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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.

Simple development and maintenance of Mobile apps and dashboards Powerful Visual Data Discovery capabilities Packed with robust analytics Free online support and training Perpetual license to use forever Quick Start Guide brings you from download through your first report

Integration BI infrastructure: All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. Metadata management: Tools should leverage the same metadata, and the tools should provide a robust way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics and report layout objects. Development tools: The platform should provide a set of programmatic and visual tools, coupled with a software developer's kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application. Collaboration: Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations.

Information Delivery Reporting: Provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities. Dashboards: Includes the ability to publish Web-based or mobile reports with intuitive interactive displays that indicate the state of a performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate real-time data from operational applications, or in conjunction with a complex-event processing engine. Ad hoc query: Enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to enable users to navigate available data sources. Microsoft Office integration: Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for document and presentation formats, formulas, data "refreshes" and pivot tables. Advanced integration includes cell locking and write-back. Search-based BI: Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. Mobile BI: Enables organizations to deliver analytic content to mobile devices in a publishing and/or interactive mode, and takes advantage of the mobile client's location awareness.

Analysis Online analytical processing (OLAP): Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a proprietary database for planning and "what if" modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk-based or in-memory). Interactive visualization: Gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns. Predictive modeling and data mining: Enables organizations to classify categorical variables, and to estimate continuous variables using mathematical algorithms. Scorecards: These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective. Prescriptive modeling, simulation and optimization: Supports decision making by enabling organizations to select the correct value of a variable based on a set of constraints for deterministic processes, and by modeling outcomes for stochastic processes.

Leaders headed by Microsoft (NASDAQ MSFT), the world's biggest software company; Oracle (NASDAQ: ORCL), the No. 1 database developer, and International Business Machines Corp. (NYSE: IBM), the No. 2 computer maker, showed off new software and analysis tools to enable retailers to make more from the consumer dollar.

The company also said that using in-house software analysis permits retailers to forecast trends and have merchandise made quickly to capitalize on them. Its “birth of a trend” analytics has determined what it calls “steampunk,” or a science-fiction and fantasy mix based around gothic machinery and 19th-century geniuses like Jules Verne and H.G. Wells will be a big trend in 2013.

ERP vendor selection. As the following graph shows, the primary candidates for ERP software were SAP, Oracle, Microsoft, Epicor, and Infor:

The cloud question. Despite the hype, only 14 percent of respondents are using ERP delivered as Software as a Service (SaaS). Although the best cloud vendors can deliver superior security and reliability than most internal IT departments, market momentum to ERP in the cloud is not there yet, as the following diagram illustrates:

Important lessons. Implementing an ERP system is always complex because the deployment drives changes to both data and processes that extend across departmental boundaries inside the organization.

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.

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.

Business intelligence is a data analysis process aimed at boosting business performance by helping corporate executives and other end users make more informed decisions.

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions.

BI encompasses a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.

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.

You can’t learn data visualization by memorizing a set of rules. You must understand why things work the way they do.

you must be able to think statistically

This doesn’t mean that you must learn advanced mathematics, nor can you do this work merely by learning how to use software to calculate correlation coefficients and p-values.

In this article we explain how you can utilize the apache authentication to restrict access to you website or parts of your website.

You have to create the files .htaccess and .htpasswd. These files are protected by the server software so you can not download or view them with your web browser.