Rocket-Powered Data Science

FEBRUARY 15, 2023

These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises. Love thy data: data are never perfect, but all the data may produce value, though not immediately.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

Timo Elliott

JANUARY 6, 2022

Like the proverbial man looking for his keys under the streetlight , when it comes to enterprise data, if you only look at where the light is already shining, you can end up missing a lot. The data you’ve collected and saved over the years isn’t free. Analyze your metadata. Real-time, cloud-based data ingestion and storage.

IT 133

IT Metadata Data-driven Data Governance 133

Rocket-Powered Data Science

JULY 6, 2021

If you include the title of this blog, you were just presented with 13 examples of heteronyms in the preceding paragraphs. This is accomplished through tags, annotations, and metadata (TAM). Smart content includes labeled (tagged, annotated) metadata (TAM). What you have just experienced is a plethora of heteronyms.

Strategy 266

Strategy Machine Learning Metadata Knowledge Discovery 266

IBM Big Data Hub

OCTOBER 4, 2022

This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. It drives a complete governance solution without the excessive costs of switching from your current data science platform. The post AI Governance: Break open the black box appeared first on Journey to AI Blog.

Metadata 101

Metadata Risk Risk Management Experimentation 101

Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. save the built model container, along with metadata like who built or deployed it. Cloudera Data Science Workbench 1.4.x

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

IBM Big Data Hub

MAY 9, 2023

It drives an AI governance solution without the excessive costs of switching from your current data science platform. The resulting automation drives scalability and accountability by capturing model development time and metadata, offering post-deployment model monitoring, and allowing for customized workflows.

Metadata 76

Metadata Risk Experimentation Dashboards 76

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Data Quality Visualization Metadata 67

Alation

AUGUST 30, 2022

Centralization of metadata. A decade ago, metadata was everywhere. Consequently, useful metadata was unfindable and unusable. We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. This universe of metadata represents a treasure trove of connected information.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

DataKitchen

AUGUST 9, 2021

The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., ….

Testing 246

Testing Data Lake Metadata Publishing 246

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. And that data is likely in clouds, in data centers and at the edge.

Snapshot 91

Snapshot Data Science Digital Transformation Metadata 91

DataKitchen

JANUARY 5, 2021

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

Testing 147

Testing Data Governance Metadata Visualization 147

Alation

FEBRUARY 20, 2020

Prior to the Big Data revolution, companies were inward-looking in terms of data. During this time, data-centric environments like data warehouses dealt only with data created within the enterprise. THE NEED FOR METADATA TOOLS. Given the characteristics of data acquisition, how should it be handled?

Metadata 52

Metadata IT Data Governance Data Warehouse 52

Alation

FEBRUARY 13, 2020

Making datasets easy to find, understand, and access is the purpose of data curation—a purpose that demands well-described datasets. Data curation is a metadata management activity and data catalogs are essential data curation technology. Who Are the Data Curators? What about Data Stewards?

Metadata 52

Metadata Data Warehouse Data Lake Data Governance 52

Ontotext

FEBRUARY 1, 2024

When our auto-categorization engine, Graphite Knowledge Studio, tags content with metadata derived from taxonomies, we also capture and store this content metadata inside the knowledge graph, thereby linking concepts to the content they describe. What do we mean by content aware knowledge graphs?

Enterprise 59

Enterprise Management Metadata Modeling 59

Rita Sallam

APRIL 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in Orlando with 4,000 of our closest D&A friends and family. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms.

Optimization 52

Optimization Machine Learning Insurance Risk 52

Jen Stirrup

OCTOBER 5, 2019

I’m particularly interested in learning more about metadata management. As I’ve written previously, I believe that metadata management is often overlooked in data science. It’s an important part of obtaining full value from data, and it should be part of the data maturity model.

Metadata 44

Metadata Data Science Risk Modeling 44

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance toolkit lets you direct, manage and monitor AI activities without the expense of switching your data science platform, even for models developed using third-party tools.

Risk 76

Risk Modeling Management Metadata 76

Alation

DECEMBER 15, 2022

June 2017: Dresner Advisory Services names Alation the #1 data catalog in its inaugural Data Catalog End-User Market Study. August 2017: Alation debuts as a leader in the Gartner MQ for Metadata Management Solutions. August 2018: Gartner names Alation a 2X Leader in the MQ for Metadata Management Solutions.

Metadata 52

Metadata Data Governance Data Quality Marketing 52

IBM Big Data Hub

SEPTEMBER 20, 2022

Automated, integrated data science tools help build, deploy, and monitor AI models. Often data scientists aren’t thrilled with the prospect of generating all the documentation necessary to meet ethical and regulatory standards. It’s not just about granting proper access to data science teams.

Metadata 57

Metadata Machine Learning Data-driven Modeling 57

Alation

FEBRUARY 20, 2020

These recommendations organize the data about your data — what the industry for many years has called metadata. And yet instead of storing this metadata in a vault accessible only by IT experts, Alation makes metadata come alive as a living data recommendation engine for the business.

