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5 Product Management Tips for Data Science Projects

  • MitulMakadia 
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Keeping data science projects on the right trajectory can be a challenge for even the best manager.

Data science management has become an essential element for companies that want to gain a competitive advantage. The role of data science management is to put the data analytics process into a strategic context so that companies can harness the power of their data while working on their data science project.

Data science management emphasizes aligning projects with business objectives and making teams accountable for results. It means ensuring that each team is in place, whether under the same office or as a distributed team. It also ensures that the team members are provided with appropriate roles and people contributing towards the project’s success. 

Remember, data science management is about transforming data into valuable customer insights and ensuring that these insights are acted upon appropriately by all stakeholders across the organization. Therefore, Data science without effective management is like playing chess without knowing how to move your pieces.

This guide will dive into some key focus areas for data science projects. You will understand the differences between different stages and how to tackle them effectively depending on your end goal with the project. We’ll also go over some strategies for optimizing data science projects and areas that may be considered challenging due to their complexity.

Provide deeper context 

Including developers and designers in the early stages of a product definition brings out the best ideas and results for the product’s success. Putting the best minds together under the same umbrella brings understanding the user, success, constraints, architectural choices, and workarounds. 

However, product management with data science has always felt like being with core development teams 25 years ago. It is tough to deal with weak understanding on both sides, specialized terminologies, and misconceptions such as “data science is easy.” 

To deal with market problems in such situations, you require to be aggressive about defining the below context:

  • Identify the key constraints and detailed use cases for your data science team. Point out the players and their roles in the project. 
  • Analyze the business goals and success metrics to boost the license revenue from new customers and reduce the churn rate. Identify the actions required to deal with customer care and increase customer satisfaction.
  • Share your user research and validation assets with the team and organization. For instance, user complaints about the poor user interface, revenue projections, and whatever connects the team members with the end-user. 

Remember that the data science projects are uncertain, and our judgment may be wrong

It is pretty easy to assume the outcomes before having an upfront investigation. When dealing with the data sets to predict the future using machine learning and AI models, the real world comes in the way of providing dirty data, entirely apparent results, and poor prediction scores.

For instance, you expect that the machine learning model can help us predict the stock market’s future based on historical data and public disclosures. Instead of proposing the same to your board of meetings directly, it is wise to prove the theory of how you can outthink the marketers and competitors on this prediction. 

Choosing / accessing data sets is crucial

The success and failure of the data science project depend upon the actual data sets and not on the intentions or intuitions. There is the possibility that some data sets are better than others, i.e., more filtered or more accessible. 

Moreover, organizations may often hide the data behind the regulatory walls, and you may have trouble accessing it. Therefore, investigate the ownership and permission for organizations’ internal data at the beginning of the project. Also, get in touch with external sources which may have acceptable use along with the identifiable consumer data and end-user permission.

Describe the accuracy required and anticipate handling “wrong” answer 

It is always said that level of accuracy is essential conversation at the very start of any data science project. We spend lots of time and effort identifying “somewhat better than a coin flip” accuracy; however, this is not enough when we put lives at risk in medical prediction applications with numerous false negatives. 

Every data science project will have something that surprises us, whether the answer is entirely wrong or teaches us something new about the real world. All you need is a plan for human review of results and escalation to humans when outcomes seem incorrect. 

“Done” means operationalized, not just having insights 

Data scientists coming from a new academic environment consider the success of product development when models meet the target audience and accuracy. The basic idea of product development is to be operationalized and incorporate the model and insights into working software. 

Being operationalized in data science can be challenging for the first time. Remember that it is unnecessary for product managers to have all the answers but instead have the right team in the room to identify and solve the given problems and issues. For instance, the fraud detection system should decide further actions in real-time if the transaction is suspected to be compromised at any given moment. 

Read Key Stages of a Data Science Project