erwin

FEBRUARY 28, 2024

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

Metadata 52

Metadata Data Governance Data Quality Technology 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 Management 52

CIO Business Intelligence

APRIL 29, 2022

Data has rights and sovereignty. Data Security: Achieving authentication, access control, and encryption without negatively impacting productivity. Data Ingestion and Processing: Ensuring that data quality, streaming, and transformation capabilities support your decision-making needs.

Digital Transformation 131

Digital Transformation Strategy Data-driven Data Strategy 131

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. Database design is often an important part of the business analyst role.

Statistics 97

Statistics Metrics Visualization Finance 97

Alation

MAY 31, 2023

This means that specialized roles such as data architects, which focus on modernizing data architecture to help meet business goals, are increasingly important to support data governance. What is a data architect? Their broad range of responsibilities include: Design and implement data architecture.

Data Governance 52

Data Governance Metadata Data Architecture Data Quality 52

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Definition of Done. When can you declare it done?

Testing 157

Testing Metadata Dashboards Statistics 157

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. Increasingly, organizations are using both.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

erwin

OCTOBER 24, 2019

. • Harvesting data – Automate the collection of metadata from various data management silos and consolidate it into a single source. Structuring and deploying data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.

Metadata 104

Metadata Management Data-driven Data Architecture 104

IBM Big Data Hub

DECEMBER 4, 2023

According to Oracle , best practices for the planning process include five categories of information: Project definition: This is the blueprint that will include relevant information for an implementation project. Oracle provides a variety of templates to facilitate item definition as data is catalogued and moved to the cloud.

Testing 79

Testing Consulting Digital Transformation Cost-Benefit 79

CIO Business Intelligence

FEBRUARY 28, 2024

And not only do companies have to get all the basics in place to build for analytics and MLOps, but they also need to build new data structures and pipelines specifically for gen AI. What you have to do as a CIO is take an architectural approach and invest in a common platform.” This is imperative for us to do.”

IT 131

IT Consulting Modeling Enterprise 131

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. The next element is the variables.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

Ontotext

MAY 30, 2019

And before we move on and look at these three in the context of the techniques Linked Data provides, here is an important reminder in case we are wondering if Linked Data is too good to be true: Linked Data is no silver bullet. 6 Linked Data, Structured Data on the Web. Linked Data and Volume.

Cost-Benefit 87

Cost-Benefit Technology Big Data Metadata 87

Alation

JULY 28, 2022

What Are the Biggest Drivers of Cloud Data Warehousing? It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. Cloud data should remove the infrastructure discussions and return attention to business, data, and outcomes.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

Ontotext

MAY 30, 2019

And before we move on and look at these three in the context of the techniques Linked Data provides, here is an important reminder in case we are wondering if Linked Data is too good to be true: Linked Data is no silver bullet. 6 Linked Data, Structured Data on the Web. Linked Data and Volume.

Cost-Benefit 75

Cost-Benefit Technology Big Data Metadata 75

Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric. Alation Data Catalog for the data fabric.

Metadata 52

Metadata IT Metrics Data-driven 52

Peter James Thomas

AUGUST 2, 2019

It includes 30 new definitions, some of which have been contributed by people like Tenny Thomas Soman, George Firican, Scott Taylor and and Taru Väre. Data Architecture – Definition (2). Data Catalogue. Data Sourcing. Geospatial Data. Reference Data (contributor: George Firican ).

Analytics 69

Analytics Data Analytics Data Architecture Statistics 69

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

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 suspect there is much less Maverick to synthetic data today.

Analytics 64

Analytics Measurement Modeling Data-driven 64

DataKitchen

AUGUST 8, 2023

On 20 July 2023, Gartner released the article “ Innovation Insight: Data Observability Enables Proactive Data Quality ” by Melody Chien. It alerts data and analytics leaders to issues with their data before they multiply. It alerts data and analytics leaders to issues with their data before they multiply.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

Alation

OCTOBER 12, 2021

The third challenge was around trusting the data. There are inconsistent definitions and inconsistent metrics, and a lack of trust in the data used in the metrics. The fourth challenge was around using the data. Spotlight friction areas and bottlenecks for data consumers (and build a solution).

Strategy 52

Strategy Enterprise Analytics Data Governance 52

BI-Survey

FEBRUARY 6, 2020

Although a common data model consisting of aligned structures and measures is needed for consistency and governance , it will not grant business departments the freedom required to efficiently analyze and interpret data. Data management has always been a challenge for companies.

Reporting 59

Reporting Optimization Enterprise Data Quality 59