AWS Big Data

JANUARY 15, 2025

In the following section, two use cases demonstrate how the data mesh is established with Amazon DataZone to better facilitate machine learning for an IoT-based digital twin and BI dashboards and reporting using Tableau. This is further integrated into Tableau dashboards. This led to a complex and slow computations.

IoT 111

IoT Machine Learning Metadata Data-driven 111

AWS Big Data

JUNE 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 98

Data Quality Data-driven Data Lake Metrics 98

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

DataKitchen

MAY 10, 2024

This process is critical as it ensures data quality from the onset. Data Ingestion: Continuous monitoring of data ingestion ensures that updates to existing data sources are consistent and accurate. Examples include regular loading of CRM data and anomaly detection. Is My Model Still Accurate?

Metrics 126

Metrics Testing Data Quality Dashboards 126

Sisense

JANUARY 14, 2021

Because of how delicate customer relationships can be, Billie expended considerable resources monitoring reported data for accuracy and fixing broken charts and reports before consumers could be affected. However, at a lean startup with a BI team of three, manually checking dozens of dashboards every morning seemed impossible.

Dashboards 85

Dashboards Testing Business Intelligence Interactive 85

DataKitchen

MAY 10, 2024

This process is critical as it ensures data quality from the onset. Data Ingestion: Continuous monitoring of data ingestion ensures that updates to existing data sources are consistent and accurate. Examples include regular loading of CRM data and anomaly detection. Is the source data of expected quality?

Data Quality 133

Data Quality Testing Software Dashboards 133

DataKitchen

MAY 10, 2021

Data errors impact decision-making. When analytics and dashboards are inaccurate, business leaders may not be able to solve problems and pursue opportunities. Data errors infringe on work-life balance. Data errors also affect careers. You and your data team can accomplish the same thing at your organization.

Testing 214

Testing Manufacturing Data Quality Statistics 214

datapine

FEBRUARY 22, 2022

IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI. Indeed, every year low-quality data is estimated to cost over $9.7

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

BizAcuity

NOVEMBER 13, 2024

Beyond mere data collection, BI consulting helps businesses create a cohesive data strategy that aligns with organizational goals. This approach involves everything from identifying key metrics to implementing analytics systems and designing dashboards.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

FineReport

MAY 31, 2023

In today’s dynamic business environment, gaining comprehensive visibility into financial data is crucial for making informed decisions. This is where the significance of a financial dashboard shines through. What is A Financial Dashboard? You can download FineReport for free and have a try!

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

AWS Big Data

SEPTEMBER 11, 2024

The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. The data quality (DQ) checks are managed using DQ configurations stored in Aurora PostgreSQL tables.

Data Architecture 111

Data Architecture Optimization Data Warehouse Metadata 111

FineReport

MAY 25, 2023

An HR dashboard functions as an advanced analytics tool that utilizes interactive data visualizations to present crucial HR metrics. Similar to various other business departments, human resources is gradually transforming into a data-centric function. What is an HR Dashboard?

Dashboards 52

Dashboards Metrics Key Performance Indicator Cost-Benefit 52

AWS Big Data

JULY 13, 2023

Target – Dashboards to be refreshed on a daily basis that would provide insights on sales, gross profit, sales pipelines, and customers. Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices.

Optimization 98

Optimization B2B Data Quality Sales 98

AWS Big Data

JULY 29, 2024

This ensures that each change is tracked and reversible, enhancing data governance and auditability. History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks.

Metadata 126

Metadata Snapshot Data Lake Metrics 126

AWS Big Data

DECEMBER 21, 2023

Movement of data across data lakes, data warehouses, and purpose-built stores is achieved by extract, transform, and load (ETL) processes using data integration services such as AWS Glue. AWS Glue provides both visual and code-based interfaces to make data integration effortless.

Analytics 111

Analytics IT Data Lake Visualization 111

CIO Business Intelligence

NOVEMBER 29, 2022

Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. Layering technology on the overall data architecture introduces more complexity. Data and cloud strategy must align.

Data Architecture 111

Data Architecture Data Integration IoT Data-driven 111

CIO Business Intelligence

NOVEMBER 7, 2023

“This has enabled every function to embrace data to make decisions, like which products to manufacture, how to price them, how much inventory to hold, and even predict when each device that we have deployed will break down,” Gupta says. Data Engineering, Data Governance, Data Integration, Data Management, Data Quality

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

DataKitchen

JUNE 19, 2023

Another way to look at the five pillars is to see them in the context of a typical complex data estate. Initially, the infrastructure is unstable, but then we look at our source data and find many problems. Our customers start looking at the data in dashboards and models and then find many issues. Was it on time?

