Skip to main content

Home/ BI-TAGS/ Group items tagged consultant

Rss Feed Group items tagged

cezarovidiu

OBIEE Consultant Jobs, Average Salary for OBIEE Consultant Jobs - 0 views

  •  
    "The first part of the table below looks at the demand for the OBIEE Consultant role in IT jobs advertised across the UK. Included is a guide to the average salaries offered in IT jobs that have cited OBIEE Consultant in their job title over the 3 months to 7 February 2013 with a comparison to the same period in the previous 2 years. The second part of the table is for comparison and provides aggregates for all Job Titles currently tracked."
cezarovidiu

Opal-Consulting - Free tools - Jasper Reports Integration - 0 views

  •  
    Jasper Reports Integration
cezarovidiu

Rittman Mead Consulting - The Changing World of Business Intelligence - 0 views

  • Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.
  • The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.
  • The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.
  • ...3 more annotations...
  • Schema on read Schema on read works on a different principle and is more common in the Big Data world. The data is not transformed in any way when it is stored, the data store acts as a big bucket. The modelling of the data only occurs when the data is read. Map/Reduce is the clearest example, the mapping is the understanding of the data structure. Hadoop is a large distributed file system, which is very good at storing large volumes of data, this is potential. It is only the mapping of this data that provides value, this is done when the data is read, not written.
  • New World Order So whereas Business Intelligence used to always be driven by the model, the ETL process to populate the model and the reporting tool to query the model, there is now an approach where the data is collected its raw form, and advanced statistical or analytical tools are used to interrogate the data. An example of one such tool is R.
  • The driver for which approach to use is often driven by what the user wants to find out. If the question is clearly formed and the sources of data that are required to answer it well understood, for example how many units of a product have we sold, then the traditional schema on write approach is best.
cezarovidiu

Top Mistakes to Avoid in Analytics Implementations | StatSlice Business Intelligence an... - 0 views

  • Mistake 1.  Not putting a strong interdisciplinary team together. It is impossible to put together an analytics platform without understanding the needs of the customers who will use it.  Sounds simple, right?  Who wouldn’t do that?  You’d be surprised how many analytics projects are wrapped up by IT because “they think” they know the customer needs.  Not assembling the right team is clearly the biggest mistake companies make.  Many times what is on your mind (and if you’re an IT person willing to admit it) is that you are considering converting all those favorite company reports.  Your goal should not be that.  Your goal is to create a system—human engineered with customers, financial people, IT folks, analysts, and others—that give people new and exciting ways to look at information.  It should give you new insights. New competitive information.  If you don’t get the right team put together, you’ll find someone longing for the good old days and their old dusty reports.  Or worse yet, still finding ways to generate those old dusty reports. Mistake 2.  Not having the right talent to design, build, run and update your analytics system.  It is undeniable that there is now high demand for business analytics specialists.  There are not a lot of them out there that really know what to do unless they’ve been burned a few times and have survived and then built successful BA systems.  This is reflected by the fact you see so many analytics vendors offer, or often recommend, third-party consulting and training to help the organization develop their business analytic skills.  Work hard to build a three-way partnership between the vendor, your own team, and an implementation partner.  If you develop those relationships, risk of failure goes way down.
  • Mistake 3.  Putting the wrong kind of analyst or designer on the project. This is somewhat related to Mistake 2 but with some subtle differences.  People have different skillsets so you need to make sure the person you’re considering to put on the project is the right “kind.”  For example, when you put the design together you need both drill-down and summary models.  Both have different types of users.  Does this person know how to do both?  Or, for example, inexperience in an analyst might lead to them believing vendor claims and not be able to verify them as to functionality or time to implement. Mistake 4.  Not understanding how clean the data is you are getting and the time frame to get it clean.  Profile your data to understand the quality of your source data.  This will allow you to adjust your system accordingly to compensate for some of those issues or more importantly push data fixes to your source systems.  Ensure high quality data or your risk upsetting your customers.  If you don’t have a good understanding of the quality of your data, you could easily find yourself way behind schedule even though the actual analytics and business intelligence framework you are building is coming along fine. Mistake 5.  Picking the wrong tools.  How often do organizations buy software tools that just sit on the shelve?  This often comes from management rushing into a quick decision based on a few demos they have seen.  Picking the right analytics tools requires an in-depth understanding of your requirements as well as the strengths and weaknesses of the tools you are evaluating.  The best way to achieve this understanding is by getting an unbiased implementation partner to build a proof of concept with a subset of your own data and prove out the functionality of the tools you are considering. Bottom Line.  Think things through carefully. Make sure you put the right team together.  Have a data cleansing plan.  If the hype sounds too good to be true—have someone prove it to you.
cezarovidiu

mysql - Best of MyISAM and InnoDB - Database Administrators - 0 views

  • Some people can make the table's row format FIXED using ALTER TABLE mydb.mytb ROW_FORMAT=Fixed; and can get a 20% increase in read performance without any other changes. This works and works effectively FOR MyISAM. This will not produce faster results for InnoDB because ... that's right ... you must consult the gen_clust_index each time.
cezarovidiu

Rittman Mead Consulting » Blog Archive » Using OBIEE against Transactional Sc... - 0 views

  • The best practice in business intelligence delivery is always to build a data warehouse.
  • Pure transactional reporting is problematic. There are, of course, the usual performance issues. Equally troublesome is the difficulty in distilling a physical model down to a format that is easy for business users to understand. Dimensional models are typically the way business users envision their business: simple, inclusive structures for each entity. The standard OLTP data model that takes two of the four walls in the conference room to display will never make sense to your average business user.
cezarovidiu

Rittman Mead Consulting » Blog Archive » Oracle Database Resource Manager and... - 0 views

  • OBIEE, at the BI Server level. lets you define query limits that either warn or stop users from exceeding certain elapsed query times or number of rows returned. Assuming you define a “standard” group for most OBIEE users, you might want to stop them from displaying reports (requests) that return more than 50,000 rows, whilst you might want to warn them if their query takes over five minutes to run.
cezarovidiu

Octavian Pantis, autorul cartii Musai List, vine la Start-Up Wall-Street. Afla cum sa f... - 0 views

  • Trainerul Octavian Pantis, managing director TMI Training & Consulting. El este autorul cartii "Musai List", un best-seller in domeniu.
cezarovidiu

Rittman Mead Consulting » Blog Archive » Event Triggers in BI Publisher 11g - 0 views

  •  
    "Event Triggers in BI Publisher 11g December 20th, 2011 by Robin Moffatt Event Triggers in BI Publisher 11g give the facility to call a function in Oracle either before or after a data set is refreshed. The function must return a boolean (true/false), and if it returns false the data model will abort execution."
cezarovidiu

Rittman Mead Consulting » Blog Archive » Upgrading OBIEE to 11.1.1.7 - 0 views

  •  
    obiee upgrade 11.1.1.7
1 - 20 of 25 Next ›
Showing 20 items per page