Business Intelligence Data Architecture Data Management Visualization 
Insight Software
JANUARY 17, 2022
Business intelligence requirements in this category may include dashboards and reports as well as the interactive and analytical functions users can perform. Consider whether you need to personalize visualizations, let users kick off workflows, or drill down into information. End-User Experience.
Astera
JANUARY 23, 2024
Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively.
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Tableau
JUNE 8, 2021
This raw data often goes through a number of transformation steps: clean and prepare, apply business rules, feature engineering, classification, scoring, and so on. . Aggregated results are then pulled into a data warehouse , or semantic layer, where business users can interact with the data using business intelligence tools. .
Astera
OCTOBER 19, 2023
While all data transformation solutions can generate flat files in CSV or similar formats, the most efficient data prep implementations will also easily integrate with your other productivity business intelligence (BI) tools. Manual export and import steps in a system can add complexity to your data pipeline.
Astera
DECEMBER 21, 2022
In comparison to cloud data warehouses, on-premise data warehouses pose certain challenges that affect the efficiency of the organizations’ analytics and business intelligence operations. Moreover, when using a legacy data warehouse, you run the risk of issues in multiple areas, from security to compliance.
Sisense
SEPTEMBER 3, 2020
One of the most common data job titles, data analysts use existing tools and algorithms to solve data-related problems (instead of inventing new ones like data scientists might do. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.
Tableau
JUNE 8, 2021
This raw data often goes through a number of transformation steps: clean and prepare, apply business rules, feature engineering, classification, scoring, and so on. . Aggregated results are then pulled into a data warehouse , or semantic layer, where business users can interact with the data using business intelligence tools. .
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