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  • Infor PM (Business Intelligence solution)

    - by Andrew
    We are currently implementing the commercial Infor PM (Performance Management) package as a business intelligence tool. Infor PM website It is apparently used by over 1,000 companies around the world, but I have found scant information about it on the net except for what's on their own website. It covers the whole range of data warehousing and BI functions with: an OLAP environment an ETL tool a report writer (called Application Studio) an add-on to Excel to connect to the data in the cubes through a pivot table etc Does anyone have any experience with using this package? How does it compare to the big players in BI (Cognos, Microsoft SSAS, Business Objects, etc). Any pitfalls I should know about? On the other hand, does it do anything better than its competitors?

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  • SQL SERVER – What is MDS? – Master Data Services in Microsoft SQL Server 2008 R2

    - by pinaldave
    What is MDS? Master Data Services helps enterprises standardize the data people rely on to make critical business decisions. With Master Data Services, IT organizations can centrally manage critical data assets company wide and across diverse systems, enable more people to securely manage master data directly, and ensure the integrity of information over time. (Source: Microsoft) Today I will be talking about the same subject at Microsoft TechEd India. If you want to learn about how to standardize your data and apply the business rules to validate data you must attend my session. MDS is very interesting concept, I will cover super short but very interesting 10 quick slides about this subject. I will make sure in very first 20 mins, you will understand following topics Introduction to Master Data Management What is Master Data and Challenges MDM Challenges and Advantage Microsoft Master Data Services Benefits and Key Features Uses of MDS Capabilities Key Features of MDS This slides decks will be followed by around 30 mins demo which will have story of entity, hierarchies, versions, security, consolidation and collection. I will be tell this story keeping business rules in center. We take one business rule which will be simple validation rule and will make it much more complex and yet very useful to product. I will also demonstrate few real life scenario where I will be talking about MDS and its usage. Do not miss this session. At the end of session there will be book awarded to best participant. My session details: Session: Master Data Services in Microsoft SQL Server 2008 R2 Date: April 12, 2010  Time: 2:30pm-3:30pm SQL Server Master Data Services will ship with SQL Server 2008 R2 and will improve Microsoft’s platform appeal. This session provides an in depth demonstration of MDS features and highlights important usage scenarios. Master Data Services enables consistent decision making by allowing you to create, manage and propagate changes from single master view of your business entities. Also with MDS – Master Data-hub which is the vital component helps ensure reporting consistency across systems and deliver faster more accurate results across the enterprise. We will talk about establishing the basis for a centralized approach to defining, deploying, and managing master data in the enterprise. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, MVP, Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, T SQL, Technology Tagged: TechEd, TechEdIn

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  • Discoverer 11g (11.1.1.2) Certified with E-Business Suite

    - by Steven Chan
    Discoverer is an ad-hoc query, reporting, analysis, and Web-publishing tool that allows end-users to work directly with Oracle E-Business Suite OLTP data.Discoverer 11g (11.1.1.2) is now certified with Oracle E-Business Suite Release 11i and 12.The latest release of Oracle Business Intelligence Discoverer 11g offers new functionality, including integration with Oracle Business Intelligence Enterprise Edition (OBIEE), published Discoverer Webservice APIs, integration with Oracle WebCenter, integration with Oracle WebLogic Server, integration with Enterprise Manager (Fusion Middleware Control) and improved performance and scalability.

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  • Discoverer 11.1.1.4 Certified with E-Business Suite

    - by Steven Chan
    Oracle Business Intelligence Discoverer is an ad-hoc query, reporting, analysis, and Web-publishing tool that allows end-users to work directly with Oracle E-Business Suite OLTP data.Discoverer 11g (11.1.1.4) is now certified with Oracle E-Business Suite Release.  Discoverer 11.1.1.4 is part of Oracle Fusion Middleware 11g Release 1 Version 11.1.1.4.0, also known as FMW 11g Patchset 3.  Certified E-Business Suite releases are:EBS Release 11i 11.5.10.2 + ATG RUP 7 and higherEBS Release 12.0.6 and higherEBS Release 12.1.1 and higher

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  • PASS Summit Preconference and Sessions

    - by Davide Mauri
    I’m very pleased to announce that I’ll be delivering a Pre-Conference at PASS Summit 2012. I’ll speak about Business Intelligence again (as I did in 2010) but this time I’ll focus only on Data Warehouse, since it’s big topic even alone. I’ll discuss not only what is a Data Warehouse, how it can be modeled and built, but also how it’s development can be approached using and Agile approach, bringing the experience I gathered in this field. Building the Agile Data Warehouse with SQL Server 2012 http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=2821 I’m sure you’ll like it, especially if you’re starting to create a BI Solution and you’re wondering what is a Data Warehouse, if it is still useful nowadays that everyone talks about Self-Service BI and In-Memory databases, and what’s the correct path to follow in order to have a successful project up and running. Beside this Preconference, I’ll also deliver a regular session, this time related to database administration, monitoring and tuning: DMVs: Power in Your Hands http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=3204 Here we’ll dive into the most useful DMVs, so that you’ll see how that can help in everyday management in order to discover, understand and optimze you SQL Server installation, from the server itself to the single query. See you there!!!!!

