CIO Business Intelligence

SEPTEMBER 14, 2023

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 that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 135

Analytics Data Science Unstructured Data Data mining 135

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 131

Analytics Data Science Statistics Unstructured Data 131

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 83

Data Science Data Analytics Prescriptive Analytics Analytics 83

Sisense

APRIL 26, 2020

In our modern digital world, proper use of data can play a huge role in a business’s success. Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Essential Big Data And Data Analytics Insights. million searches per day and 1.2

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Alation

FEBRUARY 13, 2020

This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata. Benefits of a Data Catalog. What Does a Data Catalog Do? Conclusion. Conclusion.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. In the past, data movement was defined by ETL: extract, transform, and load.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

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

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 65

Data Warehouse Data Lake Cost-Benefit Modeling 65

FineReport

JUNE 24, 2021

Originally, Excel has always been the “solution” for various reporting and data needs. However, along with the diffusion of digital technology, the amount of data is getting larger and larger, and data collection and cleaning work have become more and more time-consuming. BI software solutions (by FineReport).

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

Domino Data Lab

JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. That was the origin of big data.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

CIO Business Intelligence

JANUARY 20, 2023

Improved employee satisfaction: Providing business users access to data without having to contact analysts or IT can reduce friction, increase productivity, and facilitate faster results. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

AWS Big Data

NOVEMBER 15, 2023

The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.

Dashboards 97

Dashboards Analytics Metadata Data Warehouse 97

Cloudera

AUGUST 20, 2021

Analytical Outcome: CDP delivers multiple analytical outcomes including, to name a few, operational dashboards via the CDP Operational Database experience or ad-hoc analytics via the CDP Data Warehouse to help surface insights related to a business domain. A Robust Security Framework.

Strategy 94

Strategy Cost-Benefit Visualization Data Warehouse 94

Cloudera

DECEMBER 14, 2020

One trend that we’ve seen this year, is that enterprises are leveraging streaming data as a way to traverse through unplanned disruptions, as a way to make the best business decisions for their stakeholders. . Today, a new modern data platform is here to transform how businesses take advantage of real-time analytics.

Big Data 68

Big Data Data-driven Data Collection Enterprise 68

IBM Big Data Hub

JANUARY 10, 2023

This allows data that exists in cloud object storage to be easily combined with existing data warehouse data without data movement. The advantage to NPS clients is that they can store infrequently used data in a cost-effective manner without having to move that data into a physical data warehouse table.

Data Warehouse 52

Data Warehouse Cost-Benefit Statistics Data Processing 52

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The global commercial drone market is projected to grow from USD 8.15

Data Collection 82

Data Collection Visualization Data-driven Unstructured Data 82

Smart Data Collective

JUNE 6, 2021

While you may think that you understand the desires of your customers and the growth rate of your company, data-driven decision making is considered a more effective way to reach your goals. The use of big data analytics is, therefore, worth considering—as well as the services that have come from this concept, such as Google BigQuery.

Big Data 132

Big Data Data Analytics Analytics Cost-Benefit 132

Alation

FEBRUARY 13, 2020

More often than not, today, the key to unlocking that accomplishment sits within a tsunami of data: data collected from consumers, applications, and sensors. Every year for the last three years, NewVenture Partners has published an executive survey on AI and big data. Their latest findings? “

Machine Learning 52

Machine Learning Data-driven Big Data Technology 52

Cloudera

JANUARY 11, 2018

Data science and advanced analytics deliver more value if organizations can deploy models and algorithms on detailed data brought together from many sources, not just on summaries or samples contained in a single data warehouse or data mart.” ” And teases at the reward. You can too, as David describes….

Strategy 40

Strategy Data Warehouse Cost-Benefit Marketing 40

Domino Data Lab

FEBRUARY 4, 2019

These two points provide a different kind of risk management mechanism which is effective for science, specifically data science. Of course, some questions in business cannot be answered with historical data. Instead they require investment, tooling, and time for data collection. Brace yourselves for impact.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

FEBRUARY 22, 2022

Over the past 5 years, big data and BI became more than just data science buzzwords. Without real-time insight into their data, businesses remain reactive, miss strategic growth opportunities, lose their competitive edge, fail to take advantage of cost savings options, don’t ensure customer satisfaction… the list goes on.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. As you would guess, maintaining context relies on metadata.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

IBM Big Data Hub

AUGUST 4, 2023

Data fabric Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. This results in more marketable AI-driven products and greater accountability.

Data Architecture 63

Data Architecture Data Lake Machine Learning Data Governance 63

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. Introduction. CRM platforms).

Data Processing 89

Data Processing Data Warehouse Enterprise Visualization 89

Jet Global

MAY 20, 2022

Use these steps to find insights and patterns, so you can be the one that shares an actionable data point that leads to the upcoming big-ticket item in your strategic plan. Step #1: Data collection. Whether you are part of a small company or leading an enterprise, you will want to collect data from multiple sources.

Big Data 52

Big Data Sales Reporting Marketing 52

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

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52