Building effective solutions requires interdisciplinary knowledge and skill. It can go beyond understanding context of problem, gathering/cleaning data, feature engineering, training/testing a model and making solution available (though critical). Understanding when to buy, build, or outsource a solution is essential to innovating, scaling, and sustaining data products.  Let’s walk through the full-stack skills and considerations to enable effective analytical data products.

First let’s baseline – as the data science field matures different types of data scientist skills, roles, and responsibilities will emerge (fun note here on “battle of the Data Science Venn Diagrams…let’s not take ourselves to seriously). Being a “Analytical leader” tasked to deploy data products will require knowledge of why, when, where, and how a product will be used to achieve desired outcomes.

Driving the Success of Data Science Solutions

At a glance this may be overwhelming, but read this as a team roster not ones resume. Let’s start with 4 major skills and add details as we go.

Define a relevant problem or opportunity.

  • Ask better questions.
  • Understand end-user, supporting process, and desired outcome.
  • Analyze data within context of user, process, and outcome (EDA including unsupervised techniques).
  • Visualize data and tell a story that matters (including impact to business metrics and real outcomes)

Assessing when to build, buy, or outsource analytical solution.

  • Do we have access to data? Do we trust data? Can we govern data? Does data change often?
  • What’s the required latency of data during decision-making or execution of process
  • Does current data infrastructure support volume and variety of data to meet latency requirements? Whats the cost to get there and maintain it?
  • Do we have technical resources available to support solutions at life-cycle stages (e.g. develop, deploy, maintain, and evolve)?  All very different skills sets.

Be hands-on – establish ability to quickly build, test, fail, iterate, and deploy usable solutions

  • Do we have data and analytics workbench that enables end to end data solutions?
  • Are these workbench environments cost effective as they change with new technology (e.g. consider open-source stack)
  • Are these workbench environments scale-able, configurable, supportable, and portable?
  • Do you have people who can learn quickly and demonstrate solution within context of business problem? I’m not talking proof-of-concepts or technology demos but workable solutions.

Ensuring analytical solution is effective.

  • Are users aware of solutions and potential impact?
  • Are they actually using it and getting desired outcome?
  • Are users providing feedback on how to improve solution and are improvements being made?
  • Assess total cost of solution ownership (includes data platform, support, storage, micro-services, enhancements to remain competitive).
  • Is solution effective for users and economical to maintain and evolve (giving strategic advantage)?