erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 69

Analytics Data Warehouse Data Lake Metadata 69

erwin

MARCH 8, 2019

Following are two examples that illustrate the data governance stock check, including the Any 2 approach in action, based on real consulting engagements. Therefore, the organization needed to catalog the data it acquires from suppliers, ensure its quality, classify it, and then sell it to customers.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

Octopai

MARCH 22, 2023

Data migration can be a daunting task, especially when dealing with large volumes of data. Snowflake is one of the leading cloud-based data warehouse that provides scalability, flexibility, and ease of use. Snowflake data warehouse platform has been designed to leverage the power of modern-day cloud computing technology.

Cost-Benefit 78

Cost-Benefit Data Warehouse Unstructured Data Optimization 78

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy. The other half of our value proposition is Ontotext GraphDB.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

Data Governance 40

Data Governance Risk Metadata Management 40

Jet Global

MAY 1, 2020

The mechanical solution is to build a data warehouse. This will be the place where everybody has come together and agreed upon the data that you are going to use to manage your business, and how that data is going to be combined with which methods you’re going to use to calculate things like gross profit and net income.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

Alation

SEPTEMBER 7, 2021

In this blog we will discuss how Alation helps minimize risk with active data governance. Now that you have empowered data scientists and analysts to access the Snowflake Data Cloud and speed their modeling and analysis, you need to bolster the effectiveness of your governance models. Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

Ontotext

MARCH 18, 2020

Consequently, many data leaders today are striving to overcome these barriers by streamlining their enterprise knowledge management processes and practices. The knowledge graph model is one way of doing it and, not surprisingly, it has been in increasing demand in the last decade.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

Ontotext

MARCH 8, 2023

With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models. However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Alation

JULY 19, 2022

Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000. 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.

Metadata 59

Metadata Data Quality Statistics Data Science 59

erwin

FEBRUARY 19, 2021

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage). Map data flows.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 99

Data Lake Data Warehouse Management Risk 99

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. anomaly detection).

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. The central concept is the idea of a document.

Metadata 99

Metadata Visualization Unstructured Data Data mining 99

IBM Big Data Hub

OCTOBER 12, 2023

Connecting AI models to a myriad of data sources across cloud and on-premises environments AI models rely on vast amounts of data for training. 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.

Cost-Benefit 83

Cost-Benefit Metadata Data Governance Modeling 83

Ontotext

FEBRUARY 26, 2024

However, it still requires more effort than building a single-purpose graph, like a labeled property graph (LPG), where you can whiteboard your graph model in an hour. Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. RDF vs LPG?

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Alation

OCTOBER 18, 2022

Alation is pleased to be named a dbt Metrics Partner and to announce the start of a partnership with dbt, which will bring dbt data into the Alation data catalog. In the modern data stack, dbt is a key tool to make data ready for analysis. Lineage between dbt sources, models, and metrics.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

AWS Big Data

MARCH 3, 2023

Finally, data integrity is of paramount importance. Every event in the data source can be relevant, and our customers don’t tolerate data loss, poor data quality, or discrepancies between the source and Tricentis Analytics. Ricardo Serafim is a Senior AWS Data Lab Solutions Architect.

Software 52

Software Data Lake Testing Cost-Benefit 52

BI-Survey

FEBRUARY 23, 2023

Action items Take a holistic and long-term approach to data culture Changing the data culture in a company takes time – a change of mindset and a shift in behavior cannot come overnight. BARC’s model for systematizing the starting points for changing a data culture – the BARC Data Culture Framework – has become well established.

Metadata 52

Metadata Data-driven Data Strategy Strategy 52

datapine

AUGUST 29, 2022

Data Smart’ contains enough practical knowledge to actually start performing analyses by using good old Microsoft Excel. Best for: the seasoned BI professional who is ready to think deep and hard about important issues in data analytics and big data. The subsequent chapters focus on predictive and descriptive analysis.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

NOVEMBER 7, 2023

Centralized reporting boosts data value For more than a decade, pediatric health system Phoenix Children’s has operated a data warehouse containing more than 120 separate data systems, providing the ability to connect data from disparate systems. Companies should also incorporate data discovery, Higginson says.

Data Warehouse 138

Data Warehouse Digital Transformation Data-driven Reporting 138

Alation

AUGUST 4, 2022

Aligning business goals to data usage is a key foundation. There is no digital transformation without business-aligned data control,” Capgemini Executive Steve Jones adds. “The architectural model of big data is fundamental to this.”. Everything comes down to what organizations do with data.

Big Data 95

Big Data Digital Transformation Data Lake Data-driven 95

Rocket-Powered Data Science

MARCH 20, 2023

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. This is where SAP Datasphere (the next generation of SAP Data Warehouse Cloud) comes in.

Data Warehouse 324

Data Warehouse Metadata Digital Transformation Unstructured Data 324

Cloudera

AUGUST 26, 2020

The Cloudera Connect Technology Certification program uses a well-documented process to test and certify our Independent Software Vendors’ (ISVs) integrations with our data platform. This allows our customers to reduce spend on highly specialized hardware and leverage the tools of a modern data warehouse. .

Machine Learning 80

Machine Learning Big Data Data Warehouse Data-driven 80

AWS Big Data

APRIL 25, 2023

The following factors guided our decision: Tool close to data – It was important to have the data visualization tool as close to the data as possible. At Dafiti, the entire infrastructure is on AWS, and we use Amazon Redshift as our Data Warehouse.

Visualization 52

Visualization IT Data-driven Reporting 52

IBM Big Data Hub

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

Management 59

Management Enterprise Modeling Data Quality 59

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Andrew White

MARCH 22, 2019

I jest here; what I mean to say is that economics and rational, smart models should trump perfectly designed ideas. This ties into the failure of data governance and MDM (see first item in this list). This ties into the failure of data governance and MDM (see first item in this list). Economics over architecture.

Analytics 68

Analytics Metadata Data Governance Management 68

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. This includes cleaning, aggregating, enriching, and restructuring data to fit the desired format.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

SEPTEMBER 1, 2023

Data-Driven Decision Making: Embedded predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

Jet Global

MARCH 14, 2024

Mastering Data: Effectively Manage Your Data Download Now How Jet Analytics Enhances Microsoft Fabric Jet Analytics from insightsoftware is a complete data preparation, automation and modeling solution that enables Microsoft Dynamics customers to accelerate Dynamics ERP-ready BI projects without requiring specialist skills.

Analytics 52

Analytics Management Reporting Enterprise 52

Jet Global

FEBRUARY 23, 2024

A Centralized Hub for Data Data silos are the number one inhibitor to commerce success regardless of your business model. Through effective workflow, data quality, and governance tools, a PIM ensures that disparate content is transformed into a company-wide strategic asset.

Management 52

Management Sales Publishing Data Quality 52