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 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

Data Quality 105

Data Quality Data Warehouse Data-driven Reporting 105

BI-Survey

FEBRUARY 17, 2020

From operational systems to support “smart processes”, to the data warehouse for enterprise management, to exploring new use cases through advanced analytics : all of these environments incorporate disparate systems, each containing data fragments optimized for their own specific task. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

Timo Elliott

JANUARY 6, 2022

The SAP Data Intelligence Cloud solution helps you simplify your landscape with tools for creating data pipelines that integrate data and data streams on the fly for any type of use – from data warehousing to complex data science projects to real-time embedded analytics in business applications.

IT 135

IT Metadata Data-driven Data Governance 135

AWS Big Data

FEBRUARY 27, 2024

The following are the key components of the Bluestone Data Platform: Data mesh architecture – Bluestone adopted a data mesh architecture, a paradigm that distributes data ownership across different business units. This enables data-driven decision-making across the organization.

Data-driven 106

Data-driven Data Lake Data Quality Data Governance 106

AWS Big Data

AUGUST 1, 2024

Large-scale data warehouse migration to the cloud is a complex and challenging endeavor that many organizations undertake to modernize their data infrastructure, enhance data management capabilities, and unlock new business opportunities. This makes sure the new data platform can meet current and future business goals.

Data Warehouse 99

Data Warehouse KPI Optimization Cost-Benefit 99

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Implement data privacy policies. Implement data quality by data type and source.

Data Governance 109

Data Governance Unstructured Data Metadata Data Lake 109

BI-Survey

DECEMBER 22, 2020

Managers see data as relevant in the context of digitalization, but often think of data-related problems as minor details that have little strategic importance. Thus, it is taken for granted that companies should have a data strategy. But what is the scope of an effective strategy and who is affected by it?

Data-driven 52

Data-driven Data Strategy Strategy Enterprise 52

AWS Big Data

JULY 13, 2023

This allows for transparency, speed to action, and collaboration across the group while enabling the platform team to evangelize the use of data: Altron engaged with AWS to seek advice on their data strategy and cloud modernization to bring their vision to fruition.

Optimization 95

Optimization B2B Data Quality Sales 95

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 52

CIO Business Intelligence

FEBRUARY 16, 2023

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

DataKitchen

JUNE 24, 2021

Previously we would have a very laborious data warehouse or data mart initiative and it may take a very long time and have a large price tag. Before we jump into a methodology or even a data strategy-based approach, what are we trying to accomplish? Automate the data collection and cleansing process.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

TDAN

FEBRUARY 1, 2022

In this blog, we will discuss a common problem for data warehouses that are designed to maintain data quality and provide evidence of accuracy. Without verification, the data can’t be trusted. Enter the mundane, but necessary, task of data reconciliation. This is often a time-consuming and wasteful process.

Data Warehouse 59

Data Warehouse Data Quality IT Data Governance 59

CIO Business Intelligence

NOVEMBER 29, 2022

Layering technology on the overall data architecture introduces more complexity. Today, data architecture challenges and integration complexity impact the speed of innovation, data quality, data security, data governance, and just about anything important around generating value from data.

Data Architecture 111

Data Architecture Data Integration IoT Data-driven 111

IBM Big Data Hub

JUNE 8, 2022

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. Here are a few common data management challenges: Regulatory compliance on data use. Data quality.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56

BizAcuity

APRIL 1, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. Data governance and security measures are critical components of data strategy.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

BizAcuity

JANUARY 31, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. Data governance and security measures are critical components of data strategy.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

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 megabytes of data every second?

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

Peter James Thomas

MAY 3, 2019

Control of Data to ensure it is Fit-for-Purpose. This refers to a wide range of activities from Data Governance to Data Management to Data Quality improvement and indeed related concepts such as Master Data Management. Data Architecture / Infrastructure. Data Strategy.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 89

Data Lake Data Governance Data Architecture Machine Learning 89

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Alation

AUGUST 26, 2021

Under an active data governance framework , a Behavioral Analysis Engine will use AI, ML and DI to crawl all data and metadata, spot patterns, and implement solutions. Data Governance and Data Strategy. In other words, leaders are prioritizing data democratization to ensure people have access to the data they need.

