This week’s roundup examines how data science methods are used to turn data into value, why data visualization is fundamental to understanding data’s overall meaning, how to develop an effective analytics strategy, and optimizing your data to improve marketing ROI.
by Thor Olavsrud, Senior Writer for CIO.com
Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For most organizations, data science is employed to transform data into value that might come in the form improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like.
by Jans Aasman, CEO of Franz, Inc., featured on InfoWorld
The ability to visualize data, their relationships to one another and connections to business objectives is central to the notion of data exploration, in which users manipulate these graphical representations for greater understanding of data’s overall meaning. Data visualizations are vital for exploring knowledge graphs, which determine relationships between even seemingly unrelated datasets to indicate their relevance to specific tasks.
by Robert Pitney, Senior Data Scientist at Elder Research, featured on Predictive Analytics Times
Using analytics to achieve a sustainable competitive advantage and generate significant return on investment begins with a well-conceived analytics strategy and roadmap for success that is aligned with, and supports, the overall business strategy. An analytics assessments that includes technical, process, and cultural components can address the nuances of your organization and ensure that the conditions are right for the long-term success of your analytics initiatives.
by Andrea Steffes-Tuttle, Director of Marketing at Lityx
Marketing Optimization refers to understanding and analyzing your data to make better marketing decisions across channels. Doing this properly can result in improved ROI, increased customer response rate, increased annual revenue, decreased cost per order, and more. The challenge is to determine how to optimize your data for improved results. The amount and significance of information organizations have can be overwhelming. Think hard and define the true question you’re trying to answer. Then find the data that will answer that question.
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