CIO Business Intelligence

OCTOBER 19, 2023

The growing volume of data is a concern, as 20% of enterprises surveyed by IDG are drawing from 1000 or more sources to feed their analytics systems. Data integration needs an overhaul, which can only be achieved by considering the following gaps. This enables the system to examine the data before transforming and altering it.

Data Integration 125

Data Integration Unstructured Data Data-driven Data Warehouse 125

IBM Big Data Hub

SEPTEMBER 19, 2022

Join SingleStore and IBM on September 21, 2022 for our webinar “ Accelerating Real-Time IoT Analytics with IBM Cognos and SingleStore ”. Why real-time analytics matters for IoT systems. IoT systems access millions of devices that generate large amounts of streaming data. Inventory optimization (in retail).

IoT 52

IoT Analytics Data Warehouse Manufacturing 52

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer job description.

Analytics 127

Analytics Data Science Statistics Unstructured Data 127

AWS Big Data

MARCH 7, 2024

Organizations often need to manage a high volume of data that is growing at an extraordinary rate. At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. We think of this concept as inside-out data movement.

Data Lake 115

Data Lake Analytics Dashboards Metrics 115

CIO Business Intelligence

OCTOBER 13, 2023

Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes. Application data architect: The application data architect designs and implements data models for specific software applications.

Data Architecture 131

Data Architecture Data Warehouse Statistics Visualization 131

IBM Big Data Hub

OCTOBER 30, 2023

IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. The retailer uses these insights to optimize inventory levels, reduce costs and enhance efficiency.

Insurance 82

Insurance Data Warehouse Data-driven Unstructured Data 82

AWS Big Data

JANUARY 8, 2024

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 115

Analytics IoT Data-driven Snapshot 115

CIO Business Intelligence

FEBRUARY 16, 2023

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

Unstructured Data 104

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 104

FineReport

JULY 28, 2021

If you are curious about the difference and similarities between them, this article will unveil the mystery of business intelligence vs. data science vs. data analytics. Definition: BI vs Data Science vs Data Analytics. What is Data Science? Typical tools for data science: SAS, Python, R.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

CIO Business Intelligence

APRIL 25, 2022

The data lakehouse is a relatively new data architecture concept, first championed by Cloudera, which offers both storage and analytics capabilities as part of the same solution, in contrast to the concepts for data lake and data warehouse which, respectively, store data in native format, and structured data, often in SQL format.

Recreation/Entertainment 115

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 115

Jet Global

SEPTEMBER 4, 2020

For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

CIO Business Intelligence

JULY 10, 2023

His role now encompasses responsibility for data engineering, analytics development, and the vehicle inventory and statistics & pricing teams. The company was born as a series of print buying guides in 1966 and began making its data available via CD-ROM in the 1990s. We believe the same thing is happening right now with AI.

Data Warehouse 94

Data Warehouse Cost-Benefit Unstructured Data Machine Learning 94

Timo Elliott

JANUARY 3, 2022

It’s stored in corporate data warehouses, data lakes, and a myriad of other locations – and while some of it is put to good use, it’s estimated that around 73% of this data remains unexplored. In this way, you can turn dark data into insights and help drive business improvements. Dark variables. Learn More.

IT 143

IT Unstructured Data Data Quality Machine Learning 143

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Cloudera

SEPTEMBER 17, 2020

To address this, they focused on creating an experimentation-oriented culture, enabled thanks to a cloud-native platform supporting the full data lifecycle. This platform, including an ad-hoc capable data warehouse service with built-in, easy-to-use visualization, made it easy for anyone to jump in and start experimenting.

Experimentation 81

Experimentation Data Warehouse Dashboards Visualization 81

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model? What is a data vault?

Data Warehouse 67

Data Warehouse Data Lake Cost-Benefit Modeling 67

AWS Big Data

MARCH 25, 2024

This data store provides your organization with the holistic customer records view that is needed for operational efficiency of RAG-based generative AI applications. For building such a data store, an unstructured data store would be best. This is typically unstructured data and is updated in a non-incremental fashion.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

datapine

NOVEMBER 19, 2019

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward.

