Tagged: Index Toggle Comment Threads | Keyboard Shortcuts

  • admin 9:51 am on April 30, 2016 Permalink
    Tags: , , Index, l’avenir, mesure, ,   

    Teradata Customer Satisfaction Index (CSI): l’avenir de la mesure de la satisfaction client 

    Teradata Customer Satisfaction Index (CSI): l’avenir de la mesure de la satisfaction client



    Teradata News Releases

  • admin 9:51 am on April 14, 2016 Permalink
    Tags: , , , , Index, Metrics, , ,   

    The Future of Experience Metrics Teradata Customer Satisfaction Index Analytic Solution 

    Solution is the first to authentically measure and manage evolving customer perspective, behavior and sentiment.
    Teradata News Releases

  • admin 9:47 am on January 9, 2016 Permalink
    Tags: , Index, , , , ,   

    Customer Satisfaction Index Whats Missing in Your Net Promoter Score 

    Teradata White Papers

  • admin 9:52 am on July 18, 2015 Permalink
    Tags: , Index, , , Primary, Selection   

    Optimization in Data Modeling 1 – Primary Index Selection 

    In my last blog I spoke about the decisions that must be made when transforming an Industry Data Model (iDM) from Logical Data Model (LDM) to an implementable Physical Data Model (PDM). However, being able to generate DDL (Data Definition Language) that will run on a Teradata platform is not enough – you also want it to perform well. While it is possible to generate DDL almost immediately from a Teradata iDM, each customer’s needs mandate that existing structures be reviewed against data and access demographics, so that optimal performance can be achieved.

    Having detailed data and access path demographics during PDM design is critical to achieving great performance immediately, otherwise it’s simply guesswork. Alas, these are almost never available at the beginning of an installation, but that doesn’t mean you can’t make “excellent guesses.”

    The single most influential factor in achieving PDM performance is proper Primary Index (PI) selection for warehouse tables. Data modelers are focused on entity/table Primary Keys (PK) since it is what defines uniqueness at the row level. Because of this, a lot of physical modelers tend to implement the PK as a Unique Primary Index (UPI) on each table as a default. But one of the keys to Teradata’s great performance is that it utilizes the PI to physical distribute data within a table across the entire platform to optimize parallelism. Each processor gets a piece of the table based on the PI, so rows from different tables with the same PI value are co-resident and do not need to be moved when two tables are joined.

    In a Third Normal Form (3NF) model no two entities (outside of super/subtypes and rare exceptions) will have the same PK, so if chosen as a PI, it stands to reason that no two tables share a PI and every table join will require data from at least one table to be moved before a join can be completed – not a solid performance decision to say the least.

    The iDM’s have preselected PI’s largely based on Identifiers common across subject areas (i.e. Party Id) so that all information regarding that ID will be co-resident and joins will be AMP-local. These non-unique PI’s (NUPI’s) are a great starting point for your PDM, but again need to be evaluated against customer data and access plans to insure that both performance and reasonably even data distribution is achieved.

    Even data distribution across the Teradata platform is important since skewed data can contribute both to poor performance and to space allocation (run out of space on one AMP, run out of space on all). However, it can be overemphasized to the detriment of performance.

    Say, for example, a table has a PI of PRODUCT_ID, and there are a disproportionate number of rows for several Products causing skewed distribution Altering the PI to the table PK instead will provide perfectly even distribution, but remember, when joining to that table, if all elements of the PK are not available then the rows of the table will need to be redistributed, most likely by PRODUCT_ID.

    This puts them back under the AMP where they were in the skewed scenario. This time instead of a “rest state” skew the rows will skew during redistribution, and this will happen every time the table is joined to – not a solid performance decision. Optimum performance can therefore be achieved with sub-optimum distribution.

    iDM tables relating two common identifiers will usually have one of the ID’s pre-selected as a NUPI. In some installations the access demographics will show that other ID may be the better choice. If so, change it! Or it may give leave you with no clear choice, in which case picking one is almost assuredly better than
    changing the PI to a composite index consisting of both ID’s as this will only result in a table no longer co-resident with any table indexed by either of the ID’s alone.

    There are many other factors that contribute to achieving optimal performance of your physical model, but they all pale in comparison to a well-chosen PI. In my next blog we’ll look at some more of these and discuss when and how best to implement them.

    Jake Kurdsjuk Biopic-resize July 15

    Jake Kurdsjuk is Product Manager for the Teradata Communications Industry Data Model, purchased by more than one hundred Communications Service Providers worldwide. Jake has been with Teradata since 2001 and has 25 years of experience working with Teradata within the Communications Industry, as a programmer, DBA, Data Architect and Modeler.

    The post Optimization in Data Modeling 1 – Primary Index Selection appeared first on Data Points.

    Teradata Blogs Feed

  • admin 9:53 am on January 7, 2015 Permalink
    Tags: “Other”, , Index, sentiment   

    The “Other” CSI – Customer Sentiment Index 

    The traditional method of measuring the satisfaction of vehicle owners was to use standard surveys. While this method delivered good information, it only captured sentiment at that specific moment and did not necessarily provide an accurate picture of customer loyalty. A better way to gain a true understanding is to use a customer sentiment index (CSI) score and analytics.

    The path and pattern analysis in the Teradata® Aster Discovery Platform allows organizations to better understand events and experiences across households that own a vehicle as well as individuals. One automaker ran a four-week proof of concept (POC) using the platform to analyze seven years of data and score multi-channel customer events and touchpoints to create a CSI.

    The POC validated that CSI analytics leads to a deeper understanding of customers, which benefits the business by helping with planning, warranties, buybacks, improved revenues and much more.

    Brett Martin
    Editor in Chief
    Teradata Magazine



    Teradata Blogs Feed

Compose new post
Next post/Next comment
Previous post/Previous comment
Show/Hide comments
Go to top
Go to login
Show/Hide help
shift + esc