Smart Data Collective

SEPTEMBER 26, 2021

Data is processed to generate information, which can be later used for creating better business strategies and increasing the company’s competitive edge. The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information. .

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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. “Data is what you need to do analytics. click for book source**.

Business Intelligence 117

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Data Pine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. One of the BI architecture components is data warehousing. What Is Data Warehousing And Business Intelligence? BI Architecture Framework In Modern Business. Data integration. Data storage.

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Data Pine

MARCH 31, 2022

4) Business Intelligence Job Roles. Does data excite, inspire, or even amaze you? Do you find computer science and its applications within the business world more than interesting? If you answered yes to any of these questions, you may want to consider a career in business intelligence (BI).In

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Astera

APRIL 13, 2023

In the digital age, a data warehouse plays a crucial role in businesses across several industries. It provides a systematic way to collect and analyze large amounts of data from multiple sources, such as marketing, sales, finance databases, and web analytics. What is a Data Warehouse?

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The BAWorld

OCTOBER 17, 2024

Worry not, In this article, we will answer the following questions: What is a data warehouse? What is the purpose of data warehouse? What are the benefits of using a data warehouse? How does a data warehouse impact analytics? What are the different usages of data warehouses?

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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.

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Sisense

APRIL 26, 2020

Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Structured vs unstructured data. However, both types of data play an important role in data analysis.

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Business Analysis Knowledge Share

MARCH 15, 2023

Let’s understand what a Data warehouse is and talk through some key concepts Datawarehouse Concepts for Business Analysis Data warehousing is a process of collecting, storing and managing data from various sources to support business decision making. What is Data Warehousing?

Big Data 52

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Astera

NOVEMBER 27, 2023

It includes format checks, range checks, and consistency checks to ensure data is clean, correct, and logically consistent. Understanding the Difference: Data Profiling vs. Data Mining Data profiling and data mining are two distinct processes with different objectives and methodologies.

Data Mining 52

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Sisense

MAY 11, 2021

Here at Sisense, we think about this flow in five linear layers: Raw This is our data in its raw form within a data warehouse. We follow an ELT ( E xtract, L oad, T ransform) practice, as opposed to ETL, in which we opt to transform the data in the warehouse in the stages that follow. Dig into AI.

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Data Pine

JANUARY 24, 2021

All of our experience has taught us that data analysis is only as good as the questions you ask. Additionally, you want to clarify these questions regarding data analysis now or as soon as possible – which will make your future business intelligence much clearer. ETL data warehouse*.

Data Visualization 126

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Astera

FEBRUARY 20, 2023

A single source of truth allows healthcare organizations to apply data mining techniques to effectively detect and prevent fraud. Data Integration Challenges in Healthcare Healthcare data wields enormous power, but the sheer volume and variety of this data pose various challenges.

Healthcare 52

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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. What are Information Marts?

Agile 52

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Astera

MAY 30, 2024

With today’s technology, data analytics can go beyond traditional analysis, incorporating artificial intelligence (AI) and machine learning (ML) algorithms that help process information faster than manual methods. Data analytics has several components: Data Aggregation : Collecting data from various sources.

Data Analytics 52

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Sisense

MARCH 14, 2021

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. displaying BI insights for human users).

Big Data 52

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Astera

DECEMBER 21, 2022

Data Extraction vs. Data Mining. People often confuse data extraction and data mining. The process of data extraction deals with extracting important information from sources, such as emails, PDF documents, forms, text files, social media, and images with the help of content extraction tools.

Data Mining 52

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Astera

APRIL 27, 2023

ETL stands for Extract-Transform-Load and a data extraction ETL tool will give you the power to do all three. ETL process allows businesses to apply a complete data integration strategy with the goal of preparing data for business intelligence (BI). Here are some uses of data extraction.

E-Commerce 52

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Astera

APRIL 27, 2023

ETL stands for Extract-Transform-Load and a data extraction ETL tool will give you the power to do all three. ETL process allows businesses to apply a complete data integration strategy with the goal of preparing data for business intelligence (BI). Here are some uses of data extraction.

E-Commerce 52

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Data Pine

AUGUST 29, 2022

An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”. If we had to pick one book for an absolute newbie to the field of Data Science to read, it would be this one.

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Astera

AUGUST 28, 2024

What are data analysis tools? 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.

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Astera

FEBRUARY 7, 2024

Its versatility allows for its usage both as a database and as a data warehouse when needed. The two complement each other so you can leverage your data more easily. PostgreSQL’s compatibility with Business Intelligence tools makes it a practical option for fulfilling your data mining, analytics, and BI requirements.

E-Commerce 52

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Insight Software

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

IBM Maintenance 52

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Insight Software

MAY 1, 2023

Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. Embedded Analytics Definition Embedded analytics are the integration of analytics content and capabilities within applications, such as business process applications (e.g., that gathers data from many sources.

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Insight Software

NOVEMBER 7, 2023

The Challenges of Extracting Enterprise Data Currently, various use cases require data extraction from your OCA ERP, including data warehousing, data harmonization, feeding downstream systems for analytical or operational purposes, leveraging data mining, predictive analysis, and AI-driven or augmented BI disciplines.

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