Smart Data Collective

APRIL 5, 2022

Companies working on AI technology can use it to improve scalability and optimize the decision-making process. This feature helps automate many parts of the data preparation and data model development process. This significantly reduces the amount of time needed to engage in data science tasks. Neptune.ai.

Cost-Benefit 122

Cost-Benefit Machine Learning Data Science Unstructured Data 122

Ontotext

DECEMBER 1, 2023

Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises. from Q&A with Tim Berners-Lee ) Finally, Sumit highlighted the importance of knowledge graphs to advance semantic data architecture models that allow unified data access and empower flexible data integration.

Metadata 78

Metadata Sales Consulting Enterprise 78

IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

Data Lake 86

Data Lake Unstructured Data Management Modeling 86

Ontotext

JANUARY 6, 2021

That means removing errors, filling in missing information and harmonizing the various data sources so that there is consistency. Once that is done, data can be transformed and enriched with metadata to facilitate analysis. You can apply graph optimizations or operations such as traversals and transformations.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

Cloudera

SEPTEMBER 17, 2020

They were not able to quickly and easily query and analyze huge amounts of data as required. They also needed to combine text or other unstructured data with structured data and visualize the results in the same dashboards. Query large-sized data without loading data back and forth and incurring twice the cost.

Experimentation 80

Experimentation Data Warehouse Dashboards Visualization 80

IBM Big Data Hub

JANUARY 5, 2023

Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads. Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

Ontotext

NOVEMBER 22, 2020

Businesses wanted a way to make pie and not an in-depth understanding of forward-chaining, inferential explosion or SPARQL optimizations. Semantic Objects and the Semantic Objects Modeling Language (SOML) is a simple way to describe business objects or domain objects. Content Enrichment and Metadata Management.

Metadata 72

Metadata Cost-Benefit Unstructured Data Technology 72

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 98

Data Lake Sales Data Warehouse Snapshot 98

Cloudera

SEPTEMBER 1, 2020

DDE also makes it much easier for application developers or data workers to self-service and get started with building insight applications or exploration services based on text or other unstructured data (i.e. data best served through Apache Solr). Coordinates distribution of data and metadata, also known as shards.

Snapshot 66

Snapshot Unstructured Data Dashboards Interactive 66

Cloudera

AUGUST 30, 2018

Digital technologies are changing business models, reshaping how companies go-to-market, win new customers and drive new revenue-producing opportunities. How do I get to the next level in the data-driven journey fast enough? How do I meet a growing demand for self-serve BI, while not exploding my data warehouse budgets?

Enterprise 40

Enterprise Data-driven Digital Transformation Machine Learning 40

Sisense

MAY 28, 2020

“Not only do they have to deal with data that is distributed across on-premises, hybrid, and multi-cloud environments, but they have to contend with structured, semi-structured, and unstructured data types. That’s without mentioning outdated metadata—the data about data that provides data intelligence,” said Gopal.

Internet of Things 83

Internet of Things Metadata Data-driven Unstructured Data 83

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Answer: Better than every other vendor?

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

erwin

JUNE 27, 2019

The need for an effective data modeling tool is more significant than ever. For decades, data modeling has provided the optimal way to design and deploy new relational databases with high-quality data sources and support application development. Evaluating a Data Modeling Tool – Key Features.

Measurement 81

Measurement Modeling Unstructured Data Metadata 81

CIO Business Intelligence

MARCH 9, 2022

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 109

Data Lake Metadata Optimization Statistics 109

CIO Business Intelligence

JUNE 14, 2023

To accomplish this, we will need additional data center space, more storage disks and nodes, the ability for the software to scale to 1000+PB of data, and increased support through additional compute nodes and networking bandwidth. Focus on scalability.

Unstructured Data 64

Unstructured Data IT Manufacturing Visualization 64

FineReport

FEBRUARY 5, 2020

To put it bluntly, users increasingly want to do their own data analysis without having to find support from the IT department. Metadata management. Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally. Analytics dashboards.

Metadata 98

Metadata Dashboards Informatics Visualization 98

Cloudera

SEPTEMBER 15, 2022

Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases. There are also newer AI/ML applications that need data storage, optimized for unstructured data using developer friendly paradigms like Python Boto API. Diversity of workloads. LEGACY Bucket.

Metadata 86

Metadata Big Data Optimization Unstructured Data 86

Sisense

AUGUST 21, 2020

Gartner: “Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. Trend 5: Augmented data management. Trend 6: Cloud is a given.

Analytics 112

Analytics Machine Learning Dashboards Visualization 112

AWS Big Data

FEBRUARY 21, 2023

Data warehouses are mostly built using the dimensional model approach, which has consistently met business needs. Additionally, scalability of the dimensional model is complex and poses a high risk of data integrity issues. What is a dimensional data model?

Data Warehouse 63

Data Warehouse Data Lake Cost-Benefit Modeling 63

AWS Big Data

JULY 20, 2023

To overcome these issues, Orca decided to build a data lake. A data lake is a centralized data repository that enables organizations to store and manage large volumes of structured and unstructured data, eliminating data silos and facilitating advanced analytics and ML on the entire data.

Data Lake 84

Data Lake Analytics Snapshot Optimization 84

Alation

MARCH 21, 2023

To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations. What is big data in the travel and tourism industry? Some companies struggle to optimize their data’s value and leverage analytics effectively.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance.

Snapshot 80

Snapshot Data Lake Testing Strategy 80

Cloudera

AUGUST 21, 2020

Perhaps one of the most significant contributions in data technology advancement has been the advent of “Big Data” platforms. Historically these highly specialized platforms were deployed on-prem in private data centers to ensure greater control , security, and compliance. Streaming data analytics. . There it is.

Cost-Benefit 71

Cost-Benefit Big Data ROI Risk 71

Cloudera

OCTOBER 7, 2021

The need for a decentralized data mesh architecture stems from the challenges organizations faced when implementing more centralized data management architectures – challenges that can attributed to both technology (e.g., need to integrate multiple “point solutions” used in a data ecosystem) and organization reasons (e.g.,

Metadata 121

Metadata Cost-Benefit Enterprise Interactive 121

Smart Data Collective

OCTOBER 13, 2021

There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement. How is Data Virtualization performance optimized? In improving operational processes.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

CIO Business Intelligence

MARCH 6, 2023

Advancements in analytics and AI as well as support for unstructured data in centralized data lakes are key benefits of doing business in the cloud, and Shutterstock is capitalizing on its cloud foundation, creating new revenue streams and business models using the cloud and data lakes as key components of its innovation platform.

Data Lake 88

Data Lake Cost-Benefit Recreation/Entertainment Experimentation 88

Cloudera

APRIL 1, 2024

We scored the highest in hybrid, intercloud, and multi-cloud capabilities because we are the only vendor in the market with a true hybrid data platform that can run on any cloud including private cloud to deliver a seamless, unified experience for all data, wherever it lies. Unlike software, ML models need continuous tuning.

Unstructured Data 100

Unstructured Data Cost-Benefit Metadata Machine Learning 100

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 64

Data Architecture Data Lake Machine Learning Data Governance 64

Alation

NOVEMBER 22, 2022

This is why public agencies are increasingly turning to an active governance model, which promotes data visibility alongside in-workflow guidance to ensure secure, compliant usage. An active data governance framework includes: Assigning data stewards. Standardizing data formats. Reuse metadata productively.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Limited flexibility to use more complex hosting models (e.g.,

Data Processing 88

Data Processing Data Warehouse Enterprise Visualization 88