Astera

FEBRUARY 1, 2024

Data modeling is the process of structuring and organizing data so that it’s readable by machines and actionable for organizations. In this article, we’ll explore the concept of data modeling, including its importance, types , and best practices. What is a Data Model?

Data Modelling 40

Data Modelling Retail Documentation Visualization 40

Sisense

JULY 6, 2020

Like any complex system, your company’s EDM system is made up of a multitude of smaller subsystems, each of which has a specific role in creating the final data products. These subsystems each play a vital part in your overall EDM program, but three that we’ll give special attention to are data governance, architecture, and warehousing.

Data Management 98

Data Management Data Architecture Data Governance Government 98

Tableau

OCTOBER 8, 2021

We are excited for the next phase of the Tableau and Google Cloud partnership, including plans for deeper integration between Tableau and Looker. Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey.

Government 138

Government Digital Transformation Retail Healthcare 138

Tableau

MAY 11, 2022

Data refresh failure detection that flags the issue to data users for mitigation and downstream consumers. Data modeling for every data source created in Tableau that shows how to query data in connected database tables and how to include a logical (semantic) layer and a physical layer.

Data Warehouse 98

Data Warehouse Government Artificial Intelligence Data Discovery 98

Tableau

MAY 11, 2022

Data refresh failure detection that flags the issue to data users for mitigation and downstream consumers. Data modeling for every data source created in Tableau that shows how to query data in connected database tables and how to include a logical (semantic) layer and a physical layer.

Data Warehouse 98

Data Warehouse Government Artificial Intelligence Data Discovery 98

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

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. Your organization will need to strategize and plan carefully to execute it. Days Not Months.

Data Governance 52

Data Governance Government Data Quality Documentation 52

Sisense

SEPTEMBER 12, 2019

2 Plan your objectives (and map the supporting data). At this stage, you will need to plan your business goal. It may be an oxymoron, but with so much data out there, the more focused you are in planning your business goals, the better off you’re going to be. Go Big, go data.

Documentation 81

Documentation Government Business Intelligence Business Intelligence 81

Tableau

OCTOBER 8, 2021

We are excited for the next phase of the Tableau and Google Cloud partnership, including plans for deeper integration between Tableau and Looker. Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey.

Government 98

Government Digital Transformation Retail Healthcare 98

Astera

JULY 3, 2024

Data architecture is important because designing a structured framework helps avoid data silos and inefficiencies, enabling smooth data flow across various systems and departments. An effective data architecture supports modern tools and platforms, from database management systems to business intelligence and AI applications.

Data Architecture 40

Data Architecture Data Warehouse Data Governance Government 40

Astera

AUGUST 13, 2024

This flexibility supports adding new data sources and services, ensuring the infrastructure can grow alongside the business. Regulatory Compliance Data modernization enhances compliance with current regulations and standards. Step 5: Develop a Data Governance Framework Establish data governance policies and procedures.

Data Governance 52

Data Governance Data Architecture Data Quality Real-time Data 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 Data Quality Cloud Computing 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 Data Quality Cloud Computing Monitoring 52

Astera

JANUARY 2, 2024

When data is organized and accessible, different departments can work cohesively, sharing insights and working towards common goals. Data Governance vs Data Management One of the key points to remember is that data governance and data management are not the same concepts—they are more different than similar.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

Data Pine

SEPTEMBER 29, 2022

The quality of data is defined by different factors that will be detailed later in this article, such as accuracy, completeness, consistency, or timeliness. That quality is necessary to fulfill the needs of an organization in terms of operations, planning, and decision-making. Why Do You Need Data Quality Management?

Data Quality 96

Data Quality Monitoring Big Data Government 96

Whizlabs

MARCH 14, 2021

Data modelling and visualizations. As a business reporter, Power BI will make it easier for you to connect and integrate the data. Moreover, it will create clean and specified data models and graphs. It’s one of the most effective tools if you are a data analyst. Security and administration.

Visualization 52

Visualization Cloud Computing Data Modelling Monitoring 52

Astera

AUGUST 16, 2024

Overcome Data Migration Challenges with Astera Astera's automated solution helps you tackle your use-case specific data migration challenges. View Demo to See How Astera Can Help Why Do Data Migration Projects Fail? McKinsey reports that inefficiencies in data migration cost enterprises 14% more than their planned spending.

