Our Data Methodology: Engagement Architecture

April 29, 2020

If you’re using data to make decisions, you need solid engagement architecture to understand how you got to those decisions.

This is part two of a two-part series about establishing a strong foundation for advanced analytics. 

Now that you understand infrastructure architecture, we need to examine how you actually engage with your data. Simply enough, we call this “engagement architecture.” It includes all the ways you interact with your data as it flows through the infrastructure layers we discussed in part one of this series.

What is engagement architecture?

Most consulting firms don’t deal with data’s engagement architecture. They only focus on two of the four Vs of data: volume and velocity. But we want to give you confidence in the variety and veracity of your data. So we’re sticklers about engagement architecture.

We use a true-crime metaphor to help our clients understand engagement architecture. As any casual “Law & Order” viewer can explain, the evidence is collected, handled, analyzed, secured, and transported before it is ever presented in court. This is called the chain of evidence and it must be unbroken from the moment law enforcement obtains the evidence until the moment a lawyer submits it as an exhibit. 

If that chain is broken, even a smoking gun can be called into question. Similarly, your data is only as strong as the weakest link in your engagement architecture. Just as evidence flows from identification and collection to analysis and storage, your data flows from raw-raw data and raw conformed data to raw query-enabled data and conformed data for reporting. 

Where does your data come from?

Organizations with a firm data foundation know their data’s lineage. They know where their data is, who’s touching it, and in what state. Are they, for instance, touching the data as soon as it comes in, or are they touching it after it’s already been integrated? Are they touching it at all, or are they just going in and running a report?

The next time you read a data-driven report, ask yourself these three questions:

  1. Who curated this report?
  2. How did they source this report?
  3. Who is accountable for the data used in the report?

Here’s why these questions are important: Understanding your engagement models helps you understand how your data is curated for decision making. For example: Before you, the executive, even look at your data, an analyst has already interfered with the data’s lineage. They’ve conformed it, made certain fields available, etc. So, by the time you see the data, it’s already three generations removed from its original form. In essence, that engineer wrote the first draft of your report for you.

It comes down to this: if you’re using data to make decisions for, on behalf of, and against your customers, you need to have a very solid understanding of how you got to those decisions. And, you need to be accountable for them. 


At its most powerful, data is a business-driving asset. But the mere collection of data doesn’t add value. The practice of data — the wrangling of oodles of data into a structure that enables intelligence usage — and the service of data — constructing a user interface that enables the consumption of data for decision making — are what adds value. And they both require a supportive engagement infrastructure — infrastructure that gets the right person access to the right data to make the right decision at the right time. Of course, this is easier said than done, but we can help. 

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Vida Williams

Vida Williams, our Advanced Analytics Solutions Lead, began her career in technology as a tech writer. Today, she uses her experience as a data scientist to drive social and economic change. Vida is passionate about data inclusivity and serves as the first-ever “Innovator in Residence” at VCU’s da Vinci Center.

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