Tag Archives: Data scientist

Let business teams ask “why?” to drive data science results with guided analytics

March 12, 2018

We’ve all heard the onslaught or experienced it first-hand. An inquisitive three-year old unleashing a salvo of, “Mommy, why is this?” “Daddy, why is that?” and “What is that thing?” My recent favorite twist to this line of inquiry happened when I overheard a brave and honest parent dare to admit, “I don’t know.” The… Read More »

Analytics at scale: what data analysts need to know

March 7, 2018

With every technology company shouting “Big Data”, we are led to think analytics challenges can be solved simply by storing a whole mess of data. With current technology, storing large volumes of data is easy. It also provides absolutely no value. Value only comes from data when it is examined, manipulated, learned from, and acted… Read More »

Spend more time on analytics and less on data prep

March 6, 2018

What are the things keeping analysts and data scientists from productivity?  According to studies, it’s wrangling data. Vast volumes of data need to be sourced, collected, organized and cleansed to be useful in solving problems.  In an article by Forbes, it is estimated that about 80 percent of the time spent working with data was… Read More »

Accelerate your career in big data and analytics

November 1, 2017

The growth in popularity of data science as a subject, coupled with the global boom in data creation, and organisations looking to maximise information through big data strategies, means that more and more people are building a career in this space. However, given the embryonic nature of the category, many of those joining its ranks… Read More »

Is failure good for your data scientists?

September 25, 2017

If you’ve heard of data science (if you haven’t, where have you been and how did you find this blog?), you’ve probably heard of “fail fast”. The fail fast mentality is based on the notion that if an activity isn’t going to work, you should find out as quickly as possible, and stop doing it.… Read More »

DevOps For Data Science: Why Analytics Ops Is Key To Value

December 13, 2016

It may be a stretch to call data science commonplace, but the question “what’s next” is often heard with regard to analytics. And then the conversation often turns straight to Artificial Intelligence and deep learning. Instead, a tough love review of the current reality may be in order. The simple truth is that, as currently… Read More »

Implementing Teradata Temporal in a Physical Data Model using ERwin

June 15, 2016

There is no question that a database-supported temporal implementation gives Teradata users a powerful tool, because the time dimension has been added to data management and query processing. For the business user, the ability to ask more sophisticated time-based questions from their data warehouse, and receive more insightful answers, can yield a distinct competitive advantage.… Read More »

Let’s Be Clear: DevOps and the Agile Approach

March 2, 2016

If you are a curious individual then, like me, when you hear a new buzzword of occupational interest, you Google it and try to understand what it really means and how it fits into what you do. Right? More important, you want to know how you can apply this to your everyday work life to… Read More »

The Joy of Data Viz: The Data You Weren’t Looking For

December 8, 2015

“A good sketch is better than a long speech” – Napoléon Bonaparte I recently came across this quote on the opening page of Phil Simons’ book,  “The Visual Organization, Data Visualization, Big Data, and the Quest for Better Decisions.” It is available online. Data visualization, or Data Viz as it is often referred to, is… Read More »

The Revenge of the Schema: Why Structure Is Crucial to Success with Big Data

November 16, 2015

One way to look at progress in technology is to recognize that each new generation provides a better version of what we’ve always wanted. If you look back at the claims for Hollerith punch card-based computing or the first generation of IBM mainframes, you find that the language is recognizable and can be found in… Read More »