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The New Data Integration Requirements

In(tegrate) the Clouds

This week SnapLogic posted a presentation of the 10 Modern Data Integration Platform Requirements on the company’s blog. They are: Application integration is done primarily through REST & SOAP services. Large-volume data integration is available to Hadoop-based data lakes or cloud-based data warehouses. Integration has to support the continuum of data velocities starting from batch all the way to continuous streams.

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4 Common Data Integrity Issues and How to Solve Them

Octopai

Seen on an online dating profile: Deep, thought-out analyst type searching for a partner with integrity. Or maybe that should be an “online data-ing profile”? Seeking: data with integrity. Integrity isn’t just a coveted trait for the partner of your dreams.

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Why IoT is not the solution to supply chain issues

DataFloq

More than 10 billion IoT devices around the world are constantly adding data to already overflowing data stores. Yet global supply chain disruption persists because it is not caused by a lack of data – which is why more IoT is not the answer.

IoT 260
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GraphDB: MongoDB Document Store Integration for Large-scale Metadata Management

Ontotext

Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore as it stores data in RDF triples). It provides the core infrastructure for solutions where modeling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical. GraphDB: MongoDB Document Store Integration for Large-scale Metadata Management. What are the benefits of such integration? Fining documents.

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GraphDB: Semantic Text Similarity for Identifying Related Terms & Documents

Ontotext

Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore because it stores data in RDF triples). It provides the core infrastructure for solutions where modelling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical. . GraphDB: Semantic Text Similarity for Identifying Related Terms & Documents. Once the index is built, navigating through our data is easy.

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Metadata Management, Data Governance and Automation

erwin

erwin released its State of Data Governance Report in February 2018, just a few months before the General Data Protection Regulation (GDPR) took effect. This research showed that the majority of responding organizations weren’t actually prepared for GDPR, nor did they have the understanding, executive support and budget for data governance – although they recognized the importance of it. Download Free GDPR Guide | Step By Step Guide to Data Governance for GDPR?.

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How Metadata Makes Data Meaningful

erwin

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. Managing metadata should not be a sub-goal of data governance.

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erwin Automation Framework: Achieving Faster Time-to-Value in Data Preparation, Deployment and Governance

erwin

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value. It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). A mature and sustainable data governance initiative must include data integration.

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How Metadata Makes Data Meaningful

erwin

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. Managing metadata should not be a sub-goal of data governance.

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Low code/no code tools reap IT benefits—with caveats

CIO

Many customers find the sweet spot in combining them with similar low code/no code tools for data integration and management to quickly automate standard tasks, and experiment with new services. Data Management, IT Leadership, No Code and Low Code

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Johnson & Johnson’s big bet on intelligent automation

CIO

Anand and Sorenson, the company’s senior vice president of technology services, supply chain, data integration, and reliability engineering, proposed the creation of an enterprise-wide Intelligent Automation Council that they would chair.

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Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. So, there must be a strategy regarding who, what, when, where, why, and how is the organization’s content to be indexed, stored, accessed, delivered, used, and documented. My favorite approach to TAM creation and to modern data management in general is AI and machine learning (ML).

Strategy 208
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Overcoming Data Silos to Increase Business Agility

DataFloq

Data lies at the heart of every modern organization. This data is often trapped in silos and inaccessible to business users for reporting and analytics. Data silos prevent businesses from performing analytics and making educated choices based on said analytics.

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How a cloud-based data ecosystem is helping 3M HIS transform the healthcare business

CIO

3M 360 Encompass is a collection of applications that work together to help hospitals streamline processes, receive accurate reimbursement, promote compliance, and make data-informed decisions. This is a dynamic view on data that evolves over time,” said Koll.

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Leveraging Content Management Software to Facilitate a Cloud-First Approach

CIO

A key component to the appeal of cloud-first business models is cloud technologies’ ability to simplify processes and streamline workflows through integration and automation. quintillion bytes of data (that’s 2.5

Software 284
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Episode 2: The Challenge of Data: Using Search to Accelerate Results That Matter

CIO

As cloud migration continues apace, accessing and using the data that runs and informs your applications has become a challenge for organizations of all sizes. Cloud Search company Elastic takes the challenge of the observability of data availability and security head on.

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Overcoming Data Silos to Increase Business Agility

DataFloq

Data lies at the heart of every modern organization. This data is often trapped in silos and inaccessible to business users for reporting and analytics. Data silos prevent businesses from performing analytics and making educated choices based on said analytics.

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Informatica – Limitations of its cloud platform

BizAcuity

Informatica is a data integration tool based on ETL architecture. It provides data integration software and services for various businesses, industries and government organizations including telecommunication, health care, financial and insurance services. Informatica uses the Extract, Transform & Load (ETL) architecture which is the most popular architecture to perform data integration. Data is moved from many databases to the Data warehouse.

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Metadata Moves: Knowledge Graph Technology for Logistics

Ontotext

From container tracking through loading onto a ship and further traveling through borders and arriving at a warehouse point, the set of events and the data they are logged by across the ecosystem of logistics processes, is large and heterogeneous.

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Top 10 Big Data CRM Tools To Increase Business Sales

Smart Data Collective

Big data technology is incredibly important in modern business. One of the most important applications of big data is with building relationships with customers. To get the best benefits from this CRM software, you need to integrate it into your business system.

Big Data 109
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A Little SEMANTiCS Goes A Long Way

Ontotext

Within three days, packed with tutorials, talks and insightful keynotes, technology professionals, industry experts and researchers shared their visions and learned about new research, technologies and good practices in the fields of Linked Data and knowledge graphs.

