Data Modelling, Data Visualization and Data Warehouse

Build an Agile Data Warehouse with an Iterative Approach

Astera

NOVEMBER 14, 2023

If you have had a discussion with a data engineer or architect on building an agile data warehouse design or maintaining a data warehouse architecture, you’d probably hear them say that it is a continuous process and doesn’t really have a definite end. What do you need to build an agile data warehouse?

Data Warehouse

Data Warehouse Agile Data Modelling Data Quality

Optimize your Go To Market with AI and ML-driven Analytics platforms

BizAcuity

JULY 13, 2021

In many cases, source data is captured in various databases and the need for data consolidation arises and typically it takes around 6-9 months to complete, and with a high budget in terms of provisioning for servers, either in cloud or on-premise, licenses for data warehouse platform, reporting system, ETL tools, etc.

Data Warehouse

Data Warehouse Artificial Intelligence Data Analytics Data Analytics

Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Smart Data Collective

NOVEMBER 18, 2020

Therefore, machine learning is of great importance for almost any field, but above all, it will work well where there is Data Science. Data Mining Techniques and Data Visualization. Data Mining is an important research process.

Data Mining

Data Mining Big Data Artificial Intelligence Data Warehouse

Why Good Data Management Is Essential to Data Analytics

Insight Software

JANUARY 20, 2021

Organizations that can effectively leverage data as a strategic asset will inevitably build a competitive advantage and outperform their peers over the long term. In order to achieve that, though, business managers must bring order to the chaotic landscape of multiple data sources and data models.

Data Management

Data Management Data Analytics Data Analytics E-Commerce

The Complete Guide to Reverse ETL

Astera

JANUARY 25, 2024

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

E-Commerce

E-Commerce Data Warehouse Retail Customer Experience

Data Science vs Data Analytics: Key Differences

Astera

MAY 31, 2024

It focuses on answering predefined questions and analyzing historical data to inform decision-making. Methodologies Uses advanced AI and ML algorithms and statistical models to analyze structured and unstructured data. Employs statistical methods and data visualization techniques, primarily working with structured data.

Data Analytics

Data Analytics Data Analytics Data Warehouse Healthcare

The Future of AI in Data Warehousing: Trends and Predictions

Astera

APRIL 28, 2023

By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes.

E-Commerce

E-Commerce Supply Chain Artificial Intelligence Data Warehouse

How does Tableau power Salesforce Genie Customer Data Cloud?

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Cut costs by consolidating data warehouse investments.

Talent Acquisition

Talent Acquisition Data Warehouse Data Architecture Visualization

How does Tableau power Salesforce Genie Customer Data Cloud?

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Cut costs by consolidating data warehouse investments.

Talent Acquisition

Talent Acquisition Data Warehouse Data Architecture Visualization

What Defines the Modern Data Stack and Why You Should Care?

Astera

APRIL 27, 2023

This is where data extraction tools from companies like Matillion, Astera , and Fivetran are used to organize and prepare data for a cloud data warehouse. ELT or ETL tools , such as DBT, work within a cloud data warehouse to convert, clean, and structure data, into a format usable by data engineers and analysts.

Data Warehouse

Data Warehouse Big Data Data Analytics Data Analytics

A Complete Guide to Data Analytics

Astera

MAY 30, 2024

Statistical Analysis : Using statistics to interpret data and identify trends. Predictive Analytics : Employing models to forecast future trends based on historical data. Data Visualization : Presenting data visually to make the analysis understandable to stakeholders.

Data Analytics

Data Analytics Data Analytics Data Mining Supply Chain

Building Bridges: Data and BI Teams Partnering on an Analytics Solution

Sisense

JANUARY 15, 2021

While the BI analysts have skills to ask questions of already modeled data, they often lack the coding acumen to query massive unstructured datasets in data lakes or cloud data warehouses. Situation #2: Established company creates a data team for deeper insights.

Skillset

Skillset Data Warehouse Data Modelling Business Intelligence

What is Salesforce Data Cloud for Tableau?

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Salesforce Data Cloud for Tableau solves those challenges.

Talent Acquisition

Talent Acquisition Data Warehouse Data Architecture Visualization

SQL, Python, and R — Why You Need a Unified Analytics Stack

Sisense

AUGUST 22, 2019

What’s been missing is a way to natively integrate Python and R with the rest of the data analytics stack. Database access and data modeling in SQL should happen within the same platform that Python and R are used so that analysts can rapidly iterate on both datasets and models simultaneously.