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  • PASS Summit Preconference and Sessions

    - by Davide Mauri
    I’m very pleased to announce that I’ll be delivering a Pre-Conference at PASS Summit 2012. I’ll speak about Business Intelligence again (as I did in 2010) but this time I’ll focus only on Data Warehouse, since it’s big topic even alone. I’ll discuss not only what is a Data Warehouse, how it can be modeled and built, but also how it’s development can be approached using and Agile approach, bringing the experience I gathered in this field. Building the Agile Data Warehouse with SQL Server 2012 http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=2821 I’m sure you’ll like it, especially if you’re starting to create a BI Solution and you’re wondering what is a Data Warehouse, if it is still useful nowadays that everyone talks about Self-Service BI and In-Memory databases, and what’s the correct path to follow in order to have a successful project up and running. Beside this Preconference, I’ll also deliver a regular session, this time related to database administration, monitoring and tuning: DMVs: Power in Your Hands http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=3204 Here we’ll dive into the most useful DMVs, so that you’ll see how that can help in everyday management in order to discover, understand and optimze you SQL Server installation, from the server itself to the single query. See you there!!!!!

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  • Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Cloud in the Big Data Story. In this article we will understand the role of Operational Databases Supporting Big Data Story. Even though we keep on talking about Big Data architecture, it is extremely crucial to understand that Big Data system can’t just exist in the isolation of itself. There are many needs of the business can only be fully filled with the help of the operational databases. Just having a system which can analysis big data may not solve every single data problem. Real World Example Think about this way, you are using Facebook and you have just updated your information about the current relationship status. In the next few seconds the same information is also reflected in the timeline of your partner as well as a few of the immediate friends. After a while you will notice that the same information is now also available to your remote friends. Later on when someone searches for all the relationship changes with their friends your change of the relationship will also show up in the same list. Now here is the question – do you think Big Data architecture is doing every single of these changes? Do you think that the immediate reflection of your relationship changes with your family member is also because of the technology used in Big Data. Actually the answer is Facebook uses MySQL to do various updates in the timeline as well as various events we do on their homepage. It is really difficult to part from the operational databases in any real world business. Now we will see a few of the examples of the operational databases. Relational Databases (This blog post) NoSQL Databases (This blog post) Key-Value Pair Databases (Tomorrow’s post) Document Databases (Tomorrow’s post) Columnar Databases (The Day After’s post) Graph Databases (The Day After’s post) Spatial Databases (The Day After’s post) Relational Databases We have earlier discussed about the RDBMS role in the Big Data’s story in detail so we will not cover it extensively over here. Relational Database is pretty much everywhere in most of the businesses which are here for many years. The importance and existence of the relational database are always going to be there as long as there are meaningful structured data around. There are many different kinds of relational databases for example Oracle, SQL Server, MySQL and many others. If you are looking for Open Source and widely accepted database, I suggest to try MySQL as that has been very popular in the last few years. I also suggest you to try out PostgreSQL as well. Besides many other essential qualities PostgreeSQL have very interesting licensing policies. PostgreSQL licenses allow modifications and distribution of the application in open or closed (source) form. One can make any modifications and can keep it private as well as well contribute to the community. I believe this one quality makes it much more interesting to use as well it will play very important role in future. Nonrelational Databases (NOSQL) We have also covered Nonrelational Dabases in earlier blog posts. NoSQL actually stands for Not Only SQL Databases. There are plenty of NoSQL databases out in the market and selecting the right one is always very challenging. Here are few of the properties which are very essential to consider when selecting the right NoSQL database for operational purpose. Data and Query Model Persistence of Data and Design Eventual Consistency Scalability Though above all of the properties are interesting to have in any NoSQL database but the one which most attracts to me is Eventual Consistency. Eventual Consistency RDBMS uses ACID (Atomicity, Consistency, Isolation, Durability) as a key mechanism for ensuring the data consistency, whereas NonRelational DBMS uses BASE for the same purpose. Base stands for Basically Available, Soft state and Eventual consistency. Eventual consistency is widely deployed in distributed systems. It is a consistency model used in distributed computing which expects unexpected often. In large distributed system, there are always various nodes joining and various nodes being removed as they are often using commodity servers. This happens either intentionally or accidentally. Even though one or more nodes are down, it is expected that entire system still functions normally. Applications should be able to do various updates as well as retrieval of the data successfully without any issue. Additionally, this also means that system is expected to return the same updated data anytime from all the functioning nodes. Irrespective of when any node is joining the system, if it is marked to hold some data it should contain the same updated data eventually. As per Wikipedia - Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. In other words -  Informally, if no additional updates are made to a given data item, all reads to that item will eventually return the same value. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • timetable in a jTable

