Corinium

JUNE 6, 2019

Lack of clear, unified, and scaled data engineering expertise to enable the power of AI at enterprise scale. Regulations and compliance requirements, especially around pricing, risk selection, etc., Companies need to be able to ingest, transform, and make the data available for analytics consumption in near real time.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

BizAcuity

APRIL 1, 2023

This typically requires a data warehouse for analytics needs that is able to ingest and handle real time data of huge volumes. Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts allowing secure data sharing across the organization.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Lake Internet of Things 52

Cloudera

MAY 12, 2021

This category is open to organizations that have tackled transformative business use cases by connecting multiple parts of the data lifecycle to enrich, report, serve, and predict. . DATA FOR ENTERPRISE AI. Industry Transformation: Telkomsel — Ingesting 25TB of data daily to provide advanced customer analytics in real-time .

Digital Transformation 66

Digital Transformation Machine Learning Optimization Data Lake 66

CIO Business Intelligence

APRIL 22, 2024

Questi requisiti sono suddivisi in tre macroaree: governance, risk management e controllo della catena di fornitura. Per esempio, “i PoC aiutano a definire i parametri in base ai quali organizzare i data lake o i criteri per la digitalizzazione dei workflow.

Data Lake 105

Data Lake Testing Management IoT 105

Cloudera

JANUARY 30, 2023

Gartner defines dark data as “The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).”

IoT 80

IoT Data Architecture Data Lake Machine Learning 80

In(tegrate) the Clouds

DECEMBER 21, 2015

Aside from the Internet of Things, which of the following software areas will experience the most change in 2016 – big data solutions, analytics, security, customer success/experience, sales & marketing approach or something else? 2016 will be the year of the data lake. What are the biggest risks for software buyers in 2016?

Technology 45

Technology Internet of Things Digital Transformation Software 45

DataKitchen

JULY 27, 2023

Here are a few examples that we have seen of how this can be done: Batch ETL with Azure Data Factory and Azure Databricks: In this pattern, Azure Data Factory is used to orchestrate and schedule batch ETL processes. Azure Blob Storage serves as the data lake to store raw data.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

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

CIO Business Intelligence

FEBRUARY 28, 2024

It also requires a rethink of your business strategy to embrace advances in cloud computing, analytics, AI, IoT and automation. Or, you may have begun migrating to the cloud but now need edge computing and IoT to streamline your operations, or you may want to use AI to supercharge your business analytics.

Digital Transformation 52

Digital Transformation IoT Cost-Benefit Data Lake 52

AWS Big Data

MAY 16, 2024

H3 can also help create location-based profiling features for predictive machine learning (ML) models such as risk-mitigation models. About Amazon Redshift Thousands of customers rely on Amazon Redshift to analyze data from terabytes to petabytes and run complex analytical queries.

Data Warehouse 103

Data Warehouse Visualization Cost-Benefit Optimization 103

Cloudera

AUGUST 9, 2022

Disaster recovery and business-continuity planning is primarily focused on managing and reducing risk. Standby systems can be designed to meet storage requirements during typical periods with burstable compute for failover scenarios using new features such as Data Lake Scaling. Why disaster recovery? Conclusion.

Data Lake 75

Data Lake Data Warehouse Data-driven IoT 75

CIO Business Intelligence

MAY 12, 2022

In the hyper-competitive telecommunications market, companies that don’t achieve these superlatives risk being left in the dust by competitors. The biggest challenge for any big enterprise is organizing the data that has organically grown across the organization over the last several years. This isn’t unique to Verizon.

Testing 98

Testing Data Lake Advertising Marketing 98

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

Alation

APRIL 14, 2022

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. Pushing data to a data lake and assuming it is ready for use is shortsighted. It’s not a simple definition.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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

AWS Big Data

JUNE 12, 2023

Organizations across the world are increasingly relying on streaming data, and there is a growing need for real-time data analytics, considering the growing velocity and volume of data being collected.

Management 80

Management Metadata Testing Internet of Things 80

Cloudera

JANUARY 22, 2019

Forrester describes Big Data Fabric as, “A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, data lakes, in-memory, and NoSQL.”.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

erwin

DECEMBER 13, 2018

Data operations (DataOps) gains traction/will be fully optimized: Much like how DevOps has taken hold over the past decade, 2019 will see a similar push for DataOps. Data is no longer just an IT issue. As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.),

Data Governance 91

Data Governance IoT Internet of Things Data-driven 91

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