erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

Data Governance 40

Data Governance Risk Metadata Management 40

Cloudera

MAY 10, 2021

The introduction of CDP Public Cloud has dramatically reduced the time in which you can be up and running with Cloudera’s latest technologies, be it with containerised Data Warehouse , Machine Learning , Operational Database or Data Engineering experiences or the multi-purpose VM-based Data Hub style of deployment.

Data Warehouse 103

Data Warehouse Consulting Machine Learning Risk 103

Laminar Security

AUGUST 31, 2023

New cloud file share integrations mark a pivotal moment for holistic data security posture management in the cloud In today’s digital era, cloud file-sharing platforms have become the backbone of modern businesses, enabling swift, seamless, and efficient data collaboration.

Risk 65

Risk Data Warehouse Measurement Management 65

erwin

FEBRUARY 13, 2020

The numerous data types and data sources that exist today weren’t designed to work together, and data infrastructures have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration. Finding metadata, “the data about the data,” isn’t easy.

Metadata 135

Metadata Data Governance Management Cost-Benefit 135

erwin

OCTOBER 31, 2019

While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed. So being prepared means you can minimize your risk exposure and the damage to your reputation.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

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

Octopai

MARCH 22, 2023

Data migration can be a daunting task, especially when dealing with large volumes of data. Snowflake is one of the leading cloud-based data warehouse that provides scalability, flexibility, and ease of use. Snowflake data warehouse platform has been designed to leverage the power of modern-day cloud computing technology.

Cost-Benefit 78

Cost-Benefit Data Warehouse Unstructured Data Optimization 78

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 128

Enterprise Consulting Modeling Cost-Benefit 128

erwin

AUGUST 7, 2019

The process begins with the target or data end point, and then traversing the processes, applications, and ETL tasks in reverse from the target. The data sources that feed the ETL process are added to a “task list,” and the process is repeated for each of those sources. At each stage, the discovered pieces of lineage are documented.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

CIO Business Intelligence

NOVEMBER 23, 2022

Unlike other frameworks, EA doesn’t include a formal documentation structure; instead, it’s intended to offer a more holistic view of the enterprise. EA is also used in systems development, IT management and decision-making, and IT risk management to eliminate errors, system failures, and security breaches. Data warehouse.

Enterprise 123

Enterprise Cost-Benefit Digital Transformation Strategy 123

Octopai

DECEMBER 10, 2019

Let’s look at why: Companies need to utilize shared data to make informed decisions, but when data is sent from one department to another there is always the risk of miscommunication. This is a company-wide document that defines all important terms used internally, with customers and with vendors.

Metadata 53

Metadata Data Warehouse Reporting Business Intelligence 53

Jet Global

MAY 1, 2020

In Gartner’s report, an analyst goes to great pains to say that there is “much more risk associated to non-technology issues than there is to deploying the infrastructure, tools, and apps.”. We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. Risk to the business.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

erwin

JUNE 26, 2020

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer. It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., Clearly documents data catalog policies, rules and shares information assets.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

erwin

MAY 2, 2022

I’m not proposing that business logic be held in the database, but it needs to be documented in the data model even if it will be implemented in application code. No matter where you implement it, you must fully document the business requirements in order to produce an effective final result. Webcast: Build better data models.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

erwin

MARCH 8, 2019

Therefore, the organization needed to catalog the data it acquires from suppliers, ensure its quality, classify it, and then sell it to customers. The company wanted to assemble the data in a data warehouse and then provide controlled access to it.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

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

APRIL 27, 2022

In data warehousing, the data is extracted and transported from production database(s) into a data warehouse for reporting and analysis. Change Data Capture identifies and processes only the data that has changed and stores the changed data in a form so as to be of further use.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

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

erwin

AUGUST 15, 2022

I was pricing for a data warehousing project with just 4 TBs of data, small by today’s standards. I chose “ON Demand” for up to 64 virtual CPUs and 448 GB of memory since I wanted this data warehouse to fit entirely, or at least mostly, within memory. Figure 1: Pricing for a 4 TB data warehouse in AWS.

