Advice I would give anyone on working in Data & Analytics

Advice I would give anyone on working in Data & Analytics

September 18, 2020
directives, data, analytics

Intro #

This is just a small collection of my frequently given advice to young or newer members of the analytics industry.

If nothing else - I’d start with this:

Data Analysis exists as a scientific exploration for the purpose of driving value for it’s audience. That value can be defined as a simple aggregation of observations or, hopefully, to drive better decision making. Everything else you do will become useless if the root goal is lost:

Drive value through analytical insight.

How to use this #

Each item in the subsequent sections is a tip of the iceberg question to point you in a direction - a hint to a place to start.

The intention is to update this list periodically but also to be non-specific enough to last for the foreseeable future.

Data Layers #

1st order concepts:

  • What is my primary question to ask in this analysis? Will a given piece of data give me a solution?
  • What assumptions am I starting from for a given analysis or dataset?
  • Is answering that question within my capabilities or do I have “missing pieces”?
  • How can I prioritize my data requests?

2nd order concepts:

  • Am I using a “Reproducible” or an Adhoc approach?
  • Given an analytics question (and perhaps an answer), can I test for robustness? Longitudinal accuracy? Bias? etc.
  • How can I better balance business needs with reqest efforts (meta work)?

3rd order concepts:

  • Given an analytics solution, how can I optimize it? Scale it? Orchestrate it? Automate it?
  • Whole “data stack” perspective (What are all the components of my stack and what components add friction and where?)
  • How can I lead my organization into a more data centric reality? (Resources? Education?)

Skills / Tech #

1st order skills (manipulate some amount of data directly):

  • basic data sources (flat files, binary datasets)
  • excel tables, basic pivots, formulas etc.
  • tabling, pivoting, graphing etc.
  • a point and click analytics tool
  • select / query from a database

2nd order skills (data manipulation or analysis leverage):

  • advanced data sources (nested json data, paginated apis, etc.)
  • advanced excel knowledge (power query, power pivot, dax measures etc.)
  • complex database operations (triggers, stored procs)
  • an etl platform
  • an analytics platform
  • a version control system
  • a custom code analytics

3rd order skills (meta-manipulation, scaled):

  • profound database skillset (distributed, hdfs at scale)
  • an orchestration platform
  • software engineering skills (api / pubsub / )
  • advanced mathematical / statistics

Network #

1st order network (1-1 network development):

  • Connet with peers, alumni, or cold connect analytics professional directly.
  • Tutor, manage, or mentor others into being better within the analytics professionals.
  • Understand who among your coworkers or former coworkers can I offer additional value to directly?
  • Who in my network would I feel comfortable asking for a referral from?
  • What Meetup / Slack group exists for analytics professionals in my city / region?

2nd order network (“Always on” resource development):

  • What kind of blog / website can I maintain about myself and my projects?
  • What community can I become a contributor in? (stackoverflow, discourse, kaggle)
  • What contributions can I make to open source projects in my area? (data visualization, ML, etc.)

3rd order network (Improve the domain itself):

  • What community does not yet exist that I can create?
  • What are the bad practices that are generally perpetuated and how can they be remediated?