Metadata 64

Metadata Data Governance Dashboards Software 64

IBM Big Data Hub

AUGUST 4, 2023

Data fabric Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. Key steps include: Define business and data objectives –What are your company’s goals?

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

BI-Survey

FEBRUARY 23, 2023

“Data culture eats data strategy for breakfast” has become a popular saying among data and analytics managers and executives. Even the best data strategy cannot fulfill its potential if the data culture in the company does not match it.

Metadata 52

Metadata Data-driven Data Strategy Strategy 52

Alation

AUGUST 11, 2021

The three of us talked migration strategy and the best way to move to the Snowflake Data Cloud. As Vice President of Data Governance at TMIC, Anthony has robust experience leading cloud migration as part of a larger data strategy. This underscores the importance of having a plan that fits your data strategy.

Data Governance 52

Data Governance Insurance Metadata Strategy 52

Jet Global

FEBRUARY 25, 2025

Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to ensure its quality, accuracy, and reliability. This process is crucial for businesses that rely on data-driven decision-making, as poor data quality can lead to costly mistakes and inefficiencies.

Cost-Benefit 52

Cost-Benefit Data Collection Finance Reporting 52

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 75

Cost-Benefit Metadata Optimization Management 75

AWS Big Data

FEBRUARY 1, 2023

Clients access this data store with an API’s. Amazon S3 as data lake For better data quality, we extracted the enriched data into another S3 bucket with the same AWS Glue job. Every dataset in our system is uniquely identified by snapshot ID, which we can search from our metadata store.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Ontotext

MAY 2, 2024

They are expected to understand the entire data landscape and generate business-moving insights while facing the voracious needs of different teams and the constraints of technology architecture and compliance. Evolution of data approaches The data strategies we’ve had so far have led to a lot of challenges and pain points.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Ontotext

MARCH 8, 2023

As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 52

CIO Business Intelligence

APRIL 9, 2025

Revisiting the foundation: Data trust and governance in enterprise analytics Despite broad adoption of analytics tools, the impact of these platforms remains tied to data quality and governance. This capability has become increasingly more critical as organizations incorporate more unstructured data into their data warehouses.

Enterprise 102

Enterprise Analytics Experimentation Statistics 102

Alation

OCTOBER 21, 2021

Just as lakes benefit from the filtering power of surrounding rocks, roots, and soil to sift out incoming impurities, data lakes benefit from a diligent effort to prevent them from becoming a dumping ground for all and any data. Ungoverned data. Data governance helps keep data quality high and data literacy efforts on track.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Jet Global

JANUARY 30, 2025

Its distributed architecture empowers organizations to query massive datasets across databases, data lakes, and cloud platforms with speed and reliability. Establishing a governance policy plays a critical role in maintaining data quality and compliance by clearly defining ownership and accountability.

Data Lake 64

Data Lake Data-driven Optimization Enterprise 64

Jet Global

OCTOBER 4, 2023

Finance teams are under pressure to slash costs while playing a key role in data strategy, yet they are still bogged down by manual tasks, overreliance on IT, and low visibility on company data. Addressing these challenges often requires investing in data integration solutions or third-party data integration tools.

Finance 52

Finance Reporting Cost-Benefit Management 52

CIO Business Intelligence

NOVEMBER 25, 2024

It’s all about the data When Michele Stanton joined HGA, a national design, architecture, and engineering firm two years ago as CIO, she “learned very quickly data was the biggest challenge the company was facing.” She realized HGA needed a data strategy, a data warehouse, and a data analytics leader.

Digital Transformation 131

Digital Transformation Data Warehouse Data Governance Enterprise 131

CIO Business Intelligence

FEBRUARY 7, 2025

We bet hard on the enterprise data warehouse. Honeywell uses Snowflake for its enterprise data warehouse (EDW), and Jordan says it holds everything: bookings, billings, backlog, inventory. We run the company now as a data-driven enterprise, she says.

Data Warehouse 80

Data Warehouse Data-driven Data Strategy Metrics 80