IBM Big Data Hub

APRIL 27, 2022

Data fabric and data mesh are emerging data management concepts that are meant to address the organizational change and complexities of understanding, governing and working with enterprise data in a hybrid multicloud ecosystem. The good news is that both data architecture concepts are complimentary.

Management 81

Management Metadata Data Architecture Data Lake 81

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

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 85

Technology Data-driven Machine Learning Sales 85

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 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

IBM Big Data Hub

JANUARY 17, 2023

As part of a data fabric, IBM’s data integration capability creates a roadmap that helps organizations connect data from disparate data sources, build data pipelines, remediate data issues, enrich data quality, and deliver integrated data to multicloud platforms. Data science and MLOps.

Data-driven 56

Data-driven Enterprise Data Governance Prescriptive Analytics 56

Peter James Thomas

MAY 3, 2019

Leverage of Data to generate Insight. In this second area we have disciplines such as Analytics and Data Science. The objective here is to use a variety of techniques to tease out findings from available data (both internal and external) that go beyond the explicit purpose for which it was captured. Watch this space. [2].

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

IBM Big Data Hub

AUGUST 15, 2022

How does a data architecture impact your ability to build, scale and govern AI models? To be a responsible data scientist, there’s two key considerations when building a model pipeline: Bias: a model which makes predictions for people of different group (or race, gender ethnic group etc.)

Data mining 60

Data mining Risk Modeling Risk Management 60

CIO Business Intelligence

APRIL 29, 2022

The strategy should put formalized processes in place to quantify the value of different types of information, leveraging the skills of a chief data officer (CDO), who should form and chair a data governance committee. Data Security: Achieving authentication, access control, and encryption without negatively impacting productivity.

Digital Transformation 115

Digital Transformation Strategy Data-driven Data Strategy 115

Peter James Thomas

AUGUST 18, 2018

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.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

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. Thanks to all of these for their help. Application Programming Interface (API).

Analytics 69

Analytics Data Analytics Data Architecture Statistics 69

Alation

OCTOBER 12, 2021

Spotlight friction areas and bottlenecks for data consumers (and build a solution). Create a blueprint of data architecture to find inconsistent definitions. Build a roadmap for future data and analytics projects, like cloud computing. Evaluate and monitor data quality.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

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. Lift and shift perpetuates the same data problems, albeit in a new location. Data quality /wrangling.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

TDAN

APRIL 19, 2022

Data driven organizations are innovating faster than ever.

Management 59

Management Enterprise Forecasting Data-driven 59

Peter James Thomas

OCTOBER 30, 2018

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 remediate issues with an existing Data Architecture. It may be to introduce or expand Data Governance.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

IBM Big Data Hub

SEPTEMBER 7, 2022

We recommend that data leaders pick a small problem and solve that, then look at the impact. Do your data governance systems desperately need an overhaul or is your data visualization in need of a spruce up? Do you accept that data science takes a slight back seat while you improve the data quality?

Data Strategy 52

Data Strategy Strategy Data-driven Data Quality 52

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.

Statistics 97

Statistics Metrics Visualization Finance 97

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 52