Minimum Viable Architecture - Things on the Edge blog

Forrester BI Sandboxes (est. 2014)

5/4/2022

20 Questions

3/18/2022

There are an infinite number of questions you could ask about any business. How many actually provide the answers you need, in a way you can easily understand? The answer is 20. Obviously, there may be more and there may be less. If you can easy answer these questions, you will have a great understanding of how your business is operating.

One thing to note, these questions are roughly broken into three categories. First, companies (or profit centers) who are not yet profitable. The second group represents most companies. You are making a profit, but it fluctuates from period to period or is starting to reliably grow. The final group represents companies who are in the enviable position to be able to consider significant expansion and investment in new opportunities.

If you are currently creating metrics, reports, and dashboards, how many of these questions are you answering? It might be time to reassess the value of your current reporting. In my experience, we develop the metrics we can derive from the data we can easily access not the metrics that reflect the value we bring to our customers.

Can you quickly answer and act on these 20 questions?

For a new company or profit center:

How quickly are we turning the money we spend into cash?
How long is our cash runway?
Are we being paid on time?
Are we paying our suppliers on time?
Are we delivering late or incomplete orders because we can not afford the raw materials?

For a growing company or profit center:

Are we making money (profitable)?
Are we meeting our customer commitments?
Where is our greatest return on current operations?
Are we making the right thing, at the right time, with the right quality?
Could we take on 20% more business without affecting on-time delivery, lead-time, or quality?
Where could we make dramatic price reductions?
Where could we make dramatic delivery time reductions?
Which team is over worked?
Which decision takes the longest to make?
Who is our most important supplier?
Who is our most important competitor?
What is our largest externality?

For an established company or profit center:

Where is our greatest return on new investment?
Where should we be investing next?
What is our greatest competitive risk?

How did you score? I'm going to guess poorly. If you are not an executive in the company, or your company has adopted an "open book" policy, the answers to most of these questions are hidden. Feel free to share these with the Executives you know and have them reach out to me for additional questions.

Data Mesh Radio podcast

2/11/2022

I had the opportunity to talk with Scott Hirleman at Data Mesh Radio. We discussed identifying potential domain data team members and leveling up their skills by starting them out, building, and using an exploratory data platform.

We discussed some of the broad features of the platform and if you would like a full walkthrough, look at the recorded demo.

The Engineering Side of Data

2/1/2022

This week I had the opportunity to talk with Bob Haffner about Data Engineering for Data Discovery. Give it a listen and subscribe to his podcast / YouTube channel.

Full walk through video

1/30/2022

Sit back with your favorite snack -- It's a long one!

The data product we are building in the book is truly a "full stack" development experience. We start with an Excel file and end up with a totally automated serverless application. Yes, it looks like a lot of code, but most of it is replying the same basic patterns again and again.

Data Engineering Podcast

1/18/2022

January 15, 2022 I had the opportunity to appear on the Data Engineering Podcast with Tobias Macey.
An Introduction To Data And Analytics Engineering For Non-Programmers - Episode 255

Applications of data have grown well beyond the venerable business intelligence dashboards that organizations have relied on for decades. Now it is being used to power consumer facing services, influence organizational behaviors, and build sophisticated machine learning systems. Given this increased level of importance it has become necessary for everyone in the business to treat data as a product in the same way that software applications have driven the early 2000s. In this episode Brian McMillan shares his work on the book "Building Data Products" and how he is working to educate business users and data professionals about the combination of technical, economical, and business considerations that need to be blended for these projects to succeed.

How to estimate and prioritize

10/26/2021

This is an excerpt from the last chapter of my Building Data Products Book. At this point we've completed a number of iterations building up to a solid first pass at a sales analytics system. Borrowing from SAFe (don't @ me. I've also got plenty to say!) the next step is to take a break, continue to refactor, and think about what comes next. This is a good time to introduce some suggestions on how to prioritize what to work on when you have many possibilities.

SAFe (and many others) recommend the Weighted Shortest Job First (WSJF) method described on the blackswanfarming.com website as Cost of Delay. Good luck getting a group of people with their own priorities to agree on using actual dollar values to define value! You need to ease them into it by making the people who can assess the business value and urgency story point them, just like they made the developers use story points to determine the value of their work. (/snark)

Data and Analytics Engineering

9/10/2021

As far back as 2014, ThoughtWorks proposed the concept of an Inverse Conway Maneuver as a way to use team structure to drive the adoption of a desired architectural state. As discussed above, small independent development teams go hand in hand with the Microservices Architecture pattern.

Conway's Law asserts that organizations are constrained to produce application designs which are copies of their communication structures. This often leads to unintended friction points. The 'Inverse Conway Maneuver' recommends evolving your team and organizational structure to promote your desired architecture. Ideally, your technology architecture will display isomorphism with your business architecture. [1]

Like application developers went from front end, back end, UX, testing, and production support roles to DevOps and full-stack developer roles, the traditional data roles of business analyst, ETL developer, data modeler, and production support can be transitioned into data engineering and analytics engineering roles.

If you have Shadow IT in your world, you have already failed. - Part 3

10/28/2015

There is nothing I find more exciting than finding a small team who has build something great for their customers without the official blessing of some corporate team. Unfortunately, rolling it out to the larger company can be a disaster.

Ten self-reinforcing technologies - Part 2

10/27/2015

Continued from - Part 1
Previously published October, 21 2015

There are currently ten top contenders for the most important technologies leading businesses are using to provide a competitive edge. All of these evolved from the need to creatively solve the significant issues faced by small teams trying to quickly deliver great products. Summarized simply as: "How do we build and deliver a product customers love and are willing to pay for as quickly as possible?"