    - by chandra
    I want to create a timetable in a jTable. For the top row it will display from monday to sunday and the left colume will display the time of the day with 2h interval e.g 1st colume (0000 - 0200), 2nd colume (0200 - 0400) .... And if i click a button the timing will change from 2h interval to 1h interval. I do not want to hardcode it because i need to do for 2h, 1h, 30min , 15min, 1min, 30sec and 1 sec interval and it will take too long for me to hardcode. Can anyone show me an example or help me create an example for the 2h to 1h interval so that i know what to do? The data array is for me to store data and are there any other easier or shortcuts for me to store them because if it is in 1 sec interval i got thousands of array i need to type it out. private void oneHour() //1 interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0100", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0100 - 0200", data[2][0], data[2][1], data[2][2], data[2][3], data[2][4], data[2][5], data[2][6]}, {"0200 - 0300", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0300 - 0400", data[6][0], data[6][1], data[6][2], data[6][3], data[6][4], data[6][5], data[6][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0700", data[10][0], data[4][1], data[10][2], data[10][3], data[10][4], data[10][5], data[10][6]}, {"0700 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 0900", data[14][0], data[14][1], data[14][2], data[14][3], data[14][4], data[14][5], data[14][6]}, {"0900 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1100", data[18][0], data[18][1], data[18][2], data[18][3], data[18][4], data[18][5], data[18][6]}, {"1100 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1300", data[22][0], data[22][1], data[22][2], data[22][3], data[22][4], data[22][5], data[22][6]}, {"1300 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1500", data[26][0], data[26][1], data[26][2], data[26][3], data[26][4], data[26][5], data[26][6]}, {"1500 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1700", data[30][0], data[30][1], data[30][2], data[30][3], data[30][4], data[30][5], data[30][6]}, {"1700 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 1900", data[34][0], data[34][1], data[34][2], data[34][3], data[34][4], data[34][5], data[34][6]}, {"1900 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2100", data[38][0], data[38][1], data[38][2], data[38][3], data[38][4], data[38][5], data[38][6]}, {"2100 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2300", data[42][0], data[42][1], data[42][2], data[42][3], data[42][4], data[42][5], data[42][6]}, {"2300 - 2400", data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]}, {"2400 - 0000", data[46][0], data[46][1], data[46][2], data[46][3], data[46][4], data[46][5], data[46][6]}, }, new String [] { "Time/Day", "(Mon)", "(Tue)", "(Wed)", "(Thurs)", "(Fri)", "(Sat)", "(Sun)" } )); } private void twoHour() //2 hour interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0200", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0200 - 0400", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2400",data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]} },

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  • Innovating with Customer Needs Management

    - by Anurag Batra
    We're pleased to announce the addition of Agile Customer Needs Management (CNM) to the portfolio of PLM offerings by Oracle. CNM allows manufacturing companies to capture the voice of the customer and market, and arm their product designers with the information that they need to better meet customer requirements. It's an Enterprise 2.0 product that focuses on the quick information capture, ease of organizing information and association of that information with the product record - some of the key aspects of early stage innovation. Read on to learn more about this revolutionary new product that redefines how information is used to drive innovation.

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  • Innovation on CS

    - by guiman
    Hi all, its been some time that i've been thinking about creating something that could help the web community and leave some mark, but the problem that there are no ideas poping out of my head. So 2 questions comes to my mind: First, how did projects like Twitter, Google or any other big project that had changed our way of living in the internet get started? Secondly, do we have to force it or just keep doing what we do best and wait to that idea that could change things? While writting this question, one quote come to mind: Imagination is more important than knowledge Albert Einstein

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  • Create association between informations

    - by Andrea Girardi
    I deployed a project some days ago that allow to extract some medical articles using the results of a questionnaire completed by a user. For instance, if I reply on questionnaire I'm affected by Diabetes type 2 and I'm a smoker, my algorithm extracts all articles related to diabetes bubbling up all articles contains information about Diabetes type 2 and smoking. Basically we created a list of topic and, for every topic we define a kind of "guideline" that allows to extract and order informations for a user. I'm quite sure there are some better way to put on relationship two content but I was not able to find them on network. Could you suggest my a model, algorithm or paper to better understand this kind of problem and that helps me to find a faster, and more accurate way to extract information for an user?

