CIO Business Intelligence

DECEMBER 11, 2024

To succeed in todays landscape, every company small, mid-sized or large must embrace a data-centric mindset. This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. Implementing ML capabilities can help find the right thresholds.

Management 111

Management Data Governance Data Science Reporting 111

AWS Big Data

APRIL 8, 2025

In modern data architectures, Apache Iceberg has emerged as a popular table format for data lakes, offering key features including ACID transactions and concurrent write support. For more detailed configuration, refer to Write properties in the Iceberg documentation.

Snapshot 117

Snapshot Management Metadata Big Data 117

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. Data democratization uses a fit-for-purpose data architecture that is designed for the way today’s businesses operate, in real-time.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. A DataOps implementation project consists of three steps.

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 90

Data Lake Data Governance Data Architecture Machine Learning 90

AWS Big Data

DECEMBER 4, 2024

While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures.

Data Integration 98

Data Integration Data Lake Statistics Data-driven 98

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. Means of ensuring data integrity.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

Cloudera

JUNE 13, 2024

After countless open-source innovations ushered in the Big Data era, including the first commercial distribution of HDFS (Apache Hadoop Distributed File System), commonly referred to as Hadoop, the two companies joined forces, giving birth to an entire ecosystem of technology and tech companies.

Big Data 103

Big Data Machine Learning Contextual Data Data Lake 103

Cloudera

SEPTEMBER 15, 2022

Mark: The first element in the process is the link between the source data and the entry point into the data platform. At Ramsey International (RI), we refer to that layer in the architecture as the foundation, but others call it a staging area, raw zone, or even a source data lake. What is a data fabric?

Data Lake 83

Data Lake Data Architecture Data-driven Data Warehouse 83

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI).

Data Warehouse 100

Data Warehouse KPI Optimization Cost-Benefit 100

AWS Big Data

APRIL 19, 2023

The goal of a data product is to solve the long-standing issue of data silos and data quality. Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights.

Technology 93

Technology Data-driven Machine Learning Sales 93

Peter James Thomas

AUGUST 2, 2019

Data Architecture – Definition (2). Data Catalogue. Data Community. Data Domain (contributor: Taru Väre ). Data Enrichment. Data Federation. Data Function. Data Model. Data Operating Model. Geospatial Data. Reference Data (contributor: George Firican ).

Analytics 69

Analytics Data Analytics Data Architecture Statistics 69

IBM Big Data Hub

FEBRUARY 9, 2023

Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes. Start by identifying business objectives, desired outcomes, key stakeholders, and the data needed to deliver these objectives. So where are you in your data governance journey?

Data Governance 76

Data Governance Business Objectives Data Quality Measurement 76

TDAN

OCTOBER 18, 2022

“Technical debt” refers to the implied cost of future refactoring or rework to improve the quality of an asset to make it easy to understand, work with, maintain, and extend.

Software 94

Software Data Architecture IT Data Quality 94

erwin

APRIL 16, 2024

The data mesh framework In the dynamic landscape of data management, the search for agility, scalability, and efficiency has led organizations to explore new, innovative approaches. One such innovation gaining traction is the data mesh framework. Business Glossaries – what is the business meaning of our data?

Metadata 52

Metadata Data Quality Data Governance Modeling 52

Jedox

OCTOBER 22, 2020

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

Peter James Thomas

MAY 3, 2019

Control of Data to ensure it is Fit-for-Purpose. This refers to a wide range of activities from Data Governance to Data Management to Data Quality improvement and indeed related concepts such as Master Data Management. Data Architecture / Infrastructure.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 megabytes of data every second?

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

IBM Big Data Hub

JANUARY 25, 2023

Breaking down these silos to encourage data access, data sharing and collaboration will be an important challenge for organizations in the coming years. The right data architecture to link and gain insight across silos requires the communication and coordination of a strategic data governance program.

Data Governance 52

Data Governance Data-driven Strategy Data Architecture 52

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.

Data Strategy 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

AWS Big Data

JULY 25, 2024

Consume data assets as part of analyzing data to generate insights. Part 1: Set up account governance and identity management Before you start, compare your current cloud environment, including data architecture, to ATPCO’s environment. Clear Data quality unless you have already set up AWS Glue data quality.

Data Lake 106

Data Lake Metadata Sales Publishing 106

Ontotext

APRIL 10, 2024

Bad data tax is rampant in most organizations. Currently, every organization is blindly chasing the GenAI race, often forgetting that data quality and semantics is one of the fundamentals to achieving AI success. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Alation

OCTOBER 12, 2021

Like many, the team at Cbus wanted to use data to more effectively drive the business. “Finding the right data was a real challenge,” recalls John Gilbert, Data Governance Manager. “There was no single source of reference, there was no catalog to leverage, and it was unclear who to ask or seek assistance from.”

Strategy 52

Strategy Enterprise Analytics Data Governance 52

BI-Survey

DECEMBER 22, 2020

Business users often think that data is something technical that it is not their concern. While IT is happy to look after the technical storage and backup of data, they refer to line of business experts when it comes to quality and usability. They believe the IT department should take care of it.

Data-driven 52

Data-driven Data Strategy Strategy Enterprise 52

Peter James Thomas

OCTOBER 30, 2018

All the references I can find to it are modern pieces comparing it to the CDO role, so perhaps it is apochryphal. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data. It may be to build a new (or a first) Data Architecture. It may be to improve Data Quality.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

Ontotext

JANUARY 10, 2024

Both use cases use semantic metadata, which describes information sources with respect to a unified conceptual model , that includes ontologies, data schema, taxonomies, reference data, or other domain knowledge. They sift through documents, generate metadata, and store it in the knowledge graph.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

TDAN

AUGUST 20, 2019

We hope your Data Management career and programs are progressing well. If you have issues, please refer to DAMA.org for references, as well as the DAMA Data Management Body of Knowledge (DMBok). Good day from DAMA International. You can purchase the DMBoK at your favorite book source or via website link.

Management 40

Management Data Quality Data Architecture Data Governance 40

Ontotext

FEBRUARY 7, 2024

They have to misallocate resources because 80% of the time the data scientists are busy doing data finding, accessing, cleansing, etc. This also results in the information loss I’ve already mentioned and severely impacts our insight creation and monetizing the data. So Schemata are interoperable by design.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

Alation

JANUARY 25, 2022

The data catalog is a foundational layer of the data fabric. This zoomed-in version has references to corresponding vendor markets removed.). Using this diagram as our guide, this blog will deep-dive into each layer of the data fabric, starting with the data catalog. Alation Data Catalog for the data fabric.

Metadata 52

Metadata IT Data-driven Metrics 52

Ontotext

FEBRUARY 26, 2024

If you want to convert your data into the right insights to drive business decisions and processes, you need this data to be easily accessible and stored in a format that is flexible, accurate, and machine-readable. It must retain the context and insight of the original data and be traceable as it flows through the organization.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Andrew White

JANUARY 9, 2022

Most of D&A concerns and activities are done within EA in the Info/Data architecture domain/phases. – I remember that I tried to answer this live during the webinar. – We see most, if not all, of data management being augmented with ML. – I am not totally sure what you mean by data capture.

Analytics 64

Analytics Measurement Data-driven Modeling 64

Peter James Thomas

AUGUST 18, 2018

Convergent Evolution refers to something else. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well. So far so simple.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

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. Data observability and data lineage are complementary concepts.

Testing 130

Testing Data Governance Data Quality Data-driven 130