CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. The exam consists of 65 multiple-choice or multiple-response questions, which the candidate has 180 minutes to complete.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Cloudera

SEPTEMBER 17, 2020

By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. . Our solution: Cloudera Data Visualization.

Experimentation 80

Experimentation Data Warehouse Dashboards Visualization 80

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

datapine

MARCH 31, 2022

To simplify things, you can think of back-end BI skills as more technical in nature and related to building BI platforms, like online data visualization tools. Front-end analytical and business intelligence skills are geared more towards presenting and communicating data to others. b) If You’re Already In The Workforce.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Sisense

JULY 7, 2020

In-Warehouse Data Prep provides builders with the advanced functionality they need to rapidly transform and optimize raw data creating materialized views on cloud data warehouses. In-Warehouse Data Prep supports both AWS Redshift and Snowflake data warehouses. Additional capabilities.

Slice and Dice 92

Slice and Dice Data Warehouse Analytics Dashboards 92

datapine

SEPTEMBER 27, 2022

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

CIO Business Intelligence

JUNE 23, 2023

The focus here should be on considering all ways your customers currently consume data as well as new ways they might want to achieve better results. By incorporating data into your deliverables, you can often turn this into an annuity revenue stream where these projects become software products to monetize via a SaaS business model.

Data Strategy 114

Data Strategy Strategy Visualization Consulting 114

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. One group performed extract, transform, and load (ETL) operations to take raw data and make it available for analysis.

Data Warehouse 115

Data Warehouse Cost-Benefit Sales Data-driven 115

Sisense

APRIL 17, 2020

We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. Why use a materialized view?

Data Warehouse 90

Data Warehouse Visualization Statistics Machine Learning 90

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 80

Data Science Data Analytics Prescriptive Analytics Analytics 80

DataKitchen

JANUARY 3, 2022

Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units. Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

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

JUNE 23, 2023

Each day, TBs of new data is added to the data lake, which is then transformed, aggregated, partitioned, and compressed. In this post, we explain how Imperva’s solution enables users across the organization to explore, visualize, and analyze data using Amazon Redshift Serverless , Amazon Athena , and QuickSight.

Data Lake 71

Data Lake Cost-Benefit Dashboards Data Warehouse 71

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 87

Enterprise Data Warehouse Snapshot Cost-Benefit 87

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 135

Analytics Data Science Unstructured Data Data mining 135

Sisense

MARCH 27, 2019

With millions of streams and touch points, most businesses create gigabytes of data every minute, yet our rate of data comprehension cannot keep up. To get the most out of data, companies need to analyze it as soon as it is created—when it can provide the most immediate and relevant insights. Who Uses Real-Time BI?

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

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

datapine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

Data Quality 241

Data Quality Metrics Data-driven Management 241

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 73

Analytics Data Warehouse Data Lake Metadata 73

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.

IT 317

IT Statistics KPI Data-driven 317

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

AWS Big Data

JUNE 28, 2023

In case the data sources change, data engineers have to manually make changes in their code and deploy it again. Furthermore, the time required to build or change pipelines makes the data unfit for near-real-time use cases such as detecting fraudulent transactions, placing online ads, and tracking passenger train schedules.

Analytics 71

Analytics Data Warehouse Data Lake Data-driven 71

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. Data entities are more secure and arguably easier to master than the relational database model, but one downside is there are lots of them! Data Lakes.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

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 130

Analytics Data Science Statistics Unstructured Data 130

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. The other half of our value proposition is Ontotext GraphDB.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

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

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. Dashboards.

Dashboards 71

Dashboards Reporting Data Warehouse Visualization 71

erwin

FEBRUARY 28, 2024

Examples of complementary technologies include technology for data warehouse automation, change data capture and master data management. For data governance, it’s more complex. It actually reverse engineers the ETL/ELT technical code sets and transforms and visualizes it on a logical level. How cool is that?

Metadata 52

Metadata Data Governance Data Quality Technology 52

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

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

Data Lake 94

Data Lake Unstructured Data Management Modeling 94

Alation

JULY 13, 2021

Yet increasing complexity of data makes the old “lift-and-shift” model not just unrealistic, but risky. Businesses with complex data environments need a migration method that takes that complexity into account. The Data Race to the Cloud. This recent cloud migration applies to all who use data.

Strategy 52

Strategy Data mining Structured Data Data Governance 52

Smart Data Collective

JULY 22, 2019

Zoho Analytics is able to integrate data from a wide range of sources and turn it into a visually appealing and easy to comprehend reports for marketing, sales and other departments. Dundas transforms loads of data into visually appealing and easily comprehensible reports that can be infinitely customized.

Business Intelligence 80

Business Intelligence Cost-Benefit Dashboards Visualization 80

Jet Global

OCTOBER 7, 2020

It uses its own data mart, which cannot be customized in any way. Power BI is an analytical tool for data visualization and discovery. When working with D365 F&SCM data, it typically requires specialized programming skills to develop reports or to make changes to existing reports. What Are Data Entities?

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

Birst BI

OCTOBER 30, 2018

The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. Consider all customer interactions and their data sources as potential sources for predicting future customer behavior. Integrate the data sources of the various behavioral attributes into a functional data model.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Jet Global

OCTOBER 2, 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 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Ms SQL Girl

MARCH 30, 2015

Actionable Visualization In Power BI. Acquiring and Preparing Data for Power View and Excel. Data Modelling Patterns 101 using Power Pivot. Data Modelling Patterns 101 using Power Pivot. Power Query 101 (for Power BI Dashboard). Tips and Tricks on Charts and Data Models.

Dashboards 40

Dashboards Visualization KPI Interactive 40

Ms SQL Girl

MARCH 30, 2015

Actionable Visualization In Power BI. Acquiring and Preparing Data for Power View and Excel. Data Modelling Patterns 101 using Power Pivot. Data Modelling Patterns 101 using Power Pivot. Power Query 101 (for Power BI Dashboard). Tips and Tricks on Charts and Data Models.

Dashboards 40

Dashboards Visualization KPI Interactive 40