Category Archives: Data Warehousing

Improving CMS Star Ratings… The Secret Sauce

Many of our customers are Medicare health plans and one thing that keeps coming up in conversation is how they can transform business processes to improve star ratings. For plans covering health services, the overall score for quality of those services covers 36 different topics in 5 categories:

1. Staying healthy: screenings, tests, and vaccines

2. Managing chronic (long-term) conditions

3. Member experience with the health plan

4. Member complaints, problems getting services, and improvement in the health plan’s performance

5. Health plan customer service

Based on member feedback and activity in each of these areas, the health plans receive a rating (1-5 stars) which is published and made available to consumers. These ratings play a critical role in plan selection each Fall. The rating holds obvious value as consumers are increasingly “yelp minded,” meaning they look to online reviews from peer groups to make buying decisions. Even with this realization though, improving ratings is a challenge. There are the typical complexities of any survey: capturing a representative respondent pool, members may be negatively influenced by a single event and there are commonly emotional biases. There are also less obvious challenges associated with the data.

For example, a member with CHF may visit north of 8 providers in a month and they may or may not follow through on prescribed preventative care measures. How does CMS successfully capture the clinical and administrative data on each of these visits when patient information may be captured differently at each location? How does the health plan ensure that the CMS interpretation matches their interpretation of the visit data? In many cases, our customers have implemented an enterprise data warehouse and are doing some type of claims analysis but this analysis requires capturing new data and analyzing data in new ways.

We hear that those responsible for member ratings, retention and acquisition routinely wait >6 months to have a source or data added to a reporting database. The cycle time is too great to make a quick and meaningful impact on the ratings.

Let’s continue this discussion next week during your morning commute.

Join me as I talk with Frank Norman a Healthcare Partners at Knowledgent.

During this “drive time” webinar series, health plans will learn how to discover insights to improve CMS Star ratings.

Part 1 of the webinar series: Top 5 Reasons Why Improving CMS Star Ratings is a Challenge

Part 2 of the webinar series: Using Your Data to Improve CMS Star Ratings

Part 3 of the webinar series: Automating Insights into CMS Star Ratings

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Posted in Application Retirement, Big Data, CIO, Customers, Data Warehousing, Enterprise Data Management, Healthcare | Tagged , , | Leave a comment

Get Your Data Butt Off The Couch and Move It

Data is everywhere.  It’s in databases and applications spread across your enterprise.  It’s in the hands of your customers and partners.  It’s in cloud applications and cloud servers.  It’s on spreadsheets and documents on your employee’s laptops and tablets.  It’s in smartphones, sensors and GPS devices.  It’s in the blogosphere, the twittersphere and your friends’ Facebook timelines. (more…)

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Posted in Application ILM, B2B, Big Data, Cloud Computing, Complex Event Processing, Data Governance, Data Integration, Data Migration, Data Quality, Data Services, Data Transformation, Data Warehousing, Enterprise Data Management, Integration Competency Centers | Tagged , , , | Leave a comment

New Methods to Optimize Data Warehouse Performance and Lower Costs

Data warehouses tend to grow very quickly because they integrate data from multiple sources and maintain years of historical data for analytics.  A number of our customers have data warehouses in the hundreds of terabytes to petabytes range.  Managing such a large amount of data becomes a challenge.  How do you curb runaway costs in such an environment?  Completing maintenance tasks within the prescribed window and ensuring acceptable performance are also big challenges.

We have provided best practices to archive aged data from data warehouses.  Archiving data will keep the production data size at almost a constant level, reducing infrastructure and maintenance costs, while keeping performance up.  At the same time, you can still access the archived data directly if you really need to from any reporting tool.  Yet many are loath to move data out of their production system.  This year, at Informatica World, we’re going to discuss another method of managing data growth without moving data out of the production data warehouse.  I’m not going to tell you what this new method is, yet.  You’ll have to come and learn more about it at my breakout session at Informatica World:  What’s New from Informatica to Improve Data Warehouse Performance and Lower Costs.

I look forward to seeing all of you at Aria, Las Vegas next month.  Also, I am especially excited to see our ILM customers at our second Product Advisory Council again this year.

 

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Posted in Data Archiving, Data Governance, Data Warehousing, Database Archiving, Enterprise Data Management | Tagged , , , , , , , , | Leave a comment

Informatica World Healthcare Path

Join us this year at Informatica World!