Predictive Analytics

Predictive Analytics Data Warehouse Business Intelligence Business Intelligence

The Rise of the Data Team

Sisense

OCTOBER 14, 2020

These increasingly difficult questions require sophisticated data models, connected to an increasing number of data sources, in order to produce meaningful answers. Therein lies the power of your data team: Armed with know-how, they connect with the end user teams (internal users, product teams embedding insights, etc.)

Data Modelling

Data Modelling Embedded Analytics Data Warehouse Innovation

A Guide To Starting A Career In Business Intelligence & The BI Skills You Need

Data Pine

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

Business Intelligence Business Intelligence Data Visualization Visualization

Prevent Customer Churn: Customer Retention in the Transition to Microsoft D365 F&SCM

Insight Software

JANUARY 15, 2021

The required investment to develop reports on Power BI and Azure Data Lakes is considerable, and there are substantial liabilities to consider before making a costly long-term commitment. In many respects, it is more akin to some of the very complex data warehousing and OLAP tools of the past–perhaps with an even steeper learning curve.

Data Warehouse

Data Warehouse Supply Chain Finance Data Visualization

Workflow Fix: How UDAPs Heal Fractured Data Landscapes

Domo

APRIL 12, 2021

Data products provide a similar challenge. Between different data models, formats, and other software eccentricities, even a skilled team of data engineers and architects can struggle to make tools play nicely with one another. Many also have data visualization capabilities.

Retail

Retail Data Warehouse Business Intelligence Business Intelligence

Data Engineer vs Data Scientist: What’s the Right Fit for Your Company?

Sisense

MAY 27, 2019

As more and more data warehousing moves to the cloud, engineers increasingly find themselves working with AWS cloud services, EC2, EMR, RDS, and Redshift, other cloud-based data warehouses such as Snowflake and Google BiqQuery, cloud computing services like Microsoft Azure, and data orchestration systems such as Kubernetes.

Skillset

Skillset Cloud Computing Visualization Big Data

Microsoft Power Platform – Deciphering the Confusion

Cprime

JANUARY 27, 2021

At the heart of the Power Platform is Microsoft’s Common Data Model (Service). The CDS is a data storage service in Microsoft 365. Power BI is a set of services, apps, and connectors that together turn your unrelated sources of data into coherent, virtually immersive, and interactive insights.

User Experience

User Experience Documentation Data Warehouse Visualization

Business Intelligence Components and How They Relate to Power BI

BI Insight

MARCH 31, 2022

When I decided to write this blog post, I thought it would be a good idea to learn a bit about the history of Business Intelligence. I searched on the internet, and I found this page on Wikipedia.

Business Intelligence

Business Intelligence Business Intelligence IBM Support Data Warehouse

The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Data Pine

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

Data Quality Monitoring Big Data Government

Top Data Analytics Terms You Should Know

The BAWorld

NOVEMBER 22, 2021

Also, see data visualization. Data Analytics. Data analytics is the science of examining raw data to determine valuable insights and draw conclusions for creating better business outcomes. Data Modeling. Conceptual Data Model. Logical Data Model : It is an abstraction of CDM.

Data Analytics

Data Analytics Data Analytics Data Warehouse Data Modelling

Structured Vs. Unstructured Data

The BAWorld

SEPTEMBER 2, 2021

Non-technical users can also work easily with structured data. Structured Data Example. can be grouped in a data warehouse for marketing analysis. This is a classic example of structured data and can be efficiently managed through a database. Unstructured Data. Let us explore some examples.

Big Data

Big Data Data Warehouse Data Visualization Visualization

Top 15 Data Analysis Tools in 2024

Astera

AUGUST 28, 2024

Data analysis tools are software solutions, applications, and platforms that simplify and accelerate the process of analyzing large amounts of data. They enable business intelligence (BI), analytics, data visualization , and reporting for businesses so they can make important decisions timely.

IBM Support

IBM Support IBM Maintenance IBM cost Data Mining

Use Embedded Analytics to Modernize Workflows

Sisense

NOVEMBER 5, 2021

Nagu Nambi , Product Dev and Innovation Director at Radial, leads their Data Warehouse and Analytics Products delivery programs. He has over 24 years of experience in software development, focused on data-driven innovation, research, and enabling business transformation initiatives. Learn more.

