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  • admin 9:55 am on July 11, 2017 Permalink
    Tags: , Everything, , , Maybe’, ,   

    Maybe You Can’t Machine Learn Everything – But does that mean you shouldn’t try? 

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  • admin 9:51 am on April 2, 2017 Permalink
    Tags: , , , Everything, Flexible, Jumps, Licensing,   

    Teradata Jumps Ahead: Flexible Licensing Choices Change Everything 

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  • admin 9:52 am on September 14, 2016 Permalink
    Tags: Alles, Daten, Ende, Everything, , ohne, , vernetzt   

    Alles vernetzt alles smart und Daten ohne Ende das Internet of Everything 


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  • admin 9:51 am on July 27, 2016 Permalink
    Tags: , , , , , Everything, , , ,   

    Data Changes Everything: Teradata PARTNERS Conference Will Showcase the Business Impact of Big Data Analytics 

    Data Changes Everything: Teradata PARTNERS Conference Will Showcase the Business Impact of Big Data Analytics. ‘Prepare to be inspired,’ says conference committee president, by disruptive innovations, influencers, insights.
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  • admin 9:51 am on February 25, 2016 Permalink
    Tags: , Everything,   

    Welcome to the ‘Analytics of Everything’ 

    Consumers are constantly switching brands, forcing companies to find new ways to keep existing customers, win back old ones and appeal to new ones.

    We live in hyper-connected world. Billions of “things,” including people, businesses, machines and sensors, are interacting and creating new types of data at volumes never seen before. The Internet of Things (IoT) presents an enormous opportunity to integrate and analyze data from a diverse range of sources.

    Analytics is what gives the IoT meaning. The “Analytics of Everything” (AoE) delivers actionable insights to businesses on any area of interest at the most granular levels. These insights have the potential to disrupt and transform products, services, processes and even entire supply chains and industries.

    As the latest issue of Teradata Magazine points out, the barrier to turning so much data into value is that companies are not leveraging the information that’s available to them. In fact, some organizations are using just 1% of their data. In this issue, you’ll find out:

    This issue also features business case studies, thought leadership from today’s industry influencers and innovative technologies that can give businesses a strategic advantage. Download our free app to read it on your iPhone and iPad.

    Brett Martin

    Editor-in-Chief

    Teradata Magazine

    The post Welcome to the ‘Analytics of Everything’ appeared first on Magazine Blog.

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  • admin 9:53 am on January 29, 2016 Permalink
    Tags: , , Everything, , , Timing   

    Timing Is Everything: Why The Connected Car Needs Smarter Analytics 

    While other industries are just now coming to grips with sensor data and other forms of big data, the automotive industry can smugly say that they are veterans in this area. Since the late nineties, car manufacturers have been using data from the Engine Control Unit (ECU), Controller Area Network (CAN) and telematics to improve and enhance their vehicles.

    Fast forward to today, and while car manufacturers are comfortable with big data, there’s a new challenge looming – lots of data. The connected car has been called a “gigantic data-collection engine” for good reason.

    Just how much data do the experts think car manufacturers are going to have to be prepared for? Let me give you an illustration. Today, car makers might be downloading 100 – 200 kilobytes of data from a car, once a year, during its annual service. With the connected car, kilobytes of data can be downloaded every day. In addition, connected cars will have remote diagnostics capability to record data on-demand as needed, so engineers can study anomalies in detail.

    car
    The scale of the data deluge becomes clear when we take into account that analysts, Gartner, predict that there will be 250 million connected cars on the road by 2020.

    Just what sort of data would a car manufacturer be able to collect from a connected car? Here are just a few examples – Vehicle Generated Data, User Generated Data, Network Generated Data, Vehicle Configuration, Geolocation, Vehicle Owner, and Diagnostic Trouble Code – and that’s just the tip of the iceberg.

    It’s clear that car manufacturers will need to decide whether it makes business sense to collect all the data available, considering the cost of transferring and managing the data.

    Don’t Drive Round in Circles

    And it’s not just about deciding what data to collect. Car manufacturers also need that data to parlay into value for the company.

    With industry trends indicating that the nature of car ownership itself is changing, the data from the connected car can play an integral role in helping car makers position themselves to offer alternatives such as pay-as-you-drive insurance, car leasing or shared-usage businesses.

    For car manufacturers, their data journey should not only include data from the connected car, it should also take into account other sources of data held by the company. It is when these are analysed with the right technology, that car manufacturers can get real business insights.

    Timing is Everything

    Car manufacturers often get themselves in trouble when thinking that all this data needs to be analysed in real-time. But not only would that be prohibitively expensive, it would also be a drain on valuable resources. Instead, car manufacturers need to think about how the data can be used and to what benefit. Often analyzing the data minutes, hours, and days after the data is collected still yields actionable insights.

    Here are some examples of the value that data analytics performed at the right time rather than in real-time can provide:

    Sub-Seconds –Combining the data fed from forward facing radars, with the connection of the vehicle to infrastructure, the ability to see around corners and other cars, crashes can be prevented with seconds to spare.

    Seconds to Minutes -Traction control systems sensing slippage on a wheel sends data to other cars approaching that location, warning them of the hazardous conditions.

    Minutes
    -Transmitting alerts to owners via anti-theft devices if a vehicle is suspected to have been stolen, based on entry mode or location.

