CIO Business Intelligence

JUNE 28, 2023

Belcorp operates under a direct sales model in 14 countries. The R&D laboratories produced large volumes of unstructured data, which were stored in various formats, making it difficult to access and trace. The team leaned on data scientists and bio scientists for expert support.

Digital Transformation 99

Digital Transformation Cost-Benefit Informatics Data mining 99

AWS Big Data

MARCH 2, 2023

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

Data Lake 79

Data Lake Testing Interactive Unstructured Data 79

CIO Business Intelligence

NOVEMBER 28, 2023

With organizations seeking to become more data-driven with business decisions, IT leaders must devise data strategies gear toward creating value from data no matter where — or in what form — it resides. Unstructured data resources can be extremely valuable for gaining business insights and solving problems.

Unstructured Data 129

Unstructured Data Data-driven Visualization Data Quality 129

Cloudera

NOVEMBER 10, 2020

We have evolved with our users, from early-on Hadoop hackers needing quick access to data in the Data Lake, to a much more sophisticated SQL tool. HUE also comes with a simplistic form of pre-visualization of results and download result sets as csv files or pdfs, for local exploration or further insight sharing.

Visualization 107

Visualization Optimization Unstructured Data Dashboards 107

AWS Big Data

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Unstructured information may have a little or a lot of structure but in ways that are unexpected or inconsistent. You can integrate different technologies or tools to build a solution.

Unstructured Data 123

Unstructured Data Metadata Management Analytics 123

Sisense

OCTOBER 6, 2020

As quantitative data is always numeric, it’s relatively straightforward to put it in order, manage it, analyze it, visualize it, and do calculations with it. Spreadsheet software like Excel, Google Sheets, or traditional database management systems all mainly deal with quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Sisense

JUNE 30, 2020

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

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. Azure Data Lake Analytics.

Data Lake 115

Data Lake Analytics Data Warehouse Machine Learning 115

CIO Business Intelligence

AUGUST 18, 2022

Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. Lakehouses redeem the failures of some data lakes.

Data Lake 140

Data Lake Data Warehouse Unstructured Data Business Intelligence 140

Sisense

MAY 28, 2019

Two orthogonal approaches to data analytics have developed in this decade of BI: 1. Operating “in-data” to enable the direct query of unstructured data lakes, providing a visualization layer on top of them. Then, and only then, can they operationalize their BI on top of the cloud data lake.

Data Analytics 89

Data Analytics Data Lake Analytics Unstructured Data 89

BizAcuity

MAY 10, 2022

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. Microsoft also releases Power BI, a data visualization and business intelligence tool. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Cloudera

JULY 26, 2021

The alleviation of infrastructure and computational constraints associated with solely on-premises data platforms; Data Products can now use different deployment models (e.g., Flexibility would also allow to meet the requirements of a niche market segment that only a specific deployment model addresses. data warehousing).

Strategy 114

Strategy Data Science Marketing Unstructured Data 114

Sisense

JULY 22, 2020

The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3 , Hadoop and Microsoft Azure. Sisense provides instant access to your cloud data warehouses.

Slice and Dice 98

Slice and Dice Digital Transformation Data Warehouse Data Lake 98

Cloudera

MAY 16, 2018

This approach also relates to monitoring internal fiduciary risk by tying separate events together, such as a large position (relative to historic norms) being taken immediately after the risk model that would have flagged it was modified in a separate system. Market data: Coordinated trading among multiple parties.

Data Lake 42

Data Lake Risk Visualization Unstructured Data 42

CIO Business Intelligence

MARCH 6, 2023

Advancements in analytics and AI as well as support for unstructured data in centralized data lakes are key benefits of doing business in the cloud, and Shutterstock is capitalizing on its cloud foundation, creating new revenue streams and business models using the cloud and data lakes as key components of its innovation platform.

Data Lake 70

Data Lake Cost-Benefit Recreation/Entertainment Experimentation 70

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Limited flexibility to use more complex hosting models (e.g.,

Data Processing 88

Data Processing Data Warehouse Enterprise Visualization 88

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