1. Articles in category: Big Data and Analytics

    97-120 of 239 « 1 2 3 4 5 6 7 8 9 10 »
    1. Why analytics is eating the supply chain

      Analytics platforms are bringing new visibility into the supply chain, enabling the wholesaler to better anticipate and meet demand and offer service levels it couldn't have previously. New software is also enabling more collaboration among partners, including key customers and suppliers. New visualization tools are bringing the dynamics of the supply chain to life. Equipped with better analytics, companies can achieve tens of millions of dollars in savings along with improved service levels.

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    2. How contract modeling could reshape the NFL—and help hospitals succeed

      An exciting application of predictive modeling lies in healthcare – specifically network contracting, which encompasses the highly granular and complex agreements between payers (insurance companies and government entities such as Medicare and Medicaid) and providers (physicians, hospitals, and health systems).These new layers of complexity, can be managed and indeed capitalized on through the application of healthcare data-mining and predictive modeling – resulting in not only stronger negotiating positions for hospitals and health systems, but also greater financial security and sustainability.

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    3. Strengthening authentication through big data

      Technological advances, especially in the mobile industry, have created new possibilities, and manufacturers and vendors are offering various multi-factor solutions in the domain of biometrics, physical tokens, software tokens and mobile codes. So how can you enhance account security without disrupting the user experience? The answer might be found in big data and analytics, two trends that have proven their worth in many industries.

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    4. Turn Big Data into Big Value with Master Data Management

      Big data analysis is rapidly getting mainstream adoption in the Fortune 1000, but often without delivering strong business value, because a lack of data management quickly turns a data lake into a data swamp. If you are embarking on a big data initiative and you want to deliver business value, you can’t afford to just dump data onto Hadoop. Learn from others’ mistakes and invest in the data management capabilities you’ll need to get actionable insights. Only Master Data Management can deliver the trusted data you need for your data lake to deliver business value.

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    5. How Next-Gen Databases Elevate IT Performance

      "Data Protection for Next-Gen Databases,"  a report by Datas IO defines next-generation databases as distributed or scale-out databases or cloud databases, including NoSQL databases. Clearly, interest in these databases is on the rise, with organizations now deploying at least 10 nodes for them. Subsequently, they benefit from greater scalability and performance, with lower cost of ownership. However, many still struggle with performance, availability and data protection issues

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    6. Google launches distributed version of its TensorFlow machine learning system

      Google today announced the launch of version 0.8 of TensorFlow, its open source library for doing the hard computation work that makes machine learning possible. Normally, a small point update like this wouldn’t be all that interesting, but with this version, TensorFlow can now run the training processes for building machine learning models across hundreds of machines in parallel.

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    7. 6 Hidden Challenges Of Using The Cloud For Big Data And How To Overcome Them

      Enterprises have been slow to move big data processing to the cloud, but not for lack of trying. Most companies now use the public cloud in some form, often for SaaS applications. But enterprises have been slow to migrate big data and data warehousing to the cloud, despite cost, scalability and elasticity benefits. According to a 2014 Gartner survey, less than half of organizations with big data programs reported using the cloud in any form.

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    8. You Snooze You Lose: The Road to Business Value is Paved with Data

      The data management challenge today is exponentially more complicated. There is less business confidence in the trust and security of this heterogeneous spaghetti map of information than ever before, requiring justification to invest in even more data management competencies, such as master data management (MDM), metadata management, and data security, to name just a few. Data governance as an organizational discipline is needed now more than ever, and isn’t it time that business leaders stop expecting IT to carry this weight alone?

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    9. IoT will shake up world of data analytics, says report

      A report produced for US network operator Verizon says data analytics will become a core competency as the internet of things becomes more widely adopted by businesses. A mere 8% of enterprises that have embarked on internet of things (IoT) projects have monetized more than a quarter of the data generated by these projects.Therefore data analytic will become a core competency, with more than 50% of businesses expecting to be using more than 25% of their data by 2018-2019.

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    10. How operational analytics can help utility companies

      Data warehouses are well established in business, providing a qualitative approach to analyzing structured data. For operational analytics, data needs to be ingested from disparate manufacturing systems and open data feeds. Among the sectors that benefit from operational analytics is utilities. However, many organisations have built data-collecting infrastructure, they have yet to gain the benefits from analytics

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    11. The Tipping Point for Cloud Analytics

      Cloud has been a “trend” in IT for nearly a decade. Businesses need to be armed with the right insights at the right time to keep pace with customer expectations, market changes, and competition. The survey indicates that cloud analytics adoption has reached a tipping point: 63% of respondents say they plan to investigate, analyze, or actively deploy cloud analytics solutions over the next 12 months. Additionally, 71% said they expect to adopt a hybrid or cloud-only approach to analytics over the next three years.

