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Data

3 Problems with the Data Science Labor Market

3 Problems with the Data Science Labor Market

September 4, 2014
We are already in the era of big data, and it is only getting bigger. Every day we gain faster access to a larger volume of data, which then must be analyzed in order to fully capitalize on this expanding resource. Image courtesy of infocux via Flickr But this wealth of data can be incredibly useful. According to McKinsey, analyzing big data allows for access to more accurate and detailed information that can be applied to boost a company’s performance, provide better, narrower segmentation of customers, and offer a thorough and analytical backbone to the decision-making process. Despite its improving reputation, there are still three main problems with data science as a field:1. It is an understaffed field Even with the addition of courses in many universities related to data science and a surge in related job offerings, there is still a massive shortage of workers for [...]

Data Innovations:Human Data over Predictive Analytics

July 23, 2014
If you’ve never watched the dystopian 90s film Gattaca, go put it on your watch list. If you have, forgive my quick (inadequate) summary. The plot surrounds Vincent Freeman, a young man whose life is predetermined at birth due to an analysis of his genetic makeup to be “inadequate.” As the plot unravels, we see Vincent take ownership of his future as defies the societal determinism placed upon him. While the film is very philosophical in nature, it gives us picture of a world relying completely on prediction, instead of simply asking the people what they are capable of doing and then letting them determine the outcomes. Image courtesy of Leon Fishman via Flickr The Predictive vs. Human Data Problem Businesses are starting to evaluate the use of data innovations for insight discovery, but what isn’t being discussed enough is what form of data to use:predictive or human [...]
Letting Human Data Drive Business Level Strategy:Part 1

Letting Human Data Drive Business Level Strategy:Part 1

July 8, 2014
Everyone has a voice. In the business world, however, voices are not always heard. It depends on how well leaders listen to both their employees and their customers. It’s a scalability challenge. As technology and innovation proceeds at a rapid pace, collecting and analyzing the opinions of these stakeholders feels like an arduous and overwhelming task. Information overload is the business strategy challenge of the 21st century. We have entered the era where data has become integrated into the process of listening and how leaders construct business strategies. It’s important where we listen and how we listen. This article covers some important aspects all business leaders should take into consideration during many decision-making processes while forming business level strategy. Listen to the Doers A question:assuming a company’s people are the critical path to successful outcomes, then how come we don’t ask for their input more often? Think about [...]
4 Essential Steps for Building Scalable Data-Driven Products

4 Essential Steps for Building Scalable Data-Driven Products

May 1, 2014
An eager data scientist exclaims to the product manager, “Imagine the possibilities!” The product manager looks at him and asks, “What does it do?” The data scientist says, “We don’t know yet, but just imagine the possibilities!” Some may laugh at this conversation, others may cringe at the recent memories it invokes, and still others may utter involuntary frightful screams. Perhaps the relationship between product managers and data scientists is a necessary evil, a perpetually at odds system of checks and balances that attempts to ensure superior products for your customers and overall success of the business. So what does one do with such a recipe for frustration to scale it? Is it even possible? It is! In this post I will share the 4 essential steps to building scalable data-driven products:Teach how to learn the outcome. Effectively communicate big data. Give them a seat at the strategy [...]
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