“Many historians suggest the modern version of business intelligence evolved from the DSS database,” says the IT education site Dataversity. In the 1960s and 70s, the first data management systems and decision support systems (DSS) were developed to store and organize growing volumes of data. His research helped establish methods for creating some of IBM’s early analytics platforms. In 1958, an IBM computer scientist named Hans Peter Luhn explored the potential of using technology to gather business intelligence.
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The term business intelligence was first used in 1865 by author Richard Millar Devens, when he cited a banker who collected intelligence on the market ahead of his competitors. Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. For example: What are sales for our eastern region versus our western region this year, compared to last year?
#DSS VS BI SOFTWARE#
Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps.ĭata warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting.
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BI platforms traditionally rely on data warehouses for their baseline information.