Data Modelling, Data Quality and Presentation

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

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

Data Quality

Data Quality Monitoring Big Data Government

What are the Challenges of Applying Machine Learning in Economics and Business?

Analysts Corner

OCTOBER 30, 2024

While machine learning (ML) offers transformative potential for economics and business, applying it effectively in these fields presents unique challenges. Economic and business data are often complex, interdependent, and prone to fluctuations, making it difficult for models to maintain accuracy and relevance over time.

Data Quality

Data Quality Retail Finance Data Modelling

Harrods’ Data Analytics Transformation: Turning Challenges into Insights

Timo Elliott

DECEMBER 9, 2024

Tushir Parekh, Data Analytics Manager at Harrods, presenting at UKISUG Connect 2024 in Birmingham ICC The Starting Point: A Fragmented Data Landscape Parekh set the stage by describing the state of analytics at Harrods before their transformation began in mid-2021. A lift-and-shift approach doesnt work, Parekh warned.

Data Analytics

Data Analytics Data Analytics Government Vision

Tableau and dbt Labs: Strategic Partnership and Integration

Tableau

OCTOBER 7, 2024

This technology sprawl often creates data silos and presents challenges to ensuring that organizations can effectively enforce data governance while still providing trusted, real-time insights to the business.

Data Quality

Data Quality Government Vision Data Governance

7 Data Quality Metrics to Assess Your Data Health

Astera

APRIL 23, 2024

To do so, they need data quality metrics relevant to their specific needs. Organizations use data quality metrics, also called data quality measurement metrics, to assess the different aspects, or dimensions, of data quality within a data system and measure the data quality against predefined standards and requirements.

Data Quality

Data Quality Monitoring Data Modelling Data Warehouse

Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

ElegantJ BI

SEPTEMBER 15, 2017

One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective.

Data Governance

Data Governance Data Discovery Government Data Analytics

Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

ElegantJ BI

SEPTEMBER 15, 2017

One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective.

Data Governance

Data Governance Data Discovery Government Data Analytics

Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

ElegantJ BI

SEPTEMBER 15, 2017

One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective. Data Governance and Self-Serve Analytics Go Hand in Hand.

Data Governance

Data Governance Data Discovery Government Data Analytics

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

Augmented Analytics: Empowering Users with Deeper Intelligence

Sisense

APRIL 15, 2021

It’s the present, too, but you already know that. Your company is gathering data (and has likely been doing so for years), and you’ve probably got a system or two to glean insights from that data to make smarter decisions. Analytics is the future.

Artificial Intelligence

Artificial Intelligence Data Modelling Cloud Computing Data Discovery

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

SEPTEMBER 23, 2021

Data privacy policy: We all have sensitive data—we need policy and guidelines if and when users access and share sensitive data. Data quality: Gone are the days of “data is data, and we just need more.” Now, data quality matters. Data modeling. Data migration .

Data Governance

Data Governance Government Data Quality Change Management

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

SEPTEMBER 23, 2021

Data privacy policy: We all have sensitive data—we need policy and guidelines if and when users access and share sensitive data. Data quality: Gone are the days of “data is data, and we just need more.” Now, data quality matters. Data modeling. Data migration .

Data Governance

Data Governance Government Data Quality Change Management

Analyst, Scientist, or Specialist? Choosing Your Data Job Title

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. Business Analyst.

Chief Data Officer

Chief Data Officer Finance Data Modelling Visualization

The Benefits of Using a Data Warehouse for Healthcare Data Management

Astera

APRIL 7, 2023

Currently, around 30% of the world’s data is produced by the healthcare industry and this percentage is expected to reach 35% by 2025. The sheer amount of health-related data presents countless opportunities. Improving Data Quality and Consistency Quality is essential in the realm of data management.

Data Warehouse

Data Warehouse Healthcare Data Management Data Quality

Data Vault 101: Your Guide to Adaptable and Scalable Data Warehousing

Astera

FEBRUARY 14, 2024

Data vault is an emerging technology that enables transparent, agile, and flexible data architectures, making data-driven organizations always ready for evolving business needs. What is a Data Vault?  A data vault is a data modeling technique that enables you to build data warehouses for enterprise-scale analytics.

Data Warehouse

Data Warehouse Data Modelling Agile Data Quality

Understanding Data Cleaning & Its Importance

The BAWorld

SEPTEMBER 6, 2024

It has mainly focused on identifying the data errors, inconsistencies and inaccuracies in the data and make sure that is accurate, complete and reliable for data analytics. Why is data cleaning important? As mentioned above there are many uses of data. and in many of these source there is no control on data.

Data Analytics

Data Analytics Data Analytics Data Quality Data Modelling

Unstructured Data Challenges for 2023 and their Solutions

Astera

DECEMBER 22, 2022

It’s one of the three core data types, along with structured and semi-structured formats. Examples of unstructured data include call logs, chat transcripts, contracts, and sensor data, as these datasets are not arranged according to a preset data model. This makes managing unstructured data difficult.

Data Quality

Data Quality Data Mining Data Warehouse Data Governance

Health Data Management | Challenges and Best Practices

Astera

JANUARY 23, 2023

Billion by 2026 , showing the crucial role of health data management in the industry. Source: Precedence Research The increased volumes of information, varying in type and velocity, present immense potential to derive value from this information and aid the digital transformation of the healthcare industry.

