IBM Journey to AI blog

MAY 9, 2024

Understanding data governance in healthcare The need for a strong data governance framework is undeniable in any highly-regulated industry, but the healthcare industry is unique because it collects and processes massive amounts of personal data to make informed decisions about patient care. The consequence?

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Unite.AI

SEPTEMBER 16, 2024

Compiling data from these disparate systems into one unified location. This is where data integration comes in! Data integration is the process of combining information from multiple sources to create a consolidated dataset. Data integration tools consolidate this data, breaking down silos. The challenge?

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Unite.AI

SEPTEMBER 16, 2024

Compiling data from these disparate systems into one unified location. This is where data integration comes in! Data integration is the process of combining information from multiple sources to create a consolidated dataset. Data integration tools consolidate this data, breaking down silos. The challenge?

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Pickl AI

AUGUST 31, 2023

Almost all organisations nowadays make informed decisions by leveraging data and analysing the market effectively. However, analysis of data may involve partiality or incorrect insights in case the data quality is not adequate. What is Data Profiling in ETL? Z-score, interquartile range).

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IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Banks and their employees place trust in their risk models to help ensure the bank maintains liquidity even in the worst of times.

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Marktechpost

JUNE 5, 2024

In addition, organizations that rely on data must prioritize data quality review. Data profiling is a crucial tool. For evaluating data quality. Data profiling gives your company the tools to spot patterns, anticipate consumer actions, and create a solid data governance plan.

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Pickl AI

NOVEMBER 4, 2024

It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. The goal is to ensure that data is available, reliable, and accessible for analysis, ultimately driving insights and informed decision-making within organisations.

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IBM Journey to AI blog

JULY 3, 2024

Your data strategy should incorporate databases designed with open and integrated components, allowing for seamless unification and access to data for advanced analytics and AI applications within a data platform. This enables your organization to extract valuable insights and drive informed decision-making.

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Unite.AI

DECEMBER 5, 2023

In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making. The following elements serve as a backbone for a functional data warehouse.

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Pickl AI

JUNE 28, 2023

What is Data Mining? In today’s data-driven world, organizations collect vast amounts of data from various sources. Information like customer interactions, and sales transactions plays a pivotal role in decision-making. But, this data is often stored in disparate systems and formats. Wrapping It Up !!!

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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. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

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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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Pickl AI

JULY 8, 2024

It covers best practices for ensuring scalability, reliability, and performance while addressing common challenges, enabling businesses to transform raw data into valuable, actionable insights for informed decision-making. As stated above, data pipelines represent the backbone of modern data architecture.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Focused on addressing the challenge of agricultural data standardization, Agmatix has developed proprietary patented technology to harmonize and standardize data, facilitating informed decision-making in agriculture. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

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TransOrg Analytics

AUGUST 9, 2024

Eight prominent concepts stand out: Customer Data Platforms (CDPs), Master Data Management (MDM), Data Lakes, Data Warehouses, Data Lakehouses, Data Marts, Feature Stores, and Enterprise Resource Planning (ERP). Pros: Data Consistency: Ensures consistent and accurate data across the organization.

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Pickl AI

NOVEMBER 15, 2023

This flexibility allows organizations to store vast amounts of raw data without the need for extensive preprocessing, providing a comprehensive view of information. Centralized Data Repository Data Lakes serve as a centralized repository, consolidating data from different sources within an organization.

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Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

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Data Quality Business Intelligence ETL Data Integration 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

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TransOrg Analytics

AUGUST 21, 2024

Customer 360 initiatives are designed to bring together relevant information about individual consumers from different touch points, including but not limited to sales, marketing, customer service, and social media platforms. How Data Engineering Enhances Customer 360 Initiatives 1.

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Mlearning.ai

MAY 16, 2023

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. Finally, the transformed data is loaded into the target system.

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Pickl AI

AUGUST 9, 2024

Improved Data Navigation Hierarchies provide a clear structure for users to navigate through data. This allows for intuitive querying and reporting, making it easier for users to find the information they need. Enhanced Data Analysis By allowing users to drill down into data, hierarchies enable more detailed analysis.

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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. To utilize this data ethically, several steps need to be followed. It starts with the collection of data itself.

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The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. If you aren’t aware already, let’s introduce the concept of ETL. Redshift, S3, and so on.

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Mlearning.ai

AUGUST 10, 2023

The project I did to land my business intelligence internship — CAR BRAND SEARCH ETL PROCESS WITH PYTHON, POSTGRESQL & POWER BI 1. Section 2: Explanation of the ETL diagram for the project. Section 4: Reporting data for the project insights. ETL ARCHITECTURE DIAGRAM ETL stands for Extract, Transform, Load.

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Bugra Akyildiz

JUNE 29, 2024

This week, I will cover why I think data janitor work is dying and companies that are built in on top of data janitor work could be ripe for disruption through LLMs and what to do about it. A data janitor is a person who works to take big data and condense it into useful amounts of information. No, not really.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Schema Enforcement: Data warehouses use a “schema-on-write” approach. Data must be transformed and structured before loading, ensuring data consistency and quality.

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The MLOps Blog

MARCH 15, 2023

This is what data processing pipelines do for you. Automating myriad steps associated with pipeline data processing, helps you convert the data from its raw shape and format to a meaningful set of information that is used to drive business decisions. This ensures that the data is accurate, consistent, and reliable.

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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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Pickl AI

APRIL 26, 2024

The blog also presents popular data analytics courses, emphasizing their curriculum, learning methods, certification opportunities, and benefits to help aspiring Data Analysts choose the proper training for their career advancement. Data Warehousing and ETL Processes What is a data warehouse, and why is it important?

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Data Analysis Machine Learning ETL Explainability 52

Pickl AI

AUGUST 9, 2024

Volume It refers to the sheer amount of data generated daily, which can range from terabytes to petabytes. Organisations must develop strategies to store and manage this vast amount of information effectively. Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities.

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AWS Machine Learning Blog

AUGUST 21, 2023

Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model. This data represents transaction data for products and includes information such as customer demographics, inventory, web sales, and promotions. Tina has access to information about sales.

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Pickl AI

NOVEMBER 14, 2023

Importing Table Data Flat Files Table data flat files typically refer to structured data files where information is organized in rows and columns, resembling a table or spreadsheet.

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The MLOps Blog

AUGUST 3, 2023

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. Depending on your size, you might have a data catalog. Maybe storing and emitting open lineage information, etc.

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Pickl AI

JUNE 11, 2024

Summary: A data warehouse is a central information hub that stores and organizes vast amounts of data from different sources within an organization. Unlike operational databases focused on daily tasks, data warehouses are designed for analysis, enabling historical trend exploration and informed decision-making.

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Unite.AI

MAY 21, 2024

An additional 79% claim new business analysis requirements take too long to be implemented by their data teams. Other factors hindering widespread AI adoption include the lack of an implementation strategy, poor data quality, insufficient data volumes and integration with existing systems.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. For more information, see Zeta Global’s home page. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

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IBM Journey to AI blog

AUGUST 4, 2023

Architecture for data democratization Data democratization requires a move away from traditional “data at rest” architecture, which is meant for storing static data. Traditionally, data was seen as information to be put on reserve, only called upon during customer interactions or executing a program.

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