AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 91

Metadata Data Lake Visualization Data Transformation 91

CIO Business Intelligence

JUNE 23, 2022

Yet as companies fight for skilled analyst roles to utilize data to make better decisions , they often fall short in improving the data supply chain and resulting data quality. Without a solid data supply-chain management practices in place, data quality often suffers. First mile/last mile impacts.

Data Quality 97

Data Quality Key Performance Indicator Metrics KPI 97

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. 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

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.

Metadata 52

Metadata Data Quality Data Governance Modeling 52

AWS Big Data

NOVEMBER 9, 2023

It does this by helping teams handle the T in ETL (extract, transform, and load) processes. It allows users to write data transformation code, run it, and test the output, all within the framework it provides. As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform.

Data Warehouse 107

Data Warehouse Testing Data Quality Reporting 107

CIO Business Intelligence

JANUARY 24, 2024

Building a successful data strategy at scale goes beyond collecting and analyzing data,” says Ryan Swann, chief data analytics officer at financial services firm Vanguard. Establishing data governance rules helps organizations comply with these regulations, reducing the risk of legal and financial penalties.

Data Strategy 135

Data Strategy Strategy Unstructured Data Data Governance 135

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

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 Sales 52

CIO Business Intelligence

MARCH 4, 2024

Stakeholders are currently waging an open debate across the industry of centralization versus federated data strategies. Proponents of centralization continue to assert its effectiveness in driving operational efficiencies, enhancing analytics effectiveness, and enabling governance crucial to data security, privacy, and regulatory compliance.

Insurance 90

Insurance Strategy Data Governance Data Integration 90

Smarten

AUGUST 4, 2023

The Right Self-Serve Data Preparation Solution is Sophisticated, Easy-to-Use and Ensures User Adoption! When your enterprise decides to roll out analytics for business users, it is important to implement the right solution. Sophisticated Functionality – Don’t sacrifice functionality to get ease-of-use.

Data Lake 52

Data Lake Machine Learning Data Integration Optimization 52

CIO Business Intelligence

NOVEMBER 7, 2023

Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. The aim is to normalize, aggregate, and eventually make available to analysts across the organization data that originates in various pockets of the enterprise.

Data Warehouse 141

Data Warehouse Digital Transformation Data-driven Reporting 141

CIO Business Intelligence

FEBRUARY 16, 2023

But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. Digitizing was our first stake at the table in our data journey,” he says. Analytics, Artificial Intelligence, Data Management, Predictive Analytics

Unstructured Data 104

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 104

DataKitchen

JANUARY 23, 2023

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. We must do the same as data analytic teams.

Testing 130

Testing Data Governance Data Quality Data-driven 130

CIO Business Intelligence

DECEMBER 6, 2023

Airbus was conceiving an ambitious plan to develop an open aviation data platform, Skywise, as a single platform of reference for all major aviation players that would enable them to improve their operational performance and business results and support Airbus’ own digital transformation.

Digital Transformation 114

Digital Transformation Software Cost-Benefit Manufacturing 114

erwin

APRIL 29, 2020

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift enables you to run complex SQL analytics at scale and performance on terabytes to petabytes of structured and unstructured data, and make the insights widely available through popular business intelligence (BI) and analytics tools. Answering questions as simple as “How many unique customers do we have?”

Data Quality 79

Data Quality Testing Data Warehouse Unstructured Data 79

Ontotext

MARCH 20, 2024

Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. Studies reveal that businesses lose significant time and opportunities due to missing integrations and poor data quality and accessibility.

Data-driven 52

Data-driven Strategy Sales Data Integration 52

DataKitchen

APRIL 26, 2021

In early April 2021, DataKItchen sat down with Jonathan Hodges, VP Data Management & Analytics, at Workiva ; Chuck Smith, VP of R&D Data Strategy at GlaxoSmithKline (GSK) ; and Chris Bergh, CEO and Head Chef at DataKitchen, to find out about their enterprise DataOps transformation journey, including key successes and lessons learned.

Measurement 246

Measurement Metrics Data-driven Testing 246

Ontotext

MARCH 27, 2024

It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

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.

Reporting 52

Reporting Finance Data Quality Sales 52

Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation.

Management 83

Management Data Warehouse Digital Transformation Dashboards 83

IBM Big Data Hub

FEBRUARY 9, 2023

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

Data Governance 79

Data Governance Business Objectives Strategy Data Quality 79

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. Overall, DataOps is an essential component of modern data-driven organizations. Query> DataOps.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

Jen Stirrup

SEPTEMBER 30, 2021

Azure ML can become a part of the data ecosystem in an organization, but this requires a mindshift from working with Business Intelligence to more advanced analytics. How can we can adopt a mindshift from Business Intelligence to advanced analytics using Azure ML?

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

Sisense

OCTOBER 15, 2020

Dealing with Data is your window into the ways data teams are tackling the challenges of this new world to help their companies and their customers thrive. Streaming data analytics is expected to grow into a $38.6 Every data professional knows that ensuring data quality is vital to producing usable query results.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. Simply put, IDF standardizes data engineering processes. DataOps has emerged as an exciting solution. Transparency is key.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

Octopai

JANUARY 19, 2021

With Octopai’s support and analysis of Azure Data Factory, enterprises can now view complete end-to-end data lineage from Azure Data Factory all the way through to reporting for the first time ever.

Metadata 52

Metadata ROI Optimization Data Quality 52

Ontotext

NOVEMBER 18, 2021

Picture this – you start with the perfect use case for your data analytics product. And all of them are asking hard questions: “Can you integrate my data, with my particular format?”, “How well can you scale?”, “How many visualizations do you offer?”. Nowadays, data analytics doesn’t exist on its own.

Visualization 98

Visualization Reporting Metadata Enterprise 98

erwin

MARCH 4, 2021

Why should companies care about data governance? erwin’s 2020 State of Data Governance and Automation report found that better decision-making is the primary driver for data governance (62 percent), with analytics secondary (51 percent), and regulatory compliance coming in third (48 percent).

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

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. It follows what is called a distributed system architecture.

Technology 85

Technology Data-driven Machine Learning Sales 85

AWS Big Data

MARCH 3, 2023

For users that require a unified view of software quality, this is unacceptable. In this post, we share how the AWS Data Lab helped Tricentis to improve their software as a service (SaaS) Tricentis Analytics platform with insights powered by Amazon Redshift.

Software 52

Software Data Lake Testing Cost-Benefit 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 Risk 52

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

AWS Big Data

APRIL 5, 2023

The company decided to use AWS to unify its business intelligence (BI) and reporting strategy for both internal organization-wide use cases and in-product embedded analytics targeted at its customers. The platform also provides analytics and insights to support successful information sharing and fuel continuous improvement.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

Jet Global

MAY 28, 2019

High level, a data warehouse is a collection of business data from multiple sources used optimized for reporting, analytics and decision making. This automated process of extracting, transforming, and loading data into a data warehouse is commonly called ETL and it’s a huge advantage for analyzing your data.

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85

DataRobot Blog

MARCH 10, 2022

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52