Testing 100

Testing Data Quality Metrics Cost-Benefit 100

Andrew White

JUNE 11, 2019

Google acquires Looker – June 2019 (infrastructure/search/data broker vendor acquires analytics/BI). Salesforce closes acquisition of Mulesoft – May 2018 (business app vendor acquires data integration). There is also a lot of action in the data and analytics governance space for sure.

IT 82

IT Data Quality Data Integration Business Objectives 82

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. QuickSight offers scalable, serverless visualization capabilities.

Data Strategy 98

Data Strategy Strategy Data Warehouse Prescriptive Analytics 98

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

Ontotext

SEPTEMBER 15, 2020

Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of data integration, data and service-level management. This provides a solid foundation for efficient data integration.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

IBM Big Data Hub

JANUARY 17, 2023

This capability will provide data users with visibility into origin, transformations, and destination of data as it is used to build products. The result is more useful data for decision-making, less hassle and better compliance. Data integration. Start a trial.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

Octopai

JUNE 9, 2024

The goal was to develop sophisticated data products, such as predictive analytics models to forecast patient needs, patient care optimization tools, and operational efficiency dashboards. These data products were intended to enhance patient outcomes, streamline hospital operations, and provide actionable insights for decision-making.

IT 52

IT Data-driven Predictive Analytics Data Strategy 52

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Wayne Yaddow

FEBRUARY 26, 2025

Photo by Markus Spiske on Unsplash Introduction Senior data engineers and data scientists are increasingly incorporating artificial intelligence (AI) and machine learning (ML) into data validation procedures to increase the quality, efficiency, and scalability of data transformations and conversions.

Data Transformation 52

Data Transformation Testing Data-driven Data Quality 52

FineReport

JULY 18, 2022

The features you or your company need are core factors influencing your selection of the data analytics tool. For example, if you want the features of data visualization , such as stunning dashboards and rich charts, business intelligence tools are more suitable for you than a pure programming tool. 15 Best Data Analysis Tools.

Forecasting 52

Forecasting Dashboards Statistics Visualization 52

Jet Global

MAY 1, 2020

We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. Data quality issues. Here’s the ugly truth: Everybody has a data quality problem. This is because people won’t use BI applications that are founded on irrelevant, incomplete, or questionable data.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

Wayne Yaddow

MARCH 14, 2025

Users can apply built-in schema tests (such as not null, unique, or accepted values) or define custom SQL-based validation rules to enforce data integrity. dbt Core allows for data freshness monitoring and timeliness assessments, ensuring tables are updated within anticipated intervals in addition to standard schema validations.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

Howard Dresner

JANUARY 20, 2014

Examples: user empowerment and the speed of getting answers (not just reports) • There is a growing interest in data that tells stories; keep up with advances in storyboarding to package visual analytics that might fill some gaps in communication and collaboration • Monitor rumblings about trend to shift data to secure storage outside the U.S.

Business Intelligence 56

Business Intelligence Software Predictive Analytics Data Processing 56

Ontotext

JANUARY 6, 2022

That is why we have used GraphDB , Ontotext Platform and our significant expertise in semantic data integration to show how we can improve the quality of ENTSO-E Transparency data and develop flexible analytics by leveraging the knowledge graph approach. Spotting Data Consistency Issues. Let’s take a closer look.

Marketing 52

Marketing Data Quality Dashboards Visualization 52

Cloudera

SEPTEMBER 7, 2023

Perhaps the biggest challenge of all is that AI solutions—with their complex, opaque models, and their appetite for large, diverse, high-quality datasets—tend to complicate the oversight, management, and assurance processes integral to data management and governance. Systematize governance. Create core feedback mechanisms.

Insurance 83

Insurance Risk Data-driven Finance 83

Alation

JANUARY 27, 2022

Creating a single view of any data, however, requires the integration of data from disparate sources. Data integration is valuable for businesses of all sizes due to the many benefits of analyzing data from different sources. But data integration is not trivial. Establishes Trust in Data.

Data Governance 52

Data Governance Analytics Cost-Benefit Data-driven 52

FineReport

APRIL 9, 2024

One of the key aspects of the role of BI platforms is their ability to streamline the process of data analysis and decision-making. They offer functionalities that allow for the integration and transformation of raw data into meaningful and actionable insights.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

CIO Business Intelligence

FEBRUARY 20, 2023

To earn this cert, candidates should know how to maintain and modify Sales Cloud and Service Cloud applications; manage users, data, and security; and construct dashboards, reports, and workflows.

Consulting 87

Consulting B2B Sales Cost-Benefit 87

IBM Big Data Hub

AUGUST 4, 2023

When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard. By recognizing data as a product, it creates greater incentive to properly manage data.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

Jet Global

FEBRUARY 25, 2025

Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to ensure its quality, accuracy, and reliability. This process is crucial for businesses that rely on data-driven decision-making, as poor data quality can lead to costly mistakes and inefficiencies.

Cost-Benefit 52

Cost-Benefit Data Collection Finance Reporting 52