IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

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Data Pipeline ETL SQL Database 85

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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ETL Data Warehouse Data Quality Data Lakes 52

Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

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ETL Data Warehouse Data Quality Data Governance 52

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. What is ETL? ETL stands for Extract, Transform, Load.

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ETL Data Warehouse SQL Data Quality 52

Pickl AI

OCTOBER 15, 2024

Summary: Selecting the right ETL platform is vital for efficient data integration. Consider your business needs, compare features, and evaluate costs to enhance data accuracy and operational efficiency. Introduction In today’s data-driven world, businesses rely heavily on ETL platforms to streamline data integration processes.

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ETL Azure AWS Data Governance 52

The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Quality Data Lakes Data Warehouse Big Data 52

Data Science Dojo

SEPTEMBER 6, 2023

Defining Data Ownership: Assigning Custodianship Like a castle with appointed caretakers, data governance designates data owners responsible for different datasets. Data ownership extends beyond mere possession—it involves accountability for data quality, accuracy, and appropriate use.

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Data Warehouse Data Governance Data Quality ETL 195

Dataconomy

DECEMBER 17, 2024

Example: For a project to optimize supply chain operations, the scope might include creating dashboards for inventory tracking but exclude advanced predictive analytics in the first phase. Key questions to ask: What data sources are required? Are there any data gaps that need to be filled? What are the data quality expectations?

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Data Quality Power BI Data Engineering Data Engineering 91

Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

Data Quality 52

Data Quality Data Governance Data Warehouse Machine Learning 52

IBM Data Science in Practice

DECEMBER 7, 2022

Next Generation DataStage on Cloud Pak for Data Ensuring high-quality data A crucial aspect of downstream consumption is data quality. Studies have shown that 80% of time is spent on data preparation and cleansing, leaving only 20% of time for data analytics.

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Data Pipeline Data Quality Data Preparation ETL 130

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Also Read: Top 10 Data Science tools for 2024.

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IBM Data Science in Practice

NOVEMBER 28, 2022

As organizations steer their business strategies to become data-driven decision-making organizations, data and analytics are more crucial than ever before. The concept was first introduced back in 2016 but has gained more attention in the past few years as the amount of data has grown.

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Data Pipeline Data Quality Data Preparation Data Governance 130

Precisely

FEBRUARY 20, 2025

Follow five essential steps for success in making your data AI ready with data integration. Define clear goals, assess your data landscape, choose the right tools, ensure data quality and governance, and continuously optimize your integration processes.

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Data Silos AI AI Data Quality 69

IBM Journey to AI blog

OCTOBER 22, 2024

Indeed, IDC has predicted that by the end of 2024, 65% of CIOs will face pressure to adopt digital tech , such as generative AI and deep analytics. The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it.

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Data Silos Data Pipeline DataOps Business Intelligence 102

Data Science Blog

JUNE 27, 2023

Big Data Analytics stands apart from conventional data processing in its fundamental nature. In the realm of Big Data, there are two prominent architectural concepts that perplex companies embarking on the construction or restructuring of their Big Data platform: Lambda architecture or Kappa architecture.

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Big Data Big Data Apache Kafka Database 130

ODSC - Open Data Science

APRIL 3, 2023

As the sibling of data science, data analytics is still a hot field that garners significant interest. Companies have plenty of data at their disposal and are looking for people who can make sense of it and make deductions quickly and efficiently.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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IBM Journey to AI blog

MAY 9, 2024

Conversely, confidence in the accuracy and consistency of your data can minimize the risk of adverse health outcomes, rather than merely reacting to or causing them. Also, using predictive analytics can help identify trends, patterns and potential future health risks in your patients. These errors are crucial and can occur daily.

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Data Governance Data Silos Data Quality Predictive Analytics 85

Smart Data Collective

APRIL 29, 2020

The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. Data analytics and visualisation.

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Data Warehouse SQL Azure ETL 98

Pickl AI

AUGUST 31, 2023

However, analysis of data may involve partiality or incorrect insights in case the data quality is not adequate. Accordingly, the need for Data Profiling in ETL becomes important for ensuring higher data quality as per business requirements. What is Data Profiling in ETL?

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Data Profiling ETL Data Quality Data Wrangling 52

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. You can review the recommendations and augment rules from over 25 included data quality rules.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

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Pickl AI

DECEMBER 25, 2024

Here are some effective strategies to break down data silos: Data Integration Solutions Employing tools for data integration such as Extract, Transform, Load (ETL) processes can help consolidate data from various sources into a single repository. This allows for easier access and analysis across departments.

Data Silos 52

Data Silos Database Data Quality ETL 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. BI tools rely on high-quality, consistent data to generate accurate insights.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

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Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

JUNE 19, 2023

These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security. It is also important to establish data quality standards and strict access controls.

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ODSC - Open Data Science

OCTOBER 18, 2024

Consider these best practices when building the project charter: Collaborate with business leaders Rather than operate in isolation, interview executive sponsors and front-line decision-makers to identify pain points and the biggest opportunities for analytical solutions. However, knowledge transfer to internal teams can pose challenges.

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Data Science Data Scientist Analytics Analytics 52

Tableau

AUGUST 21, 2023

Get a quick look at tools and techniques of this dynamic data visualization platform, and uncover tips, tricks, and strategies that will propel how you work with data to create impactful visualizations and make informed decisions. Included with Data Management. Included with the Data Management SKU.

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Tableau Database Analytics Analytics 97

Precisely

APRIL 6, 2023

Data analytics and other technologies have emerged as integral elements of most businesses. Building a Trusted Single View of Critical Data Most organizations are at least somewhat aware of problems with data quality and accuracy. Data enrichment and location intelligence provide much deeper context.

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Data Quality Data Pipeline Analytics Analytics 52

Pickl AI

JULY 25, 2024

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. It supports both batch and real-time processing.

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Apache Kafka Data Lakes Data Warehouse Data Quality 52

Pickl AI

NOVEMBER 15, 2023

Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources. With all data in one place, businesses can break down data silos and gain holistic insights.

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AWS Machine Learning Blog

JANUARY 17, 2024

To obtain such insights, the incoming raw data goes through an extract, transform, and load (ETL) process to identify activities or engagements from the continuous stream of device location pings. As part of the initial ETL, this raw data can be loaded onto tables using AWS Glue.

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Clustering AWS ML ML 123

IBM Journey to AI blog

APRIL 20, 2023

Data engineers can scan data connections into IBM Cloud Pak for Data to automatically retrieve a complete technical lineage and a summarized view including information on data quality and business metadata for additional context.

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Database Data Engineering Data Engineering Data Engineer 86

Precisely

JUNE 2, 2023

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. Insufficient skills, limited budgets, and poor data quality also present significant challenges.

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Data Silos ETL Data Quality Data Pipeline 52

Dataversity

DECEMBER 7, 2020

Data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations who seek to empower more and better data-driven decisions and actions throughout their enterprises. These groups want to expand their user base for data discovery, BI, and analytics so that their business […].

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Data Warehouse Business Intelligence Business Intelligence Analytics 90

Precisely

MAY 31, 2023

When attempting to build a data strategy, the primary obstacle organizations face is a lack of resources. Teams are building complex, hybrid, multi-cloud environments, moving critical data workloads to the cloud, and addressing data quality challenges. In many cases, data arrived too late to be useful.

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Data Quality Data Governance Data Observability ETL 52