Astera

JANUARY 29, 2024

It challenges organizations to rethink their entire data lifecycle, especially within data warehouses and during data migration projects. Rainardi highlights a critical operational aspect: the retention period of personal data.

Government 145

Government Data Warehouse Data Governance Artificial Intelligence 145

Smart Data Collective

NOVEMBER 18, 2020

Data Science is an activity that focuses on data analysis and finding the best solutions based on it. Then artificial intelligence advances became more widely used, which made it possible to include optimization and informatics in analysis methods. Data Mining is an important research process. Practical experience.

Data Mining 358

Data Mining Big Data Artificial Intelligence Data Warehouse 358

Domo

APRIL 30, 2021

Data integration, not application integration. Organizations need the ability to integrate all data sources—clouds, applications, servers, data warehouses, etc. Enterprises may try to resolve the data integration issue through application integration and system orchestration. Governance and control.

Data Warehouse 98

Data Warehouse Artificial Intelligence Government Digital Transformation 98

Dataversity

NOVEMBER 29, 2021

There’s been a lot of talk about the modern data stack recently. Much of this focus is placed on the innovations around the movement, transformation, and governance of data as it relates to the shift from on-premise to cloud data warehouse-centric architectures.

Data Warehouse 130

Data Warehouse Government Innovation Artificial Intelligence 130

Astera

APRIL 28, 2023

Data management can be a daunting task, requiring significant time and resources to collect, process, and analyze large volumes of information. AI is a powerful tool that goes beyond traditional data analytics. Predictions As artificial intelligence continues to rapidly advance, its potential applications are constantly expanding.

E-Commerce 40

E-Commerce Supply Chain Artificial Intelligence Data Warehouse 40

Sisense

JANUARY 1, 2020

5 Advantages of Using a Redshift Data Warehouse. Whatever business you’re in, your company is becoming a data company. That means you need to put all that data somewhere. Chances are it’s in a data warehouse, and even better money says it’s an AWS data warehouse. Read about how Sisense BloX 2.0

Digital Transformation 98

Digital Transformation Data Warehouse Embedded Analytics Artificial Intelligence 98

Astera

NOVEMBER 30, 2023

With its foundation rooted in scalable hub-and-spoke architecture, Data Vault 1.0 provided a framework for traceable, auditable, and flexible data management in complex business environments. Building upon the strengths of its predecessor, Data Vault 2.0 To further highlight the relevance of Data Vault 2.0 Data Vault 2.0’s

Data Warehouse 52

Data Warehouse Healthcare Big Data Data Governance 52

Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

Data Warehouse 95

Data Warehouse Government Artificial Intelligence Data Discovery 95

Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

Data Warehouse 93

Data Warehouse Government Artificial Intelligence Data Discovery 93

Astera

MAY 9, 2023

Six Stages of the Data Processing Cycle The data processing cycle outlines the steps that one needs to perform on raw data to convert it into valuable and purposeful information. Data Input Data input stage is the stage in which raw data starts to take an informational form.

Data Warehouse 52

Data Warehouse Big Data Innovation Artificial Intelligence 52

Timo Elliott

OCTOBER 3, 2019

For example, Gerd Danner explained the digital core strategy of S/4HANA is key part of the journey, emphasizing that while the new platform gives you a lot more real-time analytic power, without any data duplication, you still need a data warehouse and analytics strategy over time and across different systems.

Data Warehouse 157

Data Warehouse Digital Transformation Innovation Digital 157

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.

Data Warehouse 52

Data Warehouse Data Mining Artificial Intelligence Visualization 52

Sisense

DECEMBER 23, 2019

Artificial Intelligence and machine learning are the future of every industry, especially data and analytics. AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. Use AI to tackle huge datasets.

Data Warehouse 86

Data Warehouse Artificial Intelligence Digital Transformation Innovation 86

The BAWorld

DECEMBER 23, 2023

Business Data Analyst Another distinct type is the Business Data Analyst, often seen working on data analytics projects. This role requires skills in data analytics, including knowledge of machine learning basics, artificial intelligence, and programming languages like Python.