Embedded Analytics

Embedded Analytics Data Warehouse Innovation Data Governance

Bridge the Gap Between Reporting and Data Visualization in Power BI

Insight Software

SEPTEMBER 22, 2023

Dynamics ERP systems demand the creation of a data warehouse to ensure fast query response times and that data is in a suitable format for Power BI. The skills needed to create a data warehouse are currently in short supply, leading to long lead times, high costs, and unnecessary risks.

Data Visualization

Data Visualization Visualization IBM Maintenance IBM Support

What Is Embedded Analytics?

Insight Software

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. According to the 2021 State of Analytics: Why Users Demand Better report by Hanover Research, 77 percent of organizations consider end-user data literacy “very” or “extremely important” in making fast and accurate decisions.

Embedded Analytics

Embedded Analytics IBM Support IBM Maintenance IBM cost

What is Data Mapping?

Insight Software

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. This includes cleaning, aggregating, enriching, and restructuring data to fit the desired format.

IBM Support

IBM Support IBM Maintenance Data Warehouse IBM cost

Elevate Insights: How Embedded Analytics Boost Decision Intelligence

Insight Software

APRIL 12, 2024

This intuitive approach cuts through technical barriers, transforming even non-technical users into data-savvy decision makers. Advanced Analytics Functionality to Unveil Hidden Insights Logi Symphony allows you to perform on-the-fly data modeling to swiftly adapt and integrate complex datasets directly within your existing applications.

Embedded Analytics

Embedded Analytics IBM Support IBM Maintenance Predictive Analytics

Taming the Tech Stack: Leverage Your Existing Tech Stack Securely With an Analytics Layer

Insight Software

OCTOBER 24, 2022

Analytics and data visualizations have the power to elevate a software product, making it a powerful tool that helps each user fulfill their mission more effectively. Logi Composer is the top rated low-code, turnkey analytics solution for dashboards and data visualization. Get a Demo. What to expect.

IBM Support

IBM Support IBM Maintenance Embedded Analytics IBM Expert

The Benefits, Challenges and Risks of Predictive Analytics for Your Application

Insight Software

SEPTEMBER 1, 2023

Higher Costs: In-house development incurs costs not only in terms of hiring or training data science experts but also in ongoing maintenance, updates, and potential debugging. Your content creators can customize even the tiniest details of the dashboards, data visualizations, interactions, scorecards, labels, and more that they use.

Predictive Analytics

Predictive Analytics IBM Support IBM Maintenance Embedded Analytics

Business Analysis Digest

Build an Agile Data Warehouse with an Iterative Approach

Optimize your Go To Market with AI and ML-driven Analytics platforms

Trending Sources

Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Why Good Data Management Is Essential to Data Analytics

The Complete Guide to Reverse ETL

Data Science vs Data Analytics: Key Differences

The Future of AI in Data Warehousing: Trends and Predictions

How does Tableau power Salesforce Genie Customer Data Cloud?

How does Tableau power Salesforce Genie Customer Data Cloud?

What Defines the Modern Data Stack and Why You Should Care?

A Complete Guide to Data Analytics

Building Bridges: Data and BI Teams Partnering on an Analytics Solution

What is Salesforce Data Cloud for Tableau?

SQL, Python, and R — Why You Need a Unified Analytics Stack

The Rise of the Data Team

A Guide To Starting A Career In Business Intelligence & The BI Skills You Need

Prevent Customer Churn: Customer Retention in the Transition to Microsoft D365 F&SCM

Workflow Fix: How UDAPs Heal Fractured Data Landscapes

Data Engineer vs Data Scientist: What’s the Right Fit for Your Company?

Microsoft Power Platform – Deciphering the Confusion

Business Intelligence Components and How They Relate to Power BI

The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Top Data Analytics Terms You Should Know

Structured Vs. Unstructured Data

Top 15 Data Analysis Tools in 2024

Use Embedded Analytics to Modernize Workflows

Bridge the Gap Between Reporting and Data Visualization in Power BI

What Is Embedded Analytics?

What is Data Mapping?

Elevate Insights: How Embedded Analytics Boost Decision Intelligence

Taming the Tech Stack: Leverage Your Existing Tech Stack Securely With an Analytics Layer

The Benefits, Challenges and Risks of Predictive Analytics for Your Application

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