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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.

J4L FO Designer 1.6.3

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.

Free reporting software Now enhanced for mobile intelligence Perfect solution for departments Scalable as your needs expand For Windows, Unix, Linux, Solaris, HP-UX, and AIX operating systems and any data source, including Hadoop, SAP BW, Microsoft Analysis Services, Essbase, and IBM TM1.

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1. Project Sponsorship and Governance 2. Project Management 3. Development Team (Core Team) 4. Extended Project Team

1. Project Sponsorship and Governance IT and the business should form a BI steering committee to sponsor and govern design, development, deployment, and ongoing support. It needs both the CIO and a business executive, such as CFO, COO, or a senior VP of marketing/sales to commit budget, time, and resources. The business sponsor needs the project to succeed. The CIO is committed to what is being built and how.

2. Project Management Project management includes managing daily tasks, reporting status, and communicating to the extended project team, steering committee, and affected business users. The project management team needs extensive business knowledge, BI expertise, DW architecture background, and people management, project management, and communications skills. The project management team includes three functions or members: Project development manager - Responsible for deliverables, managing team resources, monitoring tasks, reporting status, and communications. Requires a hands-on IT manager with a background in iterative development. Must understand the changes caused by this approach and the impact on the business, project resources, schedule and the trade-offs. Business advisor - Works within the sponsoring business organization. Responsible for the deliverables of the business resources on the project's extended team. Serves as the business advocate on the project team and the project advocate within the business community. Often, the business advocate is a project co-manager who defers to the IT project manager the daily IT tasks but oversees the budget and business deliverables. BI/DW project advisor - Has enough expertise with architectures and technologies to guides the project team on their use. Ensures that architecture, data models, databases, ETL code, and BI tools are all being used effectively and conform to best practices and standards.

OBIEE 11g skills, both in terms of new functionality (mapping, analyses, KPIs and Scorecards etc) and new infrastructure (WebLogic, EM, OPSS etc) A smattering of Essbase skills, focused mainly on the integration with OBIEE and Essbase (and the many workarounds and gotchas) Good ODI skills, both in terms of the basics, but also being able to write knowledge modules, integrate with OBIEE, deployment and migration Solid database skills – OBIEE gave the illusion through aggregates etc that database tuning was redundant, but time has shown it’s by far the biggest success factor in a project – get the database design and optimisation wrong, and your project is toast. You need to know partitioning, materialized views, index types, and increasingly, you need to get yourself on an Exadata project as customers are buying the technology but you can’t teach it to yourself at home BI Apps skills, but watch out for everything changing when BI Apps 11g comes out, and be prepared to learn the Fusion Apps and JDeveloper if you want to stay in the game Looking to the future, keep an eye on technologies such as in-memory (TimesTen), mid-tier caching (Coherence), plus technologies such as Business Activity Monitoring (BAM), “big data” (Hadoop, large data sets, NoSQL), complex event processing and maybe products such as Qlikview, just in case Oracle buys them, or at least to know what the competition are up to, or more importantly pitching to your boss

The other thing to bear in mind of course, if you’re an Oracle BI developer, is that you need to have great business, communication and data modeling skills.

.htaccess The .htaccess file is a simple text file placed in the directory you want the contents of the file to affect. The rules and configuration directives in the .htaccess file will be enforced on whatever directory it is in and all sub-directories as well. In order to password protect content, there are a few directives we must become familiar with. One of these directives in the .htaccess file ( the AuthUserFile directive ) tells the Apache web server where to look to find the username/password pairs. .htpasswd The .htpasswd file is the second part of the affair. The .htpasswd file is also a simple text file. Instead of directives, the .htpasswd file contains username/password pairs. The password will be stored in encrypted form and the username will be in plaintext.

There is a special program on a *nix machine that is designed to manipulate the .htpasswd file on your behalf. The name of this program is htpasswd.

The first way is to create a new .htpasswd file and add a username/password pair to the file. The second way is to add a username/password pair to an existing .htpasswd file.

Hadoop is designed to efficiently process large volumes of information by connecting many commodity computers together to work in parallel. The theoretical 1000-CPU machine described earlier would cost a very large amount of money, far more than 1,000 single-CPU or 250 quad-core machines. Hadoop will tie these smaller and more reasonably priced machines together into a single cost-effective compute cluster.

Great design, high-quality code, strong business sponsorship, accurate requirements, good project management, and thorough testing are some of the obvious requirements for successful analytics systems.

As a professional in the field, you must be able to do these things well because they form the foundation of a good analytics implementation.

Successful analytics professionals should follow a set of guiding principles.

These instructions assume that you have Infobright installed and running.   First and foremost, download Talend.  In this example, we will download Talend Open Source Data Integrator v5.0. (http://www.talend.com/download.php)  Once fully installed, download the Talend/Infobright Connector.  Ensure you download the right connector; instructions are on the download page (http://www.infobright.org/Downloads/Contributed-Software/) If you download Talend 4.0+, you’ll want the latest connector For older versions of Talend, you’ll want the 3.7 connector and lower. Once downloaded, perform the following actions: [For Windows] Copy the infobright_jni([_32|_64])bit.dll to C:\Windows\infobright_jni.dll Copy the zipped “tInfobrightOutput” directory to this directory: [Install Root of Talend] \plugins\org.talend.designer.components.localprovider_5.0.1.r74687\components\tInfobrightOutput Copy “infobright-core-3.4.jar” to [Install Root of Talend]\lib\java Running Talend in Windows If using Windows, run talend as Administrator.  If you don’t, you will see odd “Access Denied” or “Accesse Refuse” error messages when trying to use the connector.

You need to do some work on these instructions. Version 5 is not like version 4. You must run Talend 5 before the “lib\java\” folder appears.  Once it does appear, it no longer contains the .jar files like version 4; just a file “index.xml” that you have to edit to point to the infobright jar file in the components folder.

Plug-in Details: CLOB Load Have you ever used a Rich Text Editor item type in APEX? Has the amount of text ever exceeded 32k? Choke! And so begins the search for a work around... While work arounds exists they can be a little difficult find and repeat each time you need them. We designed the Enkitec CLOB Load plug-in to simplify and standardize how large amounts of text are moved from the database to APEX page items and then back to the database.

The tag-management industry is growing rapidly, as tags are critical to gathering data about your customers.

It's the early days for tag management, but the industry is growing rapidly because it's not so much about tags, but about the bigger challenge of using digital data.

Where does tag management fit in the data picture? Here's an example someone shared with me recently: He had gone to an antivirus product's website, read the reviews, and bought the software. In the days that followed, however, he suddenly began to see banner ads from that same software maker whenever he visited CNN, ESPN, and other favorite websites. The software maker knew he had visited its website, but not that he already bought the product. They were retargeting him with banner ads at unnecessary cost and no purpose.

J4L APEX FO Designer

Compatible with Apache FOP and Apex Listener.