Banking 52

Banking UML Business Analysis Business Analysis 52

Actian

SEPTEMBER 17, 2019

Currently, three primary technology shifts are combining to move beyond the capabilities and expected outcomes of Data Historian software. Modern Time-Series Databases capture multi-modal data. Outside of the OT domain, the rest of your company data is stored in standard databases and data warehouses.

Artificial Intelligence 40

Artificial Intelligence Data Warehouse Visualization Vision 40

Actian

SEPTEMBER 12, 2019

Artificial Intelligence (AI) systems seem to be everywhere and for a good reason. Data persistence enables workflow continuity and tracking across multiple systems. Artificial Intelligence is arguably the most important technological development of the modern era.

Data Requirement 40

Data Requirement Artificial Intelligence Data Warehouse Cloud Computing 40

Astera

NOVEMBER 13, 2023

But have you ever wondered how data informs the decision-making process? The key to leveraging data lies in how well it is organized and how reliable it is, something that an Enterprise Data Warehouse (EDW) can help with. What is an Enterprise Data Warehouse (EDW)?

Data Warehouse 52

Data Warehouse E-Commerce Supply Chain Real-time Data 52

Business Analysis Knowledge Share

OCTOBER 27, 2023

This may involve data from internal systems, external sources, or third-party data providers. The data collected should be integrated into a centralized repository, often referred to as a data warehouse or data lake. Data integration ensures that all necessary information is readily available for analysis.

Business Intelligence 52

Business Intelligence Business Intelligence Supply Chain Data Governance 52

Astera

MAY 24, 2023

Thanks to the rise of artificial intelligence (AI) and automation, working with this data has become easier and more efficient. The Advent of AI-Powered Tools In the current marketplace, we see a diverse range of data management tools, from data warehouses and data lakes to advanced database management systems.

Retail 40

Retail Artificial Intelligence Data Management Data Warehouse 40

Astera

JUNE 13, 2023

Machine Learning and AI Data pipelines provide a seamless flow of data for training machine learning models. This enables organizations to develop predictive analytics, automate processes, and unlock the power of artificial intelligence to drive their business forward.

E-Commerce 52

E-Commerce Data Quality Real-time Data Data Warehouse 52

Actian

MAY 21, 2019

For many years, companies have been accumulating large amounts of data with an intuitive feeling that it has value and would be put to good use to make more informed business decisions. In this case, actionable business insights are the finished product you are seeking to provide to your data consumers.

Big Data 40

Big Data Data Warehouse Artificial Intelligence Digital Transformation 40

Data Pine

MAY 9, 2019

With our introduction to business intelligence, we’re going to dispel the myths surrounding BI, explore the core business intelligence concepts, cover the BI basics, and drill down into a mix of real-life business intelligence examples and use cases. Introduction To Business Intelligence Concepts. The data warehouse.

Business Intelligence 108

Business Intelligence Business Intelligence Procurement Data Warehouse 108

Actian

FEBRUARY 24, 2020

Continuous improvement, be it through process analysis and optimization or supported by machine learning and artificial intelligence, requires RPA vendors to aggregate distributed data in a centralized location for analysis and the harvesting of enterprise insights.

Digital Transformation 40

Digital Transformation Artificial Intelligence Data Warehouse Big Data 40

Astera

JUNE 14, 2023

The ultimate goal is to convert unstructured data into structured data that can be easily housed in data warehouses or relational databases for various business intelligence (BI) initiatives. The process enables businesses to unlock valuable information hidden within unstructured documents.

Documentation 52

Documentation Supply Chain Healthcare Logistics 52

Smart Data Collective

DECEMBER 13, 2022

Five Best Practices for Data Analytics. Extracted data must be saved someplace. There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Select a Storage Platform.

Visualization 340

Visualization Data Analytics Data Analytics Artificial Intelligence 340

Actian

SEPTEMBER 17, 2019

Currently, three primary technologies work together to do the work of former data librarians and historians. Your company data is stored in databases and data warehouses. Legacy databases were good at capturing and maintaining a snapshot of data but often struggled with capturing and managing the change of data.

