There are two ways to look at the phrase “Information is an asset.”  One is metaphorically, which is the most common.  “Information is an asset, so we need to exploit it to do better stuff.”  That’s OK, but it falls short from a business sense. You can use the metaphor to launch enterprise information management (EIM) efforts, but something is always missing.

That something is the financial recognition of the asset’s value. Off hand, hardly anyone will say information has no value. But measuring what the value is creates a whole new discussion.

We have concepts around the value of information that range across all phases of the information life cycle. A recent article talks about information have no value at all until some insight is reached.  Dr. Michael Wu stated “The value of any data is only as valuable as the information and insights we can extract from it. It is the information and insights that will help us make better decisions and give us a competitive edge.” TechCrunch, November 2012.

This prompted quite the response. I and many others flat out disagree with this.  If I am a CEO, and take this literally, then I may as well shut off all information systems, since there is no “value” at all.  But we know better.  There is value in my operational and managerial data, my emails, and my documents.  If not value, then there is risk  (which, in sci fi talk, can be considered “anti-value.”)

The rubber meets the road when you talk technique. How do you value information?  Is there a formula? There may be.

Doug Laney of Gartner Group has put some serious mental cycles on this topic. Doug is getting some traction with CxO level executives.  This is not coincidental. Doug and I have worked together now and then over the years and this topic has always been an item on his “I would love to have time to do more research” list.

At the beginning, we both talked in notional terms, and presented very rough ideas as to what this might look like.   However, Doug has taken it to a higher level and it is worth any information professional’s time to check out his ideas. I am really glad to see serious treatment of the subject and to see Doug getting recognition for his research.

Doug’s labels the formal treatment of information as “Infonomics.”  To be precise, he states Infonomics is:

the economics of information and principles of information asset management. 

I can live with this definition, but it misses the practical, or “implementable” aspect of this discussion. So let’s add a term – Information Accounting, which is:

the formal accounting and measuring aspect of information asset management 

There is a nice stream of discussion on this topic on a LinkedIn group, managed by Doug Laney (and occasionally, me): http://www.linkedin.com/groups?home=&gid=4069646&trk=anet_ug_hm

The next blog will look at techniques and concepts to consider when actually valuing information.

Big data with zero relevance to your business needs is essentially the same as no data at all.

Recently, I came across an interesting article that introduced the word “evidence” into the discussion of data relevance. It’s an interesting choice because most people normally think of “evidence” in terms of forensic analysis for the purpose of building a case of some sort – against some alleged perpetrator, for some ambitious business scheme, and so on.

Evidence is any data that you can factor into a case – in other words, into a structured narrative that argues for or against some imperative. The imperative is a decision point: do this or don’t do that. It’s what lawyers do for a living. It’s what entrepreneurs do. It’s what any politician, project manager, or interested party does to build support for some course of action.

Can big data be a reliable source of evidence for whatever business case you’re building? Well, it’s not as straightforward as that. As I pointed out in this blog, data scientists, who are the primary developers of big data applications, focus on using statistical models to frame structured narratives—i.e., scenarios—that explain patterns called out in model-driven insights.

For the data scientist, visual patterns serve the core narrative-building functions of framing the opportunity, problem and solution. If you are a data scientist, many of the data-based patterns you call out concern regularities in human decision-making behavior. Past behavioral patterns in some target audience, factored as evidence into your model-based narrative, can bolster your case for or against some course of action that seeks to influence those behaviors. Churn analysis, for example, is one of the most common scenarios in decision science that usually rests on past churn behaviors among various customer segments.

The above-mentioned article discusses the possibility of auto-generating persuasive narratives by using machine-learning models to extract human-like insights from big data. I’m not as sanguine on this possibility as the author is. As they themselves point out, “the data is purely instrumental to the communication you want to support.” As far as I can see, they’re not proposing some radically disruptive new form of artificial intelligence that can reproduce the creative, cognitive, empathic, and social smarts needed to craft a case that can win the hearts and minds of real human beings.

In order to imagine that you can auto-generate a business case from raw data, you would have to boldly assume that the “evidence” in that data is self-evident, as if the “evidentiary” attribute were a form of metadata that can be easily extracted.

That’s a mistaken assumption. The evidence of any collection of facts is in the judgments of the people building and evaluating the larger case. These two processes – constructing  and deconstructing narratives – cannot be automated. You need data scientists to build big-data applications and visualizations intrinsic to data-driven narratives. You need business analysts who are subject-matter experts to collaborate closely with them in that endeavor, because it’s their responsibility to craft a case in its full business context.

Just as important, only the business analyst knows how the business case is likely to be evaluated by the ultimate decision makers.

