Lorraine argues that a universal patient identifier isn't the answer to data exchange.

Recently the Wall Street Journal posed a very timely question: “Should every patient have a unique ID number for all medical records?” Michael F. Collins, chancellor of the University of Massachusetts Medical School, wrote the “Yes: It means better care” response, while Deborah C. Peel, founder of Patient Privacy Rights, wrote the opposing view, “No: Privacy would suffer.”

While this debate is not new, recent emphasis on the value and need for data exchange, adoption of electronic medical records, and the number of data breaches have re-ignited the discussion.

Neither writer articulated that patient identification requires a comprehensive solution. This surprised me. Based upon my work in many countries around the world, I can say that a universal patient identifier (UPI) will not guarantee accurate patient identification, nor will it produce seamless data exchange. And, as both writers explained, UPIs come packaged with a whole host of issues.

A UPI isn’t a cure-all for the problems of healthcare. Instead, the industry and healthcare organizations need a comprehensive solution and approach to patient identification. Classic management tenets are at the core: people, process and technology. In fact, I presented this topic at the HIMSS Virtual conference in early December, and shared highlights in my December 15 blog post. This solution should include:

People: Currently, registration and patient access staff are among the lowest paid in most healthcare systems. We must invest in the people who are creating data. Those at the front line need a deeper understanding of the value of the data they collect. We need to clearly define – and demonstrate – how their work impacts patient care. Make them see the link between the data they collect and the power of future data analytics.

Process: While it may be obvious, it bears repeating. We must identify the data elements that are critical for patient identity and data exchange. These data deserve special attention. Multi-site organizations should standardize their processes for data capture.

Technology: Staff should use the best technology to ease patient identification, including probabilistic algorithms that can overcome the differences and errors in data capture. High-performing organizations that use this technology see error rates of less than 1%, not the 7-10% quoted in a RAND study. Additionally, the WSJ discussion overlooked the challenges of backporting a UPI to legacy records and the cost to assign a UPI, whether done by a private organization or a governmental agency. Nor can we forget the long-term costs and challenges of maintenance, as many countries with a UPI have learned.

The world’s thorniest issues, from patient identification to world hunger, don’t have silver bullet solutions. Having a UPI will help with identification, but it’s not a cure-all. Let’s open the debate and fully educate citizens, providers, and officials about all viewpoints, risks, and benefits of a UPI. Let’s learn from other countries that have faced similar challenges.

In the meantime, let’s make sure we’re investing in the management fundamentals of people, process and technology.

What would you add to our MDM library?

Blogs are great, but sometimes you need more room to really get your point across. Industry best practices change so quickly, especially in the world of MDM and data governance, making it hard to keep up with the newest releases.

Two of our MasteringDataManagement.com bloggers have gone from blog posts to full-length books. If you’ve enjoyed their blogs, definitely check out their books.

Selling Information Governance to the Business: Best Practices by Industry and Job Function – You know you want to “do” information governance. But how do you sell it to the business side of the business that controls the purse strings? MasteringDataManagement.com blogger Sunil Soares looks at the challenges faced by each industry and explains how to make your case. He previewed the book in a recent blog post.

Master Data Management & Data Governance – Larry Dubov and Alex Berson combined their stories into this fantastic reference book. Together, they tell the tale of MDM and data governance, from making the business case to building the architecture. Larry and Alex also put together a short video and blog post discussing their book. Larry originally developed some of his ideas through his blog series, Building the Business Case for MDM and Building an MDM Roadmap.

The IBM Press folks shared their list of top-selling books from last year. They also shared a discount code –  IBMEXPERIENCE – that will save you 35% at the online checkout. Below, I’ve picked some that you might find interesting.

Enterprise Master Data Management: An SOA Approach to Managing Core Information – How do MDM and SOA complement each other? How can you use the MDM reference architecture to position and design MDM solutions? Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul Van Run and Dan Wolfson all contributed their best practices and experience to this project.

