Nov/Dec 2019

In today’s world, the term “politics” generally stirs up emotional reactions, debates and strong opinions. Luckily, I won’t be delving into the realm of national political opinion. The politics I’m discussing here fall under the last of Merriam Webster’s five definitions: “competition between competing interest groups or individuals for power and leadership.” Large organizations have multiple functional areas and departments, often with competing interests and their own corporate culture – hence the term “office politics.”

• Zahir Balaporia

It’s fair to say that artificial intelligence (AI) and machine learning (ML) have become two of the biggest commercially applicable digital phenomena over the past decade. Whether it’s within e-commerce chatbots, recruitment matching software or even AI-created music, AI and ML have truly seeped into more industries than we could have ever expected.

• DJ Das

DataOps (data operations) – an automated, process-oriented methodology for optimizing the rapid collection, integration, analysis, security and integrity of data – needs to be standard practice at any insight-driven organization. DataOps improves the quality and reduces the cycle time of data analytics, while upholding security, compliance and governance policies. When implemented correctly, the practice increases the business value of data science by supporting cross-functional collaboration and accelerating time to market.

• Matt Baird

Data people come in all shapes and sizes; the modern industry analytics professional may have had his or her foundational training as a research physicist, might have cut his or her teeth optimizing ad revenue and “click” count, or may have a traditional statistics or actuarial background [1]. Surprisingly, the recent explosion in availability of data tools and expertise has not yet been accompanied by the development of firmer, contemporary frameworks for “doing” data science. Should we wait for a random search to evolve an optimal algorithm for conducting the modern data science workstream? Or are older frameworks such as CRISP-DM (Cross Industry Standard Process for Data Mining) [2] optimal and sufficiently useful models?

• Stuart Jackson
• Sean Hegarty

As more and more companies are looking to take advantage of the intelligence inherent in analytics, many wonder how best to get started when it comes to preparing data for analytics. The requirement to find, clean and organize corporate data can fall upon the data analyst. Rather than actually spending time interpreting analytics and trying to solve problems, determining answers to questions posed or predicting emerging trends, data analysts can often find themselves spending the majority of their time simply trying to prepare the data for analytics.

• Heine Krog Iversen