The Human Side of Big Data and High-Performance Analytics

Prospering from big data is not simply a matter of employing new technologies. To achieve the benefits from big data and high-performance analytics, firms will need to make some adjustments to their capabilities, even if they are already experienced users of analytics.

Big data and high-performance analytics environments are clearly different from traditional data analysis environments in many ways. This research study, however, explores the human-related differences, specifically the data scientists who do this sort of work.

Their combination of hard-core technical skills with traditional analytic capabilities makes data scientists a rare breed of professional. Recruiting, training and retaining them are challenges that will only intensify. The most successful big data organizations will be those that create or identify unique sources of data science talent.

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Centres of Excellence 101

A growing number of organizations are creating competency centers or "centers of excellence" to overcome obstacles that prevent data and analytical talent from generating enterprise-wide insight. That was the topic of a webinar in the SAS "Applying Business Analytics" series, originally broadcast in August 2010. This paper provides a summary of that webcast and makes the case for an organized, strategic and enterprise-wide approach to analytics

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Getting Value from Your Data Scientist

Simply hiring expensive data scientists isn’t enough. To create real business value with data scientists, top management must learn how to manage them effectively.

Based on a survey of more than 300 analytics professionals (of which about one-third were self-described data scientists) working in different types of US companies, this report describes: 

  • Why data scientists differ from other types of analysts.
  • Seven steps to managing data scientists for business value.
  • How analysts and data scientists view their work and their place in the organization.

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Harvard Business Review: From Data to Action

This Harvard Business Review Insight Center report examines what your data may or may not be telling you. Featuring 30 articles from researchers and practitioners in data science, marketing, and other fields, the report offer guidelines on making good use of information and turning it into profitable behaviors.

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Getting There From Here – Data Science in The Boardroom

Study after study confirms the obvious: Companies that invest in big data analytics perform at a higher level and are more profitable than their not-so-data-driven counterparts. So you would think every board of directors would be rushing to instill a top-down data-driven culture, but that hasn't happened yet. Big data analytics is used largely for customer insight (loyalty, churn, etc.) and not for top-level strategy decisions.

This white paper describes some of the reasons why this happens, and what data scientists can do to help instill a data-driven culture at the top leadership ranks of the company.

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Intelligence Quarterly Q1 2014

Today organizations are pulling in social media data, exploring machine learning, and generally aggregating more data than they ever have before. Big data is here, but do you have a strategy for managing big data? Do you need one? Can you use the same strategies you developed decades ago? You'll find the answers to these and other questions in the following pages.

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Filling the Data Scientist Gap: Schulich School of Business

York University’s Schulich School of Business addresses the lack of deep analytical skills with its MBA program.

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CIO Group: Making Big Data Approachable

In a recent study asking business and IT professionals how they make sense of their big data analysis, a resounding 98 percent said they are in the process of implementing – or have already implemented – a data visualization plan.

This CIO Market Pulse report details how organizations are combining data visualization with the power of analytics to improve decision making and promote self-service capabilities that drive collaboration.

Read about how employees who aren't data scientists or analysts are able to explore data quickly and easily, find patterns, spot inconsistencies, and get answers to queries in seconds or minutes rather than hours or days. They can then instantly create reports and share the information on the Web or mobile devices, while the IT staff is focusing on other projects.

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