Get Bold: Using Social Media to Create a New Type of Social Business –Sandy Carter’s new book offers a complete framework with practical examples and guidance. As Guy Kawasaki said, “For crying out loud, IBM ‘gets’ social media. Don’t you think it’s about time that you do? This is the book to get you started.” Of course, the fact that you’re on this blog bodes well for your social savvy – but there’s always room to learn more. Sandy blogs frequently at socialbusinesssandy.com.

Making the World Work Better: The Ideas That Shaped a Century and a Company – Kevin Maney, Steve Hamm and Jeffrey O’Brien teamed up to look back at IBM’s first century. If you enjoyed the 100 Icons of Progress or the100x100 video, you’ll appreciate this book. IBM’s history of innovation mirrors many of the technological advances of the last century, in sometimes surprising ways.

Which books are on your “must-read” list? Jim Harris maintains a nice list on ocdqblog.com. Are there others we should know about?

IBM Champion for DB2 Isaac Yassin warns, "The small details can kill you."

What do DB2 and photography have in common? IBM Information Champion Isaac Yassin says both require careful tuning and attention to detail to produce something attractive.

A self-described autodidact, Isaac has taught himself both DB2 and photography. He’s more comfortable with DB2. In fact, he teaches DB2 classes and was named an IBM Gold Consultant and IBM Information Champion for DB2.

Based in Israel, Isaac tackles many information management challenges – including data architecture and database administration and development – from a variety of angles. As founder and CEO of IYC, a consultancy focusing on DB2 engagements, Isaac is active in IDUG and heads the Israeli user group.

“I’m quite a bad manager for myself, because I drive myself very hard,” Isaac said of working for himself. But he also finds it very rewarding, particularly the teaching portion of his job. Tailoring courses for customers’ specific scenarios helps provide the best possible education.

Prior to launching IYC in 1988, Isaac worked in more traditional IT roles. He laughed as he admitted he “can’t count” how many times he dropped stacks of punched cards and tried to guess their correct order. Early in his career, punched cards were so ubiquitous that he even used one to cut and serve his wedding cake.

As technology has changed, so too have regulations governing his clients’ work. In the financial industry, each change requires adjustments in how data is handled and managed. Isaac helps his clients stay current on what they can and cannot keep, when and how to encrypt and decrypt data, and who should and shouldn’t have access to the data. With each change, Isaac explained, “You have to govern your data much more securely and deeply… and know each second what is where, why, how, who touched it, who changed anything, and why it was changed, if at all.”

Such requirements inspire a long, intricate process.  As Isaac reminded us, computers do only exactly what you tell them to, not necessarily what you want them to do. You must carefully design your database and rules from start to end so that you get exactly what you planned and wanted.

Paraphrasing the Chinese proverb, Isaac noted, “Be really, really careful what you ask for, because you may get it…. Small details can kill you.”

Complicating the matter is the changing role of DBAs. Other Champions, including Dave Beulke, have discussed this shift, and Isaac agreed. As the scope of work has increased, more people are involved in each implementation. The work itself is more fragmented, as several people take small, manageable pieces. Even with trustworthy employees, having more hands in the pot requires new rules and regulations to protect data from rogue DBAs.

Isaac vows to keep learning to stay ahead of regulatory changes. And of course, he’ll continue to share his knowledge about both DB2 and photography. To learn more about Isaac’s training courses, contact him via LinkedIn or email.

IBM Champion Isaac Yassin

Crysta shares her pile of recent MDM and data governance reads

My RSS feed keeps filling up with fantastic MDM and data governance reads. It’s like whack-a-mole: every time I think I’ve caught up, someone publishes another creative, thought-provoking post. Add active #MDM and #DataGovernance Twitter searches, and I have plenty to ponder.

Below are some of my favorites from the last couple of weeks.

DQ-View: MetaData Makes BettahMusic: Jim Harris uses music as a high-quality example of data. He imports several CDs into iTunes to demonstrate just how important metadata is. Does metadata empower a better music experience?

The MDM & Governance Ripple Effect: Justin Kern of Information Management looks at Gartner’s 2012 predictions for Information Governance and MDM.

Approximate Matching: Why should you add approximate matching to your data quality tool belt? David Loshin describes when approximate matching can help.

