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. This required additional investments in metadata. So far so simple.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Ontotext

JANUARY 10, 2024

Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. In both cases, semantic metadata is the glue that turns knowledge graphs into hubs of data, metadata, and content.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

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

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. Monitoring Job Metadata. Adding Tests to Reduce Stress.

Testing 152

Testing Metadata Dashboards Statistics 152

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. Then, you transform this data into a concise format.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

Alation

JANUARY 25, 2022

As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.

Metadata 52

Metadata IT Metrics Data-driven 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

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. This empowers individual teams to own and manage their 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. So, I hear you say, let’s share metadata and make the data self-describing.

Analytics 64

Analytics Measurement Modeling Data-driven 64

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 64

Data Architecture Data Lake Machine Learning Data Governance 64

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