4 Data Science Operating Models to Consider When Setting up a Data Science Team

4 Data Science Operating Models to Consider When Setting up a Data Science Team

‘Technology drives business’ is very much applicable to the case of how Data Science is being evaluated for adoption by many enterprises. For maximizing the value from Machine Learning projects and investments, it is essential for Businesses and IT teams to work together.

Data Science solutions evolve as the team understands the data and its patterns. As part of the solution development, the insights gathered are discussed with business teams and updates to models are done in quicker turnaround. So, these require a higher understanding of business goals.

There are 5 key aspects to building and running data science solutions. They are:

  • Use case definition & validation: Defining clearly the problem statement, mapping the data needed and methods for validating the results
  • Pattern mining & model building: Performing Data exploration, insights generation, pattern identification and building models such as predictive models
  • Model management : Defining the process for deploying models and monitoring results
  • Data Management : Provisioning the required data from the business applications and making it accessible for exploration
  • Standards, Processes and Tools: Supporting products and toolsets, defining the best practices and setting up processes for measuring use case business value

Hexaware has detailed views on how these 5 key aspects are setup and managed to define 4 unique data science operating models.

To know more about how these 5 key aspects of data science solutions enable choosing an operating model and ensure that the model chosen delivers value to business at an optimal cost, read our PoV right away.

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