erwin

APRIL 2, 2021

With more companies increasingly migrating their data to the cloud to ensure availability and scalability, the risks associated with data management and protection also are growing. Data Security Starts with Data Governance. Lack of a solid data governance foundation increases the risk of data-security incidents.

Data Governance 106

Data Governance Cost-Benefit Risk Metadata 106

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

CIO Business Intelligence

SEPTEMBER 21, 2023

The phrase “existential risk” is now everywhere—not in the sense the AI would destroy humanity, but that it would make business functions, or even entire companies, obsolete. If you take something slightly risky and make it a thousand times bigger, the risks are amplified,” he says. But it’s a sign of what’s to come. “If

Enterprise 126

Enterprise Consulting Modeling Cost-Benefit 126

Jet Global

OCTOBER 7, 2020

Confusing matters further, Microsoft has also created something called the Data Entity Store, which serves a different purpose and functions independently of data entities. The Data Entity Store is an internal data warehouse that is only available to embedded Power BI reports (not the full version of Power BI).

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Ontotext

MARCH 8, 2023

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions. Which financial institutions have filed similar risk compliance issues?

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 99

Data Lake Data Warehouse Management Risk 99

AWS Big Data

MAY 16, 2024

For more information or to get started with Amazon Redshift spatial analytics, see the documentation for querying spatial data , spatial functions , and the spatial tutorial. H3 can also help create location-based profiling features for predictive machine learning (ML) models such as risk-mitigation models.

Data Warehouse 107

Data Warehouse Visualization Cost-Benefit Optimization 107

Cloudera

JANUARY 21, 2021

While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. And you also already know siloed data is costly, as that means it will be much tougher to derive novel insights from all of your data by joining data sets.

Data Lake 93

Data Lake Data Warehouse IT Analytics 93

Cloudera

NOVEMBER 5, 2020

Many organizations struggle to meet growing and variable data warehouse demands. This is exactly what Cloudera Data Platform (CDP) provides to the Cloudera Data Warehouse. CDP is a data platform that is optimized for both business units and central IT. .

Data Lake 74

Data Lake Data Warehouse Metadata Optimization 74

Domino Data Lab

APRIL 3, 2019

Probably the best one-liner I’ve encountered is the analogy that: DG is to data assets as HR is to people. Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Most of the data management moved to back-end servers, e.g., databases. for DG adoption in the enterprise.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk. Humans can’t keep up.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

IBM Big Data Hub

MAY 9, 2023

is not just for data scientists and developers — business users can also access it via an easy-to-use interface that responds to natural language prompts for different tasks. With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs. Watsonx.ai

Data Warehouse 106

Data Warehouse Machine Learning Cost-Benefit Metadata 106

Cloudera

AUGUST 9, 2022

Data platforms are no longer skunkworks projects or science experiments. As customers import their mainframe and legacy data warehouse workloads, there is an expectation on the platform that it can meet, if not exceed, the resilience of the prior system and its associated dependencies. Why disaster recovery?

Data Lake 78

Data Lake Data Warehouse Data-driven IoT 78

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. Gaining Executive Buy-In.

Risk 83

Risk Data Lake Marketing Interactive 83

AWS Big Data

JULY 28, 2023

Organizations must comply with these requests provided that there are no legitimate grounds for retaining the personal data, such as legal obligations or contractual requirements. Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. The AWS DPA is incorporated into the AWS Service Terms.

Snapshot 83

Snapshot Metadata Measurement Data Warehouse 83

Alation

MARCH 1, 2022

Lineage helps them identify the source of bad data to fix the problem fast. Manual lineage will give ARC a fuller picture of how data was created between AWS S3 data lake, Snowflake cloud data warehouse and Tableau (and how it can be fixed). Time is money,” said Leonard Kwok, Senior Data Analyst, ARC.

Data Warehouse 52

Data Warehouse Data Lake Risk Data-driven 52

Alation

APRIL 6, 2023

Data governance is traditionally applied to structured data assets that are most often found in databases and information systems. This blog focuses on governing spreadsheets that contain data, information, and metadata, and must themselves be governed. Leaders seek to use strategic data with confidence more often.

Data Governance 52

Data Governance Metadata Cost-Benefit Structured Data 52

AWS Big Data

JUNE 6, 2023

You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions. Choose the Data quality tab.

Data Quality 75

Data Quality Data Lake Data-driven Metrics 75

Cloudera

OCTOBER 25, 2022

Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses. They combine the best of both worlds: flexibility, cost effectiveness of data lakes and performance, and reliability of data warehouses.”.

Data Processing 52

Data Processing Data Lake Cost-Benefit Risk 52

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged. Scheduling. Target Matching.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

IBM Big Data Hub

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

Cost-Benefit 76

Cost-Benefit Metadata Data Governance Modeling 76

FineReport

APRIL 11, 2023

Additionally, they provide tabs, pull-down menus, and other navigation features to assist in accessing data. Data Visualizations : Dashboards are configured with a variety of data visualizations such as line and bar charts, bubble charts, heat maps, and scatter plots to show different performance metrics and statistics.

Dashboards 40

Dashboards Business Intelligence Cost-Benefit Metrics 40

BI-Survey

NOVEMBER 23, 2021

The transactional data was stored in isolated data sets and initially served only one purpose, namely, to document the transaction that had taken place. Over time, enterprises realized that data is worth more. However, the operational data stored in data silos was not suitable for this task.

Digital Transformation 52

Digital Transformation Data Warehouse Data Lake Data-driven 52

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

datapine

AUGUST 29, 2022

The new edition also explores artificial intelligence in more detail, covering topics such as Data Lakes and Data Sharing practices. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz. 8) Data Smart: Using Data Science to Transform Information into Insight, by John W.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

erwin

JULY 17, 2019

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement. And do it without the risk of breaking everything.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

IBM Big Data Hub

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

Management 54

Management Enterprise Modeling Data Quality 54

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. Age maybe against us.

Analytics 68

Analytics Metadata Data Governance Management 68

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