Gordon Strodel, Archetype Consulting

Gordon Strodel

Data & Analytics Solution Owner for Slalom Boston. Background in Mechanical Engineering. Obsessed with solving problems elegantly and permanently.

Resume (PDF)

📍 Boston, MA

Agile for Data - Preface

Agile for Data - Preface

This is the preface of a larger series on Agile Methodology for Data Projects, aka “Agile for Data.” To see the entire set, please use this link.

A typical data project has set of activities that happen in serial and must all be accomplished before something is released to the customer which could provide business value. This typical project lifecycle is listed below.

An agile data project focuses on responding to change and releasing regular, incremental value to the business. An example project is illustrated below. Notice that business value is being deployed incrementally over the course of the project.

Traditional vs. Agile Approach

I want to clarify a few points in comparing/contrasting a traditional and agile approach.

Agile for Data is NOT:

•This is not a way to compress 38 weeks of work into 8 weeks.

•This is not a way to make your team happier and more efficient

•This is not a way to reduce the amount or types of skills sets you need on your analytics team

•This doesn’t eliminate any IT spend or technology purchases

•This is not limited to Cloud-only. (Any data technology can work with this framework.)

•This is not a “simply run faster” framework. We aren’t reducing the scope or resourcing, merely changing how they tackle, assign, and approach delivery of the work.

It can however, have the following impacts:

•Business value is delivered sooner, gets feedback more quickly, and will eventually reduce rework

•It will empower your team to be happier as their work product elevates over time as success and partnership with the business requestors increases

•This will allow your team to specialize on skill-set areas as they have interest

•Delivering large-scale data projects in this manner will allow for responding to change without complicated, costly change requests

•When paired with strategic initiatives like self-service analytics, your data will be able to evolve from a “report shop” to truly providing valuable analysis and predictive insights to the business units served

At the end of the day, if data is the new oil, we just want to find ways to get it faster, more reliably, with less rework and less technical debt. Agile methods give us a framework to commit to scope, accurately hit timelines, manage resource expectations, and ensure delivery of quality output. Why not leverage them?

I hope this preface sets the stage for the rest of the conversation. Please let me know via a comment below if you have questions along the way. Best, Gordon

Agile for Data - Introduction

Agile for Data - Introduction