Building an Effective Data Science Team
It has become an open secret that for organisations 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 organisations 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 operationalise 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 visualisation. 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 analytics model that will drive revenue creation and cost efficiencies. Depending on the size of the organisation, 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? The core components follow the data lifecycle; data engineer, data analyst and data scientist, the combination of which depends on the size of the organisation. As a rule of thumb, successful data driven organisations have 5%-8% of its employees in the data science team.
However, to make the team effective, the main ingredient that is often overlooked or even underestimated is the data science leadership role. Organisations 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 organisation achieves success in this digital era.