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  • admin 9:58 am on May 10, 2017 Permalink
    Tags: Analytical, , Equal, Self, , Sufficiency   

    Self Service Analytics Does Not Equal Analytical Self Sufficiency 


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  • admin 9:55 am on February 11, 2017 Permalink
    Tags: Analytical, , , , , Yahoo   

    Yahoo Japan Collaborating and Innovating with Analytical Ecosystems 


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  • admin 9:51 am on January 13, 2017 Permalink
    Tags: Analytical, Ecosytem, ,   

    Teradata Analytical Ecosytem e-Tutorial 


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  • admin 9:48 am on January 10, 2017 Permalink
    Tags: Analytical, , , , , , Shrewd,   

    Building the Right Analytical Ecosystem Architecture Takes Shrewd Planning 


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  • admin 9:53 am on May 26, 2016 Permalink
    Tags: , Analytical, Bauer, Jack, , , , Snarling, Tiger   

    Jack Bauer, An Analytical Real-Time Marketer, And A Snarling Tiger. Who’s Your Money On? 

    Latest imported feed items on Analytics Matters

     
  • admin 10:34 am on March 1, 2016 Permalink
    Tags: Analytical, , , , , ,   

    Hybrid Deployment for the Analytical Data Warehouse: A Case Study 

    In this latest DSC Webinar Event you will hear from the CTO of an innovative, market-leading company as he describes his firm’s journey into hybrid cloud and get fresh perspective from a well-respected analyst and editor. You’ll walk away from the webinar with a clear understanding of how YOUR team can put a new shine on your company’s analytic data warehouse investment by combining on-premises performance with cloud-based flexibility.
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  • admin 9:53 am on October 4, 2015 Permalink
    Tags: Analytical, Cycling, ,   

    Let Cycling Guide you to Analytical Success 

    “It’s not a race!” I hear you cry. Well perhaps not, but cycling in London certainly is competitive, whether that’s with other cyclists or against the thousands of other commuters which tackle London’s roads and rail networks every day in a quest to shave a minute from their journey time.

    It was on one of these days during my regular jaunt down the A3 that I considered how the (unofficial) rules for cycling in London could be used as an aid to performing successful data

    1. Never get overtaken by a Boris* bike = Leverage your technology and all data

    I don’t care iof its Chris Froome or the Terminator riding a Boris bike, you simply can’t get overtaken. They are old, heavy, thick tyred brutes, if you are overtaken by one of these, then you are either still trying to clip your shoes in, or you are doing something incredibly wrong.
    For the majority of London’s cyclists, owning a serious bit of kit is common place, so there should be no excuses when it comes to generating some speed and being agile to get around quickly. Lightweight frames, skinny tyres, a range of gears, and unnecessary lycra all help London cyclists beat the competition.
    Similarly when it comes to performing analytics, you need to ensure you keep your analytical platforms up-to-date, with the latest of analytical techniques and data handling capabilities. If you want to stay ahead, you need to leverage all data sets that you have available, both internal and external. Your competition are already doing this, so make sure to stay ahead and think innovatively about how you can tap into new sources of information. The met office, demographics, open APIs, and crowd sourced data all offer opportunities.

    *London’s self-service, cycle hire scheme. Boris Johnson was mayor of London when it was launched

    2. There is always a gap = Look for the right question

    Often when cycling it looks like the road is completely full with cars and buses (and other bikes if the weather is nice) and that there is no way through. A peek around the corner and a squeeze through a gap can often reveal a clear route ahead, if you are a road savvy cyclist. Querying your route and the options in front of you quickly and effectively allows you to continue on your journey to a destination without losing time and energy along the way.
    In the analytical world, it is key to be agile and use fail-fast techniques, so that you may quickly eliminate the less valuable lines of enquiry and instead find the golden question that will lead you down the path to successful discovery. Analysts need to have the ability and flexibility to ask lots of questions of their data and establish a platform for iterating quickly when doing so. Every question will undoubtedly lead to another, and supporting this train of thought is essential to ensuring you go down the right avenues, rather than getting caught up in a single idea, going off at a tangent and missing your goal.

    3. Be wary of EVERYTHING = don’t jump to conclusions

    OK it’s not a race, but try telling that to London’s pedestrians. Average walking speeds are at least double that of any other city in the UK and combined with a hatred for waiting to cross the road, it’s no wonder all bikes must be fitted with bells at POS. Pedestrians with a death wish, terrible road surfaces, and psychopathic couriers all put our awareness to the test. Never assume you know what’s coming and switch off from the present. Understanding the context of your situation is important to ensure you reach your destination safely.
    Analytics is all about understanding your business problems so never skip this stage and jump to conclusions about what it is you think you may have found. Take stock of your surroundings and your industry environment and establish the problems you want to address. Learn about the industry and how the business works, before trying to analyse data to reach conclusions that are biased and wrong at worst. Knowing your industry will help translate data into useful insights that meet the needs of your business.

    maps

    4. Know where you are going = what analytics techniques are available to you

    Navigating around London can be a tricky experience so it helps to know the best roads and routes available to get you around. A mix of one way systems and cycle super highways can cause confusion, hence being prepared in advance with a route mapped out is essential. Having a plan B in case of road closures or traffic jams can also help get you out of the trickiest situations.
    Analytical techniques can come in many forms and may depend on the skill set of the user. Knowing which techniques are applicable and available to you will help you get the most from your data and unearth the insights you are looking for. At times, you might even need to learn new techniques and expand your analytical tool box to get you further down your roadmap. New techniques, such as path analysis, text processing and graph analytics are changing the game.