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Unstructured Data 146

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 105

Data Lake Sales Data Warehouse Snapshot 105

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Deciding on KPIs to gauge a data architecture’s effectiveness.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

FineReport

JUNE 24, 2021

Business intelligence can assist decision-making and operation optimization, either at the operational or tactical, or strategic levels. Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. Data preparation and data processing.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

AWS Big Data

FEBRUARY 22, 2023

The data lake implemented by Ruparupa uses Amazon S3 as the storage platform, AWS Database Migration Service (AWS DMS) as the ingestion tool, AWS Glue as the ETL (extract, transform, and load) tool, and QuickSight for analytic dashboards. Data had to be manually processed by data analysts, and data mining took a long time.

Data Lake 52

Data Lake Dashboards Cost-Benefit Metadata 52

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 116

Data Lake Metadata Optimization Statistics 116

Sisense

OCTOBER 6, 2020

And, as industrial, business, domestic, and personal Internet of Things devices become increasingly intelligent, they communicate with each other and share data to help calibrate performance and maximize efficiency. The result, as Sisense CEO Amir Orad wrote , is that every company is now a data company.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Sisense

MARCH 14, 2021

However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts. 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

OCTOBER 20, 2023

We’ve seen a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With these connectors, you can bring the data from Azure Blob Storage and Azure Data Lake Storage separately to Amazon S3. Learn more in README.

Data Lake 87

Data Lake Big Data Consulting Data Warehouse 87

Cloudera

JANUARY 28, 2022

As I meet with our customers, there are always a range of discussions regarding the use of the cloud for financial services data and analytics. Customers vary widely on the topic of public cloud – what data sources, what use cases are right for public cloud deployments – beyond sandbox, experimentation efforts.

Slice and Dice 113

Slice and Dice Cost-Benefit Risk Data Governance 113

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., Ensures regulatory compliance.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

Smart Data Collective

JUNE 7, 2019

We’ll also deal with how big data stream processing can help new emerging markets in the world. What is Big Data? Big Data is defined as a large volume of structured and unstructured data that a business comes across their day-to-day operations. However, the amount of data isn’t really a big deal.

Big Data 81

Big Data Marketing Cost-Benefit Unstructured Data 81

Cloudera

AUGUST 30, 2018

How do I get to the next level in the data-driven journey fast enough? How do I meet a growing demand for self-serve BI, while not exploding my data warehouse budgets? How can I optimize my rate of return and continue to drive innovation? Tough decisions. Complex scenarios. can deliver that technology foundation. .

Enterprise 40

Enterprise Data-driven Digital Transformation Machine Learning 40

IBM Big Data Hub

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 57

Machine Learning Data Science Statistics Deep Learning 57

AWS Big Data

JULY 28, 2023

Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service delivering the best price-performance. Today, tens of thousands of customers run business-critical workloads on Amazon Redshift to cost-effectively and quickly analyze their data using standard SQL and existing business intelligence (BI) tools.

Data Lake 69

Data Lake Data Governance Data Warehouse Modeling 69

CIO Business Intelligence

JUNE 29, 2022

If you can’t make sense of your business data, you’re effectively flying blind. Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products — or new lines of business, like predictive maintenance. Microsoft. Azure Analysis Services.

Data Lake 113

Data Lake Analytics Data Warehouse Machine Learning 113

AWS Big Data

MARCH 2, 2023

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

Data Lake 120

Data Lake Data Processing Metadata Snapshot 120

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. To build a SQL query, one must describe the data sources involved and the high-level operations (SELECT, JOIN, WHERE, etc.)

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Occam's Razor

NOVEMBER 5, 2007

You are not going to solve my problem of getting a single source of truth for all my web data?" Not " singlecity ", not on the web, not in Web Analytics 2.0." But there were two concepts, one big and one small, that were key in developing my own thinking about web analytics. " Me : "Multiplicity.

Analytics 98

Analytics Experimentation Metrics Measurement 98