For example, manufacturing and distribution enterprises of all sizes would benefit from leveraging software that not only senses the daily pulse of the operations, but that also spots incongruities, analyzes the performances of multiple areas, and initiates corrective adjustments. BI tools help employees harness data which might be too complicated for manual manipulation. For instance, in departments such as purchasing and sourcing, there are constant and rapid increases in materials costs, deviations in lead times, and growth and instability in the supplier base—all of which require ever increasing buyer dexterity. BI gives organizations the ability to manage these issues proactively.
To build BI solutions within an organization, data warehousing, data integration, analytics, scorecards, and dashboards must also be considered. Each organization has its own use for some (or all) of these tools, depending on how it chooses to use the available tools. We'll look at the main BI components, and at the way BI tools can be applied within an organization.
Contemporary BI Solutions
Contemporary BI solutions enable business users to author, publish, and distribute enterprise reports via a fully integrated report writer, with an easy-to-use report creation wizard. Users can also customize and tailor reports to specific information needs. Report writing and graphing capabilities should enable even nontechnical users to create and share clear representations of complex business conditions. In addition to being easy to use, report writers must also incorporate advanced features like exception filtering and highlighting, calculations with sub-queries, rankings, drill-throughs, and so on.
Nowadays, BI tools generally provide graphical analysis of business information in multidimensional views. Most companies collect a large amount of data from their business operations; to keep track of this information, users require a wide range of software programs, along with more sophisticated database applications for departments throughout their organization. However, using multiple software programs makes it difficult to retrieve information in a timely manner and to perform analysis of the data.
BI represents all the tools and systems that play a key role in the strategic planning process by allowing a company to gather, store, access, and analyze corporate data for decision-making. Generally, these systems assist organizations in customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis, to name only a few.
Data warehousing is a collection of data designed to support management decision-making. A data warehouse (DW) contains a wide variety of data that presents a coherent picture of business conditions at a single point in time. Its purpose is to create a database infrastructure that is always online, that contains all the information from the online transaction processing (OLTP) systems (including historical data), but that is structured in such a way that it is fast and efficient for querying and analysis (as opposed to a database for processing transactions).
Separating these two functions may improve flexibility and performance. The development of a DW includes the development of systems to extract data from underlying transactional operating systems. The DW also installs a warehouse database system that provides managers flexible access to the data. The term data warehousing typically refers to the combination of many different databases across an entire enterprise. This is in contrast to a data mart, which is a database (or collection of databases) designed to help managers make strategic decisions about their business. While a DW combines databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department, although some data marts, called dependent data marts, can be subsets of larger DWs.
Dimensions of Data Integration
With the advent of data warehousing came the creation of extract, transform, and load (ETL) tools, which use metadata to transfer information from the source systems into the DW. The three functions of ETL combine to pull data out of one database and place it into another:
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Extract—the process of reading data from a database.
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Transform—the process of converting extracted data from its previous form into a form that can be placed into another database. Transformation relies on rules or lookup tables, or on the combination of data with other data. This allows disparate data sources to be merged, which creates a centralized view of organizational data.
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Load—the process of writing the data into the target database or DW.
Again, ETL tools are typically used to migrate data from one database to another, to form data marts and DWs, or to convert databases from one format or type to another. Additional tools, which also make use of structured query language (SQL), have also been developed to give users direct access to the data in the DW. With time, these query tools have become more user-friendly, and many such tools now have a parser (a program that dissects source code so that it can be translated into object code) which can turn natural language questions into valid SQL commands.
Enterprise information integration (EII) is a category of software that confronts the longstanding challenge of enterprise data integration over diverse data sources in scattered enterprise systems. Companies that have overcome the problem of scaling and managing data are now pondering how to unify their data sources and leverage them to solve near real-time business problems. To that end, EII aims to provide unified views of multiple, heterogeneous data through a distributed (“federated”) query. One way to think of EII is as a virtual database layer that allows user applications to access and query data as if it resided in a single database. In other words, the concept takes the existing database capability to merge a query across different tables, but on a virtual basis, shielding users from the underlying complexities of locating, querying, and joining data from varied data source systems.
EII is a fundamentally different approach to such data integration technologies as enterprise application integration (EAI), which provides data or process-level integration, or enterprise portals, which merely integrate data at the presentation level. EAI can be defined as the unrestricted sharing of data and business processes throughout networked applications or data sources.
EII is also different from conventional ETL tools for data warehousing because it neither moves data nor creates new data stores of integrated data. Rather, it leaves data where it is, leveraging metadata repositories across multiple foundation enterprise systems, and visibly pulls information into new applications. As a result, customers may be content to trade in expensive DWs for a data extraction and presentation layer that sits on top of existing transactional systems—but only on the condition that they receive unimpaired performance.