ElegantJ BI

JULY 11, 2017

Agility and Quality: A Vital Balancing Act! Think of data agility and data quality like a tight rope act. They want to see a QUALITY act. In data preparation, an organization may think of data preparation and data analysis as something that must always be of the highest quality – NO negotiation.

Agile 130

Agile Data Quality 130

ElegantJ BI

JULY 11, 2017

Agility and Quality: A Vital Balancing Act! Think of data agility and data quality like a tight rope act. They want to see a QUALITY act. In data preparation, an organization may think of data preparation and data analysis as something that must always be of the highest quality – NO negotiation.

Agile 130

Agile Data Quality 130

ElegantJ BI

JULY 11, 2017

Agility and Quality: A Vital Balancing Act! Think of data agility and data quality like a tight rope act. They want to see a QUALITY act. In data preparation, an organization may think of data preparation and data analysis as something that must always be of the highest quality – NO negotiation.

Agile 130

Agile Data Quality Business Intelligence Business Intelligence 130

Dataversity

FEBRUARY 8, 2021

As I’ve been working to challenge the status quo on Data Governance, I get a lot of questions about how it will “really” work. The post Dear Laura: Agile Data Governance appeared first on DATAVERSITY. I’ll be sharing these questions and answers via this DATAVERSITY® series. Last year I wrote the […].

Data Governance 130

Data Governance Government Agile Data Quality 130

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 52

Data Warehouse Agile Data Modelling Data Quality 52

Timo Elliott

DECEMBER 14, 2022

Everybody wants to innovate faster, to be more agile, to be able to react quickly to changes in today’s uncertain business environments. The industry analysts all have a similar vision of what that agile future of business looks like. You lose the roots: the metadata, the hierarchies, the security, the business context of the data.

Innovation 262

Innovation Artificial Intelligence Data Warehouse Government 262

Dataversity

NOVEMBER 22, 2023

In today’s ever-evolving landscape, leaders face a delicate balancing act when harnessing the power of AI to transform their data into valuable insights. On the one hand, the relentless speed of AI-driven advancement and fierce industry competition demand an agile, iterative approach to unlock AI’s full potential.

Innovation 162

Innovation Agile Data Governance Data Quality 162

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 52

Data Warehouse Data Modelling Agile Data Quality 52

Dataversity

OCTOBER 4, 2022

The rise of data lakes and adjacent patterns such as the data lakehouse has given data teams increased agility and the ability to leverage major amounts of data. Constantly evolving data privacy legislation and the impact of major cybersecurity breaches has led to the call for responsible data […].

Government 237

Government Cybersecurity Agile Data Governance 237

ElegantJ BI

SEPTEMBER 1, 2021

Augmented analytics features can help an SME organization to automate and enhance data engineering tasks and abstract data models, and use system guidance to quickly and easily prepare data for analysis to ensure data quality and accurate manipulation.

Data Quality 130

Data Quality Visualization Data Modelling Agile 130

ElegantJ BI

SEPTEMBER 1, 2021

Augmented analytics features can help an SME organization to automate and enhance data engineering tasks and abstract data models, and use system guidance to quickly and easily prepare data for analysis to ensure data quality and accurate manipulation.

Data Quality 130

Data Quality Visualization Data Modelling Agile 130

Dataversity

SEPTEMBER 27, 2021

DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. extract, transform, load) projects are often devoid of automated testing. The […].

Agile 246

Agile Data Warehouse Data Quality Data Architecture 246

ElegantJ BI

JUNE 13, 2019

Probably because your IT team and/or your executive management team believe it is a) too expensive, b) will take a long time to implement, c) will not be something users CAN adopt because of the need for expertise or skills.

Data Governance 130

Data Governance Data Quality Government Data Analytics 130

ElegantJ BI

JUNE 13, 2019

Probably because your IT team and/or your executive management team believe it is a) too expensive, b) will take a long time to implement, c) will not be something users CAN adopt because of the need for expertise or skills.

