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How DataLiteracy Turns Data from a Burden to a Benefit. Today, dataliteracy 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 dataliteracy? What Is DataLiteracy?
Enterprises are trying to manage data chaos. They also face increasing regulatory pressure because of global data regulations , such as the European Union’s General Data Protection Regulation (GDPR) and the new California Consumer Privacy Act (CCPA), that went into effect last week on Jan. GDPR: Key Differences.
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.
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?
Companies are leaning into delivering on data intelligence and governance initiatives in 2025 according to our recent State of Data Intelligence research. Data intelligence software is continuously evolving to enable organizations to efficiently and effectively advance new data initiatives.
Understanding the benefits of data modeling is more important than ever. Data modeling is the process of creating a data model to communicate data requirements, documenting data structures and entity types. In this post: What Is a Data Model? Why Is Data Modeling Important? What Is a Data Model?
In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). Complexity. Five Steps to GDPR/CCPA Compliance. Govern PII “at rest”.
Organizations are flooded with data, so they’re scrambling to find ways to derive meaningful insights from it – and then act on them to improve the bottom line. In today’s data-driven business, enabling employees to access and understand the data that’s relevant to their roles allows them to use data and put those insights into action.
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.
If you’re serious about a data-driven strategy , you’re going to need a data catalog. Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner. This also diminishes the value of data as an asset.
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.
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. Quite simply, metadata is data about data.
Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.
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 innovation is flourishing, driven by the confluence of exploding dataproduction, a lowered barrier to entry for big data, as well as advanced analytics, artificial intelligence and machine learning. Consumers and businesses alike have started to view data as an asset they must take steps to secure.
What Is Data Intelligence? Data Intelligence is the analysis of multifaceted data to be used by companies to improve products and services offered and better support investments and business strategies in place. Data intelligence can encompass both internal and external business data and information.
Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. A single source of data truth helps companies begin to leverage data as a strategic asset.
Intro erwin ® Data Modeler 12.5 It requires many functional elements of an organization to come together in order to reach the ultimate stages of being able to identify, understand and fully leverage the power of its data. erwinData Modeler 12.5 erwinData Modeler 12.5 bring to you?
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 democratization is a hot topic, but what does it mean? And more importantly, how can you successfully democratize data? The focus of data democratization has traditionally been on closing the delivery gap between IT and business users specifically while keeping data protected in that context.
Our team here at erwin takes great pride in this distinction because customer feedback has always shaped our products and services. The solutions work in tandem to automate the processes involved in harvesting, integrating, activating and governing enterprise data according to business requirements.
If you love our products, please vote. Year after year, customers vote Quest® products as their #1 choice in database solutions. This year Quest® (including erwin) is competing in 7 out of 29 product / solution categories: Best CDC Solution (Quest Shareplex). Best Data Governance Solution (erwinData Intelligence).
With business process modeling (BPM) being a key component of data governance , choosing a BPM tool is part of a dilemma many businesses either have or will soon face. Historically, BPM didn’t necessarily have to be tied to an organization’s data governance initiative. Choosing a BPM Tool: An Overview.
We are thrilled to introduce Quest EMPOWER 2022, a free, two-day online summit aimed to inspire you and help you develop new strategies for advancing your data intelligence, data governance, and data operations initiatives. Discover new insights into data intelligence with Donna Burbank.
The easiest way to understand a data catalog is to look at how libraries catalog books and manuals in a hierarchical structure, making it easy for anyone to find exactly what they need. Similarly, a data catalog enables businesses to create a seamless way for employees to access and consume data and business assets in an organized manner.
One of the biggest lessons we’re learning from the global COVID-19 pandemic is the importance of data, specifically using a data catalog to comply, collaborate and innovate to crisis-proof our businesses. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.
Our predictions for 2021 are rooted in what we’ve learned from the past year and the relevance of data in getting us to where we are and where we need to go. Historically, moving legacy data to the cloud hasn’t been easy or fast. However, that definition is too narrow in terms of AI’s relation to data governance.
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