erwin

JULY 17, 2019

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Ontotext

NOVEMBER 16, 2023

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Alation

FEBRUARY 13, 2020

Why do we need a data catalog? What does a data catalog do? These are all good questions and a logical place to start your data cataloging journey. Data catalogs have become the standard for metadata management in the age of big data and self-service analytics. Figure 1 – Data Catalog Metadata Subjects.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

bridgei2i

MARCH 11, 2021

Easily understandable, highly curated, and reliable data helps Machine Learning (ML) tools evolve. As long as small businesses don’t have efficient data governance strategies, they can’t properly use AI and ML-powered tools. What is a Data Governance Strategy? They have access to large amounts of data.

Key Performance Indicator 52

Key Performance Indicator Data Governance Data Lake Metadata 52

CIO Business Intelligence

MARCH 29, 2022

Lowering the entry cost by re-using data and infrastructure already in place for other projects makes trying many different approaches feasible. Fortunately, learning-based projects typically use data collected for other purposes. . You have data but don’t use it. Why does valuable data so often go unused?

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

O'Reilly on Data

JULY 28, 2020

Qualitative data collection tools (such as SurveyMonkey , Qualtrics , and Google Forms ) should be joined with interface prototyping tools (such as Invision and Balsamiq ), and with data prototyping tools (such as Jupyter Notebooks ) to form an ecosystem for product development and testing. Data Quality and Standardization.

Marketing 361

Marketing Experimentation Metrics Testing 361

Cloudera

AUGUST 4, 2021

And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. But it’s still not easy.

Data Governance 85

Data Governance Metadata Data-driven Enterprise 85

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 307

Enterprise Deep Learning Data Governance Risk 307

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Andrew White

JANUARY 9, 2022

Could you precise to which complementary research you mentioned when you talked about a data governance survey ? – Here is the one I mentioned during the webinar: The State of Data and Analytics Governance Is Worse Than You Think. – Data (and analytics) governance remains a challenge.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Alation

DECEMBER 15, 2021

Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information. Data governance requires a system.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

Alation

NOVEMBER 11, 2021

As IT leaders oversee migration, it’s critical they do not overlook data governance. Data governance is essential because it ensures people can access useful, high-quality data. Let’s take a look at some of the key principles for governing your data in the cloud: What is Cloud Data Governance?

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

Alation

FEBRUARY 17, 2023

This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. Can you have proper data management without establishing a formal data governance program?

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Alation

MARCH 17, 2021

According to the Forrester Wave: Machine Learning Data Catalogs, Q4 2020 , “Alation exploits machine learning at every opportunity to improve data management, governance, and consumption by analytic citizens. An MLDC brings many benefits, like: Enhanced data management. Data governance streamlining.

Machine Learning 52

Machine Learning Metadata Data Governance Interactive 52

Alation

JULY 6, 2021

Common Data Governance Challenges. Every enterprise runs into data governance challenges eventually. Issues like data visibility, quality, and security are common and complex. Data governance is often introduced as a potential solution. And one enterprise alone can generate a world of data.

Data Governance 52

Data Governance Metadata Data Quality Risk 52

IBM Big Data Hub

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

Alation

AUGUST 26, 2021

What Is Data Intelligence? Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.” Why keep data at all?

Metadata 64

Metadata Data Governance Dashboards Software 64

DataKitchen

JANUARY 23, 2023

Data lineage can also be used for compliance, auditing, and data governance purposes. DataOps Observability Five on data lineage: Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. What is missing in data lineage?

Testing 130

Testing Data Governance Data Quality Data-driven 130

O'Reilly on Data

DECEMBER 19, 2018

Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Toward a sustainable ML practice.

Machine Learning 258

Machine Learning Software Metadata Testing 258