Cloudera

NOVEMBER 15, 2021

In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else. The challenges of data. Data curation.

Unstructured Data 90

Unstructured Data Recreation/Entertainment Structured Data Reporting 90

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

CIO Business Intelligence

APRIL 4, 2024

Some prospective projects require custom development using large language models (LLMs), but others simply require flipping a switch to turn on new AI capabilities in enterprise software. “AI A human reviews it to make sure it makes sense, and if it does, the AI incorporates that into the learning model,” she says.

Enterprise 134

Enterprise Cost-Benefit Forecasting Sales 134

CIO Business Intelligence

FEBRUARY 16, 2023

Hence the drive to provide ML as a service to the Data & Tech team’s internal customers. All they would have to do is just build their model and run with it,” he says. That step, primarily undertaken by developers and data architects, established data governance and data integration. The offensive side?

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 105

AWS Big Data

NOVEMBER 9, 2023

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. Define data quality check task to test a package, generate docs and copy the docs to required S3 location data_quality_check = BashOperator( task_id='data_quality_check', dag=dag, bash_command=''' /usr/local/airflow/.local/bin/dbt

Data Warehouse 105

Data Warehouse Testing Data Quality Reporting 105

AWS Big Data

MARCH 7, 2023

ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Ontotext

SEPTEMBER 15, 2020

There must be a representation of the low-level technical and operational metadata as well as the ‘real world’ metadata of the business model or ontologies. Connecting the data in a graph allows concepts and entities to complement each other’s description. Consider using data catalogs for this purpose.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. The third challenge is how to combine data management with analytics.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

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. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Octopai

MARCH 22, 2023

Cost: Snowflake’s pricing model is based on usage, which means you only pay for what you use. This can be more cost-effective than traditional data warehousing solutions that require a significant upfront investment. Support for multiple data structures.

Cost-Benefit 78

Cost-Benefit Data Warehouse Unstructured Data Optimization 78

Ontotext

JUNE 22, 2023

All derived facts can be further put into context with structured data, which improves data quality and presents researchers with clear evidence and provenance for all insights Then, Ontotext’s Target Discovery provides deeper insights into the data stored in this highly-interlinked knowledge graph, where long sequences of relations can be mined.

Metrics 52

Metrics Statistics Visualization Metadata 52

AWS Big Data

JUNE 6, 2023

As the SumUp merchants user journey only starts onsite (with a Sign up or product purchase), but extends to offline card reader transactions or usage of our products from within our app and web dashboard, it is important to combine Digital Analytics data with other (backend) data sources in our Data Lake or DWH.

Analytics 55

Analytics Data Lake Testing Optimization 55

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. Reuse of knowledge from third party data providers and establishing data quality principles to populate it. The connections made through these descriptions create context.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

Peter James Thomas

AUGUST 29, 2018

My role encompasses being the business driver for the data platform that we are rolling out across the organisation and its success in terms of the data going onto the platform and the curation of that data in a governed state, depending on the consumer requirements.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Cloudera

MAY 14, 2024

To attain that level of data quality, a majority of business and IT leaders have opted to take a hybrid approach to data management, moving data between cloud, on-premises -or a combination of the two – to where they can best use it for analytics or feeding AI models.

Data Architecture 99

Data Architecture Data Governance Unstructured Data Structured Data 99

Alation

JULY 19, 2022

Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000. As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming.

Metadata 59

Metadata Data Quality Statistics Data Science 59

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

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 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Ontotext

JANUARY 10, 2024

In Computer Science, we are trained to use the Okham razor – the simplest model of reality that can get the job done is the best one. 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.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

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 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

AWS Big Data

JULY 12, 2023

With a wide array of data sources, including transactional databases, log files, and event streams, you need a simple-to-use solution capable of efficiently ingesting and transforming large volumes of data in real time, ensuring data cleanliness, structural integrity, and data team collaboration.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

Alation

OCTOBER 28, 2021

Healthcare is changing, and it all comes down to data. Leaders in healthcare seek to improve patient outcomes, meet changing business models (including value-based care ), and ensure compliance while creating better experiences. Data & analytics represents a major opportunity to tackle these challenges.

Data Governance 52

Data Governance Measurement Metrics Data Quality 52

erwin

OCTOBER 24, 2019

. • Structuring and deploying data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards. Analyzing metadata – Understand how data relates to the business and what attributes it has. Addressing the Complexities of Metadata Management.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Ontotext

JULY 29, 2021

The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

FineReport

JULY 18, 2022

Except for the rows and columns, you can also display your data through graphs and charts. For more advanced data analysis, Excel provides you with pivot tables, enabling you to analyze structured data through multiple dimensions quickly and effectively. Price: Excel is not a free tool. Python enjoys strong portability.

Forecasting 52

Forecasting Dashboards Visualization Statistics 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.

Cost-Benefit 87

Cost-Benefit Technology Big Data Metadata 87

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

Domino Data Lab

MARCH 3, 2019

What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production? There are essentially four types encountered: image/video, audio, text, and structured data.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

erwin

AUGUST 31, 2023

Q: Is data modeling cool again? In today’s fast-paced digital landscape, data reigns supreme. The data-driven enterprise relies on accurate, accessible, and actionable information to make strategic decisions and drive innovation. A: It always was and is getting cooler!!

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 266

Machine Learning Software Metadata Testing 266

Cloudera

APRIL 1, 2024

The following are key attributes of our platform that set Cloudera apart: Unlock the Value of Data While Accelerating Analytics and AI The data lakehouse revolutionizes the ability to unlock the power of data. Unlike software, ML models need continuous tuning.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MARCH 13, 2024

While Microsoft Dynamics is a powerful platform for managing business processes and data, Dynamics AX users and Dynamics 365 Finance & Supply Chain Management (D365 F&SCM) users are only too aware of how difficult it can be to blend data across multiple sources in the Dynamics environment.

Reporting 52

Reporting Finance Data Quality Sales 52