Tag Archives: Data Management
Reposted with permission
Shahid Shah’s healthcare IT, EMR, EHR, PHR, medical content, and document management advisory service. Enjoy.
Join me for a free webinar on “Understanding the Escalating Data Challenges of Meaningful Use” on Thursday, April 7th
I’ve been doing a good deal of coaching and consulting on what Meaningful Use really means to technology professionals lately so I was pleased to accept an invitation by Informatica to lead a webinar on that subject for a data management audience.
Data management professionals and the executives that they report to have now had enough time to learn how difficult meeting the escalating requirements for MU actually is; most are reporting that it’s been more work than they thought. Gone are the days when health systems thought they could just install a certified EHR and they would be able to meet the MU goals. Everyone now understands that even if they’re able to collect the measures required in the first phase of MU, the escalating data challenges of later phases will be more difficult. (more…)
I’m sure it’s no surprise to anyone, but there is much talk in the industry today regarding “data” and the management or control of it. To that end, commonly used terms such as Master Data Management (MDM) and Data Governance are sometimes used interchangeably and other times have wildly different definitions and applications. Whether or not the industry should or should not standardize on common terms and definitions is another subject altogether – and one that won’t be resolved any time soon. But, regardless of what it’s called the enterprise’s desire to better manage and control data is a hot topic, and deservedly so. But where does that leave Data Quality? (more…)
The devil, as they say, is in the detail. Your organization might have invested years of effort and millions of dollars in an enterprise data warehouse, but unless the data in it is accurate and free of contradiction, it can lead to misinformed business decisions and wasted IT resources.
We’re seeing an increasing number of organizations confront the issue of data quality in their data warehousing environments in efforts to sharpen business insights in a challenging economic climate. Many are turning to master data management (MDM) to address the devilish data details that can undermine the value of a data warehousing investment.
Consider this: Just 24 percent of data warehouses deliver “high value” to their organizations, according to a survey by The Data Warehousing Institute (TDWI). Twelve percent are low value and 64 percent are moderate value “but could deliver more,” TDWI’s report states. For many organizations, questionable data quality is the reason why data warehouses fall short of their potential. (more…)
A few months ago at the Gartner MDM Summit, I met the head of information management at MillerCoors. He recalled the challenges he faced in explaining to executives of America’s second-largest beer company why it needed master data management.
He came up with a pitch-perfect analogy.
“We make two things—beer and data,” he told executives. “We need to manage our product supply chain and our information supply chain equally efficiently.”
I love that quote because it underscores a truth in all industries—every business is an information business. Whether you make beer or diapers, automobiles or annuities, seamless integration of all information flowing through a company is key to meeting customer needs and gaining competitive advantage. (more…)
A company’s data may be one of their most significant assets and provide its greatest competitive advantage; does your organization treat it as such? In my experiences working with various organizations I am always interested to see the variety of ways in which they view their data.
The ones that are most successful in their data management practices are the ones that have established a culture of data quality. This culture starts at the executive level and permeates throughout the entire organization. Data quality issues are not viewed as an IT problem, but as an organizational problem.
These companies not only have a strong understanding of their data, but in addition they also understand the processes that create the data. The insight gained from this, better enables them to correct their data by both cleaning up the processes and implementing IT strategies to correct data in back-end systems.
Ralph Kimball has written a white paper entitled An Architecture for Data Quality. In it he discusses how to establish a culture of data quality. It is a good read and worth checking out. Lastly, I am interested in your comments in regards to how your organization treats its data. Does it consider it a strategic asset?