Business Analysis Knowledge Share

MARCH 15, 2023

Understanding the key concepts of data warehousing, such as data integration, dimensional modeling, OLAP, and data marts, is vital for business analysts who are responsible for analyzing data and providing insights that drive business performance. What is Data Warehousing?

Big Data 52

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Astera

DECEMBER 28, 2023

Airbyte vs Fivetran vs Astera: Overview Airbyte Finally, Airbyte is primarily an open-source data replication solution that leverages ELT to replicate data between applications, APIs, data warehouses, and data lakes. Like other data integration platforms , Airbyte features a visual UI with built-in connectors.

Data Warehouse 52

Data Warehouse Data Quality Documentation Data Management 52

Astera

DECEMBER 28, 2023

Airbyte vs Fivetran vs Astera: Overview Airbyte Finally, Airbyte is primarily an open-source data replication solution that leverages ELT to replicate data between applications, APIs, data warehouses, and data lakes. Like other data integration platforms , Airbyte features a visual UI with built-in connectors.

Data Warehouse 52

Data Warehouse Data Quality Documentation Data Management 52

The BAWorld

APRIL 21, 2021

It refers to the methods involved in accessing and manipulating source data and loading it into the target database. This inconsistency in data can be avoided by integrating the data into a data warehouse with good standards. The data warehouse design should accommodate both full and incremental data extraction.

Data Warehouse 52

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Data Pine

SEPTEMBER 27, 2022

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

IBM cost 117

IBM cost Data Warehouse Business Intelligence Business Intelligence 117

Astera

JANUARY 25, 2024

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

E-Commerce 52

E-Commerce Data Warehouse Retail Customer Experience 52

Data Pine

OCTOBER 25, 2022

With ‘big data’ transcending one of the biggest business intelligence buzzwords of recent years to a living, breathing driver of sustainable success in a competitive digital age, it might be time to jump on the statistical bandwagon, so to speak. One of the best books on building a BI system, hands down. click for book source**.

Business Intelligence 117

Business Intelligence Business Intelligence Big Data Data Mining 117

Astera

JANUARY 11, 2024

Siloed Data Challenges Financial institutions face several hurdles due to decentralized data. These challenges include: Legacy Systems: Outdated systems make it difficult to get the best data into your data warehouse. Divergent data sources can lead to conflicting information, undermining accuracy and reliability.

Data Warehouse 52

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Astera

APRIL 28, 2023

AI-powered ETL tools can automate repetitive tasks, optimize performance, and reduce the potential for human error. By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models and creating data visualizations.

E-Commerce 40

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Astera

FEBRUARY 16, 2024

There are several ETL tools written in Python that leverage Python libraries for extracting, loading and transforming diverse data tables imported from multiple data sources into data warehouses. Astera Aspect Astera Python Data Integration Supports various data sources and destinations with ease.

Data Warehouse 52

Data Warehouse Monitoring Data Quality Documentation 52

Astera

MAY 31, 2024

Here are the critical components of data science: Data Collection : Accumulating data from diverse sources like databases, APIs , and web scraping. Data Cleaning and Preprocessing : Ensuring data quality by managing missing values, eliminating duplicates, normalizing data, and preparing it for analysis.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 52

Astera

JULY 18, 2023

Data wrangling tools are powerful solutions designed to simplify and automate the process of data preparation. They enable data professionals to clean, transform, and organize raw data efficiently, saving countless hours of manual work while ensuring data quality and consistency.

Data Quality 52

Data Quality Visualization Data Visualization Documentation 52

Domo

JULY 8, 2021

Then there are: the vendors who provide the tools you need to create applications such as operating systems; and the SaaS applications you need to provide business value including business intelligence and data visualization tools. Look for metadata management capabilities and ways to ensure data quality.

Data Governance 52

Data Governance Government Embedded Analytics Business Intelligence 52

Astera

MAY 30, 2024

Statistical Analysis : Using statistics to interpret data and identify trends. Predictive Analytics : Employing models to forecast future trends based on historical data. Data Visualization : Presenting data visually to make the analysis understandable to stakeholders.

