Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. The post How Will The Cloud Impact Data Warehousing Technologies?

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Smart Data Collective

MAY 16, 2022

These include, but are not limited to, database management systems, data mining software, decision support systems, knowledge management systems, data warehousing, and enterprise data warehouses. Some data management strategies are in-house and others are outsourced.

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Pickl AI

JUNE 28, 2023

What is Data Mining? In today’s data-driven world, organizations collect vast amounts of data from various sources. But, this data is often stored in disparate systems and formats. Here comes the role of Data Mining. Here comes the role of Data Mining.

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Dataversity

FEBRUARY 18, 2022

Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions.

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Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

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Data Science Blog

MARCH 30, 2023

Eine bessere Idee ist es daher, Event Logs nicht in einzelnen Process Mining Tools aufzubereiten, sondern zentral in einem dafür vorgesehenen Data Warehouse zu erstellen, zu katalogisieren und darüber auch die grundsätzliche Data Governance abzusichern. Dank AI werden damit noch viel verborgenere Prozesse sichtbar.

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Analytics Vidhya

APRIL 17, 2023

Are you a data enthusiast looking to break into the world of analytics? The field of data science and analytics is booming, with exciting career opportunities for those with the right skills and expertise. So, let’s […] The post Data Scientist vs Data Analyst: Which is a Better Career Option to Pursue in 2023?

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Analytics Vidhya

JUNE 26, 2023

Introduction In today’s data-driven world, the role of data scientists has become indispensable. in data science to unravel the mysteries hidden within vast data sets? But what if I told you that you don’t need a Ph.D.

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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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Dataconomy

MAY 10, 2023

Open-source business intelligence (OSBI) is commonly defined as useful business data that is not traded using traditional software licensing agreements. This is one alternative for businesses that want to aggregate more data from data-mining processes without buying fee-based products.

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Dataconomy

APRIL 27, 2023

Conversely, OLAP systems are optimized for conducting complex data analysis and are designed for use by data scientists, business analysts, and knowledge workers. OLAP systems support business intelligence, data mining, and other decision support applications. An OLAP database may also be organized as a data warehouse.

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ODSC - Open Data Science

FEBRUARY 8, 2023

Common databases appear unable to cope with the immense increase in data volumes. This is where the BigQuery data warehouse comes into play. Conclusion Indeed BigQuery responds to all the business issues relating to the world of data (or Business Intelligence). BigQuery for Marketing: What Makes it Special?

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Alation

MARCH 29, 2022

This led to the birth of separate systems for reporting: the enterprise data warehouse. For the first time, the focus of a system became business questions, where data was denormalized. At Alation, we believe self-service has three unique stakeholders: End users trying to discover data for decision making.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data. Watsonx comprises of three powerful components: the watsonx.ai

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Alation

JULY 13, 2021

This recent cloud migration applies to all who use data. We have seen the COVID-19 pandemic accelerate the timetable of cloud data migration , as companies evolve from the traditional data warehouse to a data cloud, which can host a cloud computing environment. Complex data management is on the rise.

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ODSC - Open Data Science

JANUARY 30, 2024

This involves extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or other storage systems. Think of it as building plumbing for data to flow smoothly throughout the organization.

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Pickl AI

JANUARY 15, 2025

Data Visualization Tools These tools create visual representations of data, such as graphs and dashboards, making complex data sets easier to understand. Data Mining Tools Data mining tools analyse large datasets to discover hidden patterns or relationships within the data.

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IBM Journey to AI blog

JULY 6, 2023

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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IBM Journey to AI blog

MARCH 14, 2024

. With Db2 Warehouse’s fully managed cloud deployment on AWS, enjoy no overhead, indexing, or tuning and automated maintenance.  Netezza incorporates in-database analytics and machine learning (ML), governance, security and patented massively parallel processing.

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Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. What is business intelligence?

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Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. What is business intelligence?

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Pickl AI

MAY 7, 2024

Their tasks encompass: Data Collection and Extraction Identify relevant data sources and gather data from various internal and external systems Extract, transform, and load data into a centralized data warehouse or analytics platform Data Cleaning and Preparation Cleanse and standardize data to ensure accuracy, consistency, and completeness.

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Pickl AI

JUNE 11, 2024

Summary: A data warehouse is a central information hub that stores and organizes vast amounts of data from different sources within an organization. Unlike operational databases focused on daily tasks, data warehouses are designed for analysis, enabling historical trend exploration and informed decision-making.

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Pickl AI

JANUARY 19, 2025

Summary: Data warehousing and data mining are crucial for effective data management. Data warehousing focuses on storing and organizing data for easy access, while data mining extracts valuable insights from that data. It ensures data quality, consistency, and accessibility over time.

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Alation

AUGUST 26, 2021

Today, BI represents a $23 billion market and umbrella term that describes a system for data-driven decision-making. BI leverages and synthesizes data from analytics, data mining, and visualization tools to deliver quick snapshots of business health to key stakeholders, and empower those people to make better choices.

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