Business Intelligence Tools Overview Trust.Radius. The Data Management Problem that BI solves.Companies collect large quantities of operations data as a by product of doing business.InformationWeek. com News, analysis and research for business technology professionals, plus peertopeer knowledge sharing.Engage with our community.Huge quantities of data are stored in finance, procurement, sales, marketing systems and multiple other data repositories.Being able to analyze and understand this data is extremely important to running the business.For example, Enterprise Resource Planning ERP systems typically contain data concerning the supply chain and inventory levels in addition to financial data.HR systems contain all employee records including demographic data, salary level, and performance reviews.Customer Relationship Management CRM systems contain customer, sales pipeline, forecasting and sometimes customer support case data.The problem is that all this operational data is typically not accessible in one place for analysis in order to make decisions and provide strategic guidance to the business as a whole.Microsoft Attunity Drivers Download' title='Microsoft Attunity Drivers Download' />Controls the locking and row versioning behavior of TransactSQL statements issued by a connection to SQL Server.Specifies that statements can read rows that have.Business Intelligence Tools reviews, comparisons, alternatives and pricing.The best BI solutions for small business to enterprises.Pxky2.png' alt='Microsoft Attunity Drivers Download' title='Microsoft Attunity Drivers Download' />For example, inventory data from an ERP system could be combined with sales forecasting information to understand how to optimize inventories in response to demand.This is the problem that business intelligence systems were designed to solve.How Business Intelligence Tools Work.Traditional business intelligence solutions solve this problem by putting data into a common store called a warehouse. 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The data is then normalized removing redundancy and duplication making it easier to run queries and retrieve data for reporting.Newer data discovery and visualization platforms solve the problem differently, by either connecting directly to the various data sources, or storing data in memory for analysis and visualization.There are many different types of business intelligence technology, not all of which depend on the business warehouse paradigm.Many new approaches have emerged, and the following sections describe some of the major classes of business intelligence technology.On premise Full Stack Products.On premise full stack BI solutions have been around the longest and are now being eclipsed by newer, more flexible technology.However, thee tools still have a very large installed base, and are still very effective for managing structured data from many sources and structuring it for standard reporting across the enterprise.They have a number of key components, although every solution does not necessarily have each component of the stack Data warehouse A relational database designed specifically for data analysis instead of standard transactional processing.It acts as the conduit between operational data stores and the gaining of insight based on composite data.Slices of data from the warehouseusually summary data for a single department like sales or financeare stored in a data mart for quicker access.Extract, Transform, Load ETL The first important task is to extract the data from the various data sources and load it into a data warehouse where it is normalized organized into tables while cleaning the data and removing redundancy and inconstancies.Once it has been appropriately structured it is available for querying and analysis.OLAP or ad hoc query tools OLAP Online Analytical Processing, and its close cousin ROLAPRelational Online Analytical Processing, is a technology that allows users to query data across multiple dimensions, for building standard reports or for enabling users to ask a specific business question.Presentation layer Dashboards, scorecards and reports presenting the data to users in a visually appealing way that is easy to understand.These tools are useful for organizations that wish to deliver relatively stable operational reports in a consistent format to front line staff across the organization to help them monitor their progress or understand where performance is lagging.The advantage of this kind of enterprise reporting capability is the consistency of the data sets being used across the entire organization, which makes it easy to create alignment.It is notoriously difficult to achieve alignment if there is no common agreement about the accuracy of data, and stakeholders have different sets of data showing contradictory information.This is typically what people mean when they refer to a single source of the truth.However, on premise first, full stack BI systems are difficult to build and implement, expensive, and often difficult to learn and use.They also lack flexibility and are difficult to change once they have been built.It has been relatively common in recent years for publications and analysts to bemoan the high failure rates for BI projects, and full stack deployments are often the culprits.Implementation times for these tools can be long, because setting up the data warehouse and creating the schema are inherently IT intensive, complex tasks.When they are finally up and running, the ROI can be low, often because of usability problems.However, it should be pointed out that this does not have to be so.In recent years, a new category of data warehouse automation tools has emerged to mitigate these problems.Products like Time.Xtender, Kalido, Where.Scape, and Attunity go some way to making data warehouse creation and maintenance a far more agile and collaborative experience.These tools are capable of automating the creation of a data warehouse schema, indexes, cubes, etc.They can also create business metadata for specific business intelligence tools.In this way, they can dramatically simplify and speed up both data warehouse development and subsequent maintenance.Additionally, not all tools in this category are legacy tools.There are more modern approaches to providing end to end capabilities using newer technology.A good example of this is Sisense, which uses a more flexible version of OLAP cubes, called elasticubes, and leverages data storage provided by the chip set to eliminate some of the speed limitations of disk storage.This approach yields significant speed increases of more than 5.Full stack BI tools built on a data warehouse can still provide immense value to larger organizations with the resources to deploy and manage them, and the deep pockets required to invest in them.Example Products.Best Fit For. Organizations whose primary need is for alignment and consistency of data across a very large organization and the provision of accurate reports to line of business managers and operational employees.These tools provide a single version of the truth as a basis for decision making across an entire enterprise.Organizations with access to a highly skilled IT division, which includes ETL developers, report developers, data architects, data administrators andvery importantlycorporate trainers.However, some newer products that attempt to radically simplify both deployment and usage need far less IT oversight.Open Source Full Stack Products.The primary reason for choosing open source BI tools is often perceived cost.Commercial BI tools are still largely seen as having superior technology, while open source tools are viewed as offering good enough technology at a fraction of the price.But although download of the software can be completely free, large scale open source deployments can still turn out to be a significant investment when factoring development costs.Also, there are very often commercial versions of the products that offer capabilities that the core free product does not.These typically include enterprise level features like integrated security, connectivity to multiple data sources, administration tools, etc.It is also important to bear in mind that these are developer led tools and are designed with a developer mindset, which often means that significant development resources will be required to deploy and integrate them in an existing corporate environment.There is however renewed interest in open source BI tools today, partly fuelled by the extraordinary success of open source products like Hadoop and Revolution Analytics R, recently acquired by Microsoft, which have raised awareness of the open source approach.Example Products.Best Fit For. Open Source BI can be a good choice for organizations that have the technical expertise required to integrate the code base and make it work effectively within the organization.Typically these tool sets are very complete, due to the large number of developers working on the code base.Download Update. Star Update.Star. Download the.Double click the downloaded file.Update. Star is compatible with Windows platforms.Update. Star has been tested to meet all of the technical requirements to be compatible with.Windows 1. 0, 8. 1, Windows 8, Windows 7, Windows Vista, Windows Server 2.Windows. XP, 3. 2 bit and 6.Simply double click the downloaded file to install it.Update. Star Free and Update.Star Premium come with the same installer.Update. Star includes support for many languages such as English, German, French, Italian, Hungarian, Russian and many more.You can choose your language settings from within the program.
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