1.3 Structure data science team

During the past decade, a huge amount of data has become available and readily accessible for analysis in many companies across different business sectors. The size, complexity, and speed of increment of data suddenly beyond the traditional scope of statistical analysis or BI reporting. To leverage the big data, do you need an internal data science team to be a core competency, or can you outsource it? The answer depends on the problems you want to solve using data. If the problems are critical to the business, you can’t afford to outsource it. Also, each company has its own business context and hence needs new kinds of data or or use the results in novel ways. Being a data driven organization requires cross organization commitments to identify what data each department needs to collect, establish the infrastructure and process for collecting and maintaining that data, and the way to deliver analytical results. Unfortunately, it is unlikely that an off-the-shelf solution will be flexible enough to adapt to the specific business context. So most of the companies establish their own data science team.

Where should data science team fit? Much has been written about different ways data science function fit in the organization. In general, data science team is organized in three ways.

  1. A standalone team

Data science is an autonomous unit that is parallel to the other organizations (such as engineering, product etc.) and the head of data science reports directly to senior leadership, ideally to the CEO or at least to someone who understands data strategy and is willing to invest to give it what it needs. The advantages of this type of data organization are:

  • Data science team has autonomy and is well positioned to tackle whatever problems it deems important to the whole company.
  • It is advantageous for people in data science team to share knowledge and grow professionally.
  • It provides a clear career path for data science professionals and shows the company treats data as a first-class asset. So it tends to attract and retain top talent people.

The biggest concern of this type of organization is the risk of marginalization. Data science only has value if data drives action which requires collaboration among data scientists, engineers, product managers and other business stakeholders across the organization. If you have a standalone data science team, it is critical to choose a data science leader who is knowledgable about the applications of data science in different areas and also has strong inter-discipline communication skills. The head of data science needs to build strong collaboration with other departments.

Also, as companies grow, each department prefers to be self-sufficient and tries to hire data own analytical personal under different titles even when they can get support from the data science team. This is why it is unlikely for an already mature company to have a standalone data science team. If you start your data science team in the early stage as a startup, it is important that the CEO sets a clear vision from the beginning and sends out strong message to the whole company about accessing data support.

  1. An embedded model

There is still a head of data science but his/her role is mostly a hiring manager and coach and he/she may report to a senior manager in IT department. The data science team brings in talented people and farms them out to the rest of the company. In other words, it gives up autonomy to ensure utility. The advantages are:

  • Data science is closer to its applications.
  • There is still a data science group so it is easy to share knowledge.
  • It has high flexibility to allocate data science resource to the rest of the company.

However, there are also concerns.

  • It brings difficulty to the management since the lead of the designated team is not responsible for data science professionals’ growth and happiness while the data science managers are not directly vested in their work.
  • Data scientists are second-class citizens everywhere and it is hard to attract and retain top talent.
  1. Integrated team

There is no data science team. Each team hires its own data science people. For example, there may be a marketing analytics group consisting of data engineer, data analyst and data scientists. The team leader is a marketing manager who has an analytical mind and deep business knowledge. The advantages are obvious.

  • Data science resource aligns with the organization very well
  • Data science professionals are first-class members and valued in their own team. The manager is responsible for data science professionals’ growth and happiness.
  • The insights from data are easily put into actions.

It works well in the short term for both the company and the data science hires. However, there are also many concerns.

  • It sacrifices the professional growth of data science hires since they work in silos and specialize in specific application. It is also difficult to share knowledge across different applied areas.
  • It is harder to move people around since they are highly associated with a specific function in the organization.
  • There is no career path for data science people and so it is difficult to retain talent.

There is not an universal answer for the best way to organize data science team. It depends on the answer of many other questions. How important do you think the data science team is for your company? What is the stage of your company when you start to build data science team? Are you a startup or a relatively mature company? Data science has its own skillset, workflow, tooling, integration process and culture. If it is critical to your organization, it is the best not to bury it under any part of the organization. Otherwise data science will inevitably only serve the need for specific branch of the organization and it also impedes data democratization across the organization. How valuable it is to use data to tell the truth, how dangerous it is to use data to affirm existing opinions. No matter which way you choose, be aware of both sides of the coin. If you are looking for a data science position, it is important to know where the data science team fits in the company.