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  • Oracle Data Mining a Star Schema: Telco Churn Case Study

    - by charlie.berger
    There is a complete and detailed Telco Churn case study "How to" Blog Series just posted by Ari Mozes, ODM Dev. Manager.  In it, Ari provides detailed guidance in how to leverage various strengths of Oracle Data Mining including the ability to: mine Star Schemas and join tables and views together to obtain a complete 360 degree view of a customer combine transactional data e.g. call record detail (CDR) data, etc. define complex data transformation, model build and model deploy analytical methodologies inside the Database  His blog is posted in a multi-part series.  Below are some opening excerpts for the first 3 blog entries.  This is an excellent resource for any novice to skilled data miner who wants to gain competitive advantage by mining their data inside the Oracle Database.  Many thanks Ari! Mining a Star Schema: Telco Churn Case Study (1 of 3) One of the strengths of Oracle Data Mining is the ability to mine star schemas with minimal effort.  Star schemas are commonly used in relational databases, and they often contain rich data with interesting patterns.  While dimension tables may contain interesting demographics, fact tables will often contain user behavior, such as phone usage or purchase patterns.  Both of these aspects - demographics and usage patterns - can provide insight into behavior.Churn is a critical problem in the telecommunications industry, and companies go to great lengths to reduce the churn of their customer base.  One case study1 describes a telecommunications scenario involving understanding, and identification of, churn, where the underlying data is present in a star schema.  That case study is a good example for demonstrating just how natural it is for Oracle Data Mining to analyze a star schema, so it will be used as the basis for this series of posts...... Mining a Star Schema: Telco Churn Case Study (2 of 3) This post will follow the transformation steps as described in the case study, but will use Oracle SQL as the means for preparing data.  Please see the previous post for background material, including links to the case study and to scripts that can be used to replicate the stages in these posts.1) Handling missing values for call data recordsThe CDR_T table records the number of phone minutes used by a customer per month and per call type (tariff).  For example, the table may contain one record corresponding to the number of peak (call type) minutes in January for a specific customer, and another record associated with international calls in March for the same customer.  This table is likely to be fairly dense (most type-month combinations for a given customer will be present) due to the coarse level of aggregation, but there may be some missing values.  Missing entries may occur for a number of reasons: the customer made no calls of a particular type in a particular month, the customer switched providers during the timeframe, or perhaps there is a data entry problem.  In the first situation, the correct interpretation of a missing entry would be to assume that the number of minutes for the type-month combination is zero.  In the other situations, it is not appropriate to assume zero, but rather derive some representative value to replace the missing entries.  The referenced case study takes the latter approach.  The data is segmented by customer and call type, and within a given customer-call type combination, an average number of minutes is computed and used as a replacement value.In SQL, we need to generate additional rows for the missing entries and populate those rows with appropriate values.  To generate the missing rows, Oracle's partition outer join feature is a perfect fit.  select cust_id, cdre.tariff, cdre.month, minsfrom cdr_t cdr partition by (cust_id) right outer join     (select distinct tariff, month from cdr_t) cdre     on (cdr.month = cdre.month and cdr.tariff = cdre.tariff);   ....... Mining a Star Schema: Telco Churn Case Study (3 of 3) Now that the "difficult" work is complete - preparing the data - we can move to building a predictive model to help identify and understand churn.The case study suggests that separate models be built for different customer segments (high, medium, low, and very low value customer groups).  To reduce the data to a single segment, a filter can be applied: create or replace view churn_data_high asselect * from churn_prep where value_band = 'HIGH'; It is simple to take a quick look at the predictive aspects of the data on a univariate basis.  While this does not capture the more complex multi-variate effects as would occur with the full-blown data mining algorithms, it can give a quick feel as to the predictive aspects of the data as well as validate the data preparation steps.  Oracle Data Mining includes a predictive analytics package which enables quick analysis. begin  dbms_predictive_analytics.explain(   'churn_data_high','churn_m6','expl_churn_tab'); end; /select * from expl_churn_tab where rank <= 5 order by rank; ATTRIBUTE_NAME       ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK-------------------- ----------------- ----------------- ----------LOS_BAND                                      .069167052          1MINS_PER_TARIFF_MON  PEAK-5                   .034881648          2REV_PER_MON          REV-5                    .034527798          3DROPPED_CALLS                                 .028110322          4MINS_PER_TARIFF_MON  PEAK-4                   .024698149          5From the above results, it is clear that some predictors do contain information to help identify churn (explanatory value > 0).  The strongest uni-variate predictor of churn appears to be the customer's (binned) length of service.  The second strongest churn indicator appears to be the number of peak minutes used in the most recent month.  The subname column contains the interior piece of the DM_NESTED_NUMERICALS column described in the previous post.  By using the object relational approach, many related predictors are included within a single top-level column. .....   NOTE:  These are just EXCERPTS.  Click here to start reading the Oracle Data Mining a Star Schema: Telco Churn Case Study from the beginning.    

