CIO Business Intelligence

FEBRUARY 16, 2023

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

Unstructured Data 103

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 103

AWS Big Data

JUNE 6, 2023

In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

Analytics 55

Analytics Data Lake Testing Optimization 55

Alation

JUNE 2, 2022

A data catalog is a central hub for XAI and understanding data and related models. While “operational exhaust” arrived primarily as structured data, today’s corpus of data can include so-called unstructured data. These methods and their results need to be captured, but how? Other Technologies. Conclusion.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

CIO Business Intelligence

APRIL 4, 2024

Like other CIOs, Katrina Redmond has been inundated with opportunities to deploy AI that promise to speed business and operations processes, and optimize workflows. In some data migration activity we’ve observed a 40% increase in various steps along the way and an increase in speed.” That gets the fear factor down,” she says.

Enterprise 131

Enterprise Cost-Benefit Forecasting Sales 131

O'Reilly on Data

DECEMBER 19, 2018

We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data. With machine learning, the challenge isn’t writing the code; the algorithms are implemented in a number of well-known and highly optimized libraries.

Machine Learning 262

Machine Learning Software Metadata Testing 262

Ontotext

JULY 6, 2023

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. There are four groups of data that are naturally siloed: Structured data (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

AWS Big Data

MARCH 7, 2023

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning. ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

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.

Cost-Benefit 87

Cost-Benefit Technology Big Data Metadata 87

Ontotext

JANUARY 6, 2021

A knowledge graph can be used as a database because it structures data that can be queried such as through a query language like SPARQL. You can apply graph optimizations or operations such as traversals and transformations. It’s context that enriches meaning and enables understanding.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

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.

Cost-Benefit 75

Cost-Benefit Technology Big Data Metadata 75

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

JULY 29, 2021

The two distinct threads interlacing in the current Semantic Web fabrics are the semantically annotated web pages with schema.org (structured data on top of the existing Web) and the Web of Data existing as Linked Open Data. Below, we outline the two directions in which we at Ontotext see and build the Semantic Web.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Alation

MAY 16, 2023

What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The various steps along the pipeline involve transforming, optimizing, cleaning, filtering, integrating, and aggregating the data. The end result is data that is ready to be analyzed.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Domino Data Lab

MARCH 3, 2019

There are essentially four types encountered: image/video, audio, text, and structured data. Even so, it’s likely that reproducibility and hyperparameter optimization are problems which tend to show up after the earlier issues have been resolved and when there are larger, more complex ML projects underway.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

CIO Business Intelligence

MARCH 9, 2022

Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio. They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics.

Data Lake 105

Data Lake Unstructured Data Data Warehouse Data Quality 105

Alation

MAY 16, 2023

What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The various steps along the pipeline involve transforming, optimizing, cleaning, filtering, integrating, and aggregating the data. The end result is data that is ready to be analyzed.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Alation

OCTOBER 28, 2021

It defines how data can be collected and used within an organization, and empowers data teams to: Maintain compliance, even as laws change. Uncover intelligence from data. Protect data at the source. Put data into action to optimize the patient experience and adapt to changing business models.

Data Governance 52

Data Governance Measurement Metrics Data Quality 52

Timo Elliott

JANUARY 22, 2019

But it magnifies any existing problems with data quality and data bias and poses unprecedented challenges to privacy and ethics. Comprehensive governance and data transparency policies are essential. Understanding and optimizing the customer experience is the bedrock of successful digital transformation.

Analytics 89

Analytics Machine Learning Unstructured Data Digital Transformation 89

erwin

AUGUST 31, 2023

Let’s explore the continued relevance of data modeling and its journey through history, challenges faced, adaptations made, and its pivotal role in the new age of data platforms, AI, and democratized data access. Embracing the future In the dynamic world of data, data modeling remains an indispensable tool.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

Jet Global

MAY 9, 2024

ETL pipelines are commonly used in data warehousing and business intelligence environments, where data from multiple sources needs to be integrated, transformed, and stored for analysis and reporting. For example, retail companies can monitor sales transactions as they occur to optimize inventory management and pricing strategies.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52