    Hours-Detecting quality issues of cars in the field or targeting offers and services to connected owners as the car passes a certain position.

    Days
    –By analyzing the usage patterns and behaviors of customers, car companies can propose deals for pay-as-you-drive insurance or information on a car-sharing program.

    Months -Feeding usage information back to design teams, so that changes can be implemented, for instance, if sensor read-outs suggest that back doors of certain models are not often opened and closed, design teams can make a decision to only manufacture a 3-door version of that model instead of the 5-door version.

    This approach of performing analytics on the data at the right time, rather than in real time, means that companies can put the ability to query the data in the hands of frontline staff, not just strategic or middle-management levels.

    Call centre operatives, showroom sales staff and service centre repair engineers can see all the other touch points and conversations that a particular consumer has had with the company. This means that they can respond intelligently to the customer, which, in the long-term, means satisfied and loyal customers, better efficiency and profitability.

    If you’re interested in this topic, you will find in-depth analysis and innovative examples of how connected car data is being used in Winning the Connected Car Data Wars.

    This post first appeared on Forbes TeradataVoice on 29/10/2015.

    The post Timing Is Everything: Why The Connected Car Needs Smarter Analytics appeared first on International Blog.

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  • admin 9:46 am on November 4, 2015 Permalink
    Tags: , , Everything, , , , listener, , , , ,   

    Teradata pushes Analytics of Everything message as it launches real time listener and integrated Hadoop and Aster UDA 

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  • admin 9:58 am on June 4, 2015 Permalink
    Tags: Everything, wellforgotten   

    Everything new is a well-forgotten old 

    What does database development have to do with fashion? The answer is cycles.

    Fashion seems to recycle itself every 20 years. Some schools of thought call it the nostalgia trends. When we are young we look up to our parents fashion style and when we reach young adult age, we begin, sometimes subconsciously, to copy them and their style. Hence, the 20 year cycle.

    However, some fashion items never go out of fashion. They seem to survive the test of time. Such as ‘Le Smoking’,the women tuxedo suits. Ever since it was created by Yves Saint Laurent in 1969, it has been revoked and remodel yearly but remains an iconic fashion piece.

    Image 1

    Image source: http://www.speak-fashion.de/fashion_history/classics/fashion-history-classics-women%E2%80%99s-tuxedo

    Other things follow this 20 year rebirth cycle. What was in fashion 2 decades ago suddenly become “new” and a very sought after fashion trend. Only to disappear into oblivion, until the next rebirth. Remember, the large shoulder pads of the 1980? No, think Balmain vs Michael Jackson jacket.

    Who can forget the 80’s! The decade that fashion forgot! Leg warmers, mini skirts, sweater/jackets with enormous shoulder pads all worn at the same time in neon colors. I will never be seen in any of those items again!

    The development of Database Management Systems (DBMS) follows its own 20 year cycle, No SQL to SQL and back again. Although, the definition of a database remains the same: ”an organised collection of data”, the ways to organise and address this collection seems to be going in and out of fashion.Think of a database as an equivalent to YSL tuxedo that never goes out of fashion. On the other hand, SQL DBMS follow 10-20 year cycle ever since it was developed by Dr Codd for his relational model [1].

    I am not suggesting that SQL will go out of fashion, like the big shoulder pads.

    Quite the opposite, it is more like the little black dress [2]. Once iconized by Coco Chanel in the 1926 it remains a simple and very effective tool.

    Image 3

    Image source: http://www.foreignmag.net/fashion/2015/3/14/the-little-black-dress-lbd

    If fashion cycles, at least to some extent, are driven by nostalgia and our desire for individuality the developments in DBMS are driven by the progress in the hardware, software and the emergence of a new variety of data.

    Prior to 1970, DBMS where navigational, meaning, entries where linked data sets, which formed a large network, No SQL. In the late 70‘s early 80’s the rise in the computing power and the birth of a desktop computer lead to commercial development of the relational database systems, SQL .

    In 2000, new data type such as XML files and web logs lead to the development post-relational databases, which became known as NoSQL ( Not Only SQL) databases. Come 2013 and we are back to the NewSQL, a RDBMS that tries to combine the best of both worlds, performance of NoSQL with an order of RDBMS [3].

    So is SQL DBMS out of fashion, dead? Certainly not! Everything has its place and time. There is still a need for a “Law and Order” of SQL DBMS as well as the flexibility of NoSQL in order to accommodate new types of data.

    Image 4

    1. Codd, Edgar F (June 1970). “A Relational Model of Data for Large Shared Data Banks”. Communications of the ACM (Association for Computing Machinery) 13 (6): 377–87.doi:10.1145/362384.362685. Retrieved 2007-06-09.
    2. http://en.wikipedia.org/wiki/Little_black_dress
    3. http://en.wikipedia.org/wiki/Database

    Tatiana Bokareva is a Data Scientist for Teradata Aster, the market leader in big data analytics. Tatiana is responsible for data mining, analytics and ultra-fast analysis of unstructured, semi structured data using Teradata Aster advance analytics platform.

    The post Everything new is a well-forgotten old appeared first on International Blog.

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  • admin 9:49 am on April 29, 2015 Permalink
    Tags: , Everything, , , softwaredefined, ,   

    How the IT universe moves to software-defined data warehouse life, and everything 

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