Each time I start a new project helping a team build analytic into their application, I start at the same place: the plan. Often I’ll hear the same thing — “we don’t need to talk about that; we’re past all that.” That’s what I thought too, back when I built my first data application. It wasn’t until later in the project, when I started having some issues, that I realized I’d skipped over a few things in the plan.

Starting with a shared plan for your analytics is an essential first step in a successful project. These eight steps will help ensure that you don’t accidentally skip one of these early project steps and end up paying the price later.

Here’s the first checklist:

Produced by OmniGraffle 7.10.2 2019-06-07 21:03:15 +0000 Canvas 1 Layer 1 Copyright 2019 NextWave Business Intelligence Has an executive sponsor been appointed? Appointing a sponsor or champion for the project can be a key factor in its success. This isn’t the person who performs the actual implementation or the procurement person — it’s the person who will shepherd the project through, cradle to grave. Having this person in place ensures that project doesn’t stall or even die out at the first roadblock. Is the project essential to revenue growth, customer retention, or market positioning?  Why are you doing your analytics project? If it’s because “everyone has analytics” and you’d like some (don’t laugh — I’ve hear this one before), it’s a sign that the project isn’t critical and therefore, not a priority. Ensure that you’ve identified exactly how your analytics relate to revenue, retention, or strengthening your position in the market. Will this be considered a profit-center for the business? Are the analytics going to be a profit center for your business? Projects that are counted on to deliver profit to the business tend to be under a bit more scrutiny than those that are “nice to haves.” And, if done right, analytics absolutely can deliver new revenue streams. If your project isn’t considered a revenue driver, is it really going to be a priority? Is the executive team in agreement what the data product should be/do? I’ve walked into situations where, when polled, each executive has a different opinion of what constitutes “success” for the data product. Some say dashboards, others would be happy with reports, while still others expect data discovery capability. Whatever the goal of the analytic application, make sure that every one of the executive team is in agreement. Have goals been set? Analytics projects rarely seem to have goal. “Implement analytics” is often the only charge given to the project leader. This is a mistake that can lead to a successfully implemented technology that fails to deliver any benefit to the business. Set goals for personas served, data problems addressed, users on-boarded, daily utilization, etc. BEFORE starting the implementation process. Has a timeline been set and is it reasonable? It seems simple, but people (ahem, me) skip this one all the time. Set a fixed deadline for the implementation of the initial stage of the analytic deployment — and make sure everyone agrees that it can be achieved. Is budget available and realistic? Analytic teams tend to underestimate the cost of implementing analytics — especially if they’re doing much of the work in-house. Make sure you’ve accounted for the cost of data wrangling, cleansing, QA, training, and all the other gotcha’s that are baked into every project. Has a priority been set for the project? Make sure your analytic project is a key priority for the business. This is easier if it’s tied to revenue or other customer targets (see #2). Given the number of stakeholders, from IT to DevOps to business units involved in the average data product, it’s essential that everyone involved understand the priority given to the effort. Analytics Readiness Checklist #1: Planning the project Text BUSINESS INTELLIGENCE

(You can download a PDF of this checklist here.)

That’s Checklist #1: Planning. It might seem like this isn’t completely focused on the data and analytics part of the project and that’s for a reason — you’re not there yet. These steps are absolutely essential to the success of an analytic project. Don’t be tempted to gloss over them — they are key to ANY major initiative.

In the next checklist in the series, we’ll cover data readiness.