    5. Discover new places and share= be at the bleeding edge of analytics and don’t be selfish!

    Cycling opens a world of opportunities to discover new things and places that a bus, tube, or car commuter wouldn’t ordinarily see. Social media ensures that any hidden gems a cyclist stumbles across are immediately Instagramed or Facebooked to ensure networks are aware of our escapades and to encourage others to join the experience.
    Analytics gives organisations the opportunity to do something unique in their market, make new discoveries and remain amongst the most innovative businesses. Gartner’s research found that organisations can be great at innovating within a function but often struggle to replicate the same success across the business as a whole*. So don’t be selfish, when you do something good and uncover some actionable insights, break through the silos and share it with other areas. A structure that allows communication and sharing of techniques will ensure everyone benefits.
    So there we have it, my top 5. Hopefully following these simple guidelines will lead you to something wheely spoketacular…

    * “Organizational Principles for Pricing Advanced Analytics and Data Science Teams” 04 September 2013, Gartner

    The post Let Cycling Guide you to Analytical Success appeared first on International Blog.

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  • admin 9:46 am on May 30, 2015 Permalink
    Tags: Analytical, , , , , , ,   

    From Keeping the Lights on to Driving More Value from Your Analytical Environment 


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  • admin 10:34 am on May 12, 2015 Permalink
    Tags: Analytical, , , , , , , , , ,   

    Webinar: Managed Services: From Keeping the Lights on to Driving More Value from Your Analytical Environment 

    Gain expert insight from Louise O’Neill, Partners for Teradata Managed Services Center of Expertise (CoE), and Judy Dobson, Teradata Managed Services Delivery Partner, as they discuss the many ways Teradata Managed Services can give you a competitive edge.
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  • admin 9:51 am on May 1, 2015 Permalink
    Tags: Analytical, , Converting, , Private, ,   

    Your First Step To Converting Your Analytical Warehouse To a Private Cloud 

    So your organisation has decided to commence discussion with your vendor to move your current analytical platform to a private cloud service. How do you go about making the first step? What areas do you need to consider?   How far do you go in converting existing functions to an outsourced service? How do you go about constructing a new service agreement around an Analytical Warehouse as a Service (AWaaS)?

    Many organisations want to develop a data warehouse environment that have the flexibility in provisioning for additional capacity with the certainty and predictability of cost. They also wish to avoid the lumpy capital expenditure associated with large platform upgrades and refreshes.   Most of the focus has been in changing the financial arrangements, converting from a capital expenditure model to an operating expense model (rent instead of buy).

    However, there are various factors that need to be considered if one is to fully leverage the opportunities presented when converting to a private cloud service model. To add to the complexity, it is not uncommon for various stakeholders to have different interpretation of what a new service should cover.

    The following article shares a framework that Teradata uses to help in developing a new cloud service construct. The framework is used in developing an initial position – a straw man –to clarify requirements and intent beyond the new service. The straw man provides a foundation to build the new service contract.

    The figure below summarises the nine levers to consider in designing your AWaaS.

     

     

     

     

     

    Each lever is discussed further below.

     

     

     

     

     

     

     

     

     

     

     

     

     

    Who will be responsible for day to day operations of the warehouse environment including DBA, backup, network, batch jobs, program tuning, administration, fault management etc. ?

     

     

     

     

     

     

     

     

     

    A higher Flex capacity increases risk and will therefore increase provisioning cost for the vendor.

     

     

     

     

     

     

     

     

     

     

     

     

    Most clients provide shared a data centre facilities. Future transition to Vendor hosted Cloud offerings should also be considered. 

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    Organisations that have rudimentary capacity planning processes can seek support from the vendor and include this as part of the service contract.

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    Longer period generally reduces cost and needs to include direct cost and included service features.

     

     

     

     

     

     

     

     

     

    Our experience working with other organisation on several of his AWaaS type arrangement is that each client is unique. Defining the requirements and target state is an iterative process as both client and vendor better understand the implications of the service construct.

    The form below is an example of a completed initial description. Having this straw man will help in developing a common understanding and can highlight conflicts and inconsistencies.

     

     

     

     

     

     

     

    This framework was very helpful in our initial discussions with organisation who wish to explore their options.   Having this framework helped both clients and vendor better understand the requirements and to commence co-operate design of a new service solution.

    Renato Manongdo is a Senior Financial Services Industry Consultant at Teradata ANZ and is also the practice lead for Business Value Measurement in Asia Pacific. Connect with Renato Manongdo on Linkedin.

     

     

    The post Your First Step To Converting Your Analytical Warehouse To a Private Cloud appeared first on International Blog.

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