Multi-Occupancy: Henrik Sorensen recounts the challenge of aligning reference data with real-world location data. His post is inspired by a recent move to London, where addresses are handled differently than in Copenhagen.

Business Forecast: What 2012 Holds for Information Managers: Our Sunil Soares joined Stephen Powers (Forrester), Dan Kernan (SAP) and Matt Durham (Software AG) for this roundtable discussion hosted by DM Radio’s Eric Kavanagh and Jim Ericson. Big Data, data governance, My predictions were pretty good.

Data Governance – V (Need for Data Discovery): I just discovered this series from Namit Kabra. This post covers using data discover to find relationships among your data. The four prior posts feature basics of data governance, business glossaries, the need for data governance, and data masking. I can’t wait for the next post.

Video: David Birmingham of Brighlight Consulting Talks About the Power of Netezza: I stumbled on this recent video as I prepared to interview David as part of the ChampionSpeak series. Netezza is a relatively recent IBM acquisition, and I enjoyed finally getting a better understanding of what exactly Netezza does. (Hint: data warehouses!)

Identifying the Right Source of Master Data: Our own @MDMGeek talks about one of the crucial first steps of any MDM implementation: determining which sources of master data to include.

Of course, I’d be remiss if I didn’t mention the posts published right here on MasteringDataManagement.com:

Big Data Governance: A Framework. Sunil Soares has noticed two major trends converging. So what happens when Big Data meets Data Governance? He defines “Big Data Governance” and shares a framework he’s developed.

More Mobile, More Competitive Government: Stephan Zoder muses on whether government should – and can – compete with private industry. Specifically, is our personal data more secure in government databases or those owned by private companies?

Predictive Data Governance for Business Transformation: Steven Adler – founder and chairman of IBM’s Data Governance Council – talks about the emerging role of predictive data governance. He also previews the Feb 28-29 Predictive Data Governance Forum in New York.

What have you been reading lately?

Sunil explains how Big Data and Data Governance are converging - and what it means for businesses.

I am starting to see a convergence of two major trends in the marketplace: information governance and Big Data. We are coining the term “Big Data Governance” to reflect this emerging trend. I define Big Data Governance as the formulation of policy to optimize, secure, and leverage Big Data as an enterprise asset by aligning the objectives of multiple functions.

Here is the framework that I have developed to establish the scope of information governance:

1. Master Data Governance
   This includes a single view of customers, materials, vendors, employees and chart of accounts. Each data domain has specific attributes that need to be fit for purpose. For example, phone number is an important attribute for the customer data domain, because it is important for an enterprise to have valid contact information in case of need.
2. Reference Data Governance
   This includes data that is relatively static such as codes for countries, states or provinces, currencies, industries and customer segments.
3. Big Data Governance
   This includes social media (Twitter feeds, blogs, Facebook pages, LinkedIn profiles), cell phone GPS data, sensor data, weather data, etc. These data tend to be operational in nature and meet the three “V” criteria – volume, velocity, and variety.

Most of my clients are implementing information governance programs today. These programs focus on the governance of master data and, to a lesser extent, reference data. Based on my conversations, I expect that clients will increasingly focus on the governance of big data in the next 12-18 months.

Big Data Governance programs need to focus on issues that are similar to other information governance initiatives. For example, these programs need to address the following:

• Information Lifecycle Management – Big Data programs need to ensure that storage costs do not spiral out of control.
• Data Quality – Organizations need to establish what level of data quality is “good enough” because of the high volume and velocity of Big Data.
• Metadata – Big Data Governance needs to create sound metadata to avoid situations such as where a company bought the same dataset twice because it was named differently within two different repositories.
• Privacy – Enterprises need to be very specific about adherence to privacy concerns, such as leveraging social media analytics.

All said and done, 2012 should be a breakout year for Big Data Governance programs.

This post originally appeared on January 24 on Sunil’s personal blog. Sunil also examined how different industries are facing this challenge in an October post, The Convergence of Information Governance & Big Data.

