CIO Business Intelligence

NOVEMBER 14, 2022

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

Data mining 125

Data mining Data-driven Statistics OLAP 125

Cloudera

JULY 21, 2022

Cloudera users can securely connect Rill to a source of event stream data, such as Cloudera DataFlow , model data into Rill’s cloud-based Druid service, and share live operational dashboards within minutes via Rill’s interactive metrics dashboard or any connected BI solution. Cloudera Data Warehouse).

Metrics 86

Metrics Slice and Dice Data Warehouse Dashboards 86

AWS Big Data

MAY 28, 2024

Welcome back to our exciting exploration of architectural patterns for real-time analytics with Amazon Kinesis Data Streams! The key to unlock the full potential of this real-time data lies in your ability to effectively make sense of it and transform it into actionable insights in real time.

IoT 90

IoT Analytics Dashboards Data-driven 90

AWS Big Data

MAY 25, 2023

Many organizations today are using AWS Glue to build ETL pipelines that bring data from disparate sources and store the data in repositories like a data lake, database, or data warehouse for further consumption. The email is formatted via HTML output that provides tables for the aggregated job run data.

Reporting 69

Reporting Metrics Optimization Data Lake 69

Sisense

JULY 7, 2020

Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data. Today’s organizations are more data-driven than ever. In-Warehouse Data Prep supports both AWS Redshift and Snowflake data warehouses.

Slice and Dice 92

Slice and Dice Data Warehouse Analytics Dashboards 92

AWS Big Data

MAY 17, 2023

During that same time, AWS has been focused on helping customers manage their ever-growing volumes of data with tools like Amazon Redshift , the first fully managed, petabyte-scale cloud data warehouse. From 2019 to now, Wang reports the amount of data the company holds has grown by a factor of 20.

Data Warehouse 111

Data Warehouse Cost-Benefit Sales Data-driven 111

O'Reilly on Data

AUGUST 18, 2020

We’ll share why in a moment, but first, we want to look at a historical perspective with what happened to data warehouses and data engineering platforms. Lessons Learned from Data Warehouse and Data Engineering Platforms. Data Science and Machine Learning Require Flexibility. Consider user interfaces.

Data Science 358

Data Science Machine Learning Data Warehouse Deep Learning 358

DataKitchen

JANUARY 3, 2022

For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports. Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

AWS Big Data

NOVEMBER 9, 2023

The aim was to bolster their analytical capabilities and improve data accessibility while ensuring a quick time to market and high data quality, all with low total cost of ownership (TCO) and no need for additional tools or licenses. Flat files – Other systems supply data in the form of flat files of different formats.

Data Warehouse 104

Data Warehouse Testing Data Quality Reporting 104

datapine

SEPTEMBER 27, 2022

Some of these ‘structures’ may include putting all the information; for instance, a structure could be about cars, placing them into tables that consist of makes, models, year of manufacture, and color. With a MySQL dashboard builder , for example, you can connect all the data with a few clicks.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Cloudera

DECEMBER 21, 2020

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. A Cloudera Data Engineering service exists. The Data Scientist.

Dashboards 97

Dashboards Visualization Data Warehouse Data Lake 97

Jet Global

SEPTEMBER 4, 2020

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. The SQL query language used to extract data for reporting could also potentially be used to insert, update, or delete records from the database.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

datapine

MARCH 31, 2022

Being able to clearly see how the data changes in time is what makes it possible to extract relevant conclusions from it. For this purpose, you should be able to differentiate between various charts and report types as well as understand when and how to use them to benefit the BI process. Business Intelligence Job Roles.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

FineReport

JULY 28, 2021

Definition: BI vs Data Science vs Data Analytics. Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 71

Analytics Data Warehouse Data Lake Metadata 71

datapine

SEPTEMBER 29, 2022

In this article, we will detail everything which is at stake when we talk about DQM: why it is essential, how to measure data quality, the pillars of good quality management, and some data quality control techniques. But first, let’s define what data quality actually is. 4 – Data Reporting.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Jet Global

OCTOBER 7, 2020

For organizations considering a move to Microsoft Dynamics 365 Finance & Supply Chain Management (D365 F&SCM), or for those in the early stages of an implementation project, defining a clear strategy for curating data is a key to developing a comprehensive approach to reporting and analytics. What Are Data Entities?

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

CIO Business Intelligence

SEPTEMBER 14, 2023

They’re often responsible for building algorithms for accessing raw data, too, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 129

Analytics Data Science Unstructured Data Data mining 129

IBM Big Data Hub

AUGUST 28, 2023

For more than 10 years, the publisher has used IBM Cognos Analytics to wrangle its internal and external operational reporting needs. And in the last few years, the team realized there was an opportunity to expand beyond centralized operational reporting to enable further business growth.

