Tag Archives: Data Management
Some interesting news hit UK headlines last year that companies could be made to give the public greater access to their personal transaction data in an electronic, portable and machine-readable format. That’s if the midata project has anything to do with it.
Launched in April 2011 midata is part of the UK Government’s consumer empowerment strategy, Better Choices: Better Deals. Essentially, it’s a partnership between government, consumer groups and major businesses. Its aim is to give consumers access to the data that they produce, from the likes of household utilities, and banking, to internet transactions and high street loyalty cards. (more…)
Thomas Davenport, visiting professor at Harvard University and author of the watershed book Competing on Analytics, is once again making waves across the datasphere with his proclamation of data scientist as the “sexiest job of the 21st century.”
To many readers here at the Perspectives site, of course, this is not news, as many data professionals have increasingly been recognizing – and are being recognized – for the increasing power of information in driving new insights and business opportunities. (more…)
There are those who look at the emerging world of cloud computing as a trend to efficiency. It gives them the ability to leverage resources using much more cost effective models, where those resources are provisioned and shared amongst many consumers.
However, in the quest for efficiency, we often overlook the functionality of “the cloud.” Or, the usefulness of placing core features in a centralized location, which thus provides better control and governance, as well as efficiency. This leads to the placement of core enterprise data management services in the cloud, such as Master Data Management, or MDM. (more…)
In my previous post I discussed effective stakeholder management and communications as a key enabler of successful data quality delivery. In this blog, I will discuss the importance of demonstrated project management fundamentals.
Large-scale, complex enterprise Data Quality and Data Management efforts are characterized by numerous activities and tasks being performed iteratively by multiple resources, across multiple work streams, with high volume units of work (i.e. dozens of source systems and data objects, hundreds of tables, thousands of data elements, hundreds of thousands of data defects and millions of records). Without the means to effectively define, plan and manage these efforts, success is nearly impossible. (more…)
We have been looking at how data management issues can be classified, and in my last post I provided five categories, but broken them down into two groups: Systemic and System. The systemic issues are ones in which process or management gaps allow data flaws to be introduced. A good example occurs when consumers of reports from the data warehouse insist that the data sets are incomplete, and the root cause is that the processes in which the data is initially collected or created do not comply with the downstream requirement for capturing the missing values. (more…)
The banking sector has been through the mill over the past couple of years. Yet as the sector works through the aftermath of the economic turmoil and seeks to innovate with customer service initiatives, many are taking big risks over potential loss of customer data.
Why? Well because adequate safeguards may not be in place to protect confidential data during the testing and development of new web-based services and applications. Worse though, keeping bank accounts secure is not the only risk they’re running – many may not be meeting the data privacy standards required by the regulators. (more…)
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…)