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?

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.

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.

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.

In 2012, Jon Case thinks we'll shift from worrying about the architecture of "MDM in the cloud" solutions to their benefits

I was asked recently what 2012 holds for MDM. But in thinking about 2012, I kept reminding myself of a topic from 2011 that just never seemed to gel: 'MDM in the cloud.' Looking forward at 2012, I think we'll actually see this hot 2011 topic emerge in a more clear and useful way.

Early in 2011, I lost count of the number of analysts, customers and business partners who expressed curiosity about 'MDM in the cloud.' But everyone I spoke with had different, often vague, opinions about what it means, or why it matters. Most people were wrapped up in the technical architecture that such a solution might require - multi-tenancy, remote hosting, etc.

In 2012, I believe that the MDM market will shift focus away from the technical architecture itself and towards the benefits that are associated with 'cloud,' regardless of the technical architecture. The market will realize that they don't care about 'MDM in the cloud' as much as they care about three key topics:

Cost of Ownership

There's no denying that a typical 'cloud' application changes the cost of ownership equation, as the customer doesn't need their own servers or IT staff to administer the system. Instead, the customer replaces the up-front perpetual license cost with a subscription- or service-based recurring cost.

You will absolutely see MDM vendors and MDM systems integrators toying with the hosting and SaaS licensing models as one way of addressing cost of ownership - but I believe most vendors will do so without undertaking a re-architecture of their software to make the software a true 'cloud' application.

I also believe that the most sophisticated MDM vendors will go beyond the hosting and licensing factors to truly address a more important factor in cost of ownership - simplicity. This could take many forms: Simplicity of creating, loading, and maintaining an MDM hub; simplicity of integrating to that MDM hub; and simplicity of extending the MDM hub to address more use cases over time. Addressing simplicity will impact cost of ownership in the long run more than tweaking the hosting or licensing models.

Ease of Sharing and Consuming Information

Another perception of cloud applications is that they are inherently better at sharing information since they were designed to live remotely. I think the ease with which you can consume or share master data from an MDM solution is completely divorced from whether that MDM solution is 'in the cloud.' And I think customers are increasingly aware of this. So in 2012, there will be less people asking, “Do you offer MDM in the cloud?“ and more asking, “How does your MDM solution make it easier for me to consume master data?”

Ease of Integration with Other Cloud Applications

Lastly, there is a perception that a cloud-based application is just inherently better at integrating with other cloud-based applications. Just like the information-sharing topic above, I believe an MDM solution can and should be capable of integrating easily with cloud-based applications independent of whether the MDM solution is itself 'in the cloud.' And I believe customers will realize that the important question is not, “Do you offer MDM in the cloud?” but, “What capabilities do you offer to quickly and easily integrate with my other existing cloud-based applications?” regardless of whether those existing cloud-based applications might be contributing master data, consuming master data, or both.

What other trends do you predict for 2012, whether they are related to 'MDM in the cloud' or not? Please comment freely! Also see Larry Dubov’s 8 predictions for the relationship between MDM and data governance.

Having analytics at your fingertips enables you to act quickly.

One of my biggest gripes with data warehousing marketing (and pretty much corporate IT and Data Management) is that we promote these multi-million dollar solutions that companies bank their well-being on with either a bunch of talking heads, cutesy videos or even cardboard cut outs of hardware at trade shows. Yes, I know that it is cost effective for the latter point (I have shipped my share of full racks and well, not a fun way to blow your quarterly budget) – but there is something to be said about actually ‘touching’ something before you purchase it.

Our acquisition of the Netteza product line really opened my eyes to a number of tactics that support this. Being able to ‘Test Drive’ your hardware on site through their appliance – simple and sweet – was great, and it was just cool to look at as well.

The Smart Analytics System 5710 unfortunately does not have the slick green shell like our other Netezza products, but it does have the footprint of a pizza box (have an empty rack slot?) and the pricepoint that could drop it into any organization’s shopping list. At under $50K, the Smart Analytics 5710 has become some sort of phenomenon as an impulse buy in IT this season. Add to that, the Intel Xeon powered 3630s from System x are a screamer to boot.

Back to the video though. Aside from touching it, we wanted to show ‘how’ this system is used. These are actual screenshots, and actual reports and dashboards that you get (out of the box) with the InfoSphere Warehouse Packs. With the Smart Analytics 5710, you plug it in, turn it on and then load your data – And if you are using the Packs, well – you have reports and dashboards available on demand as well – out-of-the-box.

As for being able to ‘touch’ these systems, I will chat more in the coming weeks about our plans there. Rest assured that you will have multiple opportunities to see and play with the Smart Analytics System 5710 in the next few months. In the meantime, if you have any questions – just let me know, or leave a comment below.