Data Governance 130

Data Governance Data Quality Government Data Analytics 130

ElegantJ BI

JUNE 13, 2019

Reduce the time to prepare data for analysis. Engender social BI and data popularity. Balance agility with data governance and data quality. So, why wouldn’t your organization want to implement Data Preparation Software that is easy enough for every business user?

Data Governance 130

Data Governance Data Quality Government Data Analytics 130

Dataversity

APRIL 11, 2023

The enormous amount of data in circulation has allowed enterprises to automate, advance, or accelerate business development with the help of agile methodologies. Thus, it is crucial to manage and streamline quality test data.

Data Management 130

Data Management Agile Data Quality Data Governance 130

BizAcuity

MARCH 29, 2019

The main reasons why customers perceive the cloud as an advantage for Data Lakes are better security, faster deployment time, better availability, frequent feature and functionality updates, more elasticity, better geographic coverage, and costs linked to actual utilization. Numbers are only good if the data quality is good.

Healthcare 256

Healthcare Data Quality Big Data Visualization 256

Dataversity

MARCH 17, 2023

The last few years have demonstrated how critical it is for businesses to maintain agility and make fast, confident decisions to stay ahead.

Artificial Intelligence 130

Artificial Intelligence Agile Data Quality Data Governance 130

Timo Elliott

JANUARY 4, 2021

And finally, agility. As I’ve said, data storytelling isn’t fundamentally about technology. Second, getting a glimpse into the future. People really want to know what’s about to happen, so there’s lot of interest in leading indicators, scenario planning, and predictive technologies. The key takeaways. But technology can help!

Planning 348

Planning Real-time Data Data Visualization Finance 348

Analysts Corner

OCTOBER 31, 2023

Commercial : Customer Relationship Management (CRM) systems that integrate customer data and preferences to identify greater business opportunities in personalized campaigns and actions. Management : monitoring transactional data from business operations to generate indicators at various levels.

Artificial Intelligence 246

Artificial Intelligence Monitoring Healthcare Banking 246

Astera

JUNE 13, 2023

Acting as a conduit for data, it enables efficient processing, transformation, and delivery to the desired location. By orchestrating these processes, data pipelines streamline data operations and enhance data quality. Techniques like data profiling, data validation, and metadata management are utilized.

E-Commerce 52

E-Commerce Data Quality Real-time Data Data Warehouse 52

Astera

FEBRUARY 28, 2024

Data Governance and Documentation Establishing and enforcing rules, policies, and standards for your data warehouse is the backbone of effective data governance and documentation. Keeping track of metadata can help you understand the data, facilitate data integration , enable data lineage tracing, and enhance data quality.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

NOVEMBER 1, 2023

Data Integration: A data warehouse enables seamless integration of data from various systems and eliminates data silos and promotes interoperability and overall performance. Data-driven Finance with Astera Download Now Who Can Benefit from a Finance Data Warehouse?

Data Warehouse 52

Data Warehouse Finance Data Quality Banking 52

Astera

OCTOBER 19, 2023

Easy-to-Use, Code-Free Environment By eliminating the need for writing complex code, data preparation tools reduce the risk of errors. These tools allow users to manipulate and transform data without the potential pitfalls of manual coding. The tool also lets users visually explore data through data exploration and profiling.

IBM Support 40

IBM Support Data Quality IBM Maintenance Real-time Data 40

Inflexion Analytics

SEPTEMBER 10, 2023

Retailers used tools like Google Big Query to aggregate customer intelligence gathered in store with customer intelligence data gathered online from customer searches. They then combined it with data from operational and transactional systems to gain a strategic advantage in data quality and completeness.

Retail 52

Retail Supply Chain Innovation Digital 52

Astera

AUGUST 8, 2023

What matters is how accurate, complete and reliable that data. Data quality is not just a minor detail; it is the foundation upon which organizations make informed decisions, formulate effective strategies, and gain a competitive edge. to help clean, transform, and integrate your data.