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  • Project structure: where to put business logic

    - by Mister Smith
    First of all, I'm not asking where does business logic belong. This has been asked before and most answers I've read agree in that it belongs in the model: Where to put business logic in MVC design? How much business logic should be allowed to exist in the controller layer? How accurate is "Business logic should be in a service, not in a model"? Why put the business logic in the model? What happens when I have multiple types of storage? However people disagree in the way this logic should be distributed across classes. There seem to exist three major currents of thought: Fat model with business logic inside entity classes. Anemic model and business logic in "Service" classes. It depends. I find all of them problematic. The first option is what most Fowlerites stick to. The problem with a fat model is that sometimes a business logic funtion is not only related to a class, and instead uses a bunch of other classes. If, for example, we are developing a web store, there should be a function that calcs an order's total. We could think of putting this function inside the Order class, but what actually happens is that the logic needs to use different classes, not only data contained in the Order class, but also in the User class, the Session class, and maybe the Tax class, Country class, or Giftcard, Payment, etc. Some of these classes could be composed inside the Order class, but some others not. Sorry if the example is not very good, but I hope you understand what I mean. Putting such a function inside the Order class would break the single responsibility principle, adding unnecesary dependences. The business logic would be scattered across entity classes, making it hard to find. The second option is the one I usually follow, but after many projects I'm still in doubt about how to name the class or classes holding the business logic. In my company we usually develop apps with offline capabilities. The user is able to perform entire transactions offline, so all validation and business rules should be implemented in the client, and then there's usually a background thread that syncs with the server. So we usually have the following classes/packages in every project: Data model (DTOs) Data Access Layer (Persistence) Web Services layer (Usually one class per WS, and one method per WS method). Now for the business logic, what is the standard approach? A single class holding all the logic? Multiple classes? (if so, what criteria is used to distribute the logic across them?). And how should we name them? FooManager? FooService? (I know the last one is common, but in our case it is bad naming because the WS layer usually has classes named FooWebService). The third option is probably the right one, but it is also devoid of any useful info. To sum up: I don't like the first approach, but I accept that I might have been unable to fully understand the Zen of it. So if you advocate for fat models as the only and universal solution you are welcome to post links explaining how to do it the right way. I'd like to know what is the standard design and naming conventions for the second approach in OO languages. Class names and package structure, in particular. It would also be helpful too if you could include links to Open Source projects showing how it is done. Thanks in advance.

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  • How to show or direct a business analyst to a data modelling subject?

    - by AaronLS
    Our business analysts pushed hard to collect data through a spreadsheet. I am the programmer responsible for importing that data. Usually when they push hard for something like this, I never know how well it will work out until a few weeks later when I have time assigned to work on the task of programming the import of the data. I have tried to do as much as possible along the way, named ranges, data validations, etc. But I usually don't have time to take a detailed look at all the data and compare to the destination in the database to determine how well it matches up. A lot of times there will be maybe a little table of items that somehow I have to relate to something else in the database, but there are not natural or business keys present that would allow me to do so. Make the best of this, trying to write something that can compare strings and make a best guess at it and then go through the effort of creating interfaces for a user to match the imported data to the destination. I feel like if the business analyst was actually creating a data model, they would be forced to think about these relationships, and have an appreciation for the need of natural or business keys to be part of the spreadsheet for the purposes of smoothly importing the data. The closest they come to business analysis is a big flat list of fields, and that would be fine if it were like any other data dictionary and include data types+relationships, but it isn't. They are just a bunch of names. No indication of what type of data they might hold, and it is up to me to guess. When I have pushed for more detail, they say that it is just busy work. How can I explain the importance of data modelling? How can I tell them what it is and how to do it? It feels impossible, because they don't have an appreciation for its importance. They do however, usually have an interest in helping out in whatever way they can, it's just this in particular has never gotten a motivated response.

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  • How to show or direct a business analyst to do data modelling?

    - by AaronLS
    Our business analysts pushed hard to collect data through a spreadsheet. I am the programmer responsible for importing that data. Usually when they push hard for something like this, I never know how well it will work out until a few weeks later when I have time assigned to work on the task of programming the import of the data. I have tried to do as much as possible along the way, named ranges, data validations, etc. But I usually don't have time to take a detailed look at all the data and compare to the destination in the database to determine how well it matches up. A lot of times there will be maybe a little table of items that somehow I have to relate to something else in the database, but there are not natural or business keys present that would allow me to do so. Make the best of this, trying to write something that can compare strings and make a best guess at it and then go through the effort of creating interfaces for a user to match the imported data to the destination. I feel like if the business analyst was actually creating a data model, they would be forced to think about these relationships, and have an appreciation for the need of natural or business keys to be part of the spreadsheet for the purposes of smoothly importing the data. The closest they come to business analysis is a big flat list of fields, and that would be fine if it were like any other data dictionary and include data types+relationships, but it isn't. They are just a bunch of names. No indication of what type of data they might hold, and it is up to me to guess. When I have pushed for more detail, they say that it is just busy work. How can I explain the importance of data modelling? How can I tell them what it is and how to do it? It feels impossible, because they don't have an appreciation for its importance. They do however, usually have an interest in helping out in whatever way they can, it's just this in particular has never gotten a motivated response.

