Jet Global

OCTOBER 22, 2020

Power BI provides users with some very nice dashboarding and reporting capabilities. Unfortunately, it also introduces a mountain of complexity into the reporting process. Let’s begin with an overview of how data analytics works for most business applications. The first is an OLAP model.

Modeling 52

Modeling OLAP Reporting Sales 52

Sisense

AUGUST 11, 2019

BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics. See an example: Explore Dashboard. Confused yet?

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

Jet Global

APRIL 2, 2019

But business intelligence software , built to give businesses the opportunity to collect, unify, sort, tag, analyze, and report on the vast amounts of data at their disposal, must be a focus for businesses hoping to gain an AI advantage down the road. It All Starts with Data. So how is the data extracted?

Enterprise 80

Enterprise OLAP Business Intelligence Internet of Things 80

Jet Global

AUGUST 14, 2019

Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

Jet Global

SEPTEMBER 4, 2020

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. The SQL query language used to extract data for reporting could also potentially be used to insert, update, or delete records from the database.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

AWS Big Data

MARCH 7, 2023

Consumption This pillar consists of various consumption channels for enterprise analytical needs. It includes business intelligence (BI) users, canned and interactive reports, dashboards, data science workloads, Internet of Things (IoT), web apps, and third-party data consumers.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Jet Global

OCTOBER 2, 2019

Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Sisense

FEBRUARY 16, 2021

Every aspect of analytics is powered by a data model. A data model presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. Live models run queries directly against the data source.

Modeling 52

Modeling OLAP Data Warehouse Cost-Benefit 52

Sisense

SEPTEMBER 3, 2019

First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). For example, if you are using Redshift solely for analytics purposes, you can scale the cluster up with more nodes when this happens and resume work once it is complete.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78