What are the implications of more mobile, competitive government agencies?

Last time, we looked at private sector business models around the mobility concept. Today, we will look at the public sector. Upfront, let me assure you this is not a partisan manifesto.

To a large degree, the same transformation I described for the private sector could occur in the public sector. However, the public sector usually lacks the same profit motivation.

Many daily experiences combine private enterprises with government functions. Every time you get a business license, hire an employee, verify background or employment, check on a transfer payment recipient, pay your taxes, or submit a regulatory report, you’re combining the two. For example, in the US, the e-file tax system connects the filer (or filer’s accountant), the federal and state governments, and the bank-managed ACH payment system.

Moreover, formal public-private partnerships tightly link the two worlds, despite their separate motivations.  There is no reason why their historic focus around utilities, transportation, and health infrastructure could not extend to the IT world.

The government may charge a premium for these IT services. But many citizens would find it valuable and desirable to have them managed by highly-regulated body less motivated by profit. Maybe we need a government’s bill of rights that allows new ways to make money from services, beyond taxes. The current budget could certainly use it.

Until recently, for example, the US Postal Service (a semi-governmental agency) actually turned a profit. Legally, the Postal Service must remain revenue-neutral. But last year, they tallied a $10 billion loss. Sen. Claire McCaskill aims to fight this with a “write more letters campaign,” and the USPS itself is running ads claiming “a refrigerator has never been hacked.” Imagine if the USPS was empowered to be more competitive and try a variety of things. For example, they could provide special purpose vehicles (SPV) for managing email or payment service. Some state-owned European postal services successfully offer this.

For folks worried about Big Brother government abusing your data, privately-owned corporations face their own data privacy problems. More than 100 million T.J. Maxx customers were affected by an 18-month identity theft operation ending in 2007. In recent weeks, Zappos admitted that data for its 24 million customers has been compromised.

Legal ramifications for reputation and, ultimately, profits may encourage private companies to guard against data loss. But the Patriot Act and other post-9/11 regulations abroad have already extended the government’s reach into private sector data.

Regardless of sector and use case, all of these integrated, cloud-based business models and processes rely on the same thing: shared master data! This includes the full spectrum of master data: all domains, types (transactional and analytical), high and low latency.

However, we must consider the dark side of any public-private experiments. If you’re scared by Facebook tracking behavior after log-off and indefinitely keeping your history, think what a government agency with a private-sector offering may mean. To compete, such offerings will likely maximize their leverage in the legislative process and may lack the necessary oversight.

Consider a new class of “robber barons” like Carnegie, Rockefeller and Astor. Rather than controlling coal mines and steel mills, they control key steps in the information flow between government and the business world. Their goal is to drive the most profitable steps in the delivery of services and products.

They control what data you see and use, as well as the processes and standards to do so. They will know everything about you, and like Max Headroom, they’ll use this to sell you the next gadget. They’ll use regulation to maintain their advantage.

On the bright side, these IT barons would likely be forced into more transparency, rather than buying political influence, given the formalization of the structures and that state of social media today.

It will be a cool but scary world. But it’s less scary if you know what you’re doing. You could even wipe yourself off the grid.

The Data Governance Council brainstorms to meet the challenges of Predictive Data Governance

Data Governance programs are popping up all over the globe. It isn't hard to get one started anymore. But it is hard to be good at it and to make it last. In fact, I see more programs taking one step forward and two steps back – narrowing focus to demonstrate results – to fall in line with other IT projects than charting a clear path towards larger transformation.

But let’s be clear – Data Governance is about Business Transformation. We can't change organizational behavior to take data seriously if we can't change how we work.

We in the Data Governance Council have a vision that Data Governance is a coordination of people collaborating on common goals and purposes – to use data as an asset. That vision requires that piecemeal project management of data issues must evolve into systemic governance structures and methods, whose goals and purposes themselves transcend the people, applications and interactions.

Until last year, we didn't fully know how to close the gap between where we are today and where we'd all like to go. But today we see the way forward, and the Data Governance Council is embarking on a bold new program to develop Predictive Governance: systemic ways of describing our world and modeling potential interactions to understand what works and how to improve it.