Publishing 50

Publishing Analytics Business Intelligence Operational Reporting 50

DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. Model Deployment Customers need the flexibility to deploy models into different environments.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

Jet Global

MARCH 25, 2020

While JD Edwards transactional data is required to run period close reports, analyze trends, and prepare forecasts for planning and budgeting, it comes with a lot of complexity. JD Edwards World has no less than 1600 tables of data to support just its business applications. 1 – What are all your reporting needs? .

Reporting 45

Reporting Finance Sales Visualization 45

FineReport

MAY 6, 2020

Metode statistik yang berkaitan dengan data mining: analisis regresi logistik multi variasi, analisis regresi non linear, analisis diskriminan, dll. Metode kuantitatif: analisis axis waktu, model probabilitas, optimalisasi. Prinsip database: model data, desain database. Data Warehouse. Bahasa Programing.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy. Another complication was the existing data silos.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Sisense

JUNE 8, 2020

Datasets are on the rise and most of that data is on the cloud. The recent rise of cloud data warehouses like Snowflake means businesses can better leverage all their data using Sisense seamlessly with products like the Snowflake Cloud Data Platform to strengthen their businesses.

Data Warehouse 89

Data Warehouse Data-driven Dashboards Sales 89

CDW Research Hub

APRIL 20, 2020

Many organizations move from a traditional data warehouse to a hybrid or cloud-based data warehouse to help alleviate their struggles with rapidly expanding data, new users and use cases, and a growing number of diverse tools and applications. Connecting ThoughtSpot and Snowflake is a simple 3-step process.

Data Warehouse 52

Data Warehouse Consulting Data-driven Structured Data 52

AWS Big Data

JUNE 28, 2023

When data is used to improve customer experiences and drive innovation, it can lead to business growth. times more likely than beginners to report at least 20% revenue growth. However, to realize this growth, managing and preparing the data for analysis has to get easier.

Analytics 69

Analytics Data Warehouse Data Lake Data-driven 69

DataKitchen

OCTOBER 5, 2021

Data operations (or data production) is a series of pipeline procedures that take raw data, progress through a series of processing and transformation steps, and output finished products in the form of dashboards, predictions, data warehouses or whatever the business requires. Their product is the data.

Testing 152

Testing Dashboards Measurement Experimentation 152

datapine

JANUARY 24, 2021

Additionally, incorporating a decision support system software can save a lot of company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems. If nothing can be changed, there is no point of analyzing data. Who are they?

IT 317

IT Statistics KPI Data-driven 317

Jet Global

JULY 23, 2019

With multiple sessions on VBA, macros, Jet products, data visualization, Power BI, PivotTables, dashboards, and the latest technology in Microsoft Excel – Excelapalooza is your reporting and analytics dreamland. Check out this sample of more than 60 sessions: Build Stunning Dashboards with Power BI. PivotTables 101.

Dashboards 71

Dashboards Reporting Data Warehouse Visualization 71

Alation

MARCH 29, 2022

In the 1970s, data was confined to mainframes and primitive databases. Reports required a formal request of the few who could access that data. The 1980s ushered in the antithesis of this version of computing — personal computing and distributed database management — but also introduced duplicated data and enterprise data silos.

Business Intelligence 96

Business Intelligence Reporting Data mining Data-driven 96

Occam's Razor

APRIL 4, 2011

What's the difference between web reporting and web analysis? So here I what I actually said: If you see a data puke then you know you are looking at the result of web reporting, even if it is called a dashboard. Learn more about it here: Action Dashboard.]. Someone asked me this very simple question today.

Reporting 127

Reporting Metrics Dashboards Statistics 127

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved. Data engineer job description.

Analytics 119

Analytics Data Science Statistics Unstructured Data 119

Andrew White

DECEMBER 9, 2019

The outline of the call went as follows: I was taking to a central state agency who was organizing a data governance initiative (in their words) across three other state agencies. All four agencies had reported an independent but identical experience with data governance in the past.

Analytics 66

Analytics Data Lake Data Governance Metadata 66

Jet Global

MAY 1, 2020

Recently released is a report by Gartner about all the things that can go wrong in your BI implementation. In Gartner’s report, an analyst goes to great pains to say that there is “much more risk associated to non-technology issues than there is to deploying the infrastructure, tools, and apps.”. Learn how to prepare your data for BI.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

Jet Global

AUGUST 14, 2019

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing. So how is the data extracted?

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

Jet Global

APRIL 2, 2019

Many of the features frequently attributed to AI in business, such as automation, analytics, and data modeling aren’t actually features of AI at all. It All Starts with Data. Which problems do disparate data points speak to? Enter data warehousing. So how is the data extracted?

Enterprise 80

Enterprise OLAP Business Intelligence Internet of Things 80

Jet Global

JULY 2, 2019

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73