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  • Use of Business Parameters in BPM12c

    - by Abhishek Mittal-Oracle
    With the release of BPM12c, a new feature to use Business Parameters is introduced through which we can define a business parameter which will behave as a global variable which can be used within BPM project. Business Administrator can be the one responsible to modify the business parameters value dynamically at run-time which may bring change in BPM process flow where it is used.This feature was a part of BPM10g product and was extensively used. In BPM11g, this feature is not present currently.Business Parameters can be defined in 2 ways:1. Using Jdev to define business parameters, and 2. Using BPM workspace to define business parameters.It is important to note that business parameters need to be mapped with a valid organisation unit defined in a BPM project. If the same is not handled, exceptions like 'BPM-70702' will be thrown by BPM Engine. This is because business parameters work along with organisation defined in a BPM project.At the same time, we can use same business parameter across different organisation units with different values. Business Parameters in BPM12c has this capability to handle multiple values with different organisation units defined in a single BPM project. This enables business to re-use same business parameters defined in a BPM project across different organisations.Business parameters can be defined using the below data types:1. int2. string 3. boolean4. double While defining an business parameter, it is mandatory to provide a default value. Below are the steps to define a business parameter in Jdev: Step 1:  Open 'Organization' and click on 'Business Parameters' tab.Step 2:  Click on '+' button.Step 3: Add business parameter name, type and provide default value(mandatory).Step 4: Click on 'OK' button.Step 5: Business parameter is defined. Below are the steps to define a business parameter in BPM workspace: Step 1: Login to BPM workspace using admin-username and password.Step 2: Click on 'Administration' on the right top side of workspace.Step 3: Click on 'Business Parameters' in the left navigation panel under 'Organization'. Step 4:  Click on '+' button.Step 5: Add business parameter name, type and provide default value(mandatory).Step 6: Click on 'OK' button.Step 7: Business parameter is defined. Note: As told earlier in the blog, it is necessary to define and map a valid organization ID with predefined variable 'organizationalUnit' under data associations in an BPM process before the business parameter is used. I have created one sample PoC demonstrating the use of Business Parameters in BPM12c and it can be found here.

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  • Embedded Model Designing -- top down or bottom up?

    - by Jeff
    I am trying to learn RoR and develop a webapp. I have a few models I have thought of for this app, and they are fairly embedded. For example (please excuse my lack of RoR syntax): Model: textbook title:string type:string has_many: chapters Model: chapter content:text has_one: review_section Model: review_section title:string has_many: questions has_many: answers , through :questions Model: questions ... Model: answers ... My question is, with the example I gave, should I start at the top model (textbook) or the bottom most (answers)?

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  • Big GRC: Turning Data into Actionable GRC Intelligence

    - by Jenna Danko
    While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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  • CRM@Oracle Series: Showcasing Innovation with Oracle Customer Hub

    - by tony.berk
    When is having too many customers a challenge? It is not something too many people would complain about. But from a data perspective, one challenge is to keep each customer's data consistent across multiple enterprise systems such as CRM, ERP, and all of your other related applications. Buckle your seat belts, we are going a bit technical today... If you have ever tried it, you know it isn't easy. If you haven't, don't go there alone! Customer data integration projects are challenging and, depending on the environment, require sharp, innovative people to succeed. Want to hear from some guys who have done it and succeeded? Here is an interview with Dan Lanir and Afzal Asif from Oracle's Applications IT CRM Systems group on implementing Oracle Customer Hub and innovation. For more interesting discussions on innovation, check out the Oracle Innovation Showcase.

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  • Oracle Adattárház Referencia Architektúra, a legjobb gyakorlatból

    - by Fekete Zoltán
    Hogyan építsünk adattárházat, hogyan kapcsoljuk össze a rendszereinkkel? Mi legyen az az architektúra, mellyel a legkisebb kockázattal a legbiztosabban érünk célba? Ezekre a kérdésekre kaphatunk választ az Oracle Data Warehouse Reference Architecture leírásból. Letöltheto a következo dokumentum: Enabling Pervasive BI through a Practical Data Warehouse Reference Architecture

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  • How Do You Actually Model Data?