Traditional scientific analysis says that to understand a problem you have to take apart the issue and decompose it into all its components and sub-components and find the root cause.

But this assumes there is always just one root cause and one thing to blame:

Data Quality in our branch operations is atrocious, so we have to fix our incentive structure.

Our network was hacked and our customer data was exposed, so fire the CISO.

It’s almost irresistible to search for scapegoats to common problems using simple cause and effect analysis.

For example, people rarely ever imagine admit that individual data quality problems are symptomatic of larger systemic challenges in the information supply chains we have created over decades to handle information flows from source to target

And no CEO expects that network hacks are the result of systemic weaknesses in IT systems that are themselves a reflection of organizational culture and priorities.

It’s hard to accept that people created the systems that enable poor data quality, global jurisdictional jungles, metadata misunderstanding, lax security, privacy invasions, and Big Data mischief. No one deliberately creates these problems. No one wants them to continue. But they do continue nonetheless because people really don't understand the elements and interdependencies of the systems they have created.

The point of Predictive Governance is that we work in large ecosystems and we must work to understand them. If we can't describe our ecosystems, we can't rise above the superstitions and organizational behaviors that constantly hold us back.

At our February Predictive Governance Forum in New York City, we will explore the ideas and methods behind Predictive Governance, new Enterprise Data Governance solutions that integrate multiple business and IT domains, and Internet Jurisdiction and Multi-Stakeholder Governance in the context of global regulatory confusion as an archetype of Predictive Governance Challenges.

These are big problems and we are working on big solutions.

See the agenda. Read our blogs. Understand our mission. Be prepared to interact.

This is a thought leadership forum for change. Join the IBM Data Governance Council at our meeting in New York on February 28 and 29 and make a difference. If you’re unable to come in person, join the InfoGov Community, an online forum for sharing ideas, best practices and global dialogue.

IBM Champion for DB2 Dave Beulke advises performance reviews for all

Now that 2012 is firmly underway, you may have already forgotten your resolutions. But IBM Champion for DB2 Dave Beulke urges you to make – and keep – at least one more: conduct a “Performance Review” of DB2 and your other systems.

As a DB2 consultant, author and teacher with expertise in database performance, data warehouses and internet applications, Dave speaks from a trove of experience. For the last 14 years, he has guided clients through performance reviews that have saved millions of dollars. “It’s amazing the inefficiencies that have crept into the systems over the years,” he explained during our recent interview, citing several examples. In one case, a performance review saved a client 42% on their mainframe system costs.

Some of the problems stem from structural changes in the industry. As Dave described, “The ratio of DBAs to systems and the number of developers to DBAs is dwindling. The DBAs are forced to be more custodians of the databases instead of policing the performance and the various improvements in DB2 that can be leveraged.”

Today’s DBAs have far more on their plates and are often taking care of Oracle and SQL server in addition to DB2. This dilutes their expertise and leaves less time to learn how to incorporate new feature or best practices. As a result, inefficiencies develop and eventually accumulate.

That’s where professional consultants like Dave come in. Involved with DB2 since 1988, when he worked on version 1.2, Dave has done it all. He helped build one of the first data warehouses for EF Hutton, a company trying to understand the success of investments used by their clients. Dave also managed Spiegel’s database systems from 1990-1997, developing all the DB2 applications that involved 3 terabytes of data for merchandising, order entry and customer service for Spiegel, Eddie Bauer and Newport News. More recently, Dave has been busy designing analytics systems for healthcare companies. Recently, he implemented a 22 billion row data warehouse that analyzes data about claims and patient care.

Dave is an IBM DB2 Gold Consultant who has co-authored exams for V8 and V7 z/OS DB2 Certification and Business Intelligence Certification. Dave has been a columnist for DB2 Magazine and an instructor for The Data Warehouse Institute. Like several of our other IBM Champions, Dave is very active in the DB2 community. He is an IDUG past president and speaks at user group meetings every year.

