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 130

Data Governance Data Discovery Government Data Analytics 130

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 130

Data Governance Data Discovery Government Data Analytics 130

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 130

Data Governance Data Discovery Government Data Analytics 130

Analysts Corner

JUNE 18, 2023

Python, Java, C#) Familiarity with data modeling and data warehousing concepts Understanding of data quality and data governance principles Experience with big data platforms and technologies (e.g., Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

Data Warehouse 130

Data Warehouse Data Quality Documentation Data Governance 130

The Data Administration Newsletter

DECEMBER 20, 2022

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

Data Governance 52

Data Governance Government Data Quality Data Modelling 52

Dataversity

JUNE 19, 2023

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

Monitoring 130

Monitoring Data Modelling Data Quality Data Governance 130

Astera

JUNE 12, 2024

Data lineage is an important concept in data governance. It outlines the path data takes from its source to its destination. Understanding data lineage helps increase transparency and decision-making for organizations reliant on data. This complete guide examines data lineage and its significance for teams.

Data Governance 52

Data Governance Government Data Quality Monitoring 52

Dataversity

MAY 1, 2023

Many organizations have mapped out the systems and applications of their data landscape. Many have modeled their data domains and key attributes. The remainder of this point of view will explain why connecting […] The post Connecting the Three Spheres of Data Management to Unlock Value appeared first on DATAVERSITY.

Data Management 244

Data Management Documentation Data Modelling Data Quality 244

Astera

AUGUST 30, 2024

Data Migrations Made Efficient with ADP Accelerator Astera Data Pipeline Accelerator increases efficiency by 90%. Try our automated, data model-driven solution for fast, seamless, and effortless data migrations. Automate your migration journey with our holistic, data model-driven solution.

Data Governance 52

Data Governance Government Data Quality Documentation 52

Astera

MARCH 28, 2024

So, organizations create a data governance strategy for managing their data, and an important part of this strategy is building a data catalog. They enable organizations to efficiently manage data by facilitating discovery, lineage tracking, and governance enforcement.

Data Governance 52

Data Governance Government Data Discovery Data Quality 52

Astera

JANUARY 2, 2024

Securing Data: Protecting data from unauthorized access or loss is a critical aspect of data management which involves implementing security measures such as encryption, access controls, and regular audits. Organizations must also establish policies and procedures to ensure data quality and compliance.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

Astera

FEBRUARY 28, 2024

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

FEBRUARY 27, 2024

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

FEBRUARY 27, 2024

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

JULY 3, 2024

It creates a space for a scalable environment that can handle growing data, making it easier to implement and integrate new technologies. Moreover, a well-designed data architecture enhances data security and compliance by defining clear protocols for data governance.

Data Architecture 40

Data Architecture Data Warehouse Data Governance Government 40

Astera

AUGUST 13, 2024

Data-first modernization is a strategic approach to transforming an organization’s data management and utilization. It involves making data the center and organizing principle of the business by centralizing data management, prioritizing data quality , and integrating data into all business processes.

Data Governance 52

Data Governance Data Architecture Data Quality Real-time Data 52

Astera

JULY 13, 2023

MDM ensures data consistency, reduces duplication, and enhances data quality across systems. It is particularly useful in scenarios where data integrity, data governance, and data quality are of utmost importance, such as customer data management, product information management, and regulatory compliance.

Data Warehouse 40

Data Warehouse Data Quality Data Governance Master Data Management 40

Astera

FEBRUARY 16, 2024

It facilitates the seamless collection, consolidation, and transformation of data from diverse sources and systems into a unified and standardized format. The advantages of this integration extend beyond mere organization; it significantly improves data quality and accuracy.

Finance 52

Finance Banking Data Quality Artificial Intelligence 52

Astera

MAY 13, 2024

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. Remember to monitor and validate your data to ensure it remains accurate, complete, and relevant.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

Astera

JANUARY 23, 2024

Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. Traditional data warehouses with predefined data models and schemas are rigid, making it difficult to adapt to evolving data requirements.

