Convert From Big Data to Smart Data

TDAN

Did you know that 90% of all data has been generated over the last 2 years? Big Data has been an important topic in the marketing scene for quite some time.

Deep Learning Can Make a Difference

TDAN

Deep learning, as defined by MathWorks, is a system of artificial intelligence that is built around learning by example. Multiple industries have already understood the benefits that deep learning brings to their operational capabilities.

The Non-Invasive Data Governance Framework – The Details

TDAN

The third and final part of the Non-Invasive Data Governance Framework details the breakdown of components by level, providing considerations for what must be included at the intersections.

We Need Data Ethics Now

TDAN

What are the ethics of collecting, managing, and analyzing data? Is it going to come to regulations enforced by government and industry consortiums to make businesses think about data ethics with the goal of truly treating data ethically?

Lean Data Governance Strategies

TDAN

The goal of data governance is to ensure the quality, availability, integrity, security, and usability within an organization. The way that you go about this is up to you.

Database Administration Impacted by the Connected Economy

TDAN

Before the end of the decade, the number of connected objects is projected to expand greatly. According to several different analysts, the number of connected objects by 2020 could be as low as 26 billion or as high as 50 billion. But even the low end of that range is quite large.

The Unfolding of the Data-Centric Paradigm – Part 1

TDAN

There is a movement to upend traditional thinking about information systems by putting data and meaning at the center of strategy, architecture, and system development sequencing. It is the most recent of a series of data-centric waves which, over several decades, have attempted to remedy the entrenched application-centric paradigm.

IT 40

The latest edition of The Data & Analytics Dictionary is now out

Peter James Thomas

After a hiatus of a few months, the latest version of the peterjamesthomas.com Data and Analytics Dictionary is now available. Business Glossary (contributor: Tenny Thomas Soman ). Data Architecture – Definition (2). Data Catalogue. Data Community.

Big Data Hadoop vs. Traditional RDBMS

TDAN

Apache Hadoop is a comprehensive ecosystem which now features many open source components that can fundamentally change an enterprise’s approach to storing, processing, and analyzing data.

Common Data Modeling Mistakes and Their Impact

TDAN

Although data modeling has been around for over 30 years, it ranks among the top areas from which database application problems arise. Today’s data modeling […].

DB Pros Need to Know Cloud Migration

TDAN

In 1961, Professor John McCarthy was the first to publicly suggest in a speech at the centennial celebration of the Massachusetts Institute of Technology that: “Computing may someday be organized as a public utility just as the telephone system is a public utility.

Agile vs. Lean Project Management

TDAN

The perfect project management style has yet to be discovered. For the curious project owner, exploring all the different project management methodologies is an enriching endeavor.

DAMA International Community Corner: Announcements & New Chapters

TDAN

Welcome to DAMA Corner, a source of information for data management professionals here in TDAN.com, an industry-leading publication for people interested in learning about data administration, data management disciplines, and best practices. Each column provides an update on the professional organization DAMA International, and an opportunity to share your experience with other professionals that are passionate about data! […].

Artificial Intelligence Hope – Reinvent the ‘Broken’ Physician-Patient Relationship

TDAN

The physician job has become less and less focused on patients, and more and more preoccupied with the administrative and data-entry tasks that require attention and time. A study by the University of Wisconsin and the American Medical Association (AMA) found that primary care physicians spend almost 6 hours on EHR data entry during a […].

A Simple Data Capability Framework

Peter James Thomas

As part of my consulting business , I end up thinking about Data Capability Frameworks quite a bit. Sometimes this is when I am assessing current Data Capabilities, sometimes it is when I am thinking about how to transition to future Data Capabilities. Data Strategy.

Convergent Evolution

Peter James Thomas

No this article has not escaped from my Maths & Science section , it is actually about data matters. That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Of course some architectures featured both paradigms as well.

Learn Microsoft BI Stack

Ms SQL Girl

This is a much belated follow up to my very first blog post Open Your Mind To Business Intelligence from 4 years ago. Chapter 2 Designing an Eff ective Business Intelligence Architecture. Chapter 3 Selecting the Data Architecture that Fits Your Organization.

Key Differences between a Traditional Data Warehouse and Big Data

Perficient Data & Analytics

Traditional data warehouse solutions were originally developed out of necessity. In order to run the business, every company uses enterprise resource planning (ERP) and CRM applications to manage back-office functions like finance, accounts payable, accounts receivable, general ledger, and supply chain, as well as front-office functions like sales, service, and call center. So how do you make the data gathered more useful? That’s where business intelligence comes into play.

Snowflake: A New Blueprint for the Modern Data Warehouse

Sirius Computer Solutions

Companies today are struggling under the weight of their legacy data warehouse. These old and inefficient systems were designed for a different era, when data was a side project and access to analytics was limited to the executive team. Data migration and integration.

Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?

Ontotext

With several examples, you will see how knowledge management can be made smarter using the potential of semantic technology to fuse data, analyze relationships, detect patterns and infer new facts from enriched datasets. .

Why Your BI and Analytics Platform Should be Cloud-Agnostic

Sisense

With the introduction of VMware in the 1990s, developers embraced the ability to run their applications on virtual machines that could then run on any physical machine architecture. What does it mean for your data? You Should be Able to Store Your Data In Any Cloud.

Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?

Ontotext

Whether you refer to the use of semantic technology as Linked Data technology or smart data management technology, these concepts boil down to connectivity. Connectivity in the sense of connecting data from different sources and assigning these data additional machine-readable meaning.

Three Trends for Modernizing Analytics and Data Warehousing in 2019

Cloudera

Data analytics priorities have shifted this year. Growth factors and business priority are ever changing. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. Big Data Technologies and Architectures.

Meet the newest Data Superheros: The Sixth Annual Data Impact Awards Finalists Are…

Cloudera

Drum roll… Starting from well over 100 nominations, we are excited to announce the finalists for this year’s Data Impact Awards ! Two weeks from today we will announce the winners at the Data Impact Awards Celebration on Tuesday, 11th September the week of Strata Data 2018 , New York.

Topics to watch at the Strata Data Conference in New York 2019

O'Reilly on Data

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. Strata attracts the leading names in the fields of data management, data engineering, analytics, ML, and artificial intelligence (AI).