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 97

Data Governance Government Data Quality Change Management 97

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

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. Combining datasets is key to unlocking more advanced insights.

Chief Data Officer 97

Chief Data Officer Finance Data Modelling Visualization 97

Sisense

JANUARY 14, 2021

VP of Business Intelligence Michael Hartmann describes the problem: “When an upstream data model change was introduced, it took a few days for us to notice that one of our Sisense charts was ‘broken.’ We believe this can help teams be more proactive and increase the data quality in their companies,” said Ivan.

E-Commerce 85

E-Commerce Business Intelligence Business Intelligence Data Modelling 85

Astera

JUNE 12, 2024

Data Governance establishes framework, policies, and processes for managing data assets within an organization. Focus Flow of data Origin and history of data Management and control of data assets Purpose Ensure data quality, traceability, and compliance. How was the data created?

Data Governance 52

Data Governance Government Data Quality Monitoring 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

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

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

Astera

APRIL 28, 2023

Data management can be a daunting task, requiring significant time and resources to collect, process, and analyze large volumes of information. AI is a powerful tool that goes beyond traditional data analytics. Smart Data Modeling Another trend in data warehousing is the use of AI-powered tools for smart data modeling.

E-Commerce 40

E-Commerce Supply Chain Artificial Intelligence Data Warehouse 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

DECEMBER 29, 2023

In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way. Free Download What is a Data Warehouse? Data is organized into two types of tables in a dimensional model: fact tables and dimension tables.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Astera

DECEMBER 29, 2023

In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way. Free Download What is a Data Warehouse? Data is organized into two types of tables in a dimensional model: fact tables and dimension tables.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Astera

APRIL 24, 2024

A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.

Data Governance 52

Data Governance Government Data Architecture Data Quality 52

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

Business Analysts

APRIL 5, 2023

It requires careful planning, analysis, and collaboration between IT and business teams. Data analysis and modelling : AI projects require large amounts of data to train machine learning models. This involves identifying stakeholders, developing communication plans, and providing training and support to end-users.

Artificial Intelligence 52

Artificial Intelligence Business Analysis Business Analysis Requirements Gathering 52

Marutitech

JANUARY 21, 2022

The initial step for any data science management process is to define the team’s appropriate project goal and metrics, i.e., a data science strategic plan. Hence, if they are provided with the manager role, they will skimp on data science management. . What is the CRISP-DM Process Model? Scrubbing data .

Project Management 113

Project Management Data Mining Data Analytics Data Analytics 113

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

Astera

JANUARY 12, 2024

It was developed by Dan Linstedt and has gained popularity as a method for building scalable, adaptable, and maintainable data warehouses. Self-Serve Data Infrastructure as a Platform: A shared data infrastructure empowers users to independently discover, access, and process data, reducing reliance on data engineering teams.

Data Architecture 52

Data Architecture Data Warehouse Data Quality Government 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

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

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

Fivetran is also not an ideal solution if you are looking for a complete enterprise-grade data management solution as it doesn’t support data governance or offer advanced capabilities to improve data quality. All you need to do is just drag and drop the transformations in the data flow designer.

Data Warehouse 52

Data Warehouse Data Quality Data Management Government 52

Astera

FEBRUARY 14, 2024

Identify the source systems, data entities, and stakeholders involved. Your Salesforce data migration plan should also be clear about the timelines, resources, and responsibilities. Specify how data will be transformed and mapped during the migration process. Data Loading: Load the transformed data into Salesforce.

IBM Support 52

IBM Support IBM Maintenance IBM cost Data Quality 52

Astera

AUGUST 16, 2024

McKinsey reports that inefficiencies in data migration cost enterprises 14% more than their planned spending. 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.

Digital Transformation 52

Digital Transformation Planning Government Cloud Computing 52

Astera

JANUARY 24, 2024

Key Features of Astera It offers customized data quality rules so you can get to your required data faster and remove irrelevant entries more easily. It provides multiple security measures for data protection. Features built-in data quality tools, such as the Data Quality Firewall, and error detection.

Data Warehouse 52

Data Warehouse Data Quality Data Management Data Security 52

Astera

NOVEMBER 14, 2023

Main Components of Astera’s Data Warehouse Builder With that said, almost any organization can deploy an agile data warehousing solution, provided that it has the right technology stack fueling the initiative. Not only that, you can enjoy the benefits in various scenarios as well.

Data Warehouse 52

Data Warehouse Agile Data Modelling Data Quality 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

Astera

MAY 31, 2024

Here are the critical components of data science: Data Collection : Accumulating data from diverse sources like databases, APIs , and web scraping. Data Cleaning and Preprocessing : Ensuring data quality by managing missing values, eliminating duplicates, normalizing data, and preparing it for analysis.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 52

Astera

MAY 25, 2023

Incident Response Having a robust incident response plan in place is necessary in the event of a data breach. The plan should outline steps to contain the breach, analyze the scope, notify affected parties, and remediate vulnerabilities. Plans should be tested and updated regularly to ensure effectiveness.

Big Data 40

Big Data Data Security Monitoring Data Quality 40

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

MAY 30, 2024

Variety : Data comes in all formats – from structured, numeric data in traditional databases to emails, unstructured text documents, videos, audio, financial transactions, and stock ticker data. Veracity: The uncertainty and reliability of data. Veracity addresses the trustworthiness and integrity of the data.

Data Analytics 52

Data Analytics Data Analytics Data Mining Supply Chain 52

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 52

E-Commerce Data Warehouse Retail Customer Experience 52

Astera

OCTOBER 27, 2023

Practical Tips To Tackle Data Quality During Cloud Migration The cloud offers a host of benefits that on-prem systems don’t. Here are some tips to ensure data quality when taking your data warehouse to the cloud. Many cloud data warehouses use cost-based optimization to parse queries. We've got both!

Data Warehouse 52

Data Warehouse Data Quality Big Data Predictive Analytics 52

Actian

JUNE 4, 2019

A combination of size, deployment option (on-premise or cloud) and the sophistication of analytics tools that come with the warehouse will drive the cost of your data warehouse. You should plan for these main cost categories: Setup – These are the costs to acquire and configure the data warehouse solution.

Data Warehouse 40

Data Warehouse Digital Transformation Data Quality Data Modelling 40

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

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