FineReport

JUNE 7, 2024

Criteria for Top Data Visualization Companies Innovation and Technology Cutting-edge technology lies at the core of top data visualization companies. Embrace FanRuan’s transformative technologies to elevate your data visualization experience to unprecedented heights!

Visualization 52

Visualization Predictive Analytics Dashboards Predictive Modeling 52

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. What is the difference between business analytics and data analytics? Business analytics is a subset of data analytics. What is business analytics?

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

CIO Business Intelligence

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. What are the four types of data analytics?

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 89

Metadata Data Lake Visualization Data Transformation 89

IBM Big Data Hub

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 70

Risk Modeling Management Metadata 70

bridgei2i

NOVEMBER 17, 2020

Over time, as technology started entering every business process, the second wave of automation came with technology implementation. Automation through technology had the same outcomes as the first wave though. Automation through technology had the same outcomes as the first wave though. So is technology.

Deep Learning 85

Deep Learning Manufacturing Optimization Data-driven 85

Ontotext

MARCH 20, 2024

Unleashing the Power of Data Connections Zenia Graph isn’t just another data solution company. We are pioneers in knowledge graphs – a revolutionary technology that goes beyond simple data analytics, aggregation, and reporting. Zenia Graph’s Salesforce Accelerator makes this a reality.

Data-driven 52

Data-driven Strategy Sales Data Integration 52

CIO Business Intelligence

JUNE 13, 2022

New technologies hit the market, existing ones evolve, business needs change on a dime, staff comes and goes. Technology has to move much closer to the customer, but there are still intermediaries. CIOs still have interpreters, they have interpreters between the actual users and the technology organization,” he says.

IT 144

IT Digital Transformation Strategy Internet of Things 144

AWS Big Data

APRIL 11, 2023

Such a solution should use the latest technologies, including Internet of Things (IoT) sensors, cloud computing, and machine learning (ML), to provide accurate, timely, and actionable data. In the inventory management and forecasting solution, AWS Glue is recommended for data transformation.

Forecasting 76

Forecasting Management IoT Data-driven 76

DataRobot Blog

MARCH 10, 2022

As a result of these technological advancements, the manufacturing industry has set its sights on artificial intelligence and automation to enhance services through efficiency gains and lowering operational expenses. Python is unarguably the most broadly used programming language throughout the data science community.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

AWS Big Data

DECEMBER 21, 2023

Movement of data across data lakes, data warehouses, and purpose-built stores is achieved by extract, transform, and load (ETL) processes using data integration services such as AWS Glue. AWS Glue provides both visual and code-based interfaces to make data integration effortless.

Analytics 97

Analytics IT Data Lake Visualization 97

Anmut

MARCH 24, 2022

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success.

ROI 52

ROI Measurement Data-driven Data Transformation 52

Sisense

OCTOBER 15, 2020

As real-time analytics and machine learning stream processing are growing rapidly, they introduce a new set of technological and conceptual challenges. Technological challenges to handling streaming data. Stateful transformations. Ori has a B.A. in Computer Science and an MBA. Ori has a B.A. Mei has a B.S.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

DataKitchen

JULY 27, 2023

You can use it for big data analytics and machine learning workloads. Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. Azure Blob Storage serves as the data lake to store raw data.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

Alation

FEBRUARY 2, 2023

Understanding data and analytics ensures that data is used effectively and helps troubleshoot any problems. These core capabilities are necessary for successfully managing a data product. Successful Product Managers Use the Right Tools & Technologies The right tools and tech stack can make or break a project.

Management 52

Management Data-driven Visualization Strategy 52

AWS Big Data

FEBRUARY 6, 2023

He has a specialty in big data services and technologies and an interest in building customer business outcomes together. Jiseong Kim is a Senior Data Architect at AWS ProServe. He also understands how to apply technologies to solve big data problems and build a well-designed data architecture.

Dashboards 76

Dashboards Optimization Data Lake Cost-Benefit 76

Sisense

MARCH 14, 2021

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

AWS Big Data

FEBRUARY 6, 2023

He has a specialty in big data services and technologies and an interest in building customer business outcomes together. Jiseong Kim is a Senior Data Architect at AWS ProServe. He also understands how to apply technologies to solve big data problems and build a well-designed data architecture.

Cost-Benefit 52

Cost-Benefit Data Lake Dashboards Big Data 52

O'Reilly on Data

JANUARY 17, 2023

Think about what the model results tell you: “Maybe a random forest isn’t the best tool to split this data, but XLNet is.” ” If none of your models performed well, that tells you that your dataset–your choice of raw data, feature selection, and feature engineering–is not amenable to machine learning.

