bridgei2i

MARCH 11, 2021

From eliminating the need for human assistance in repetitive tasks to reducing the risk of human errors in manual processes – AI can do a lot for large-scale businesses. With improved data cataloging functionality, their systems can become responsive. The post How Can Small Businesses Benefit from an AI Data Company?

Key Performance Indicator 52

Key Performance Indicator Data Governance Data Lake Metadata 52

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

CDW Research Hub

APRIL 18, 2022

The complexity and cost of SIEM solutions and the number of resources that security consumes can easily swallow a large portion of an enterprise’s budget, causing many organizations to fall behind in the security data race. Security data lakes can reduce organizations’ reliance on SIEM solutions.

Data Lake 75

Data Lake Enterprise Data Collection Risk 75

Alation

FEBRUARY 13, 2020

Today, we’re announcing that Alation has closed a $50 million Series C funding led by Sapphire Ventures, with participation from new investor Salesforce Ventures and our existing investors Costanoa Ventures, DCVC (Data Collective), Harmony Partners and Icon Ventures.

Enterprise 52

Enterprise Data Lake Machine Learning Data-driven 52

Smart Data Collective

OCTOBER 26, 2019

Having cost-effective off-site backup allows companies to focus more on their methodology for backing up data than the price of that method. Closer sites for data storage mean lower cost, but a higher risk to the company. Big Data Storage Concerns. Further sites may be less cost-effective but more secure. Conclusion.

Big Data 45

Big Data Data Lake Cost-Benefit Measurement 45

BizAcuity

MARCH 1, 2019

At the backend, based on the data collected, data is stored in data lakes. Such data is collected from hundreds, thousands and millions of users. Then AI/ML algorithms are run on this collected data. to make better decisions and risk assessments.

IoT 56

IoT Internet of Things Big Data Data-driven 56

Alation

FEBRUARY 20, 2020

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. Due to these needs, hybrid cloud data lakes emerged as a logical middle ground between the two consumption models. earthquake, flood, or fire), where the data collected does not need to be as tightly controlled.

Data Lake 52

Data Lake ROI Metadata Cost-Benefit 52

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance.

Snapshot 85

Snapshot Data Lake Testing Strategy 85

Cloudera

JANUARY 21, 2021

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. Of course you don’t want to re-create the risks and costs of data silos your organization has spent the last decade trying to eliminate. Must you be: .

Data Lake 93

Data Lake Data Warehouse IT Analytics 93

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

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

CIO Business Intelligence

JANUARY 30, 2024

Only a few enterprises have adopted fully automated ESG data collection and monitoring tools; the majority still depend on unreliable manual practices,” Everest’s Narayanan says. From there, CIOs can determine the most relevant pieces of data and how to source and automate the gathering of that data, IDC’s Cravens says.

Reporting 115

Reporting Data Quality Strategy Data-driven 115

Cloudera

DECEMBER 16, 2022

There are six key capabilities that cement our leadership in enterprise data platforms: 1-Hybrid and multi-cloud portability and scale to support the most demanding workloads. Anything else requires integration, sometimes between multiple vendors, which means complexity and risk. 5-Integrated open data collection.

Management 88

Management Metadata Machine Learning Data Lake 88

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. Additionally, scalability of the dimensional model is complex and poses a high risk of data integrity issues.

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Modeling 65

Cloudera

SEPTEMBER 8, 2021

Putting aside operational efficiency for a moment, let us now consider the constraint “accelerated time frames” If data flows, ETL pipelines, BI reports and machine learning pipelines all need to be rewritten or heavily modified, this can significantly extend the time to value and increase the risk of moving to the cloud.

Data Lake 75

Data Lake Enterprise Data-driven Data Strategy 75

Cloudera

AUGUST 4, 2021

Data governance used to be considered a “nice to have” function within an enterprise, but it didn’t receive serious attention until the sheer volume of business and personal data started taking off with the introduction of smartphones in the mid-2000s.

Data Governance 83

Data Governance Metadata Data-driven Enterprise 83

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 85

Metadata Data Lake Optimization Strategy 85

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

Alation

FEBRUARY 1, 2022

Today organizations view data as the “new oil”, an asset that, if used wisely, can support innovation while providing a meaningful competitive advantage and a better customer experience. And with data collection and replication growing so quickly, governance is more important than ever. Curious to learn more?

Data Lake 52

Data Lake Data Governance ROI Cost-Benefit 52

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Architecture 63

Data Architecture Data Lake Machine Learning Data Governance 63

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. 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. The Right Tools.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

CIO Business Intelligence

JANUARY 20, 2023

But to get maximum value out of data and analytics, companies need to have a data-driven culture permeating the entire organization, one in which every business unit gets full access to the data it needs in the way it needs it. This is called data democratization. Security and compliance risks also loom.

Data Lake 135

Data Lake Data-driven Finance Data Architecture 135

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52