THE DATA DOWNLOAD

Topics: Big Data

Why do relatively few companies have success with big data?

This is the next interview in a new series in which we ask our unique and diverse Datasource Consulting staff to share a bit about themselves, both professionally and personally. We've been amazed and inspired at their talents, hobbies, and aspirations and we hope you are, too! 

Topics: Data Governance, Data Virtualization, Blog, Master Data Management, Enterprise Data

By:  David Crolene, Vice President, Delivery

The verdict is in and Data Virtualization (DV) is here to stay.  This is driven by many factors such as the maturation of vendor technologies (e.g., heterogeneous join optimization, row level security, and high availability), increases in computing power, and a decrease in time end users are willing to wait for access to data. 

Topics: Meet Datasource

This is the first interview in a new series in which we ask our unique and diverse Datasource Consulting staff to share a bit about themselves, both professionally and personally. We've been amazed and inspired at their talents, hobbies, and aspirations and we hope you are, too! 

Topics: Master Data Management

Q: How instrumental was the ability to get the organization to a point where business processes became repeatable, and led to a more efficient data governance model where naming standards and taxonomy conversations made progress?

Topics: Data Governance

Data governance has a bad rap. Rightly or wrongly, people think that it’s just too much to take on, that it introduces additional – and unnecessary – layers of bureaucracy, that its procedures are onerous, confusing, and restrictive. In practice, unfortunately, this is frequently the case.

It doesn’t have to be.

Topics: Data Governance

With the focus that companies have on everything “data” – analytics, reporting, dashboards, Big Data, predictive analytics, decision science and so on – data governance is also increasing in importance. A good data governance program gets business stakeholders involved in deciding on data definitions and supporting or sponsoring consistent data usage across an organization.

Topics: Agile, Data Governance, Enterprise Data

Many organizations are now recognizing the need for data governance but are still struggling with the right way to structure it. A good approach: be agile!

Topics: Data Quality

Recently, we outlined the top five data quality problems in enterprise data management and offered best practices to solve those challenges. This post explores the topic further to highlight five additional roadblocks associated with managing the critical data of an organization. Organizations that take time to decipher the root cause behind data challenges will run more successful enterprise data programs. They’ll also have a foundation in place for sustainable growth with an enterprise-wide view of customers, manufacturing, supply chains, sales, and operations. 

Topics: Data Quality

Top Ten Data Quality Problems: Part I

By: Sally McCormack, Data Quality Competency Director, Datasource Consulting, LLC

Problems with data quality are costly to an enterprise. When facing the potential for missed opportunities, uninformed decision-making, non-compliance sanctions, and low customer satisfaction, today’s business leaders are making data quality a priority in their organizations’ data management programs. An Experian report found that 88 percent of companies see a direct effect of inaccurate data on their bottom line, losing an average of 12 percent of their revenue. In a similar study by Database Marketing, organizations estimate that they could increase sales by nearly a third (29%) with corrected customer data. (Source: Internal Results)