Building effective analytical solutions requires interdisciplinary knowledge and skill. Though very critical, it can go beyond understanding context of problem, gathering/cleaning data, testing/training a model and making solution available. Understanding when to buy, build, or outsource, assessing total cost of ownership vs. realized value are essential to sustain effective solutions. Below I will attempt to walk through the full-stack skills and considerations to enable effective analytic products.
First let’s baseline – as the data science field matures different types of data scientists 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 provide effective data solutions.
At a glance this may be overwhelming, but read this as a team roster not one persons resume. Let’s start with 3 major skills and add details as we go.
1 – 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 performance metrics)
2 – 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.
3 – Ensuring analytical solution is effective.
- Are users aware of solutions and potential impact to them, if utilized?
- 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 (more strategic consideration)?