Dave’s approach is reflected by his company’s name, Pragmatic Solutions. By taking a methodical, thorough look at existing systems through performance reviews, Dave can help identify areas for improvement that can save storage or processing costs. As companies are looking to maximize efficiency, this approach is growing more popular. In fact, the day after our interview, Merriam-Webster named “pragmatic” as their 2011 word of the year.

Start your year off right by preparing for your own performance review – and by listening to our full podcast interview with Dave. You can also visit DaveBeulke.com for Dave’s blog, resources and upcoming speaking events.

Dave Beulke, IBM Champion for DB2

When analytics turns data into information, healthcare can be transformed.

We’re swimming in data, but are we transforming it into usable information?

As we embark on 2012, we’re looking ahead at the next steps for transforming healthcare. Josh Constine of TechCrunch got in the spirit with 6 Big HealthTech Ideas that Will Change Medicine in 2012. Constine named Artificial Intelligence (citing IBM Watson and Siri) and Big Data as two of the six.

Both concepts are particularly timely. Mandates require more and more data to be collected and stored in electronic health records. But all that data collection and expense is ill used if it gets trapped in silos.

As Lorraine Fernandes, IBM’s Global Healthcare Ambassador, recently asked, “How do we harness the power of all this electronic data?”

Fernandes and Dr. Bill Rollow, GBS Solutions Leader for Healthcare Value and Transformation, discussed the promise of Watson-inspired analytics in their recent video.

Transforming healthcare comes with ambitious goals: improving the quality and safety of healthcare while controlling costs and creating new efficiencies for patients, providers, and payers.

The challenge is that the vast majority of healthcare data is unstructured, whether doctors’ notes, x-rays or other test results. But a longitudinal view of patients and providers paves the way for incredible potential rewards.

Deep analytics – like those powered by IBM Watson – present a world of possibilities. The data previously stuck in silos can be translated into information that underpins better outcomes, best practices and more cost-effective solutions. Patients could save copays or time by avoiding duplicate, unnecessary tests that may create safety risks. Providers gain on both sides of the equation, as they can provide better care while streamlining their interactions with payers.

As Dr. Rollow explained, “The paradox is, there is a relatively large amount of data, or potential data, out there, but the amount of information that we have that’s usable in clinical decision making is small.” Analytics aims to change this.

Watch the video to hear more about how analytics can transform healthcare. What do you predict for healthcare analytics in 2012?

Which Information Management buzzwords will dominate 2012?

Quick, which buzzwords and concepts will be most important to Information Management professionals in 2012?

Some of our bloggers have already shared their thoughts. Larry Dubov made 8 predictions about the changing relationship between MDM and data governance. Jon Case looked at “MDM in the cloud,” predicting that the industry will focus less on architecture and more on benefits.

But we’re not done. Sunil Soares, Director of Information Governance with IBM’s Software Group, will participate in a DM Radio discussion on Thursday, January 12 at 3 PM ET, “What 2012 Holds for Information Managers.”

Hosts Eric Kavanagh and Jim Ericson will interview Sunil, as well as analyst Stephan Powers (Forrester Research), Dan Kiernan (SAP) and Matt Durham (Software AG).

I’m eager to hear what they say, as the conversation could take so many directions. I’m assuming analytics will be high on the list, and of course they’ll discuss information governance. But what else will they fit into an hour? Architecture? Data quality? Cloud trends?

I’m reminded of the 2008 IBM commercial for Buzzword Bingo. For the uninitiated, Buzzword Bingo entails picking several keywords that are commonly used and placing them on a Bingo card. During a meeting or conference, players mark when each word is mentioned, with a goal of completing a row.

The more cynical view Buzzword Bingo as picking terms that are overused to the point of losing their meaning. But I see it as a means of identifying the terms that drive our industry, for better or worse. And it can be interesting to try to predict which terms will be used – or ignored.

So register for Thursday’s DM Radio session, and think about which terms you expect to hear. Which more esoteric terms may pop up? You could also generate your own Buzzword Bingo card.

I’ll be live-tweeting the session under @IBM_InfoSphere, and the DM Radio folks will make the recording available afterwards.