Dashboards Data Warehouse Marketing OLAP 
CIO Business Intelligence
NOVEMBER 14, 2022
A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.
Jet Global
SEPTEMBER 4, 2020
For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.
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In(tegrate) the Clouds
OCTOBER 7, 2016
As the data visualization, big data, Hadoop, Spark and self-service hype gives way to IoT, AI and Machine Learning, I dug up an old parody post on the business intelligence market circa 2007-2009 when cloud analytics was just a disruptive idea. Thanks to The OLAP Report for lots of great market materials.
FineReport
AUGUST 14, 2020
For popular reporting tools on the market, you can refer to: Best Reporting Tools List in 2020 and How to Choose. Based on the process from data to knowledge, a standard reporting system’s functional architecture is shown below. It is composed of three functional parts: the underlying data, data analysis, and data presentation.
Jet Global
JUNE 5, 2019
Data drives everything in the business world, from manufacturing to supply chain logistics to retail sales to customer experience to post-sale marketing and beyond, data holds the secrets to making processes more efficient, production costs cheaper, profit margins higher and marketing campaigns more effective.
Jet Global
APRIL 20, 2020
CRM software has gone through a similar transformation, starting with sales force automation, and more recently evolving into a new breed of products that support digital marketing campaigns through email, social media, and online advertising. It enabled finance professionals to view, filter, and analyze their data along multiple dimensions.
Octopai
JANUARY 8, 2020
As a company’s data landscape grows and evolves, more computing “horsepower” is needed to perform the ETL and OLAP cube processing required to populate data warehouses and drive reports and dashboards. With automated metadata management, you can correlate data source growth with the performance of these processes.
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