AWS Big Data

FEBRUARY 29, 2024

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. Implement data privacy policies. Implement data quality by data type and source.

Data Governance 95

Data Governance Unstructured Data Metadata Data Lake 95

Jedox

OCTOBER 22, 2020

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it. Data analytics is not new.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

AWS Big Data

NOVEMBER 9, 2023

Building a data platform involves various approaches, each with its unique blend of complexities and solutions. In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform.

Data Warehouse 105

Data Warehouse Testing Data Quality Reporting 105

AWS Big Data

APRIL 4, 2024

Griffin is an open source data quality solution for big data, which supports both batch and streaming mode. In today’s data-driven landscape, where organizations deal with petabytes of data, the need for automated data validation frameworks has become increasingly critical.

Data Quality 62

Data Quality Data Lake Data Warehouse Data-driven 62

datapine

SEPTEMBER 27, 2022

We have already given you our top data visualization books , top business intelligence books , and best data analytics books. Our next book is dedicated to anyone who wants to start a career as a data scientist and is looking to get all the knowledge and skills in a way that is accessible and well-structured.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

CIO Business Intelligence

MARCH 9, 2022

Data-driven organizations understand that data, when analyzed, is a strategic asset. Organizations are expected to experience 30-40% data growth annually , which creates greater data protection responsibility and increases the data management burden. Cloudera and Dell Technologies for More Data Insights.

Data-driven 105

Data-driven Analytics Machine Learning Optimization 105

CIO Business Intelligence

NOVEMBER 22, 2022

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices. Here’s a quick rundown of seven major trends that will likely reshape your organization’s current data strategy in the days and months ahead.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

CIO Business Intelligence

JUNE 23, 2023

According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital business objectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.

Data Strategy 113

Data Strategy Strategy Visualization Consulting 113

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Smart Data Collective

APRIL 29, 2020

What is data management? Data management can be defined in many ways. The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated.

Management 83

Management Data Warehouse Digital Transformation Dashboards 83

IBM Big Data Hub

JUNE 8, 2022

However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key. Here are a few common data management challenges: Regulatory compliance on data use.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56

Laminar Security

DECEMBER 13, 2023

Understanding AI for data discovery Data discovery is a process that involves understanding where data resides in your environment, including all public clouds , data warehouses, SaaS applications, cloud file shares, and on-prem storage. Data discovery is an essential process in today’s data-driven world.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

DataKitchen

JANUARY 19, 2022

DataKitchen acts as a process hub that unifies tools and pipelines across teams, tools and data centers. DataKitchen could, for example, provide the scaffolding upon which a Snowflake cloud data platform or data warehouse could be integrated into a heterogeneous data mesh domain. About the Author. Abhinav Tiwari.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

CIO Business Intelligence

AUGUST 9, 2022

Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer job description.

Analytics 130

Analytics Data Science Statistics Unstructured Data 130

Alation

AUGUST 30, 2022

We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. We pioneered the world’s first machine learning data catalog, centralizing technical, operational, business and behavioral metadata into one place. This expansive map of tools constitutes today’s modern data stack.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

datapine

MAY 21, 2019

“BI is about providing the right data at the right time to the right people so that they can take the right decisions” – Nic Smith. Data analytics isn’t just for the Big Guys anymore; it’s accessible to ventures, organizations, and businesses of all shapes, sizes, and sectors.

Business Intelligence 183

Business Intelligence Cost-Benefit Dashboards ROI 183

Octopai

DECEMBER 28, 2022

For most data analysts and end-users, the vast majority of data activity is spent in the zoomed-in view of their data environment: analytics and reporting. Power BI is a robust data analytics and reporting platform, with the ability to draw in data from a variety of sources, then model, visualize and derive insights.

Reporting 52

Reporting Data Quality Visualization Data Warehouse 52

AWS Big Data

JUNE 6, 2023

In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

Analytics 55

Analytics Data Lake Testing Optimization 55

Jet Global

JANUARY 20, 2021

As the volume of available information continues to grow, data management will become an increasingly important factor in effective business management. Lack of proactive data management, on the other hand, can result in incompatible or inconsistent sources of information, as well as data quality problems.

Data Analytics 95

Data Analytics Management Analytics Data Warehouse 95

BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 Big Data Ecosystem.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Essential Big Data And Data Analytics Insights. million searches per day and 1.2

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. By understanding the power of ETL, organisations can harness the potential of their data and gain valuable insights that drive informed choices. Both approaches aim to improve data quality and enable accurate analysis.

Data Quality 85

Data Quality Data Warehouse Data-driven Reporting 85

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

MARCH 9, 2022

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI.

Data Lake 115

Data Lake Unstructured Data Data Warehouse Data Quality 115

AWS Big Data

JULY 13, 2023

To succeed further, their vision of data requires it to be accessible and actionable to all, with key roles and responsibilities defined by those who produce and consume data, as shown in the following figure. Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices.

Optimization 91

Optimization B2B Data Quality Sales 91

AWS Big Data

JULY 12, 2023

Introduction to Amazon Redshift Amazon Redshift is a fast, fully-managed, self-learning, self-tuning, petabyte-scale, ANSI-SQL compatible, and secure cloud data warehouse. Thousands of customers use Amazon Redshift to analyze exabytes of data and run complex analytical queries.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. You need to process this to make it ready for analysis.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

CIO Business Intelligence

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 128

Analytics Machine Learning Software Data mining 128

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

CIO Business Intelligence

MARCH 22, 2022

But the sheer volume of the world’s data is expected to nearly triple between 2020 and 2025 to a whopping 180 zettabytes. How do you introduce AI into your data and analytics infrastructure? To companies entrenched in decades-old business and IT processes, data fiefdoms, and legacy systems, the task may seem insurmountable.

Analytics 111

Analytics Key Performance Indicator Data Warehouse Data-driven 111

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

Metadata 59

Metadata Data Quality Statistics Data Science 59

IBM Big Data Hub

DECEMBER 7, 2023

Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses. They are seamlessly integrated with cloud-based data warehouses, facilitating the collection, storage and analysis of data from various sources.

OLAP 58

OLAP Slice and Dice Cost-Benefit Data Warehouse 58

IBM Big Data Hub

OCTOBER 27, 2022

When Newcomp Analytics started working with chocolatier Lindt Canada more than 15 years ago to support their supply chain, Lindt had no full-time IT personnel for analytics. Yet Newcomp continues to be an essential and trusted partner, helping the company keep up with the high volume of analytics solutions it needs to address.

Slice and Dice 52

Slice and Dice Analytics Sales Business Intelligence 52

IBM Big Data Hub

APRIL 27, 2022

Read: The first capability of a data fabric is a semantic knowledge data catalog, but what are the other 5 core capabilities of a data fabric? A data fabric also streamlines deriving insights from data through better data observability and data quality by automating manual tasks across data platforms using machine learning.

Management 80

Management Metadata Data Architecture Data Lake 80

Smart Data Collective

SEPTEMBER 26, 2021

Working with massive structured and unstructured data sets can turn out to be complicated. It’s obvious that you’ll want to use big data, but it’s not so obvious how you’re going to work with it. So, let’s have a close look at some of the best strategies to work with large data sets. Summarize and sample your data at query time.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

IBM Big Data Hub

OCTOBER 12, 2023

Once trained and deployed, models also need reliable access to historical and real-time data to generate content, make recommendations, detect errors, send proactive alerts, etc. They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources.

Cost-Benefit 77

Cost-Benefit Metadata Data Governance Modeling 77

IBM Big Data Hub

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.

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76