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We live in a data-rich, insights-rich, and content-rich world. 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. Plus, AI can also help find key insights encoded in data.
Having a clearly defined digitaltransformation strategy is an essential best practice for successful digitaltransformation. But what makes a viable digitaltransformation strategy? Constructing A DigitalTransformation Strategy: Data Enablement. Probably not.
What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise.
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. What Is Metadata? Harvest data.
For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Together, these capabilities enable terminal operators to enhance efficiency and competitiveness in an industry that is increasingly datadriven.
Data is the foundation of innovation, agility and competitive advantage in todays digital economy. As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Data quality is no longer a back-office concern.
It’s time to consider data-driven enterprise architecture. The traditional approach to enterprise architecture – the analysis, design, planning and implementation of IT capabilities for the successful execution of enterprise strategy – seems to be missing something … data. That’s right. This is what we call the Mezzo.
What Is Metadata? Metadata is information about data. A clothing catalog or dictionary are both examples of metadata repositories. Indeed, a popular online catalog, like Amazon, offers rich metadata around products to guide shoppers: ratings, reviews, and product details are all examples of metadata.
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.
erwin has once again been positioned as a Leader in the Gartner “2020 Magic Quadrant for Metadata Management Solutions.”. The post erwin Positioned as a Leader in Gartner’s 2020 Magic Quadrant for Metadata Management Solutions for Second Year in a Row appeared first on erwin, Inc.
Data has been the driving force of the decade. Digital pioneers like Amazon, Netflix and Uber account for some of the most extreme market disruption their respective industries have faced. Many organizations have tried and failed to become truly “data-driven,” and many organizations will continue to do so.
As we enter 2021, we will also be building off the events of 2020 – both positive and negative – including the acceleration of digitaltransformation as the next normal begins to be defined. When the pandemic first hit, there was some negative impact on big data and analytics spending. Now is the time.
Understanding the data governance trends for the year ahead will give business leaders and data professionals a competitive edge … Happy New Year! Regulatory compliance and data breaches have driven the data governance narrative during the past few years. Constructing a DigitalTransformation Strategy.
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. Constructing a DigitalTransformation Strategy: How Data Drives Digital.
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. Then you must bulk load the legacy data.
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Ask a CIO where their focus lies and ‘digitaltransformation’ as well as ‘growth’ will come into the conversation quite quickly. Both rely virtually entirely on the enterprise leveraging of data. Governing digitaltransformation.
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DigitalTransformation. Data Security & Risk Management. Data Center Consolidation. Data Governance (knowing what data you have and where it is). DigitalTransformation. The key driver of modern EA is the demand for digitaltransformation. Compliance/Legislation.
In the data-driven era, CIO’s need a solid understanding of data governance 2.0 … Data governance (DG) is no longer about just compliance or relegated to the confines of IT. Today, data governance needs to be a ubiquitous part of your organization’s culture. Creating a Culture of Data Governance.
Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value. The What: Data Governance Defined. Data governance has no standard definition.
Not Documenting End-to-End Data Lineage Is Risky Busines – Understanding your data’s origins is key to successful data governance. Not everyone understands what end-to-end data lineage is or why it is important. Data Lineage Tells an Important Origin Story. Who are the data owners?
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.
Data-driven insights are only as good as your data Imagine that each source of data in your organization—from spreadsheets to internet of things (IoT) sensor feeds—is a delegate set to attend a conference that will decide the future of your organization.
Battle Creek, Michigan — July 18, 2023 — Octopai, a global leader in data lineage and business intelligence automation, and Demand Chain AI, a pioneer in AI-driven demand forecasting and supply chain optimization, have today announced a strategic partnership.
Organizations with a solid understanding of data governance (DG) are better equipped to keep pace with the speed of modern business. In this post, the erwin Experts address: What Is Data Governance? Why Is Data Governance Important? What Is Good Data Governance? What Are the Key Benefits of Data Governance?
How Data Literacy Turns Data from a Burden to a Benefit. Today, data literacy is more important than ever. Data is now being used to support business decisions few executives thought they’d be making even six months ago. So, what is data literacy? What Is Data Literacy? Data Literacy Definition.
The need for data mapping tools in light of increasing volumes and varieties of data – as well as the velocity at which it must be processed – is growing. Data mapping tools have always been a key asset for any organization looking to leverage data for insights. Isolated units of data are essentially meaningless.
Consider that e-commerce’s acceleration due to the pandemic saw retailers’ digital sales penetration realize 10 years of growth in just the first three months of 2020 alone. . In summary, predicting future supply chain demands using last year’s data, just doesn’t work. DigitalTransformation is not without Risk.
Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations. Data Governance Is Business Transformation. Maturity Levels.
We’re excited about our recognition as a March 2020 Gartner Peer Insights Customers’ Choice for Metadata Management Solutions. The solutions work in tandem to automate the processes involved in harvesting, integrating, activating and governing enterprise data according to business requirements.
The right set of tools helps businesses utilize data to drive insights and value. But balancing a strong layer of security and governance with easy access to data for all users is no easy task. Another option — a more rewarding one — is to include centralized data management, security, and governance into data projects from the start.
Over the years, organizations have invested in creating purpose-built, cloud-based data lakes that are siloed from one another. A major challenge is enabling cross-organization discovery and access to data across these multiple data lakes, each built on different technology stacks.
This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. Software development, once solely the domain of human programmers, is now increasingly the by-product of data being carefully selected, ingested, and analysed by machine learning (ML) systems in a recurrent cycle.
This article is based on a podcast Ron Powell conducted with Sharon Graves, Enterprise Data and BI Tools Evangelist for GoDaddy, about data curation, data stewardship, and data catalogs. His focus is on business intelligence, analytics, big data, and data warehousing. What did that involve?
To transform Fujitsu from an IT company to a digitaltransformation (DX) company, and to become a world-leading DX partner, Fujitsu has declared a shift to data-driven management. The platform consists of approximately 370 dashboards, 360 tables registered in the data catalog, and 40 linked systems.
Every day, organizations of every description are deluged with data from a variety of sources, and attempting to make sense of it all can be overwhelming. By 2025, it’s estimated we’ll have 463 million terabytes of data created every day,” says Lisa Thee, data for good sector lead at Launch Consulting Group in Seattle. “For
Every company is becoming a data company. In Data-Powered Businesses , we dive into the ways that companies of all kinds are digitallytransforming to make smarter data-driven decisions, monetize their data, and create companies that will thrive in our current era of Big Data.
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