Artificial Intelligence 9

Artificial Intelligence Data Warehouse Digital Transformation Data Management 9

Data Pine

JANUARY 24, 2021

You don’t have to do all the database work, but an ETL service does it for you; it provides a useful tool to pull your data from external sources, conform it to demanded standard and convert it into a destination data warehouse. ETL data warehouse*. 7) Who are the final users of your analysis results?

Data Visualization 126

Data Visualization Data Analytics Data Analytics Data Quality 126

Data Pine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. If you’d like some resources in this area, we have posts on related business intelligence books and business intelligence podcasts you can use to start your research. Business Intelligence Job Roles.

Business Intelligence 95

Business Intelligence Business Intelligence Data Visualization Visualization 95

Dataversity

AUGUST 1, 2022

Everything is already digital-first, totally connected, in the cloud, and powered by data, everywhere, all the time. The age of digital transformation is over. It’s too late to be debating whether you should digitally transform your organization. The world has already digitally transformed. Digital-first organizations won.

Digital Transformation 246

Digital Transformation Digital Innovation Data Warehouse 246

GAVS Technology

NOVEMBER 3, 2020

As evident in most hospitals, these information are usually scattered across multiple data sources/databases. Hospitals typically create a data warehouse by consolidating information from multiple resources and try to create a unified database. Limitations of Current Methods.

Healthcare 52

Healthcare Data Mining Data Warehouse Data Management 52

Actian

SEPTEMBER 12, 2019

Artificial Intelligence (AI) systems seem to be everywhere and for a good reason. Data persistence enables workflow continuity and tracking across multiple systems. Artificial Intelligence is arguably the most important technological development of the modern era.

Data Requirement 8

Data Requirement Artificial Intelligence Data Warehouse Cloud Computing 8

Dataversity

APRIL 30, 2021

The goal of digital transformation remains the same as ever – to become more data-driven. We have learned how to gain a competitive advantage by capturing business events in data. Events are data snap-shots of complex activity sourced from the web, customer systems, ERP transactions, social media, […].

Digital Transformation 246

Digital Transformation Digital Data Warehouse Data Governance 246

Tableau

APRIL 18, 2022

A data fabric is an emerging data management design that allows companies to seamlessly access, integrate, model, analyze, and provision data. Instead of centralizing data stores, data fabrics establish a federated environment and use artificial intelligence and metadata automation to intelligently secure data management. .

Data Quality 99

Data Quality Government Artificial Intelligence Data Warehouse 99

Tableau

APRIL 18, 2022

A data fabric is an emerging data management design that allows companies to seamlessly access, integrate, model, analyze, and provision data. Instead of centralizing data stores, data fabrics establish a federated environment and use artificial intelligence and metadata automation to intelligently secure data management. .

Data Quality 97

Data Quality Government Artificial Intelligence Data Warehouse 97

BizAcuity

APRIL 14, 2022

Currently in the market, organizations look at on-premises, cloud storage, hybrid and multi-cloud storage options based on the kind of data they have and decide between data lakes, data warehouses or both depending on the kind of data they have and their long term goals. Enterprise Big Data Strategy.

Big Data 130

Big Data Data Analytics Data Analytics Data Management 130

Astera

JANUARY 18, 2024

It involves a careful evaluation of different solutions to identify the one that aligns most effectively with the organization’s data integration requirements and long-term goals. Its platform includes: ReportMiner for unstructured data extraction in bulk. Automate and orchestrate your data integration workflows seamlessly.

IBM Support 52

IBM Support IBM Maintenance IBM cost Data Warehouse 52

Sisense

FEBRUARY 24, 2021

Business analysts, who may not have the coding skills needed to derive value from the data, need a suite of self-service features that are easy to use without assistance from the data team. Many large organizations either have a central data warehouse or are in the process of creating one.

Chief Data Officer 69

Chief Data Officer Data Warehouse Big Data Innovation 69