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 95

Data Lake Unstructured Data Data Warehouse Data Quality 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 Poor data quality.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

DataKitchen

JANUARY 25, 2022

Ensuring that data is available, secure, correct, and fit for purpose is neither simple nor cheap. Companies end up paying outside consultants enormous fees while still having to suffer the effects of poor data quality and lengthy cycle time. . When a job is automated, there is little advantage to outsourcing. .

Consulting 244

Consulting Testing Data Lake Data Quality 244

AWS Big Data

DECEMBER 13, 2023

For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users. Data quality check – The data quality module enables you to perform quality checks on a huge amount of data and generate reports that describe and validate the data quality.

Metadata 91

Metadata Data Lake Visualization Data Transformation 91

AWS Big Data

FEBRUARY 29, 2024

To provide a response that includes the enterprise context, each user prompt needs to be augmented with a combination of insights from structured data from the data warehouse and unstructured data from the enterprise data lake. Implement data privacy policies. Implement data quality by data type and source.

Data Governance 97

Data Governance Unstructured Data Metadata Data Lake 97

Alation

MARCH 21, 2023

Why is data analytics important for travel organizations? With data analytics , travel organizations can gain real-time insights about customers to make strategic decisions and improve their travel experience. How is data analytics used in the travel industry?

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

DataKitchen

AUGUST 3, 2021

First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex data lake maintained by a specialized team drowning in technical debt. See the pattern?

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

Ontotext

FEBRUARY 12, 2024

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

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

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines support data science and business intelligence projects by providing data engineers with high-quality, consistent, and easily accessible data.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

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

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 63

Data Quality Data Lake Data Warehouse Data-driven 63

AWS Big Data

MARCH 1, 2023

Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. Transform CSV to Parquet Raw CSV files are converted to Parquet data format with AWS Glue.

Data Lake 87

Data Lake Deep Learning Modeling Interactive 87

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.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

AWS Big Data

MAY 1, 2024

Excel-based data utilization Microsoft Excel is available on almost everyone’s PC in the company, and it helps lower the hurdles when starting to utilize data. However, Excel is mainly designed for spreadsheets; it’s not designed for large-scale data analytics and automation.

Dashboards 113

Dashboards Data-driven Publishing Cost-Benefit 113

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

Optimization 92

Optimization B2B Data Quality Sales 92

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Architecture 60

Data Architecture Data Lake Machine Learning Data Governance 60

AWS Big Data

DECEMBER 21, 2023

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. AWS Glue provides both visual and code-based interfaces to make data integration effortless. About the authors Basheer Sheriff is a Senior Solutions Architect at AWS.

Analytics 98

Analytics IT Data Lake Visualization 98

Alation

AUGUST 4, 2022

Many CIOs argue the rise of big data pushed people to use data more proactively for business decision-making. Big data got“ more leaders and people in the organization to use data, analytics, and machine learning in their decision making,” says former CIO Isaac Sacolick. Big data can grow too big fast.

Big Data 95

Big Data Digital Transformation Data Lake Data-driven 95

AWS Big Data

JANUARY 30, 2023

Many customers need an ACID transaction (atomic, consistent, isolated, durable) data lake that can log change data capture (CDC) from operational data sources. There is also demand for merging real-time data into batch data. Delta Lake framework provides these two capabilities.

Insurance 82

Insurance Data Lake Data-driven Management 82

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

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 57

Data-driven Enterprise Data Governance Data Lake 57

AWS Big Data

JUNE 6, 2023

Unless, of course, the rest of their data also resides in the Google Cloud. 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.

Analytics 55

Analytics Data Lake Testing Optimization 55

DataKitchen

OCTOBER 11, 2021

The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. First, a business analyst consolidates data from some public websites, an SFTP server and some downloaded email attachments, all into Excel. Adding Tests to Reduce Stress.

Testing 152

Testing Metadata Dashboards Statistics 152

CIO Business Intelligence

MARCH 22, 2022

Which type(s) of storage consolidation you use depends on the data you generate and collect. . One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Set up unified data governance rules and processes.

Analytics 90

Analytics Key Performance Indicator Data Warehouse Data-driven 90

CIO Business Intelligence

NOVEMBER 28, 2023

With each game release and update, the amount of unstructured data being processed grows exponentially, Konoval says. This volume of data poses serious challenges in terms of storage and efficient processing,” he says. To address this problem RetroStyle Games invested in data lakes. Quality is job one.

Unstructured Data 117

Unstructured Data Data-driven Visualization Data Quality 117

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

AWS Big Data

NOVEMBER 9, 2023

It allows users to write data transformation code, run it, and test the output, all within the framework it provides. Use case The Enterprise Data Analytics group of a large jewelry retailer embarked on their cloud journey with AWS in 2021. AWS Glue – AWS Glue is used to load files into Amazon Redshift through the S3 data lake.

Data Warehouse 107

Data Warehouse Testing Data Quality Reporting 107

IBM Big Data Hub

APRIL 27, 2022

Since its uniquely metadata-driven, the abstraction layer of a data fabric makes it easier to model, integrate and query any data sources, build data pipelines, and integrate data in real-time. This improves data engineering productivity and time-to-value for data consumers. What’s a data mesh?

Management 81

Management Metadata Data Architecture Data Lake 81

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

Alation

JANUARY 27, 2022

How do businesses transform raw data into competitive insights? Data analytics. Modern businesses are increasingly leveraging analytics for a range of use cases. Analytics can help a business improve customer relationships, optimize advertising campaigns, develop new products, and much more. What is Data Analytics?

Data Governance 52

Data Governance Analytics Cost-Benefit Data-driven 52

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. Overall, DataOps is an essential component of modern data-driven organizations. Query> DataOps.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

IBM Big Data Hub

DECEMBER 7, 2023

C-OLAP optimized data storage for faster query processing, while IM-OLAP stored data in memory to minimize data access latency and enable real-time analytics. Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses.

OLAP 59

OLAP Slice and Dice Cost-Benefit Data Warehouse 59

FineReport

MARCH 24, 2024

Rapid technological advancements and extensive networking have propelled the evolution of data analytics, fundamentally reshaping decision-making practices across various sectors. In this landscape, data analysts assume a pivotal role, tasked with interpreting data to drive informed decision-making.

Statistics 52

Statistics Data mining Visualization Reporting 52

Andrew White

JANUARY 11, 2021

Note: Delivery of data, analytics solutions and the sustainment of technology, data and services is a question. Does Data warehouse as a software tool will play role in future of Data & Analytics strategy? Data lakes don’t offer this nor should they. Governance. Product Management.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

AWS Big Data

DECEMBER 18, 2023

Solution overview One of the common functionalities involved in data pipelines is extracting data from multiple data sources and exporting it to a data lake or synchronizing the data to another database. His core area of expertise include Technology Strategy, Data Analytics, and Data Science.

Metadata 123

Metadata Visualization Data Lake Data-driven 123

AWS Big Data

NOVEMBER 15, 2023

As the organization receives data from multiple external vendors, it often arrives in different formats, typically Excel or CSV files, with each vendor using their own unique data layout and structure. DataBrew is an excellent tool for data quality and preprocessing.

Metadata 86

Metadata Sales Data Lake Big Data 86