Data Analytics 52

Data Analytics Data Analytics Data Mining Supply Chain 52

Astera

MAY 6, 2024

Type of Data Mining Tool Pros Cons Best for Simple Tools (e.g., – Data visualization and simple pattern recognition. Simplifying data visualization and basic analysis. A key aspect of data preparation is the extraction of large datasets from a variety of data sources. Advanced Tools (e.g.,

Data Mining 52

Data Mining Supply Chain Data Quality Visualization 52

Astera

OCTOBER 19, 2023

Easy-to-Use, Code-Free Environment By eliminating the need for writing complex code, data preparation tools reduce the risk of errors. These tools allow users to manipulate and transform data without the potential pitfalls of manual coding. Manual export and import steps in a system can add complexity to your data pipeline.

IBM Support 40

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Data Pine

AUGUST 29, 2022

Best for: the seasoned BI professional who is ready to think deep and hard about important issues in data analytics and big data. An excerpt from a rave review: “…a tour de force of the data warehouse and business intelligence landscape. Khan Analytic Philosophy: A Very Short Introduction by Michael Beaney.

Big Data 105

Big Data Data Analytics Data Analytics Data Mining 105

ElegantJ BI

MARCH 19, 2021

One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa).

Data Quality 130

Data Quality Agile Business Intelligence Business Intelligence 130

ElegantJ BI

MARCH 19, 2021

One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa).

Data Quality 130

Data Quality Agile Business Intelligence Business Intelligence 130

ElegantJ BI

MARCH 19, 2021

One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa).

Data Quality 130

Data Quality Agile Business Intelligence Business Intelligence 130

Data Pine

FEBRUARY 22, 2022

This should also include creating a plan for data storage services. Are the data sources going to remain disparate? Or does building a data warehouse make sense for your organization? Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI.

Business Intelligence 87

Business Intelligence Business Intelligence Chief Data Officer Government 87

Astera

AUGUST 28, 2024

Data analysis tools are software solutions, applications, and platforms that simplify and accelerate the process of analyzing large amounts of data. They enable business intelligence (BI), analytics, data visualization , and reporting for businesses so they can make important decisions timely.

IBM Support 40

IBM Support IBM Maintenance IBM cost Data Mining 40

Astera

APRIL 10, 2023

Source: Gartner As companies continue to move their operations to the cloud, they are also adopting cloud-based data integration solutions, such as cloud data warehouses and data lakes. Establish and enforce data quality rules to ensure clean and consistent data across all environments.

Data Management 52

Data Management IBM Maintenance Real-time Data IBM Support 52

Insight Software

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

IBM Maintenance 52

IBM Maintenance Data Warehouse IBM Support Real-time Data 52

Insight Software

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

IBM Support 52

IBM Support IBM Maintenance Data Warehouse IBM cost 52

Insight Software

OCTOBER 11, 2023

Existing applications did not adequately allow organizations to deliver cost-effective, high-quality interactive, white-labeled/branded data visualizations, dashboards, and reports embedded within their applications. Join disparate data sources to clean and apply structure to your data.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance User Experience 52

Insight Software

SEPTEMBER 1, 2023

These include data privacy and security concerns, model accuracy and bias challenges, user perception and trust issues, and the dependency on data quality and availability. Data Privacy and Security Concerns: Embedded predictive analytics often require access to sensitive user data for accurate predictions.

Predictive Analytics 52

Predictive Analytics IBM Support IBM Maintenance Embedded Analytics 52

Insight Software

MAY 8, 2024

Logi Symphony harnesses the strengths of two recent insightsoftware acquisitions, Logi Analytics and Dundas BI, to enable software teams to rapidly design, build, and embed interactive dashboards, pixel-perfect reports and data visualizations with fast connectivity and access to modern data infrastructure.

Business Intelligence 52

Business Intelligence Business Intelligence IBM Support Embedded Analytics 52

Insight Software

APRIL 14, 2022

Having accurate data is crucial to this process, but finance teams struggle to easily access and connect with data. Improve data quality. Some functional areas use business intelligence and data visualization tools, but operate in isolation with their own data sets, driving decisions related to that function only.

IBM Maintenance 52

IBM Maintenance Finance IBM cost IBM Support 52

Insight Software

MARCH 4, 2024

Moving data across siloed systems is time-consuming and prone to errors, hurting data quality and reliability. Unleash the power of storytelling by showcasing your ESG achievements with engaging visuals. Manual processes and juggling multiple tools won’t cut it under the ever-changing CSRD regulations.

Supply Chain 52

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Insight Software

SEPTEMBER 22, 2023

About Logi Symphony Logi Symphony enables software teams to rapidly design, build, and embed interactive dashboards and data visualizations with fast connectivity and access to modern data infrastructure. Connect to any data source. Align data with ETL, data performance, data quality, and data structure.

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