Digital Transformation 52

Digital Transformation Planning Government Cloud Computing 52

Astera

MAY 13, 2024

Data Aggregation Types and Techniques There are various types of data aggregation. Your requirements and how you plan to use the data will determine which approach suits your use case. Temporal As the name suggests, temporal aggregation summarizes data over specified time intervals.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

Actian

OCTOBER 7, 2019

If a credential must be updated, then it occurs within the data hub, and all the subscribing applications can continue using the connection. Data hubs also simplify the data governance requirements as the data is persisted at a central location.

Data Warehouse 10

Data Warehouse Government Data Governance Agile 10

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 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

Actian

OCTOBER 7, 2019

If a credential must be updated, then it occurs within the data hub, and all the subscribing applications can continue using the connection. Data hubs also simplify the data governance requirements as the data is persisted at a central location.

Data Warehouse 40

Data Warehouse Government Data Governance Agile 40

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.

Data Warehouse 52

Data Warehouse Data Quality Data Management Government 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

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

Cprime

DECEMBER 21, 2022

With traditional data integration projects, there are complex Extract Transform Load (ETL) workflows to clean data and transform it into the precise format needed by target systems. The global Integration Platform as a Service (iPaaS) market value is approximately $1.9 billion, and it is expected to reach $10.3 Conclusion.

Data Management 52

Data Management Real-time Data Data Governance Vision 52

Astera

JUNE 3, 2024

Relational databases are excellent for applications that require strong data integrity , complex queries, and transactions, such as financial systems, customer relationship management systems (CRM), and enterprise resource planning (ERP) systems. Data volume and growth: Consider the current data size and anticipated growth.

Data Warehouse 52

Data Warehouse Documentation E-Commerce Data Management 52

Astera

APRIL 24, 2024

Data aggregation tools allow businesses to harness the power of their collective data, often siloed across different systems and formats. By aggregating data, these tools provide a unified view crucial for informed decision-making, trend analysis, and strategic planning. Who Uses Data Aggregation Tools?

IBM Support 52

IBM Support IBM cost IBM Maintenance Visualization 52

Sisense

NOVEMBER 5, 2021

This learning process also helps drive Radial’s Data governance strategy, helping us understand data retention needs by business area, availability of data (live vs archive), data separation and security, and more. and create accurate forecasts they can use to plan for the future. What should we do?”

Embedded Analytics 52

Embedded Analytics Data Warehouse Innovation Data Governance 52

Astera

JANUARY 12, 2024

Modern data architecture is characterized by flexibility and adaptability, allowing organizations to seamlessly integrate structured and unstructured data, facilitate real-time analytics, and ensure robust data governance and security, fostering data-driven insights.

Data Architecture 52

Data Architecture Data Warehouse Data Quality Government 52

Analysts Corner

JANUARY 6, 2025

Other strategies: Be ready to face new forms of digital threats: protect your assetssoftware, data, models and algorithms from cyberthreats. when managing your data assets and implementing the semantic layer) to implement things with proven methods,faster. Leverage industry standards (e.g.

Government 130

Government Digital Artificial Intelligence Healthcare 130

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. It is a complex and challenging task that requires careful planning, analysis, and execution.

IBM Support 52

IBM Support IBM Maintenance Data Warehouse IBM cost 52

Insight Software

AUGUST 15, 2024

MDM is necessary for maintaining data integrity and consistency across your organization, but it can be complex and time-consuming to manage different data sources and ensure accurate data governance. With Power ON’s user management features, you can enhance collaboration and ensure robust data governance.

IBM Maintenance 52

IBM Maintenance IBM Support Data Governance Government 52

Insight Software

AUGUST 30, 2024

AI can also be used for master data management by finding master data, onboarding it, finding anomalies, automating master data modeling, and improving data governance efficiency. From Chaos to Control: Navigating Your Supply Chain With Actionable Insights Download Now Is Your Data AI-Ready?

Supply Chain 52

Supply Chain IBM Maintenance IBM Support IBM cost 52

Insight Software

OCTOBER 24, 2022

With an embedded analytics development environment, software teams can avoid getting bogged down in intensive data modeling efforts, instead streamlining data connectivity to a broad range of modern data sources and formats.

IBM Support 52

IBM Support IBM Maintenance Embedded Analytics IBM Expert 52

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 52

IBM Maintenance IBM Support Supply Chain IBM Expert 52