Embed not working? Watch the video, Business Insight at your Fingertips: IBM Smart Analytics 5710, on YouTube.

Which topics were hottest in 2011?

We’ve had quite a year on the Mastering Data Management blog! We welcomed 20 new bloggers, launched the ChampionSpeaks series of IBM Champion interviews, looked at the implications of IBM’s Watson project, and launched InfoSphere MDM v10. Below are our 11 “most viewed” posts, which present a nice time capsule of the year nearly over.

Introducing IBM InfoSphere Master Data Management – Right before IOD11, Rick Clements announced the launch of InfoSphere MDM v10. This post attracted a lot of attention and helped introduce several features of the new release, more of which were detailed in the days that followed. (A summary of all the announcements, features and use cases was also highly viewed.)

Watson and our Future – Weeks before Watson took on Jeopardy champions Ken Jennings and Brad Rutter, Jarrett Goldfedder looked at the possible implications of Watson. As Jarrett concluded, “The part I most anticipate is the moment when the simulated voice of Watson answers that first question. That’s the moment when we get a glimpse of our future and can feel the rush of excitement that occurs the moment you realize that you are seeing something that can change the world.”

Watson & Healthcare Analytics – Watson was the talk of February’s HIMSS conference, particularly the potential to collaborate with physicians and aid in diagnosing complex cases. IBM Research’s Chalapathy Neti, Director of Healthcare Transformation, discussed how Watson can help with the analytics that are changing healthcare.

MDM vs Sensemaking – Jeff Jonas explained that, though MDM and Sensemaking have very different missions, they can be used collaboratively with incredible results. Jeff also detailed the value of bad data – an interesting departure from the usual focus on data quality. The key is to identify “Bad Data Bad vs Bad Data Good.”

Why Isn’t Data Quality Managed Proactively? – Responding to posts from Jim Harris and David Loshin, Larry Dubov examines the “Catch 22” of data quality: why organizations tend to manage data quality only after problems are revealed, when they’re more expensive and difficult to resolve. This post kicked off his fantastic Quantifying Information Assets series.

Series: MDM Vision & Value – We know the benefits of MDM, but getting a project off the ground requires a clearly-defined vision with tangible value. Lise Neely offers three key insights for MDM strategy and walks organizations through articulating – and calculating – the value of MDM. Lise also offered important reminders about mapping benefits to beneficiaries and not overlooking the “softer” MDM benefits.

Metric-Driven Information Governance – Inevitably, someone will ask your information governance team, “So what have you done for me lately?” Sunil Soares explained why you need metrics – but not too many – that can clearly demonstrate the value of information governance. He included a pair of real-world examples.

IBM Champion Cüneyt Göksu on DB2 & Databases – Our interview with Champion for DB2 Cüneyt Göksu was the most-viewed of our popular new ChampionSpeaks blog and podcast series. Cüneyt provided an international perspective on DB2, how databases have evolved and the role of community for users.

Why Information Governance is a Market, Not Just a Process – Sunil Soares states, “The "anything and everything" nature of Information Governance can be a challenge for practitioners,” before explaining how Information Governance has become a market in and of itself. Also, he uses an intriguing elephant analogy.

Metadata for Strategic Information Governance – November saw a flurry of posts around metadata. David Corrigan responded to a pair of posts by Jim Harris, writing about metadata’s critical role as a cornerstone of effective information integration and governance. As David explained, “A well thought-out metadata strategy is often the difference between starting a tactical information integration and governance project, and truly expanding to an enterprise-wide strategic integration and governance architecture.”

Trust & Verify: How Data Quality Can Foster IT-Business Alignment – Guest blogger Eric Kavanagh previewed a June webinar he presented with our Patrick Connolly. The duo spoke about the vital importance of trust in data quality initiatives. After all, Eric said, “If you don’t trust data, you’re likely not going to rely on it for making decisions.” The webinar is still available on demand and definitely worth a watch.

Did I miss your favorite post? What do you hope to see in 2012?

Happy Holidays!

Larry looks at the past relationship between data governance and MDM and offers 8 predictions for 2012

As an industry, we’ve been increasingly focused on Master Data Management (MDM) and Data Governance or Information Governance over the last decade. As we prepare for 2012, I’d like to provide some historical perspective on the commonalities and difference between the two areas

Both MDM and data governance developed from similar needs. Specifically, enterprise demands to establish better control over continuously growing volumes of enterprise data (especially core enterprise data also known as master data) drove the growth of both data governance and MDM at the beginning of the 21st century. Both terms were adopted by the market in 2003-2004, resulting in the formation of multiple MDM and data governance groups, organizations and institutions. Consulting companies started aggressively forming MDM and data governance practices.

