{"id":5395,"date":"2021-04-21T16:26:17","date_gmt":"2021-04-21T15:26:17","guid":{"rendered":"https:\/\/www.theoaklandgroup.co.uk\/?p=5395"},"modified":"2021-04-21T16:26:17","modified_gmt":"2021-04-21T15:26:17","slug":"the-four-phases-of-a-project-analytics-roadmap","status":"publish","type":"post","link":"https:\/\/weareoakland.com\/blog\/the-four-phases-of-a-project-analytics-roadmap\/","title":{"rendered":"The Four Phases of a Project Analytics Roadmap"},"content":{"rendered":"

In this article, we continue our discussion into the process of building out a complete Project Analytics capability for your major\u00a0<\/span><\/i>projects<\/span><\/i>\u00a0initiatives.\u202f<\/span><\/i>\u00a0<\/span>\u00a0<\/span><\/p>\n

Last week, we explored the engineering architecture required for a modern Project Analytics capability. This week, we look at how to create a roadmap from your Project Analytics pilot into a production-ready solution that is fit for the future.<\/span>\u00a0<\/span><\/p>\n

Introducing the Phases of Project Analytics Deployment\u00a0<\/strong><\/p>\n

Phase 1: Data Architecture Conception \/ Proof of Viability<\/span>\u00a0<\/span><\/p>\n

We covered most of this phase in last week\u2019s article that unpacked the topic of\u00a0<\/span>data engineering for Project Analytics<\/span><\/span><\/a>.\u202f<\/span>\u00a0<\/span><\/p>\n

Key milestones in phase 1 are to determine the availability of the data you require, and the viability of the architecture you have in mind for your Project Analytics initiative.<\/span>\u00a0<\/span><\/p>\n

Coming out of this phase should be a roadmap of how your solution will be built and an \u2018illustrator\u2019 that supports any internal business case activity.<\/span>\u00a0<\/span><\/p>\n

Phase 2: Building the Core Platform\u00a0<\/strong><\/p>\n

During this phase, you will begin to leverage your earlier data engineering exploration to increase the level of data \u2018ingestion\u2019 into your Project Analytics platform. The goal here is to build a live demonstrator that possesses enough functionality to deliver value to the business.<\/span>\u00a0<\/span><\/p>\n

For example, in phase 2\u00a0<\/span>you\u2019re<\/span>\u00a0looking to demonstrate:<\/span>\u00a0<\/span><\/p>\n

Live connectivity to core systems:<\/span><\/b>\u202f<\/span>\u00a0<\/span><\/p>\n

You\u2019re looking to demonstrate that your analytics platform can pull in operational data, in an acceptable timeframe, and translate this live data into some form of\u00a0<\/span>insight<\/span>\u00a0<\/span><\/p>\n

Data organisation\/<\/span><\/b>productionisation<\/span><\/b>:<\/span><\/b>\u202f<\/span>\u00a0<\/span><\/p>\n

As discussed earlier in this series, a lot of major projects reporting in the past has required substantial manual cleansing and manipulation of the data before\u00a0<\/span>it\u2019s<\/span>\u00a0deemed \u2018fit for purpose\u2019 enough to hand over to senior management.\u202f<\/span>\u00a0<\/span><\/p>\n

In phase 2,\u00a0<\/span>you\u2019ll<\/span>\u00a0start letting go of those manual processes and start relying on the data engineering platform you created in Phase 1 to better organise and manipulate the data in a more automated fashion. You may still be some way off a fully operational, signed off solution, but your team should be putting in the right processes to ensure a more predictable result from the inbound operational data.<\/span>\u00a0<\/span><\/p>\n

Blocker removal:\u00a0<\/span><\/b>At this phase of the journey,\u00a0<\/span>it\u2019s<\/span>\u00a0not uncommon to start experiencing some blockers to your progress, these typically fall into the following categories:<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n