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Doing Cloud Migration and Data Governance Right the First Time

erwin

So if you’re going to move from your data from on-premise legacy data stores and warehouse systems to the cloud, you should do it right the first time. And as you make this transition, you need to understand what data you have, know where it is located, and govern it along the way.

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Announcing the GA of Cloudera DataFlow for the Public Cloud on Microsoft Azure

Cloudera

After the launch of Cloudera DataFlow for the Public Cloud (CDF-PC) on AWS a few months ago, we are thrilled to announce that CDF-PC is now generally available on Microsoft Azure, allowing NiFi users on Azure to run their data flows in a cloud-native runtime. . Processing Streaming Data.

KPI 95
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Data Governance Best Practices – Where to Start

DataFloq

Long recognized as a must in the data-driven world, data governance has never been easy for big and tiny organizations alike. Today, the complexities associated with adopting data governance best practices are greater than ever. What is data governance?

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The 6-Step Guide to Integrating Business Intelligence and Analytics

Smart Data Collective

Business Intelligence is the practice of collecting and analyzing data and transforming it into useful, actionable information. Business Intelligence uses methods and tools like machine learning to take massive, unstructured swaths of data and turn them into easy-to-use reports.

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What’s the Current State of Data Governance and Automation?

erwin

I’m excited to share the results of our new study with Dataversity that examines how data governance attitudes and practices continue to evolve. Defining Data Governance: What Is Data Governance? . 1 reason to implement data governance.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

Data lineage is the journey data takes from its creation through its transformations over time. Tracing the source of data is an arduous task. Data Lineage Use Case: From Tracing COVID-19’s Origins to Data-Driven Business. Five Benefits of Data Lineage.

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The unCatalog and Less Cow Bell

Andrew White

The State of Play with Data and Analytics Governance. Data and analytics (or data, or information) governance is a shambles for many organizations, and a success for very few. See Data and Analytics Governance as a Business Capability: A Gartner Trend Insight Report.

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The What, Why and How of Web Reports

FineReport

We’re living in a world where data is proliferating, and the traditional way of storing and displaying data may cause you to lose your way. Web Portal as Shield for Authorization and Data Management.

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5 keys to a successful API strategy

CIO

For enterprises seeking to overhaul their operations and offerings, application programming interfaces (APIs) have become key facilitators of digital transformation — especially as organizations increasingly move data and applications to the cloud. Codify data ownership.

Strategy 254
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Turning the page

Cloudera

This means we can double down on our strategy – continuing to win the Hybrid Data Cloud battle in the IT department AND building new, easy-to-use cloud solutions for the line of business. And, the Enterprise Data Cloud category we invented is also growing.

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Comparing DynamoDB and MongoDB for Big Data Management

Smart Data Collective

A growing number of companies are discovering the benefits of investing in big data technology. Companies around the world spent over $160 billion on big data technology last year and that figure is projected to grow 11% a year for the foreseeable future.

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More Definitions in the Data and Analytics Dictionary

Peter James Thomas

The peterjamesthomas.com Data and Analytics Dictionary is an active document and I will continue to issue revised versions of it periodically. Data Asset. Data Audit. Data Classification. Data Consistency. Data Controls. Data Curation (contributor: Tenny Thomas Soman ). Data Democratisation. Data Dictionary. Data Engineering. Data Ethics. Data Integrity. Data Lineage. Data Platform.

Testing 43
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The quest for high-quality data

O'Reilly on Data

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. As model building become easier, the problem of high-quality data becomes more evident than ever. The problem is even more magnified in the case of structured enterprise data. Data integration and cleaning.

Modeling 183
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It Takes A Village To Raise An Enterprise Knowledge Graph

Ontotext

In the two decades of building knowledge graphs with the W3C consortium standards (which are essentially a collaborative effort, fitting the need for interoperable and future-proof data management), we have discovered the power of synergies.

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Data Modeling 101: OLTP data modeling, design, and normalization for the cloud

erwin

How to create a solid foundation for data modeling of OLTP systems. As you undertake a cloud database migration , a best practice is to perform data modeling as the foundation for well-designed OLTP databases. Data modeling helps you right-size cloud migrations for cost savings.

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Modern Data Modeling: The Foundation of Enterprise Data Management and Data Governance

erwin

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. Metadata management is the key to managing and governing your data and drawing intelligence from it.

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How to Implement Longview Tax in Six Easy Steps

Insight Software

Automate your organization’s tax data collection and processing. Documents that need to be sourced in this initial phase include workpapers for a representative sample of the company entities (e.g.,

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KGF22: Knowledge Graphs and The Not So Quiet Cognitive Revolution

Ontotext

Yet, knowledge graph projects are hard to manage not only because graph databases still aren’t the first thing enterprises consider when seeking a solution to their data problem, but also because the understanding about what a knowledge graph can and cannot do is still unclear.

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7 Benefits of Metadata Management

erwin

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives.

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. Weaving the Semantic Web with Semantic Annotations and Linked Open Data.

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RDF-Star: Metadata Complexity Simplified

Ontotext

And yeah, the real-world relationships among the entities represented in the data had to be fudged a bit to fit in the counterintuitive model of tabular data, but, in trade, you get reliability and speed. They create reliable, consistent and communicable models for representing data.

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The Benefits of Data Management Automation: 8 Tips to Automate Data Management

erwin

As organizations deal with managing ever more data, the need to automate data management becomes clear. Last week erwin issued its 2020 State of Data Governance and Automation (DGA) Report. Protecting data came in last place. That’s a lot of data to manage!