Data Quality 52

Data Quality IBM Support IBM Maintenance Monitoring 52

Astera

MAY 25, 2023

That’s how it can feel when trying to grapple with the complexity of managing data on the cloud-native Snowflake platform. They range from managing data quality and ensuring data security to managing costs, improving performance, and ensuring the platform can meet future needs.

Data Quality 52

Data Quality Government Data Governance Monitoring 52

Business Analysis Knowledge Share

OCTOBER 27, 2023

Introduction In today’s data-driven landscape, businesses have recognized the paramount importance of harnessing the power of data to stay competitive and agile. Business Intelligence (BI) has emerged as a critical tool for organizations seeking to gain insights from their data and make informed decisions.

Business Intelligence 52

Business Intelligence Business Intelligence Supply Chain Data Governance 52

Dataversity

JANUARY 21, 2022

Businesses have long struggled to find the balance between compliance and agility. This is especially true when it comes to Data Governance. Effective Data Governance ensures that […].

Data Governance 130

Data Governance Government Agile Data Quality 130

Astera

NOVEMBER 16, 2022

This way, you can modernize your data Infrastructure with minimal risk of data loss. Hybrid cloud integration optimizes IT performance and provides agility, allowing you to expand your workload on the cloud. Understand and assess potential data quality challenges in a hybrid cloud environment. Data Quality.

Data Architecture 52

Data Architecture Data Quality Data Warehouse Data Governance 52

Astera

OCTOBER 10, 2023

Modern companies heavily rely on data to drive their decision-making processes, but data consistency and quality can lead to inaccurate conclusions. Gartner’s 2018 report highlights that organizations incur an average cost of $15 million annually due to poor data quality. What Is Data Standardization?

Data Quality 52

Data Quality Artificial Intelligence Monitoring Visualization 52

Dataversity

AUGUST 30, 2021

DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. extract, transform, load) projects are often devoid of automated testing. The […].

Agile 130

Agile Data Warehouse Data Quality Data Architecture 130

Smart Data Collective

OCTOBER 29, 2023

With a powerful suite of analytics tools available today – such as predictive analytics, prescriptive analysis, customer segmentation and lead scoring – organizations now have access to critical information that can equip them with the power to make data-driven decisions quickly and accurately. How do they do this?

Portfolio Management 262

Portfolio Management Agile Big Data Predictive Analytics 262

Insight Software

APRIL 9, 2023

Plagued by supply chain disruptions and price inflation, finance teams are at the forefront of organizational efforts to strategize and remain agile in changing circumstances. Data Quality Consistency Data quality and consistency is the foundational challenge.

Supply Chain 52

Supply Chain Finance Predictive Analytics Data Quality 52

Astera

FEBRUARY 27, 2024

Automated data extraction solutions significantly improve medical insurance claims processing by expediting and enhancing the entire workflow. It’s wise to invest in modern automation solutions as they are equipped with data quality rules.

Healthcare 52

Healthcare Documentation Pharmaceutical Data Quality 52

Astera

NOVEMBER 30, 2023

With its foundation rooted in scalable hub-and-spoke architecture, Data Vault 1.0 provided a framework for traceable, auditable, and flexible data management in complex business environments. Building upon the strengths of its predecessor, Data Vault 2.0 Data Vault 2.0 What’s New in Data Vault 2.0? Data Vault 2.0

Data Warehouse 52

Data Warehouse Healthcare Big Data Data Governance 52

Timo Elliott

JANUARY 5, 2022

The first one is: companies should invest more in improving their data quality before doing anything else. To make a big step forward with data science, you first need to do that painful work. That’s an awful waste of resources. Yves: Why are all those projects failing? Timo: I see two main reasons.

Digital Transformation 288

Digital Transformation Chief Data Officer Innovation Digital 288