The Dreaded Question: “Why Doesn’t This Metric in System A Exactly Match System B?”

Imagine if you will, a prospect hires you to come in and design a new analytics implementation using a new tool. Everything is going great and you’re getting all the information you need from the stakeholder interviews you’ve conducted. You’ve identified all the systems in play, including the incumbent analytics tool that is going to be replaced. Your client continuously tells you how error prone and incorrect it is. You complete the initial release of the new analytics implementation and start working with your client on the maturity roadmap to continuously enhance their new implementation. You’re making progress, but after a few months some users start to question why the numbers with the new implementation are different from the previous system. The new implementation has been tested thoroughly and there are no major issues. You reassure your client that the numbers are accurate. Even though they called the former system error prone they insist in comparing the new to the old and since the new system generates different numbers, it must be wrong. This seems like the perfect episode of the Twilight Zone. It’s something that is encountered frequently with some variation. This is something that I’ve experienced in the past and still experience today. No matter how much you confirm the new analytics implementation is accurate, your client still defers to the previous one. There are multiple reasons for this. They had used it for so long they were used to the information that was coming from it. They made decisions based on the information. Anything different must be wrong. It’s a Trap! I was involved in a...

Adobe Analytics Amazon Alexa Skill

If you are already familiar with the Adobe Analytics APIs then creating an Adobe Analytics-Amazon Alexa Skill is a pretty simple process.     Building Alexa Skills with the Alexa Skills Kit STEP 1: Create a New Skill If you haven’t already, your first step is to create an Amazon Developer account. Once you have an account, then you can create your first skill. From the Alexa Skills area, click ‘Add a New Skill’ STEP 2: Skill Information Name: This is the name of your Skill should you choose to deploy it to the Alexa App Store. Invocation Name: This is the name a user will use to call your Skill e.g. Alexa ask Adobe…. Application Id: This is a unique identifier for your application and is use as a check, within your endpoint script (more on that later), to ensure that the service calling your script is your Alexa Skill and no one else.   STEP 3: Interaction Model   The Interaction Model defines how users will interact with your skill. Intent Schema The Intent Schema is a JSON Object that defines the key words and phrases that are spoken by a user and how it maps to your Skill. For this example, we are building a very basic skill that simply accepts a reporting time period as an input. We accomplish this by defining an intent, GetDate, and a Slot that will hold the key reporting time period values. The Slot is defined using a name, ReportDate, and in this instance a list of valid dates for the Skill defined as LIST_OF_DATES.  Custom Slot Types In this example, we are using a custom type...