Harnessing the Power of Big Data Big Opportunity for Retailers to Win Customers

Retailers generate large volumes of data across their supply chain and at various customer touch points across omni-channel operations. Harnessing the power of big data – big opportunity for retailers to win customers.

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Pivotal

Pivotal’s cloud native platform drives software innovation for many of the world’s most admired brands. With millions of developers in communities around the world, Pivotal technology touches billions of users every day. After shaping the software development culture of Silicon Valley's most valuable companies for over a decade, today Pivotal leads a global technology movement transforming how the world builds software.

OTHER WHITEPAPERS
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Data-Driven Business Models: Challenges and Opportunities of Big Data

whitePaper | June 6, 2017

This report draws on interviews with 28 business leaders and stakeholder representatives from the UK and US in order to answer the following questions: How is (big) data being used; what is a ‘big data business model’? What are the main obstacles to exploitation of big data in the economy? What can and should be done to mitigate these challenges and ensure that the opportunities provided by big data are realised? There is optimism about profit potential, but experts caution that big data does not automatically lead to profits. Many business leaders do not see ‘big data’ as a new phenomenon; rather it is perceived as being a continuation of a process by which companies seek competitive advantage or efficiency through the application of (data) science and technology.

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THE INTERCONNECT FOR THE EXASCALE ERA

whitePaper | July 9, 2019

Crays next-generation Shasta supercomputer represents a major advancement in the flexibility and capability of Cray supercomputers. The exascale-class system will be the basis of Crays converged architecture for simulation, analytics, AI, and data management over the next decade and beyond.

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BETTING ON BIG DATA

whitePaper |

The time is ripe for big data and analytics initiatives to pull leaders ahead of the pack. New and varied sources of data, including weblogs, sensors and social media streams, are weaving their way into the analytics mix, making it easier than ever to create connections that convert into actionable insights. By analyzing everything from consumer transactions to location data, organizations are realizing key business advantages and achieving a measurable impact on revenue.

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Data Science and Open Source Software

whitePaper | June 8, 2018

The skills of a data scientist. Non-technical skills. Education: Computer Science, Mathematics, Statistics. Domain Knowledge. Intellectual Curiosity. Communication Skills. Balance between technical, communication and presentation skills. Technical skills. Programming: R, Python. Machine Learning/Data. Mining: scikit-learn, KNIME, TensorFlow, Theano, PyTorch. Big Data Processing: Hadoop, Spark, Flink or Storm.

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Big Data Analysis with Revolution R Enterprise

whitePaper | August 10, 2010

The R language is well established as the language for doing statistics, data analysis, data-mining algorithm development, stock trading, credit risk scoring, market basket analysis and all manner of predictive analytics. However, given the deluge of data that must be processed and analyzed today, many organizations have been reticent about deploying R beyond research into production applications. The main barrier is that R is a memory-bound language. All data used in calculations — vectors, matrices, lists, data frames, and so forth — all need to be held in memory. Even for modern computers with 64-bit address spaces and huge amounts of RAM, dealing with data sets that are tens of gigabytes and hundreds of millions of rows (or larger) can present a significant challenge. The problem isn’t just one of capacity, that is, being simply being able to accommodate the data in memory for analysis. For mission-critical applications, performance is also a prime consideration: if the overnight analysis does not complete in time for the open of business the next day, that’s just as much of a failure as an out-of-memory error. And with data set sizes growing rapidly, scalability is also of concern: even if the in-memory analysis completes today, the IT manager still needs the confidence that the production run will complete — on time! — as the data set grows.

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Advanced ‘Big Data’ Analytics with R and Hadoop

whitePaper |

Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge.

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Spotlight

Pivotal

Pivotal’s cloud native platform drives software innovation for many of the world’s most admired brands. With millions of developers in communities around the world, Pivotal technology touches billions of users every day. After shaping the software development culture of Silicon Valley's most valuable companies for over a decade, today Pivotal leads a global technology movement transforming how the world builds software.

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