白皮书

To help you avoid pitfalls in your data architecture, Karen López describes how to: understand the benefits of an effective enterprise data modeling effort; identify seven common mistakes that organizations can make in developing enterprise data models; and establish a plan to address those issues with ER/Studio

Use these strategies to define and deploy a successful enterprise data model in your company.

While data architects like to collaborate amongst themselves using their own procedures and language, they need to consider improving our interaction with our business clients. This paper discusses five things you can do right now to improve collaboration with business users.

In this whitepaper, data management expert Kelle O’Neal leads you through the process of creating a sustainable data governance program:

• Managing the expansion of data governance
• Embedding the program
• Identifying the difficulties of creating sustainable governance
• Assessing the current state of your data governance

Guerilla governance speeds up the process and reduces costs by focusing scarce resources on projects that add the most value to your organization.

In this whitepaper, data management expert Kelle O’Neal shows you how to:

• Spot key differences between guerilla and enterprise governance
• Develop, quantify, and justify your data governance program
• Create your own plan of action you can execute right now

To help you make a business case for data governance within your own organization, we’ve teamed up with Kelle O’Neal on a whitepaper called “Making the Business Case for Data Governance” that walks you through a step-by-step process to:

• Quantify the benefits of data governance in your own organization
• Establish the scope of a data governance program
• Show a clear connection between data governance and the ability to meet business objectives 

Download the whitepaper “How NOT to Govern Data: 5 Pitfalls to Avoid” to discover:

• The top 5 mistakes you can make in your data governance program that will virtually guarantee failure
• Concrete strategies to avoid these pitfalls from the beginning, or correct them once you’ve got them
• How to incorporate best practices into your broader data governance framework

This expert whitepaper will show you the true, cascading costs of using tools such as Visio or Excel for data modeling. You’ll learn how using tools built specifically for data modeling can help you achieve up to a 387% ROI in year one.

During the last few years, several database market dynamics have led many people to question the utility of data modeling. In particular, the advent of XML information management, growing frustration with traditional relational database management system (RDBMS) capabilities and vendor relationships, and the expanding influence of cloud platforms have led many organizations to reconsider their commitments to the practice of data modeling. Read this paper for perspectives on why data modeling is more important than ever before, and that organizations that seek to fully leverage database market dynamics must redouble their focus on data modeling.

Almost all business applications track information about people and organizations, recording information about customers, suppliers, subsidiaries, departments, employees, and contractors redundantly in many different systems. For this reason, it is very difficult to keep key information such as client contact data consistent and accurate. Examples of applications that store information about people and organizations include sales, marketing, purchasing, order entry, invoicing, project management, and accounting.

Summary: We need data governance in our organizations and we need it to be done correctly. We as data professionals cannot solve this problem alone. In fact, we can’t solve it at all. This is a business issue. We cannot force compliance into systems after the fact, nor can we engineer it into solutions without business ownership of their governance and compliance processes. This paper lays out 5 things we must do to ensure our data governance, stewardship and compliance programs are successful.