Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. Data virtualization is becoming more popular due to its huge benefits. Maximizing customer engagement.

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

CIO Business Intelligence

JANUARY 24, 2024

Overlooking these data resources is a big mistake. The proper use of unstructured data will become of increasing importance to IT leaders,” says Kevin Miller, CTO of enterprise software developer IFS. “It Creating data silos Denying business users access to information because of data silos has been a problem for years.

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

erwin

SEPTEMBER 17, 2020

Data automation reduces the loss of time in collecting, processing and storing large chunks of data because it replaces manual processes (and human errors) with intelligent processes, software and artificial intelligence (AI). Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

erwin

MARCH 4, 2021

In addition to drivers like digital transformation and compliance, it’s really important to look at the effect of poor data on enterprise efficiency/productivity. The Benefits of erwin Data Intelligence.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. For users that require a unified view of software quality, this is unacceptable.

Software 52

Software Data Lake Testing Dashboards 52

Jen Stirrup

SEPTEMBER 30, 2021

AI vs ML vs Data Science vs Business Intelligence. Before we dive in, let’s define strands of AI, Machine Learning and Data Science: Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

DataKitchen

DECEMBER 9, 2022

Both DevOps and DataOps are approaches to managing and optimizing the development and deployment of software and other applications, with a focus on collaboration, automation, and continuous improvement. Query> Is DataOps something that can be solved with software or is it more of a people process? Query> When do DataOps?

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

Ontotext

FEBRUARY 12, 2024

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

AWS Big Data

JULY 27, 2023

DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code. The over 200 transformations it provides are now available to be used in an AWS Glue Studio visual job. Now that we identified the data quality issues to address, we need to decide how to deal with each case.

Visualization 98

Visualization Cost-Benefit Publishing Data Quality 98

erwin

APRIL 16, 2024

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. erwin also provides data governance, metadata management and data lineage software called erwin Data Intelligence by Quest.

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Metadata Data Quality Data Governance Modeling 52

AWS Big Data

APRIL 19, 2023

Background The success of a data-driven organization recognizes data as a key enabler to increase and sustain innovation. The goal of a data product is to solve the long-standing issue of data silos and data quality. You can also explore Neptune notebooks demonstrating ML and data science for graphs.

Technology 105

Technology Data-driven Machine Learning Sales 105

Jet Global

MAY 28, 2019

A data warehouse is typically used by companies with a high level of data diversity or analytical requirements. As the complexity and volume of data used in the enterprise scales and organizations want to get more out of their analytics efforts, data warehouses are gaining more traction for reporting and analytics over databases.

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85

Octopai

AUGUST 11, 2024

Just as a navigation app provides a detailed map of roads, guiding you from your starting point to your destination while highlighting every turn and intersection, data flow lineage offers a comprehensive view of data movement and transformations throughout its lifecycle.

Metadata 40

Metadata Visualization Data Quality Data Governance 40

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 75

Cost-Benefit Metadata Optimization Management 75

AWS Big Data

APRIL 5, 2023

“Each of these tools were getting data from a different place, and that’s where it gets difficult,” says Jeroen Minnaert, head of data at Showpad. “If If each tool tells a different story because it has different data, we won’t have alignment within the business on what this data means.”

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

Jet Global

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.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MARCH 13, 2024

Complex Data Structures and Integration Processes Dynamics data structures are already complex – finance teams navigating Dynamics data frequently require IT department support to complete their routine reporting. Prospective Customer Current Customer Partner Hidden Do you resell software?

Reporting 52

Reporting Finance Data Quality Sales 52

Jet Global

MARCH 14, 2024

Jet’s interface lets you handle data administration easily, without advanced coding skills. You don’t need technical skills to manage complex data workflows in the Fabric environment. Data Lineage and Documentation Jet Analytics simplifies the process of documenting data assets and tracking data lineage in Fabric.

Analytics 52

Analytics Management Reporting Data Quality 52

Jet Global

AUGUST 24, 2023

It streamlines data integration, ensures real-time access to accurate information, enhances collaboration, and provides the flexibility needed to adapt to evolving ERP systems and business requirements. Data transformation ensures that the data aligns with the requirements of the new cloud ERP system.

Finance 52

Finance Reporting Data Integration Data Warehouse 52

CIO Business Intelligence

MARCH 26, 2025

For many CIOs, preparing their data for even one AI project is a tall order. As they embark on their AI journey, many people have discovered their data is garbage, says Eric Helmer, chief technology officer for software support company Rimini Street. They arent sure where it is among hundreds of different systems in some cases.

Data Quality 129

Data Quality Forecasting Unstructured Data Data-driven 129

BI-Survey

MARCH 6, 2025

For data management teams, achieving more with fewer resources has become a familiar challenge. While efficiency is a priority, data quality and security remain non-negotiable. Developing and maintaining data transformation pipelines are among the first tasks to be targeted for automation.

Data Warehouse 52

Data Warehouse Metadata Unstructured Data Data-driven 52

AWS Big Data

FEBRUARY 25, 2025

If data mapping has been enabled within the data processing job, then the structured data is prepared based on the given schema. This output is passed to next phase where data transformations and business validations can be applied. After this step, data is loaded to specified target.

Data Processing 58

Data Processing Machine Learning Data-driven Cost-Benefit 58

DataKitchen

JANUARY 23, 2023

Five on DataOps Observability : DataOps Observability is the ability to understand the state and behavior of data and the software and hardware that carries and transforms it as it flows through systems. It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed.

Testing 130

Testing Data Governance Data Quality Data-driven 130