole data lifecycle of data management, analysis, and science processes.

A new Harvard Business Review Analytic Services and IBM study shows 72% of business leaders believe they are susceptible to disruption within three years but only 14% are prepared to respond. Since 90% of today’s data was created in the last two years, how have organizations invested in their data science team to cope with the new challenges?

In the data lifecycle, there are three key roles within a data science team: data engineers, data analysts, and data scientists.

Intuitively, data engineers are at the start of the data lifecycle. One of the activities of data engineers is to establish and operationalize the enterprise-wide data governance regime to ensure that the right level of data quality is observed. Other responsibilities include effective data management, database management, data architecture, and implementing data security systems.
Data analysts are well-versed in the art of data exploration, manipulation, and visualization. In today’s environment, data analysts are also expected to be able to execute statistics tests, have knowledge of running experiments, and build simple business models.

Data scientists digest the large amount of data coupled with business knowledge, create an analytics model that will drive revenue creation and cost efficiencies. Depending on the size of the organization, the roles of data analysts and data scientists could be merged, although it has been said that a good data scientist will have roots in data analysis.

So, what are the ingredients of an effective data science team?

Tip #1: Build a strong team

The core components follow the data lifecycle; data engineer, data analyst, and data scientist, the combination of which depends on the size of the organization. As a rule of thumb, successful data-driven organizations have 5%-8% of their employees in the data science team.

Tip #2: Leader is key

However, to make the team effective, the main ingredient that is often overlooked or even underestimated is the data science leadership role. Organizations must install an experienced director or head of data science at the senior management level. This individual should have an extensive background in establishing and running an operational data science team. Coupled with business acumen and commercial nous, the director of data science will ensure that the organization achieves success in this digital era.

Tip #3: Look for internal top talent and upskill them

The best data science team does not need to come from IT or computer science background. Non-technical professionals who transition and turn into data science and analytics roles can bring their past experience and expertise to improve and innovate better. Forming a data science team with people from different departments is the ultimate secret recipe for success.

Unsure of where to start?

We are here to help! We work with organizations to develop a strategic framework to align people, systems, and processes with an AI-powered system.

Discover how matured Data-Driven Organizations form an effective Data Science team based on 6 pillars of Data-Driven Organization.

CADS has developed our own proprietary model of the Data-Driven Organization Roadmap. The unique model allows an organization to have a bird-eye view of missing gaps that block its transition to do more with data.

Schedule a consultancy call with us to explore how you can leverage our technology to accelerate the transformation to become a matured data-driven organization in the next 1 – 2 years.

Visit www.thecads.com/data-driven-organization to explore more details about our program.

 

Navigate Through Digital Transformation - MBA in Business Analytics
MBA in Business Analytics
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