Data Lake 52

Data Lake Modeling Unstructured Data Data Warehouse 52

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. The main challenges were risk mitigation and agility in analytics.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Ontotext

MARCH 18, 2020

The many data warehouse systems designed in the last 30 years present significant difficulties in that respect. When designing a system’s architecture, it’s impossible to know from the start all the ways data will be used. The first challenge here is how to enable agile enterprise information management.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

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. These processes could include reports, campaigns, or financial documentation.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

JUNE 20, 2023

Alation: And you likely had plenty of data even before the acquisition. The challenge wasn’t that we had nothing to document. Data from any other brands we acquired or built were based on the same schema, so an engineering team looking for data would say, “We know about where it is, but we don’t know exactly where it is.”

Technology 52

Technology Data Governance Data Warehouse Software 52

Smart Data Collective

JANUARY 22, 2020

Small companies value the ability to store documents in the cloud and conveniently manage them. Large business players appreciate the opportunity to save money on the acquisition and maintenance of their own data storage infrastructure. What measures are essential to keep your sensitive data confidential? Data Security.

Data Processing 57

Data Processing Measurement Risk Data Warehouse 57

Octopai

JUNE 1, 2020

Data Warehousing – For data to have value for your company, it needs to be stored in an accessible location. Correct data mapping facilitates the creation of usable, searchable data warehouses.

Metadata 52

Metadata Data Warehouse Data Integration Finance 52

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

Cloudera

OCTOBER 13, 2020

This utility raises awareness of clusters that may present risks during an upgrade to CDP due to, for example, an unsupported of the operating system currently in use. The upgrade advisor utility is focused on the environment and platform in use but doesn’t take into consideration use-cases, the actual cluster data, or workflows in use.

Risk 107

Risk Software Management Publishing 107

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

Smart Data Collective

JUNE 7, 2019

Volume – Companies gather data from different sources such as business transactions, social media, and other relevant data. Variety – It means all data can be presented in a variety of formats – from structured numeric data to the unstructured ones, which include text documents, audio, video, and email.

Big Data 81

Big Data Marketing Cost-Benefit Unstructured Data 81

IBM Big Data Hub

JUNE 2, 2023

improved document management capabilities, web portals, mobile applications, data warehouses, enhanced location services, etc.) .” For example, the core systems technology landscape for each state could be a mainframe legacy system with varying degrees of maturity, portability, reliability and scalability.

Consulting 84

Consulting Cost-Benefit Technology ROI 84

CIO Business Intelligence

JUNE 7, 2022

Owczarek was recently involved in a project to develop an AI-based product for the pharmaceutical industry that would support the manual reviewing of research papers and documents with natural language processing. AI strategists can also help organizations obtain the data they need to fuel AI effectively.

Machine Learning 145

Machine Learning Data Science Consulting Metrics 145

Cloudera

MAY 22, 2018

With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. This leads to wasted time and effort during research and collaboration or, worse, compliance risk.

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

datapine

JANUARY 24, 2021

Additionally, you want to clarify these questions regarding data analysis now or as soon as possible – which will make your future business intelligence much clearer. ETL data warehouse*. 1) What exactly do you want to find out? 7) Who are the final users of your analysis results?

IT 317

IT Statistics KPI Data-driven 317

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 110

Data Warehouse Visualization Cost-Benefit Optimization 110

Alation

FEBRUARY 13, 2020

A robust data catalog provides many other capabilities including support for data curation and collaborative data management, data usage tracking, intelligent dataset recommendations, and a variety of data governance features. Benefits of a Data Catalog. Improved data efficiency.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Cloudera

NOVEMBER 25, 2020

For example, it includes patient samples (blood, blood pressure, temperature, and more), patient information (age, gender, where they have lived, family situation, and other details), and treatment history, most of which is currently found only in paper documents. The Vision of a Discovery Data Warehouse.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

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 102

Data Lake Data Warehouse Management Risk 102