Metadata 52

Metadata Analytics Dashboards Sales 52

DataRobot Blog

SEPTEMBER 1, 2022

These and many other questions are now on top of the agenda of every data science team. To quantify how well your models are doing, DataRobot provides you with a comprehensive set of data science metrics — from the standards (Log Loss, RMSE) to the more specific (SMAPE, Tweedie Deviance). Learn More About DataRobot MLOps.

Metrics 145

Metrics Statistics Modeling Data Science 145

Andrew White

JULY 5, 2023

I blogged recently about the high level of hype and confusion across Data and Analytics just a few months ago. Here is the original blog from March 2023: Summing Up Three Days at Gartner’s Data and Analytics Conference in Orlando, Florida, USA. The other 96% of data in the catalog has little meaning to them.

Metrics 52

Metrics Metadata Modeling Data Quality 52

Domino Data Lab

JUNE 13, 2019

Domino Data Science Field Notes provide highlights of data science research, trends, techniques, and more, that support data scientists and data science leaders accelerate their work or careers. . “In the same spirit as [Recht et al., ” They also were able to.

Testing 83

Testing Data Science Experimentation Metadata 83

Ontotext

APRIL 15, 2020

Ontotext’s knowledge graph technology is at the core of Cochrane’s data architecture developed by our partners from Data Language. Here, GraphDB is used for storing the ontology models, the vocabulary, the content metadata and the graphs from the PICO ontology.

Publishing 105

Publishing Metadata Data mining Technology 105

AWS Big Data

MAY 4, 2023

Amazon’s Open Data Sponsorship Program allows organizations to host free of charge on AWS. Over the last decade, we’ve seen a surge in data science frameworks coming to fruition, along with mass adoption by the data science community. Data scientists have access to the Jupyter notebook hosted on SageMaker.

Data Processing 90

Data Processing Metadata Informatics Interactive 90

Cloudera

FEBRUARY 11, 2019

The January 2019 “Magic Quadrant for Data Management Solutions for Analytics” provides valuable insights into the status, direction, and players in the DMSA market. In this blog, we share our takeaways as they relate to the DMSA market trends. Cloudera’s Positioning. Cloudera is positioned furthest for “Completeness of Vision.”

Management 43

Management Metadata Analytics Machine Learning 43

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

AWS Big Data

JUNE 7, 2023

This is a guest blog post co-written with Sumesh M R from Cargotec and Tero Karttunen from Knowit Finland. They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed.

Metadata 55

Metadata Data Lake Machine Learning Big Data 55

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Andrew White

OCTOBER 22, 2021

If you follow my blog for any period of time you will know that for most years I have attended our annual Gartner IT Symposium I do a day-in-the-life blog of an analyst. Analytics Tactics (known outcome/known data/BI/analytics v unknown outcome/unknown data/data science/ML) 11. Data Hub Strategy 10.

IT 52

IT Data Lake Strategy Data Science 52

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. on roadmap).

Testing 131

Testing Metadata Risk Data Science 131

Cloudera

JUNE 7, 2022

Only Cloudera has the power to span multi-cloud and on-premises with a hybrid data platform. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 109

IT Data Architecture Unstructured Data Big Data 109

Andrew White

DECEMBER 9, 2019

The client had recently engaged with a well-known consulting company that had recommended a large data catalog effort to collect all enterprise metadata to help identify all data and business issues. Modern data (and analytics) governance does not necessarily need: Wall-to-wall discovery of your data and metadata.

Analytics 66

Analytics Data Lake Data Governance Metadata 66

Data Virtualization

MARCH 21, 2024

The POC was for a data. The post Getting the Fundamentals Right for Gen AI appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Integration 59

Data Integration Management IT Consulting 59

Cloudera

JUNE 18, 2022

The first generation of the Hive Metastore attempted to address the performance considerations to run SQL efficiently on a data lake. It provided the concept of a database, schemas, and tables for describing the structure of a data lake in a way that let BI tools traverse the data efficiently.

Data Lake 91

Data Lake Data Warehouse Machine Learning Cost-Benefit 91

Cloudera

MAY 12, 2021

Use cases could include but are not limited to: workload analysis and replication, migrating or bursting to cloud, data warehouse optimization, and more. Data Champions: OVO (PT Visionet Internasional) — Using advanced, intelligent data analytics and machine learning to increase customer conversion rates.

Digital Transformation 67

Digital Transformation Machine Learning Optimization Data Lake 67

IBM Big Data Hub

MAY 9, 2022

As discussed in the previous section data virtualization and data cataloging help get the right data to the right people by making it easier to find the data that best fits their needs and access it. Automated metadata generation is essential in order to turn a manual process into one that is better controlled.

Management 57

Management Data Quality Metadata Data Integration 57