    Since the 1970’s Developers, Analysts and DBAs have been able to represent concepts and relations in the form of data through the use of generic symbols.  But what is data modeling?  The first time I actually heard this term I could not understand why anyone would want to display a computer on a fashion show runway. Hey, what do you expect? At that time I was a freshman in community college, and obviously this was a long time ago.  I have since had the chance to learn what data modeling truly is through using it. Data modeling is a process of breaking down information and/or requirements in to common categories called objects. Once objects start being defined then relationships start to form based on dependencies found amongst other existing objects.  Currently, there are several tools on the market that help data designer actually map out objects and their relationships through the use of symbols and lines.  These diagrams allow for designs to be review from several perspectives so that designers can ensure that they have the optimal data design for their project and that the design is flexible enough to allow for potential changes and/or extension in the future. Additionally these basic models can always be further refined to show different levels of details depending on the target audience through the use of three different types of models. Conceptual Data Model(CDM)Conceptual Data Models include all key entities and relationships giving a viewer a high level understanding of attributes. Conceptual data model are created by gathering and analyzing information from various sources pertaining to a project during the typical planning phase of a project. Logical Data Model (LDM)Logical Data Models are conceptual data models that have been expanded to include implementation details pertaining to the data that it will store. Additionally, this model typically represents an origination’s business requirements and business rules by defining various attribute data types and relationships regarding each entity. This additional information can be directly translated to the Physical Data Model which reduces the actual time need to implement it. Physical Data Model(PDMs)Physical Data Model are transformed Logical Data Models that include the necessary tables, columns, relationships, database properties for the creation of a database. This model also allows for considerations regarding performance, indexing and denormalization that are applied through database rules, data integrity. Further expanding on why we actually use models in modern application/database development can be seen in the benefits that data modeling provides for data modelers and projects themselves, Benefits of Data Modeling according to Applied Information Science Abstraction that allows data designers remove concepts and ideas form hard facts in the form of data. This gives the data designers the ability to express general concepts and/or ideas in a generic form through the use of symbols to represent data items and the relationships between the items. Transparency through the use of data models allows complex ideas to be translated in to simple symbols so that the concept can be understood by all viewpoints and limits the amount of confusion and misunderstanding. Effectiveness in regards to tuning a model for acceptable performance while maintaining affordable operational costs. In addition it allows systems to be built on a solid foundation in terms of data. I shudder at the thought of a world without data modeling, think about it? Data is everywhere in our lives. Data modeling allows for optimizing a design for performance and the reduction of duplication. If one was to design a database without data modeling then I would think that the first things to get impacted would be database performance due to poorly designed database and there would be greater chances of unnecessary data duplication that would also play in to the excessive query times because unneeded records would need to be processed. You could say that a data designer designing a database is like a box of chocolates. You will never know what kind of database you will get until after it is built.

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  • Oracle Financial Analytics for SAP Certified with Oracle Data Integrator EE

    - by denis.gray
    Two days ago Oracle announced the release of Oracle Financial Analytics for SAP.  With the amount of press this has garnered in the past two days, there's a key detail that can't be missed.  This release is certified with Oracle Data Integrator EE - now making the combination of Data Integration and Business Intelligence a force to contend with.  Within the Oracle Press Release there were two important bullets: ·         Oracle Financial Analytics for SAP includes a pre-packaged ABAP code compliant adapter and is certified with Oracle Data Integrator Enterprise Edition to integrate SAP Financial Accounting data directly with the analytic application.  ·         Helping to integrate SAP financial data and disparate third-party data sources is Oracle Data Integrator Enterprise Edition which delivers fast, efficient loading and transformation of timely data into a data warehouse environment through its high-performance Extract Load and Transform (E-LT) technology. This is very exciting news, demonstrating Oracle's overall commitment to Oracle Data Integrator EE.   This is a great way to start off the new year and we look forward to building on this momentum throughout 2011.   The following links contain additional information and media responses about the Oracle Financial Analytics for SAP release. IDG News Service (Also appeared in PC World, Computer World, CIO: "Oracle is moving further into rival SAP's turf with Oracle Financial Analytics for SAP, a new BI (business intelligence) application that can crunch ERP (enterprise resource planning) system financial data for insights." Information Week: "Oracle talks a good game about the appeal of an optimized, all-Oracle stack. But the company also recognizes that we live in a predominantly heterogeneous IT world" CRN: "While some businesses with SAP Financial Accounting already use Oracle BI, those integrations had to be custom developed. The new offering provides pre-built integration capabilities." ECRM Guide:  "Among other features, Oracle Financial Analytics for SAP helps front-line managers improve financial performance and decision-making with what the company says is comprehensive, timely and role-based information on their departments' expenses and revenue contributions."   SAP Getting Started Guide for ODI on OTN: http://www.oracle.com/technetwork/middleware/data-integrator/learnmore/index.html For more information on the ODI and its SAP connectivity please review the Oracle® Fusion Middleware Application Adapters Guide for Oracle Data Integrator11g Release 1 (11.1.1)