Even though foundationally both disciplines serve the same goal, they grew somewhat independently.  MDM created advanced technology constructs and, most notably, MDM data hubs. Data governance (information governance) practices and frameworks have been developed in a variety of focus areas, including data quality, data protection, data visibility and security, data archival, data definitions, content management, data warehousing, metadata management, etc – but  not MDM.

Interestingly enough, most established data governance methodologies are relatively light on standards, good practices and techniques recently developed by MDM. Data governance methodologies and practices often failed to meet requirements of MDM practitioners.

With that background, I’ve made 8 predictions for 2012 and beyond.  Do you agree? Disagree? Share your comments below.

1. Data governance methodologies will incorporate master data more holistically, developing master data governance as a significant part of any generic data governance or information governance methodology. For master data governance to be effective, it has to be aware of and rely on modern MDM techniques, technologies, and applications of probabilistic science to master data quality.

2.  Master data governance councils will grow in significance to become a common and integral part of more general data governance councils. For the organizations where data governance does not exist or function properly, a master data governance council can become a seed for an effective, working data governance council

3.  The notion of master data governance policies and good practices around them will evolve. These practices will be defined in terms of well-established policy conditions, resources and KPIs, enforcement methods, monitoring and reporting standards.

4.  Data stewardship will continue to be better aligned with master data governance policies. We’ve seen this begin to happen, but the alignment will strengthen.

5. The trend of integration between MDM and BPM will accelerate to enable more collaborative and proactive data governance. This will further strengthen MDM technologies and make even more services oriented constructs rather than just data repositories.

6.  The MDM-BPM integration will accelerate development of MDM-powered applications to meet new demands driven by liquidity policies or other requirements. Examples include Global Account Opening and Maintenance, Global Policy Issuance, Customer On-boarding, MDM-centric Credit Application Processing, MDM-enabled Call Center Applications, MDM-centric Marketing Campaign Management, Laboratory Test Management, Credit Risk applications and others.

7.  The hub-of-hubs architecture will grow in market consequence and draw significant attention from those responsible for data governance. The term “hub-of-hubs” applies to an MDM solution where multiple jurisdiction-specific hubs work in concert for enterprise governance while preserving their jurisdiction-specific ownership, privacy and control. The hub-of-hubs architecture seems promising to resolve trade-offs between local or divisional needs for data privacy and ownership and global or corporate needs for the golden customer record.

8.  The MDM-specific glossary will solidify and standardize on a few hundred critical terms. This will accelerate MDM implementations and improve operations and MDM adoption. At present, the lack of common MDM terminology slows down many initiatives and makes them more expensive than they can be with a better defined glossary.

What are your predictions for 2012?

Anju explains how MDM can help improve your business processes

Did you know that “Business Process Improvement” is the #1 driver for MDM? Recently, my colleagues Alex Eastman, Trey Anderson and I spoke at a webcast featuring the business process improvements that IBM Master Data Management v10 can enable.

But this wasn’t your typical dry presentation. How many webcasts have you attended where “The Grumpy Old Man” from the classic days of Saturday Night Live (SNL) is referenced? For those of you unfamiliar with the character, he complains about advances in the modern world and contrasts them to the way things were in the past. No matter how difficult or uncomfortable those experiences were he’d say, "That’s the way things were, and we liked it!"

We discussed 3 main topics:

Information Consumption: I focused on IBM’s commitment to Information Consumption as a key objective of IBM’s MDM philosophy and practice. We at IBM find it important to understand how our customers plan to consume their master data in their business environments. By understanding how you use master data, we can help facilitate that information consumption. We highlighted two key ways to achieve this: MDM Powered-Applications, and by combining BPM Express with InfoSphere MDM v10.

MDM-Powered Applications: Next, Alex focused on MDM-Powered Applications. After defining what exactly MDM-Powered Apps are, he dug into how they facilitate business process improvement and their role in the MDM Journey. Alex also introduced the Grumpy Old Man analogy of MDM, describing how MDM was thought of in the past, and contrasting that with new ways of getting the most out of MDM implementations. Read Alex’s recent post on this, “Not Your Grandmother’s MDM.”

Business Process Management: Finally, Trey discussed how process-oriented data stewardship enhances master data quality. By taking advantage of new process management capabilities, customers can configure workflow-based solutions that deliver data stewardship and data governance applications. This enables customers to improve data steward effectiveness by enforcing master data quality policies though process implementation. As a result, organizations see increased productivity with workflow-managed assignments and distribution, and improved visibility through reporting and dashboards. Trey recently blogged about this in more detail.

If you didn’t catch the live webcast, you can access the replay on demand. How has MDM helped you improve your business processes?