We have a great line up of speakers and events to help you become a data driven healthcare organization… I’ve provided a few highlights below:

Participate in the Informatica World Keynote sessions with Sohaib Abbasi and Rick Smolan who wrote “The Human Face of Big Data”  — learn more via this quick YouTube video: http://www.youtube.com/watch?v=7K5d9ArRLJE&feature=player_embedded

With more than 100 interactive and in-depth breakout sessions, spanning 6 different tracks, (Platform & Products, Architecture, Best Practices, Big Data, Hybrid IT and Tech Talk), Informatica World is an excellent way to ensure you are getting the most from your Informatica investment. Learn best practices from organizations who are realizing the potential of their data like: Ochsner Health, Sutter Health, UMass Memorial, Qualcomm and Paypal.

Finally, we want you to balance work with a little play… we invite you to network with industry peers at our Healthcare Cocktail Reception on the evening of Wednesday, June 5th and again during our Data Driven Healthcare Breakfast Roundtable on Thursday, June 6th.

See you there!

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Posted in Application Retirement, B2B, Complex Event Processing, Data Integration, Data Integration Platform, Data masking, Data Migration, Data Warehousing, Healthcare, Informatica Events, Master Data Management, Uncategorized | Tagged , | Leave a comment

Turn the Data Warehouse into a Glass House

Jim Harris, OCDQ

The data warehouse’s goal is timely delivery of trusted data to support decision-enabling insights. However, it’s difficult to get insights out of an environment that’s hard to see inside of. This is why, as much as is possible given the necessities of data privacy, a data warehouse should be turned into a glass house, allowing us to see data quality and business intelligence challenges as they truly are.

Trusted data is not perfect data. Trusted data is transparent data, honest about its imperfections, and realistic about the practical trade-offs between delivery and quality. You can’t fix what you can’t see, but even more important, concealing or ignoring known data quality issues is only going to decrease business users’ trust of the data warehouse. Perfect data is impossible, but the more control enforced wherever data originates, and the more monitoring performed wherever data flows, the better overall data quality will be in the warehouse. (more…)

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Posted in Data Privacy, Data Quality, Data Warehousing | Tagged , | 1 Comment

Data Chaos: Public Enemy Number One

This year marks the 20th anniversary for Informatica. Twenty years of solving the problem of getting data from point A to point B, improving its quality, establishing a single view and managing it over its life-cycle. Yet after 20 years of innovation and leadership in the data integration market, when one would think the problem had been solved, all data had been extracted, transformed, cleansed and managed, it actually hasn’t — companies still need data integration. Why?  Data is complicated business. And with data increasingly becoming central to business survival, organizations are constantly looking for ways to unlock new sources of it, use it as an unforeseen source of insight and do it all with greater agility and at lower cost. (more…)

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Posted in Big Data, CIO, Data Integration, Data Integration Platform, Data Warehousing | Tagged , , , , , , , | Leave a comment

Is It Time For a New ICC Book?

In a recent visit to a client, three people asked me to autograph their copies of Integration Competency Center: An Implementation Guidebook. David Lyle and I published the book in 2005, but it was clear from the dog-eared corners and book-mark tabs that it is still relevant and actively being used today.  Much has changed in the last seven years including the emergence of Big Data, Data Virtualization, Cloud Integration, Self-Service Business Intelligence, Lean and Agile practices, Data Privacy, Data Archiving (the “death” part of the information life-cycle), and Data Governance.  These areas were not mainstream concerns in 2005 like they are today.  The original ICC (Integration Competency Center) book concepts and advice are still valid in this new context, but the question I’d like readers to comment on is should we write a new book that explicitly provides guidance for these new capabilities in a shared services environment? (more…)

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Posted in Data Governance, Data Integration, Data Quality, Data Services, Data Warehousing, Integration Competency Centers | Tagged | Leave a comment

I Can’t Describe It, “But I Know It When I See It”

So wrote Potter Stewart, Associate Justice of the Supreme Court in Jacobellis v. Ohio opinion (1964). He was talking about pornography. The same holds true for data. For example, most business users have a hard time describing exactly what data they need for a new BI report, including what source system to get the data from, in sufficiently precise terms that allow designers, modelers and developers to build the report right the first time. But if you sit down with a user in front an analyst tool and profile the potential source data, they will tell you in an instant whether it’s the right data or not. (more…)

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Posted in Data Integration, Data Migration, Data Quality, Data Services, Data Warehousing, Enterprise Data Management, Integration Competency Centers, Profiling | Tagged , , , | Leave a comment