6 New Articles on Harnessing the Power of Big Data

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From disruptive technologies to predictive analysis, big data is driving transformation and innovation. However, some organizations’ efforts are falling flat due to inherent challenges with harnessing the power of big data. This week, we’re looking at how businesses can achieve success with big data analysis and overcome obstacles on the way.

1. Top 10 Disruptive Technologies in Pharma

By Chanice Henry, published on Pharma IQ

Disruptive technology and big data

Pharma isn’t immune to disruptive technologies. Enterprises are actively changing the industry’s culture, or showing true potential to do so. These organizations include microchip clinical trials set to provide information about human organs and artificial intelligence aiding decision-making in patient treatment. Along with other companies that are generating powerful analytics, many of these disruptive technologies are undergirded by big data.

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2. Predictive Analytics Builds a More Agile Business

By Samuel Greengard, published on CIO Insight

Predictive analytics – the key to innovation

Businesses have used the ability to look at the past and make decisions to improve outcomes. Now, predictive data is changing the game, allowing organizations to see into the future and gain unique insights. Many are banking on its ability to allow organizations to innovate via a much more granular look at the market, trends and customers. With predictive analysis, businesses can gain a deeper insight into more complex issues, and drive operational efficiencies to gain a competitive edge, increase profitability and improve organizational performance.

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3. 5 Lessons from Data Analytics Ninjas

By Ann All, published on Enterprise Apps Today

Data analytics fuels big data initiatives

With companies like Wal-Mart and BC Hydro expanding on successful initiatives, data analytics is helping drive better use of big data. Some of the principles these companies follow from which other organizations wanting to improve business operations can learn include: Promoting best practices for data use and governance; using data visualization; training use on data analytics skills; and weeding out the good data from the bad. Organizations that are able to make good use of data via analytics work to first find a problem that needs solving, creating a goal to pursue and the driving program development from there.

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4. Avoiding The Drift Into Analytics Oblivion: Turning Your Business Into An Analytics-Driven One

By Scott Langfeldt, published on Forbes

Could big data mean a business ultimately becomes obsolete?

The right recipe for success now means using analytics, big data and fast processing, but it’s not the only key to making it in the fast-changing marketplace. Even with significant investments in analytics, some organizations are failing to harness the power of big data. Due to increased market pressures and competitive environments, in some cases, analytic failures are making businesses obsolete. The deciding factor of success lies in knowing how to act on data, and how quickly an organization takes to respond.

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5. How CIOs can truly use big data effectively

By David Weldon, published on FierceCIO

Big data disappoints without effective analysis

Big data delivers vast opportunities for organizations to improve outcomes and drive profits – but only when data management and analysis are effective. This challenge is fast becoming one of the most difficult components for an agile organization. Effective analysis helps organizations manage their storehouse of data and make sense of it. The way to get ahead is not to merely collect data. Without proper analysis of big data, organizations can’t drive research and development, sales and marketing, and more.

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6. 6 Causes Of Big Data Discrepancies

By Lisa Morgan, published on InformationWeek

Big Data – Big Possibilities

Dangerous data discrepancies leads to different results, a huge frustration for organizations across industries. Identifying and levering big data involves understanding data quality, faithfully running data-cleansing procedures, employing true algorithms, using the right models, understanding model complexity, and recognizing interpretations can differ. The key is to know that human activity can impact big data analysis and take steps to prevent inherent uncertainties in the outcomes from affecting it.

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