CIO Business Intelligence

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. It’s a fluid situation.”

Analytics 130

Analytics Machine Learning Data mining Software 130

CIO Business Intelligence

JANUARY 24, 2024

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

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 122

Data Warehouse Testing Data Quality Reporting 122

erwin

MARCH 4, 2021

Instead, he suggests they put data governance in real-world scenarios to answer these questions: “What is the problem you believe data governance is the answer to?” Or “How would you recognize having effective data governance in place?”. The Benefits of erwin Data Intelligence. Where is it?

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

erwin

SEPTEMBER 17, 2020

It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

Smart Data Collective

APRIL 29, 2020

The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. Microsoft Azure.

Management 98

Management Data Warehouse Digital Transformation Dashboards 98

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. The offensive side? That takes its own time. The company’s Findability.ai

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. For an example, refer to How JPMorgan Chase built a data mesh architecture to drive significant value to enhance their enterprise data platform. Platform architects define a well-architected platform.

Data Warehouse 105

Data Warehouse KPI Optimization Cost-Benefit 105

Ontotext

NOVEMBER 18, 2021

We can kind of do it within one enterprise, agreeing on certain templates, but when we start going cross-enterprise, or when we start integrating legacy data, it will be a lot of work doing that by hand. OntoRefine is a data transformation tool that lets you unite plenty of data formats and get them into your triplestore.

Visualization 98

Visualization Reporting Metadata Enterprise 98

AWS Big Data

APRIL 19, 2023

However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to know which data products they can join to create new insights. This is especially true in a large enterprise with thousands of data products.

Technology 98

Technology Data-driven Machine Learning Sales 98

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 76

Data Governance Business Objectives Data Quality Measurement 76

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.

Data Governance 40

Data Governance Risk Metadata Management 40

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 Machine Learning Data Quality 52

Ontotext

MARCH 27, 2024

They also don’t have features for enterprise data management such as schema language, data validation capabilities, interoperable serialization formats, or a proper modeling language. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 53

Modeling Metadata Data Quality Enterprise 53

Octopai

JUNE 9, 2024

For HealthCo, this meant they could finally see how data moved from its source through various transformations to its final destination. This visibility was crucial for identifying and rectifying data quality issues quickly, ensuring consistent and reliable insights.

IT 52

IT Data-driven Predictive Analytics Data Strategy 52

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

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

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

Alation

AUGUST 11, 2022

But there are only so many data engineers available in the market today; there’s a big skills shortage. So to get away from that lack of data engineers, what data mesh says is, ‘Take those business logic data transformation capabilities and move that to the domains.’ Let’s take data privacy as an example.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Finally, data integrity is of paramount importance. Every event in the data source can be relevant, and our customers don’t tolerate data loss, poor data quality, or discrepancies between the source and Tricentis Analytics.

Software 52

Software Data Lake Testing Cost-Benefit 52

DataRobot Blog

MARCH 10, 2022

DataRobot and Snowflake Jointly Unleash Human and Machine Intelligence Across the Industrial Enterprise Landscape. 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. Native Python Support for Snowpark.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

Alation

OCTOBER 28, 2021

To make good on this potential, healthcare organizations need to understand their data and how they can use it. These systems should collectively maintain data quality, integrity, and security, so the organization can use data effectively and efficiently. Why Is Data Governance in Healthcare Important?

Data Governance 52

Data Governance Measurement Data Quality Metrics 52

Data Virtualization

JULY 20, 2023

Reading Time: < 1 minute In this post, I’m going to cover logical data management and its impact on data mesh architectures. But there’s a lot of confusion in the marketplace today between different types of architectures, specifically data mesh and data fabric, so I’ll.

Management 40

Management Data Integration IT Data Quality 40

Rocket-Powered Data Science

MARCH 20, 2023

We could further refine our opening statement to say that our business users are too often in a state of being data-rich, but insights-poor, and content-hungry. This is where we dispel an old “big data” notion (heard a decade ago) that was expressed like this: “we need our data to run at the speed of business.”

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Machine Learning 325

AWS Big Data

DECEMBER 4, 2024

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 98

Metadata Data Governance Data Quality Data-driven 98

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 Dashboards 246

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

Ontotext

FEBRUARY 12, 2024

As organizations become more data-driven, different use cases will always require different types of transformations, putting a heavy load on the centralized teams. For large enterprises, data mesh distributes data ownership and reduces dependencies between services.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

CIO Business Intelligence

NOVEMBER 7, 2023

In fact, as companies undertake digital transformations , usually the data transformation comes first, and doing so often begins with breaking down data — and political — silos in various corners of the enterprise. Some of this data might previously have been accessible to only a small number of groups or users.

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

Jet Global

MAY 28, 2019

Whether the reporting is being done by an end user, a data science team, or an AI algorithm, the future of your business depends on your ability to use data to drive better quality for your customers at a lower cost. So, when it comes to collecting, storing, and analyzing data, what is the right choice for your enterprise?

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85

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

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.

Data Lake 52

Data Lake Machine Learning Data Integration Data Quality 52

Wayne Yaddow

MARCH 28, 2025

Unleashing GenAIEnsuring Data Quality at Scale (Part2) Transitioning from individual repository source systems to consolidated AI LLM pipelines, the importance of automated checks, end-to-end observability, and compliance with enterprise businessrules. T Introduction There are several opportunities (and needs!)

Data Quality 52

Data Quality Data Integration Data Governance Metadata 52

CIO Business Intelligence

MARCH 26, 2025

Give up on using traditional IT for AI The ultimate goal is to have AI-ready data, which means quality and consistent data with the right structures optimized to be effectively used in AI models and to produce the desired outcomes for a given application, says Beatriz Sanz Siz, global AI sector leader at EY.

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

Businesses of all sizes are challenged with the complexities and constraints posed by traditional extract, transform and load (ETL) tools. These intricate solutions, while powerful, often come with a significant financial burden, particularly for small and medium enterprise customers.

Data Processing 53

Data Processing Machine Learning Data-driven Cost-Benefit 53

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. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52