AI / Data Science Operating Model: Teams, Processes

data science operating model

Realizing value from AI/data science or machine learning projects requires the coordination of many different teams based on an appropriate operating model. If you want to build an effective AI/data science operation, you need to create a data science operating model that outlines the steps involved in how teams are structured, how data science projects are implemented, how the maturity of data science practice is evaluated and an overall governance model which is used to keep a track of data science initiatives. In this blog post, we will discuss the key components of a data science operating model and provide examples of how to optimize your data science process.

AI / Data science operating model

AI / Data science forms one of the workstreams of data analytics and one of the key pillars of digital transformation. At times, AI/data science is also referred to as advanced analytics. Data science teams work very closely with data management, data engineering, and data visualization teams to develop data products that can be used to drive business decisions.

Operating models are important for data-driven organizations because they provide a clear and concise framework for data product development. The data science operating model provides a standard process for data product development that can be followed by data science teams across the organization. Depending on the size, complexity, and business drivers, the following different operating model structures can be implemented for data science:

  • Centralized data science team: In this model, all data science activities are centralized within a single team or organization. This structure is well suited for organizations that have a clear business need for data science and want to invest in building a dedicated data science capability.
  • Decentralized data science teams: In this model, data science activities are distributed across different business functions/divisions or based on different product lines. This structure is well suited for organizations that follow the following structure:
  • Data science teams based on business divisions: In this model, data science teams are aligned with specific business divisions. For example, in a pharma company, there are different divisions such as oncology, neurology, and cardiology. One can set up data science teams in each of these divisions.
  • Data science teams based on product lines: In this model, data science teams are aligned with specific product lines. For example, in a retail company, there are different product lines such as apparel, cosmetics, and home goods. One can set up data science teams for each of these product lines. In another example such as a bank, there are different product lines such as retail banking, corporate banking, and investment banking. One can set up data science teams for each of these product lines.
  • Geographically distributed data science teams: In this model, data science activities are distributed across different geographical regions. For example, in a global company, data science teams can be set up in different continents such as Europe, Asia, and North America.
  • Hybrid data science teams: In this model, data science activities are centralized within a single team or organization, but there are also decentralized data science teams that work on specific projects. These teams are formed and diffused on-demand. This structure is well suited for organizations that want to invest in building a dedicated data science capability, but also want to decentralize data science activities to different business functions/divisions or product lines.

AI / Data science development lifecycle/process

The following represents some of the key steps for the implementation of AI / data science projects:

  • Data collection and data preparation: This is the first step in any data science project. Data scientists need to collect data from various sources, clean it, and prepare the data for analysis.
  • Exploratory data analysis: In this step, data scientists analyze the data to understand the relationships between different variables. They also try to identify any patterns or trends in the data.
  • Model development: In this step, data scientists develop predictive models using the data. They also evaluate different models to find the one that best fits the data.
  • Model deployment: In this step, data scientists deploy the model in a production environment and monitor its performance. They also retrain the model on new data as it becomes available.

Data science team structure

At a very high level, the following is what the composition of a data science team would look like. For details, you may check out my other blog – data science team structure – roles & responsibilities

  • Product manager: Product managers are responsible for defining the data science roadmap and priorities. They work with business stakeholders to understand the business problem and translate it into a data science problem.
  • Data science architect: Data science architect is responsible for architecting and designing data science solutions. They work with data scientists and help them design scalable and efficient data science solutions. They work with product managers to identify one or more data science solutions for a given business problem.
  • Data scientists: Data scientists are responsible for data analysis, model development, and model deployment. They use their statistical and machine learning skills to solve business problems.
  • Data engineer: Data engineer is responsible for data ingestion, data processing, and data warehousing. They work with data scientists to understand the data requirements and help them design efficient data pipelines.
  • MLOps engineer: MLOps engineer is responsible for the operationalization of data science solutions. They work with data scientists and data engineers to help them deploy and manage data science models in production.

AI / Data science maturity model

The following are different stages of data science maturity that can be tracked for the effectiveness of the data science operating model:

  • Ad-hoc: In this stage, data science projects are undertaken on an ad-hoc basis with no clear process or structure in place. This is typically seen in organizations that are just starting out with data science.
  • Foundation: In this stage, data science projects are undertaken using a defined process. There is typically a data science team in place with clear roles and responsibilities. However, the process is still ad-hoc and there is no formal governance in place.
  • Enabler: In this stage, data science projects are undertaken using a defined process with formal governance in place. There is typically a data science center of excellence (CoE) in place with clear roles and responsibilities. The CoE provides guidance and support to data science teams across the organization.
  • Transformational: In this stage, data science is embedded into the organization’s culture and processes. Data science teams work closely with business teams to identify and solve business problems. There is a clear data science roadmap in place with priorities set by the business.

AI / Data science governance model

The following represents some of the key aspects of the governance of AI / data science projects, teams, processes, etc.

  • Roles and responsibilities: There should be clear roles and responsibilities for data science teams.
  • Standards and procedures: There should be clear standards and procedures for data science projects. This includes standards for data quality, model development, model deployment, etc.
  • Tools and technologies: There should be clear guidelines on the use of tools and technologies for data science projects. This includes guidelines on the use of open-source tools, proprietary tools, cloud-based tools, etc.
  • Communication and collaboration: There should be clear communication and collaboration channels between data science teams and other teams in the organization. This includes communication channels between data scientists, business stakeholders, IT staff, etc.
  • Formal governance: There should be a formal governance structure in place for data science projects. This includes a data science steering committee, a data science center of excellence, etc.

The data science operating model is a data-driven approach to developing data products and services. The data science operating model includes four main steps: setting up an enterprise-wide data science team structure, setting up a data science development lifecycle, setting up or hiring a data science team, setting up a data science maturity model, and setting up data science governance model. It is important to set up an operating model to realize the full potential of data science in an organization. If you would like to learn more, please leave your comments below or contact me.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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