Agile 52

Agile Data Warehouse Data Quality Government 52

Astera

NOVEMBER 30, 2023

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0

Data Warehouse 52

Data Warehouse Big Data Healthcare Data Governance 52

Dataversity

FEBRUARY 19, 2021

Enterprises are modernizing their data platforms and associated tool-sets to serve the fast needs of data practitioners, including data scientists, data analysts, business intelligence and reporting analysts, and self-service-embracing business and technology personnel. Click to learn more about author Tejasvi Addagada.

Business Intelligence 147

Business Intelligence Business Intelligence Data Modelling Data Discovery 147

The Data Administration Newsletter

JANUARY 17, 2024

Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations.

Data Modelling 122

Data Modelling Data Quality Data Governance Government 122

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 52

Data Quality Data Mining Data Warehouse Data Governance 52

Astera

JANUARY 18, 2024

As far as the destinations are concerned, Fivetran supports data warehouses and databases, but it doesn’t support most data lakes. It also offers limited data transformation capabilities and that too through dbt core, which is an open source tool. Change data capture (CDC) for all relational databases in one platform.

Data Warehouse 52

Data Warehouse Data Quality Data Management Government 52

Dataversity

FEBRUARY 17, 2021

Click to learn more about author Steve Zagoudis. Successful problem solving requires finding the right solution to the right problem. We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” – Russell L.

Data Warehouse 130

Data Warehouse Data Modelling Data Governance Data Quality 130

Dataversity

JULY 12, 2021

In my eight years as a Gartner analyst covering Master Data Management (MDM) and two years advising clients and prospects at a leading vendor, I have seen first-hand the importance of taking a multidomain approach to MDM. Click to learn more about author Bill O’Kane.

Master Data Management 130

Master Data Management Data Management Data Quality Data Governance 130

Dataversity

MARCH 18, 2022

Redman) served as the judge in a mock trial of a data architect (played by Laura Sebastian Coleman) […]. The post What Data Practitioners Need to Know (and Do) About Common Language appeared first on DATAVERSITY. Weinberg [1] In March 2019, one of us (Thomas C.

Chief Data Officer 130

Chief Data Officer Digital Transformation Data Governance Data Modelling 130

Dataversity

MARCH 1, 2021

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Click to learn more about author Wayne Yaddow.

Data Warehouse 130

Data Warehouse Data Governance Data Modelling Data Quality 130

Astera

JANUARY 25, 2024

Reverse ETL, used with other data integration tools , like MDM (Master Data Management) and CDC (Change Data Capture), empowers employees to access data easily and fosters the development of data literacy skills, which enhances a data-driven culture.

E-Commerce 52

E-Commerce Data Warehouse Retail Customer Experience 52

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 Analytics.

Business Analysis 46

Business Analysis Business Analysis Requirements Gathering Documentation 46

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. Astera does all the heavy lifting involved in a data migration.

Digital Transformation 52

Digital Transformation Planning Government Cloud Computing 52

The Data Administration Newsletter

JUNE 21, 2022

Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].

Master Data Management 78

Master Data Management Data Modelling Data Quality Data Governance 78

Astera

JUNE 3, 2024

Data needs Data structure: Analyze the type of data the organization needs to store—structured, semi-structured, or unstructured. Relational databases excel with structured data, while NoSQL options cater to more flexible data models.

Data Warehouse 52

Data Warehouse Documentation E-Commerce Data Management 52

The Data Administration Newsletter

AUGUST 7, 2024

My column today is a follow-up to my article “The Challenge of Data Consistency,” published in the May 2023 issue of this newsletter. In that article, I discussed how semantic encoding (also called concept encoding) is the go-to solution for consistently representing master data entities such as customers and products.

Data Quality 52

Data Quality Data Modelling Data Governance Artificial Intelligence 52

The Data Administration Newsletter

AUGUST 2, 2022

In part 1 of this series, I shared how our reactions to data can cause a great deal of suffering and discussed the following 3 principles to identify how this suffering can occur as well as ways to alleviate suffering: Attachment is a root cause of suffering ‘Not knowing’ has unlimited possibilities Always being ‘right’ […].

Data Modelling 52

Data Modelling Data Quality Data Governance Government 52

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 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98