Are You Up to Date on the Latest Big Data News of the Week?
According to a recent Big Data Made Simple article, the increasing volume and detail of the information captured is growing exponentially. A few years ago this meant data could be managed by a few in-house specialists, but today the implications of data are wide reaching.
There are logical challenges in managing, sorting, and maintaining this data. There are also subsequent benefits. That said, very few companies have truly mastered how to persistently turn insights into action resulting in profits. In pharma analytics specifically, experience can dictate the difference between a quality set of impactful, widely utilized analytics or an expensive set of reports that do not drive bottom line.
This week’s selection is about things C-suite and marketers need to know about big data with a special Nostradamus big data predictions bonus. Happy reading!
3 Things Marketers Need to Know About Big Data
Published on Big Data Made Simple
This article presents three key ways marketers need to be aware of big data. 1) Big Data will become the basis for competitive advantage 2) Creatives will take a back seat to data scientists and 3) No matter how daunting the task of mastering big data sounds, get started now. Read on to discover the implications of big data for leaders in every sector.
4 Common C-suite Mistakes That Can Ruin Your Potential Big Data ROI
By H.O. Maycotte, published on Forbes
Big Data has boomed and companies across all industries are buying into platforms and tools that allow them to capture, unify, analyze and put action to disparate sources of user or customer data. According to IDC, big data technology and services will reach a $32.4 billion market by 2017, a growth rate six times higher than the overall information and communication technology market. And with the likes of Sephora, L’Oreal, Wal-Mart etc. buying into the big data tech and services economy, it’s likely it won’t take till 2017 to outpace the forecast. This Forbes contributed article reviews the biggest mistakes and take aways C-suite need to be aware of when it comes to this hot topic.
11 Ways Data Analysis Can Boost Your Bottom Line
By Karen A. Frenkel, published on CIO Insight
Companies recognize the need to analyze the data they amass, but the ROI is often unclear. Depending on the organization, industry and type of data at hand, analysis can either lead to lucrative insights or come up dry. Clearly, many companies also want to hire data analysts, making data analyst one of the world’s most in-demand jobs, according to Gartner. CIOs should understand how to deal with hybrid data, the combination of structured and unstructured data, and how to shine light on dark data, which is collected but unused despite its value. Dev Patel presents 11 nuggets of advice on how data analysts can increase revenue for their company and help solve the ROI problem.
Big Data, Big Pharma and You. All of You.
By Tom Ulrich, published on Vector Blog
Where is the next generation of therapeutic innovations going to come from? Population-level genomic studies? The fitness trackers on everyone’s wrist? Mining electronic medical records? People’s tweets, Yelps and Facebook posts? How about all of the above?
What all of these things have in common is data. Lots of it. Some of it represents kinds of data that didn’t exist 5 or 10 years ago, but all of it is slowly beginning to fuel the pharma sector’s efforts to create the next blockbuster drug or targeted therapeutic. At least, it should be. But as Forbes contributor, David Shaywitz, noted this spring, big pharma has in some ways been late to dive into the Big Data revolution. Discover how big data and digital health opportunities can ultimately improve healthcare.
Nostradamus on Big Data: 5 Predictions
By Srikant Sastri, published on Big Data Made Simple
It’s almost 450 years since Nostradamus wrote his best-seller, “Les Propheties”, and gave everyone sleepless nights ever since.
What predictions would he make today about Big Data in the 2015-2020 era? Read on to discover 5 big data predictions that may or may not realize.