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    12. How the needs of mid-sized organizations help drive analytics accessibility

      Cloud, open source, database flexibility, self-service, diverse licensing options and mobile flexibility supported a natural shift in the business intelligence landscape, making it easier for small and mid-market businesses to adopt a broader range of BI offerings. Overall, the solutions that now exist within the marketplace for all organizations offers a breadth and depth the likes of which have never been seen before. The mid-market has benefited from technology advancements and self-service BI access. 

       

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    13. The Keys to Putting IoT Data to Work for Your Organization

      Much of the talk about the Internet of Things (IoT) focuses on the “things” themselves – wearables, sensors, iBeacons, and other network-connected machines. However, the greatest value for organizations comes from combining the data generated by these devices with other customer or operational data to uncover insights and establish predictive models. This is the incredible promise of IoT, but without the ability to link data from the smart, networked “things” with other business data, its value is limited.

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    14. A Sea Change in the Data Market

      The data market is facing a set of inflection points: data location, processing, and management. As these changes drive increased volume, variety, and importance of data, the role of data integration and management becomes increasingly important. In every industry, there are inflection points that change the face of the market by creating new opportunities. Many vendors attempt to address each of these three major inflections with bits and pieces of technology. 

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    15. Blockchain applications for healthcare

       Blockchains can authenticate access to medical information, quickly and securely. New models that share medical records are emerging, as Health information exchanges (HIE) and all-payer claim databases (APCD) have become obsolete with blockchain. Patient-generated health data (PGHD) has tremendous potential with wearable technology. Innovative leaders can see the how the patient experience is evolving.

       

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    16. Sap's Hana Vora bridges divide between enterprise and Hadoop data

      Aiming to help bring contextual analytics to the data organizations have stored in Hadoop, enterprise systems and other distributed data sources, SAP Tuesday announced the general availability of in-memory query engine SAP HANA Vora. SAP debuted the software last September, though Ken Tsai, vice president, head of Cloud Platform & Data Management, Product Marketing, at SAP, says the company has been working on it behind the scenes for years.

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    17. Unlocking blockchain for the underbanked

      In an age when companies are looking for innovative ways to optimize internal processes, make and verify transactions and increase data management and security practices, the blockchain stands out as the candidate to solve their problems. Bitcoin still remains the largest example of the blockchain technology, but there have been other examples of distributed ledgers springing up around the world.

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    18. KPMG signs up IBM's Watson for help with auditing

      IBM has spent a lot of work applying Watson to healthcare applications. But Watson can tackle other problems, too, and a new customer offers an example of how it can help with professional services. On Tuesday, KPMG said it plans to start using Watson to help with its auditing and other services. Advisers plan to use Watson to analyze large volumes of structure and unstructured financial data, improving their ability to analyze anomalies and decide how to handle them.

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    19. Microsoft is bringing SQL Server to Linux

      SQL Server, Microsoft’s flagship relational database product, is now available on Linux in the form of an early private preview, with a full launch planned for mid-2017. Until now, SQL Server was strictly a Windows product, but as Scott Guthrie, Microsoft’s executive vice president of its cloud and enterprise group, writes today, the company has decided that it’s time to bring it to Linux as well

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    20. How to justify the purchase of a data integration tool

      The growing importance of business intelligence and data analytics applications in driving business decision making has made data integration's vital role in the enterprise crystal clear. From gathering data, transforming it into useful information and delivering it to the business users or processes that need it, data integration routines provide the crucial link between a variety of source and target systems. This article explores how other organizations are using these platforms to meet their needs.

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    21. Analytics Modernization: Data as Your Competitive Advantage

      If your business strategy involves competing on analytics, then now is the time to re-think your data management architecture. Investing in big data technology and new analytics tools is not enough to ensure the success of your analytics strategy. Success depends on building a highly productive and flexible data management architecture to fuel that strategy with clean, complete, timely, and secure data.

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    22. Accelerate Time to Value with Healthcare Discovery Analytics

      Electronic Medical Record (EMR) adoption, big data, and other technology trends are generating large volumes and varieties of data for analysis. Healthcare organizations need to invest in the data itself, instead of having an application-centric (or EMR-centric) view of data, which provides an incomplete snapshot of the patient by limiting the view to data within each individual application. 

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