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  • Unique Business Value vs. Unique IT

    - by barry.perkins
    When the age of computing started, technology was new, exciting, full of potential and had a long way to grow. Vendor architectures were proprietary, and limited in function at first, growing in capability and complexity over time. There were few if any "standards", let alone "open standards" and the concepts of "open systems", and "open architectures" were far in the future. Companies employed intelligent, talented and creative people to implement the best possible solutions for their company. At first, those solutions were "unique" to each company. As time progressed, standards emerged, companies shared knowledge, business capability supplied by technology grew, and companies continued to expand their use of technology. Taking advantage of change required companies to struggle through periodic "revolutionary" change cycles, struggling through costly changes that were fraught with risk, resulted in solutions with an increasingly shorter half-life, and frequently required altering existing business processes and retraining employees and partner businesses. The pace of technological invention and implementation grew at an ever increasing rate, making the "revolutionary" approach based upon "proprietary" or "closed" architectures or technologies no longer viable. Concurrent with the advancement of technology, the rate of change in business increased, leading us to the incredibly fast paced, highly charged, and competitive global economy that we have today, where the most successful companies are companies that are good at implementing, leveraging and exploiting change. Fast forward to today, a world where dramatic changes in business and technology happen continually, a world where "evolutionary" change is crucial. Companies can no longer afford to build "unique IT", nor can they afford regular intervals of "revolutionary" change, with the associated costs and risks. Human ingenuity was once again up to the task, turning technology into a platform supporting business through evolutionary change, by employing "open": open standards; open systems; open architectures; and open solutions. Employing "open", enables companies to implement systems based upon technology, capability and standards that will evolve over time, providing a solid platform upon which a company can drive business needs, requirements, functions, and processes down into the technology, rather than exposing technology to the business, allowing companies to focus on providing "unique business value" rather than "unique IT". The big question! Does moving from "older" technology that no longer meets the needs of today's business, to new "open" technology require yet another "revolutionary change"? A "revolutionary" change with a short half-life, camouflaging reality with great marketing? The answer is "perhaps". With the endless options available to choose from, it is entirely possible to implement a solution that may work well today, but in 5 years time will become yet another albatross for the company to bear. Some solutions may look good today, solving a budget challenge by reducing cost, or solving a specific tactical challenge, but result in highly complex environments, that may be difficult to manage and maintain and limit the future potential of your business. Put differently, some solutions might push today's challenge into the future, resulting in a more complex and expensive solution. There is no such thing as a "1 size fits all" IT solution for business. If all companies implemented business solutions based upon technology that required, or forced the same business processes across all businesses in an industry, it would be extremely difficult to show competitive advantage through "unique business value". It would be equally difficult to "evolve" to meet or exceed business needs and keep up with today's rapid pace of change. How does one ensure that they do not jump from one trap directly into another? Or to put it positively, there are solutions available today that can address these challenges and issues. How does one ensure that the buying decision of today will serve the business well for years into the future? Intelligent & Informed decisions - "buying right" In a previous blog entry, we discussed the value of linking tactical to strategic The key is driving the focus to what is best for your business, handling today's tactical issues while also aligning with a roadmap/strategy that is tightly aligned with your strategic business objectives. When considering the plethora of possible options that provide various approaches to solving today's complex business problems, it is extremely important to ensure that vendors supplying those options, focus on what is best for your business, supplying sufficient information, providing adequate answers to questions, addressing challenges, issues, concerns and objections honestly and openly, and focus on supplying solutions that are tailored for, and deliver the most business value possible for your business. Here are a few questions to consider relative to the proposed options that should help ensure that today's solution doesn't become tomorrow's problem. Do the proposed solutions: Solve the problem(s) you are trying to address? Provide a solid foundation upon which to grow/enhance your business? Provide tactical gains that align with and enable your strategic business goals/objectives? Provide an infrastructure that can be leveraged with subsequent projects? Solve problems for the business overall, the lines of business, or just IT? Simplify your current environment Provide the basis for business: Efficiency Agility Clarity governance, risk, compliance real time business visibility and trend analysis Does your IT staff have the knowledge/experience to successfully manage the proposed systems once they are deployed in production? Done well, you will be presented with options tailored to your business, that enable you to drive the "unique business value" necessary to help your business stand out from others, creating a distinct competitive advantage, delivering what your customers need, when they need it, so you can attract new customers, new business, and grow top line revenue, all at a cost that provides a strong Return on Investment/Return on Assets. The net result is growth with managed cost providing significantly improved profit margin and shareholder value.

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