Machine Learning 333

Machine Learning Predictive Modeling Software Modeling 333

Smart Data Collective

JULY 25, 2019

One study by McKinsey and Company showed that big data solutions could cut healthcare costs by $450 billion a year. If the use of modern technologies by businesses and different organizations is about convenience and saving time and money, when it comes to hospitals, it is about health care and saving lives. Conclusion.

Big Data 97

Big Data Data Transformation Management Software 97

CIO Business Intelligence

JUNE 29, 2022

Taking the broadest possible interpretation of data analytics , Azure offers more than a dozen services — and that’s before you include Power BI, with its AI-powered analysis and new datamart option , or governance-oriented approaches such as Microsoft Purview. Azure Data Factory. Datamarts in Power BI.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

AWS Big Data

NOVEMBER 15, 2023

They can use their own toolsets or rely on provided blueprints to ingest the data from source systems. Once released, consumers use datasets from different providers for analysis, machine learning (ML) workloads, and visualization. The difference lies in when and where data transformation takes place.

Dashboards 96

Dashboards Analytics Metadata Data Warehouse 96

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different. Model Development.

IT 351

IT Testing Experimentation Software 351

AWS Big Data

JULY 6, 2023

It has not been specifically designed for heavy data transformation tasks. Step Functions helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines. Note that Lambda is a general purpose serverless engine.

Machine Learning 130

Machine Learning Visualization IoT Digital Transformation 130

Alation

JUNE 28, 2022

Scale effectively: Leverage taxonomies to ensure consistent modeling outcomes when introducing new data sets or changing business demands. Track data lineage: Document data origins, record data transformation and movement, and visualize flow throughout the entire data lifecycle. Supports Strong Data Culture.

Metadata 52

Metadata Management Data Governance Machine Learning 52

IBM Big Data Hub

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

IBM Big Data Hub

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. Automated development: Automates data preparation, model development, feature engineering and hyperparameter optimization using AutoAI.

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

AWS Big Data

OCTOBER 11, 2023

The data in the machine-readable files can provide valuable insights to understand the true cost of healthcare services and compare prices and quality across hospitals. The availability of machine-readable files opens up new possibilities for data analytics, allowing organizations to analyze large amounts of pricing data.

Visualization 97

Visualization Dashboards Data-driven Gap analysis 97

AWS Big Data

JULY 20, 2023

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. Why did Orca build a data lake?

Data Lake 88

Data Lake Analytics Snapshot Optimization 88

IBM Big Data Hub

MARCH 14, 2024

Try Db2 Warehouse SaaS on AWS for free   Netezza SaaS on AWS IBM® Netezza® Performance Server is a cloud-native data warehouse designed to operationalize deep analytics, data mining and BI by unifying, accessing and scaling all types of data across the hybrid cloud. Netezza

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76

Alation

AUGUST 11, 2022

In the technology industry, even the most incremental trends often get framed in next-generation terminology. DataOps sprung up to connect data sources to data consumers. The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Tools became stacks.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

AWS Big Data

FEBRUARY 21, 2023

We use Apache Spark as our main data processing engine and have over 1,000 Spark applications running over massive amounts of data every day. These Spark applications implement our business logic ranging from data transformation, machine learning (ML) model inference, to operational tasks.

Cost-Benefit 55

Cost-Benefit Informatics Optimization Management 55

CIO Business Intelligence

JUNE 14, 2022

By most accounts, companies are making the necessary investments, as evidenced by the majority of heads of IT (52%) saying data analytics and machine learning will drive the most IT investment at their organizations this year, according to CIO.com’s State of the CIO survey. Anatomy of a data strategy. What actually works.

Data Transformation 145

Data Transformation Data-driven Data Strategy Strategy 145

bridgei2i

SEPTEMBER 3, 2020

So Ronnie comes with about 15 years of experience in leadership positions, across technology and the media industry. Ronobijay: Sure, I think it would, you know, what used to be anathema till a few months back, you know, data transformation is real now, right? Thank you for taking the time to speak with us today, Ronnie.

Enterprise 94

Enterprise Insurance Digital Transformation Interactive 94

AWS Big Data

OCTOBER 18, 2023

To look for fraud, market manipulation, insider trading, and abuse, FINRA’s technology group has developed a robust set of big data tools in the AWS Cloud to support these activities. Melody Yang is a Senior Big Data Solutions Architect for Amazon EMR at AWS. or later installed.

Big Data 81

Big Data Data Processing Interactive Testing 81

Ontotext

DECEMBER 9, 2020

We all want to solve the interesting data challenges, build analytics, generate graph embeddings and train smart machine learning models over our knowledge graph data. This leads to lots of small data fetches to/from GraphDB over the network. Custom code also tends to over-fetch data that is not required.

Modeling 130

Modeling Management Optimization Metrics 130

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 85

Enterprise Data Warehouse Data Lake Optimization 85