This week’s roundup discusses how marketers can get more out of analytics, what data scientists really do, characteristics of high performing brands using analytics, and why investment in analytics is essential to getting ahead.
How to Empower a Marketing Team with Analytics
by Sam Miller, Digital Consultant at Cognifide, featured on Fourth Source
Good analytics starts with asking the right questions, not by sifting through a mountain of data to see what turns up. A good starting point is to develop a plan for analytics, and to do this, it can be helpful to take a systematic approach to looking at what you want to achieve. This article identifies six key areas for marketing teams to develop an analytics plan around.
What Data Scientists Really Do, According to 35 Data Scientists
by Hugo Bowne-Anderson, Data Scientist and Educator at DataCamp, featured on Harvard Business Review
What, exactly, is it that data scientists do? As the host of the DataCamp podcast DataFramed, Hugo Bowne-Anderson has spoken with over 30 data scientists across a wide array of industries and academic disciplines. Among other things, he has asked them about what their jobs entail.
75% of High Performing Brands Conduct Advanced Analytics, Study Finds
provided by Lexer, featured on PR Newswire
Among the key findings in the recently released 2018 Data Culture Study – an industry report on the challenges marketers face in operationalizing data to improve the customer experience – over 75% of high performing brands have in-house analytics and insights. The report, published by Customer Data Platform, Lexer, has revealed high performing brands use data much more often.
6 Reasons Why Investment In Analytics Is Essential
contributed by Forbes Insights Team, featured on Forbes
The ability to capitalize on data insights and analytics can make or break a company. And big data, artificial intelligence, and predictive analytics have every organization scrambling for an advantage—or fearing disruption. To reap the rewards of data-driven business initiatives, enterprises must make targeted investments in traditional and emerging analytics tools, as well as in underlying IT infrastructures to support them.
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