AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 91

Data Lake Data Warehouse Data-driven B2B 91

DataRobot

JANUARY 20, 2022

Specialized teams from DataRobot and Snowflake will enable ICSs to mitigate data governance and model bias risk with confidence. Public sector data sharing. ICSs can reduce the time taken to build population health registries and predictive models by up to 90 percent.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

Jet Global

FEBRUARY 5, 2019

states that about 40 percent of enterprise data is either inaccurate, incomplete, or unavailable. This poor data quality translates into an average of $15 million per year in a ripple effect of financial loss, missed opportunities, and high-risk decision making. Because bad data is the reason behind poor analytics. .

Data Quality 69

Data Quality Data Warehouse Business Intelligence Data Governance 69

CIO Business Intelligence

SEPTEMBER 21, 2022

CMOs need to look for ways to leverage customer data to deliver superior and highly tailored experiences to customers. CIOs need to ensure that the business’ use of data is compliant, secure, and done according to best practices. They need to assure the board that the risk from data is minimised.

Risk 80

Risk Data Lake Marketing Interactive 80

erwin

APRIL 27, 2022

Best Data Governance Solution (erwin Data Intelligence). Best Data Modeling Solution (erwin Data Modeler). Best Data Security Solution (Quest ApexSQL). In data warehousing, the data is extracted and transported from production database(s) into a data warehouse for reporting and analysis.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

IBM Big Data Hub

OCTOBER 12, 2023

Accounting for the complexities of the AI lifecycle Unfortunately, typical data storage and data governance tools fall short in the AI arena when it comes to helping an organization perform the tasks that underline efficient and responsible AI lifecycle management. And that makes sense.

Cost-Benefit 78

Cost-Benefit Metadata Data Governance Modeling 78

Jet Global

FEBRUARY 23, 2024

Load : Once data transformation is complete, the transformed data is loaded into the target system, such as a data warehouse, database, or another application. ETL tools ensure that the data is loaded efficiently and accurately into the destination. Data warehouses can be complex, time-consuming, and expensive.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 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 60

Data Architecture Data Lake Machine Learning Data Governance 60

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

erwin

FEBRUARY 21, 2019

Data is the key to recognizing customers, personalizing experiences, making helpful recommendations, ensuring items are in stock, tracking deliveries and more. Digital Transformation in Retail – What’s the Risk? says retailers need to achieve a level of what he calls “data intelligence.”

Digital Transformation 69

Digital Transformation Data Governance Risk Sales 69

erwin

SEPTEMBER 25, 2020

Therefore, the visual representation provided by a data model gives organizations the confidence to design their proposed systems and take them live. Data modeling is a critical component of metadata management , data governance and data intelligence. Automate data model and database schema generation.

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

BizAcuity

APRIL 14, 2022

Be it supply chain resilience, staff management, trend identification, budget planning, risk and fraud management, big data increases efficiency by making data-driven predictions and forecasts. With adequate market intelligence, big data analytics can be used for unearthing scope for product improvement or innovation.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

erwin

JUNE 26, 2020

It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., legacy systems, data warehouses, flat files stored on individual desktops and laptops, and modern, cloud-based repositories.). Clearly documents data catalog policies, rules and shares information assets.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

IBM Big Data Hub

MARCH 14, 2024

By using decades of database expertise in performance by IBM and combining it with AWS’s scalability, security and governance features, customers can achieve enhanced flexibility, agility and cost efficiency in the cloud. It enables secure data sharing for analytics and AI across your ecosystem.

Cost-Benefit 77

Cost-Benefit Metadata Optimization Management 77

IBM Big Data Hub

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Jet Global

JULY 2, 2019

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

Octopai

DECEMBER 10, 2019

Well, scoot over Templeton, because it’s also going to be the year of the Automated Business Glossary for business intelligence and data governance teams everywhere. Standard terms in sales might have a different understanding in accounting, and crossed wires can lead to problems.

Metadata 53

Metadata Data Warehouse Reporting Business Intelligence 53

datapine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. The program manager should lead the vision for quality data and ROI.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

Metadata 64

Metadata Data Governance Dashboards Software 64

FineReport

MAY 31, 2023

Furthermore, we will introduce some business intelligence solution that excels in simplifying the process of creating and utilizing a financial dashboard effectively. Finance and accounting teams often deal with data residing in multiple systems, such as accounting software, ERP systems, spreadsheets, and data warehouses.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Jet Global

OCTOBER 24, 2022

This approach also introduces risk when it comes to integration and upgrades, creating potential headaches further down the road. In a world rife with security risks, that should be a concern to every software vendor. In a world rife with security risks, that should be a concern to every software vendor.

Analytics 52

Analytics Dashboards Software Reporting 52

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

MARCH 22, 2019

This ties into the failure of data governance and MDM (see first item in this list). A data hub strategy should be economical, not perfected; and a data hub does not collect data like a data warehouses or data lake does – they are very different things. Strategy is Learning By Doing.

Analytics 68

Analytics Metadata Data Governance Management 68

Domino Data Lab

JULY 2, 2019

Of course, if you use several different data management frameworks within your data science workflows—as just about everybody does these days—much of that RDBMS magic vanishes in a puff of smoke. Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?”

Metadata 105

Metadata Machine Learning Data Science Data-driven 105