Rick Kapalko

With degrees in Analysis and Management, Mr. Kapalko has spent two decades in both project management of agile development and contract management of operations optimization. He is adept at managing the solution path - realizing business value from engineering projects, people, processes, and data.

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Blogs from this Author

gears

Using Predictive Analytics in a Smart Factory

So you want a smart factory? Something that drives greater value by combining business processes and physical machines. Update your manufacturing processes from legacy automation to a fully connected stream of data. The solution translates a specific business problem into a data problem that can be solved with predictive analytics. Many manufacturing systems operate at […]

Data Center Interior

Predictive Model Ensembles: Pros and Cons

Many recent machine learning challenges winners are predictive model ensembles. We have seen this in the news. Data science challenges are hosted on many platforms. Techniques included decision trees, regression, and neural networks. And, winning ensembles used these in concert. But, let’s understand the pros and cons of an ensemble approach. Pros of Model Ensembles […]

Randall Bruder 3 I4nvi9d1k Unsplash

Data Wrangling – Comparing Three Predictive Analytic Techniques

I spend a bit of time data wrangling. I try to pay mind to what the predictive analytic technique needs. Likewise, it does things on its own too. Then again, when interpreting results, I think on it again. Worse, when I try to compare models or create an ensemble, I really need to know. So, […]

Data Ethics – The Profound Impacts of New Technology

Data ethics and security have been around a long time – as long as data. Constantly evolving. But, continued tech advancement has made a profound impact. So, data security and data ethics is more important than ever. 1. Magnified Relevance Tech has enabled an exponential increase of data. We know this. The more data, the […]

Web API Using Azure

Machine Instruction 3.0: Express Your Desire

The evolution of expressing our machine instruction. Let’s get there together. Machine Instruction 1.0 In the past, we taught our computers what to do by expressing our desires with exact instruction. Classic computer programming that listed step by step instructions. We taught our machines how to behave. Arguably, even legacy artificial intelligence worked this way. Of course, this way of directing […]

agile backlog groom

OLAP and Hadoop: The 4 Differences You Should Know

OLAP and Hadoop are not the same. OLAP is a technology to perform multi-dimensional analytics like reporting and data mining. It has been around since 1970. Hadoop is a technology to perform massive computation on large data. Around since 2002. They can be used together but there are differences when choosing between using Hadoop/MapReduce data […]

Machine Learning Models Have People Skills

I like seeing patterns across domains. Consider machine learning models and employees. Both models and people can be appraised. What is an employee appraisal? Employee “performance appraisal has three basic functions: (1) to provide adequate feedback to each person on his or her performance; (2) to serve as a basis for modifying or changing behavior […]

Mtn Range

5 Methods to Decompose a User Story

This is a quick guide to decompose a user story. There are lots of resources that explain methods to decompose a user story. In this case, there is a parallel with Perficient Denver office lunch time learning session. This is not rocket science, but rather, a quick guide to facilitate a forum. Therefore, use this article […]

Marketing Automation is a Quantifiable Cycle

Marketing Automation (MA) is the effort to better manage [online] marketing channels. Mark Polly calls out a 2014 Frost & Sullivan Report to say MA will be a $1.9B industry by 2020. And Marisa Lather explains some ways to get your MA going the right way. Marketing Automation vs CRM The classic “purchase funnel” is a […]

consumers

Minimum Viable Product (MVP) May Not Be Maximum Value

In software development, the Minimum Viable Product (MVP) is the quickest time to release for something useful. All these words are loaded: “quickest,” “release,” and “useful.”  Noting that development is a change, the definition [of a one-time] MVP depends on your planned current vs. future business state. Yes, I emphasize that your MVP vision depends […]

Aspen trees

Antifragile Software: 6 Things to Know and Watch

This blog is a summary of the antifragile sw movement. There is lots of links. Loosely, Antifragile is the property of thriving through volatility and surprise.  There is an antifragile sw manifesto, but note also that this goes beyond robust/resilient. As part of a Digital Transformation, here are six things to think about. 1. Adaptive Failure […]

Agile Backlog Groom

Hype Cycle for 3 Phases of an Agile Backlog Groom

I was reading about the Gartner Hype Cycle and realized the same curve could be applied to Agile Backlog Groom (or simply Grooming).  Grooming is the process for an agile scrum team to absorb new work and add details. Every piece of work, no matter the size, follows this generic evolution. That is, the following […]

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