Data Management

Data Management IBM Maintenance Healthcare IBM cost

All You Need to Know About Data Aggregation

Astera

MAY 13, 2024

Besides being relevant, your data must be complete, up-to-date, and accurate. Automated tools can help you streamline data collection and eliminate the errors associated with manual processes. Enhance Data Quality Next, enhance your data’s quality to improve its reliability.

Supply Chain

Supply Chain E-Commerce Healthcare Data Quality

Data Science vs Data Analytics: Key Differences

Astera

MAY 31, 2024

Two key disciplines have emerged at the forefront of this approach: data science vs data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. It allows you to retrieve and manipulate data efficiently.

Data Analytics

Data Analytics Data Analytics Data Warehouse Healthcare

Business Analysis vs Business Analytics

MindsMapped

JULY 18, 2020

Business Analytics mostly work with data and statistics. They primarily synthesize data and capture insightful information through it by understanding its patterns. Business Analysts and Business Analytics – Differences. Business Analytics.

Business Analysis

Business Analysis Business Analysis Requirements Gathering Documentation

The Complete Guide to Reverse ETL

Astera

JANUARY 25, 2024

Reverse ETL combined with data warehouse helps data analysts save time allowing them to focus on more complex tasks such as making sure their data is high quality, keeping it secure and private, and identifying the most important metrics to track. Data Models: These define the specific sets of data that need to be moved.

E-Commerce

E-Commerce Data Warehouse Retail Customer Experience

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

Data Migration Challenges: Strategies for a Smooth Transition

Astera

AUGUST 16, 2024

Let’s look at some reasons data migration projects fail: Risk of Data Integrity Loss Data quality maintenance is crucial to a smooth data migration process, especially when dealing with large volumes of data. This can lead to the organization losing a lot of valuable data after migration.

Digital Transformation

Digital Transformation Planning Government Cloud Computing

How to Manage your Data Science Project: An Ultimate Guide

Marutitech

JANUARY 21, 2022

Hence, if they are provided with the manager role, they will skimp on data science management. . What is the CRISP-DM Process Model? One of the essential tasks of data science management is ensuring and maintaining the highest possible data quality standards. Why Do You Need It? .

Project Management

Project Management Data Mining Data Analytics Data Analytics

Unveiling 10.3: Astera Introduces New, Innovative AI Features and Enhanced Capabilities

Astera

AUGUST 22, 2023

Reusable Artifacts: Astera’s data preparation scripts are reusable across various artifacts (dataflow, workflow, analytics workbench), promoting efficient and consistent data processing throughout multiple workflows. It offers an instant preview and feedback on data quality, helping you ensure the accuracy and integrity of your data.

Innovation

Innovation Documentation Data Analytics Data Analytics

Top Data Analytics Terms You Should Know

The BAWorld

NOVEMBER 22, 2021

Completeness is a data quality dimension and measures the existence of required data attributes in the source in data analytics terms, checks that the data includes what is expected and nothing is missing. Consistency is a data quality dimension and tells us how reliable the data is in data analytics terms.

Data Analytics

Data Analytics Data Analytics Data Warehouse Data Modelling

The Top 7 Data Aggregation Tools in 2024

Astera

APRIL 24, 2024

This blog reviews the top 7 data aggregation tools, exploring how each solution ensures that every byte of an organization’s data is harnessed for strategic insights. What are Data Aggregation Tools? Advanced Data Transformation : Offers a vast library of transformations for preparing analysis-ready data.

IBM Support

IBM Support IBM Maintenance IBM cost Visualization

Save Time and Stress with Dynamics Data Merging from Atlas

Insight Software

MARCH 13, 2024

Complex Data Structures and Integration Processes Dynamics data structures are already complex – finance teams navigating Dynamics data frequently require IT department support to complete their routine reporting.

IBM Maintenance

IBM Maintenance IBM Support Supply Chain IBM Expert

Business Analysis Digest

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

What are the Challenges of Applying Machine Learning in Economics and Business?

Trending Sources

Harrods’ Data Analytics Transformation: Turning Challenges into Insights

Tableau and dbt Labs: Strategic Partnership and Integration

7 Data Quality Metrics to Assess Your Data Health

Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

Why Good Data Management Is Essential to Data Analytics

Augmented Analytics: Empowering Users with Deeper Intelligence

How to: Focus on three areas for a holistic data governance approach for self-service analytics

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Analyst, Scientist, or Specialist? Choosing Your Data Job Title

The Benefits of Using a Data Warehouse for Healthcare Data Management

Data Vault 101: Your Guide to Adaptable and Scalable Data Warehousing

Understanding Data Cleaning & Its Importance

Unstructured Data Challenges for 2023 and their Solutions

Health Data Management | Challenges and Best Practices

All You Need to Know About Data Aggregation

Data Science vs Data Analytics: Key Differences

Business Analysis vs Business Analytics

The Complete Guide to Reverse ETL

A Complete Guide to Data Analytics

Data Migration Challenges: Strategies for a Smooth Transition

How to Manage your Data Science Project: An Ultimate Guide

Unveiling 10.3: Astera Introduces New, Innovative AI Features and Enhanced Capabilities

Top Data Analytics Terms You Should Know

The Top 7 Data Aggregation Tools in 2024

Save Time and Stress with Dynamics Data Merging from Atlas

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