My favorite part of the event, and any of our events really, is meeting all the people in person I’ve worked with throughout the year producing our webinars, both speakers and attendees. Many people who have attended our webinars and have visited our site came to our DATAVERSITY booth with content requests. Love it! We are more than happy to put our researchers and writers to the task. It is my firm philosophy that we cannot go wrong with requested materials. Some requests for education materials:

• What is an Information Product?
• How do you manage outsourcing your data?
  • With so many development work being outsourced, how do you manage the data they are producing
• More Information on Data Discovery Products such as Tableau

Another exciting result of the conference is talking to several people who will be working with DATAVERSITY on various projects. John Ladley, President of IMCue Solutions, and Gwen Thomas, Founder of the Data Governance Institute, will be blogging for us. John will blog about various topics including Data Governance and Master Data Management. Gwen will be blogging about what’s happening in the world of Chief Data Officers. There are also a couple of other potential exciting new bloggers in the works as well. More to come on that!

We’re excited about the reception we’ve received from you, our subscribers, on our initial Research Paper releases. At the conference we were able to sit down with a couple Industry Experts to generate a few more papers. Coming end May, beginning of June we will release our Data Governance Research Paper written in partnership with Bob Seiner, sponsored by ASG and the Data Governance Professionals Organization (DGPO). Dan McCreary and Ann Kelly, our partners in our NoSQL Now! Conference, will help us generate a Research Paper on Database technologies. David Loshin, President of Knowledge Integrity, will be working with us on a fall Research Paper on Information Architecture to be published in October, Sponsored by Denodo.

Another exciting outcome of the conference was my chance to sit down with Andrea Dierna from Morgan Kaufmann Publishers. She and I have been talking for a couple years now on how we can partner together and we finally have the resources to implement our strategies. Starting immediately, we have swapped Editorial Calendars. Anytime we have a blogger or webinar speaker who has a book published with Morgan Kaufmann (MK), MK will provide us a discount code for DATAVERSITY subscribers. We’ve had a lot of requests for books lately, so very excited we can provide discounts to you through our partnership. We’ll also be producing some webinars with some of their authors. Many of their authors are Industry Experts who are very much tied in with Enterprise Data World having given many well received presentations at the conference over the years. The final phase will be for us to implement a bookstore so you can get all the recommended books in one location with the discount codes readily available.

Some very exciting news for our international folks, we’re going to start doing some webinars in multiple languages! Pablo Ribaldi, Solution Manager for Information Governance & Quality at the Church of Jesus Christ, will be delivering some webinars in Spanish. He’s going to start with a couple of webinars on Data Governance and Master Data Management. Mario Faria, Data Strategy Advisor of Boa Vista, who was also the first Cheif Data Officer in Brazil, will be delivering a webinar in Portuguese, How to Create a Successful Data Organization. We’re very excited about these!

And one of my favorite results of the conference is the “DATAVERSITY T-shirt around the World.” We gave away every last one of the T’s we brought with us. They are American Apparel 50/50 blend unisex T’s. We worked hard to invest in quality and they are so soft, even those who weren’t interested initially in another T walked away with one in their hands. We were asked if we’d be interested in posting pics of people in their DATAVERSITY T, and of course we said yes! We love the idea and love even more that it was requested! So if you have a T, tweet us your picture @Dataversity or post it to our Facebook page: http://www.facebook.com/Dataversity. Or bring us a printed copy to our next conference and we’ll start a collage board. And thanks Chris Bradley for getting it started!

Chris Bradley (@InfoRacer) sporting the DATAVERSITY T in Bath, England.

Have I mentioned lately we have the best subscribers? Thanks for being so interactive with all we do!!! And as always, if you have additional ideas, suggestions, or questions, don’t hesitate to shoot me an email.

Regards,

Shannon Kempe

Executive Editor

Governance programs can’t survive unless there is demonstrable long-term financial benefit for the business.

I’m just back from Enterprise Data World. If you missed it, start making plans for EDW 2014 in Austen. EDW is the gathering of the Data Management clans, and I love it because I never come away without some new ideas, and some old ideas clarified.

One EDW highlight for me this year was a session on sustainable governance presented by Kelle O’Neal of First San Francisco Partners. To my regret I had to leave at the halfway point, but not before my thoughts about the economics of governance had started to gel.  On the way out I chatted briefly with Asim Mohammad of Bank of America and he confirmed my thoughts about the cost/value issue and the gelling process continued as I traveled home.

The basic thought is very simple. No matter what organizational structure you adopt, or what processes you put in place, your governance environment won’t be self-sustaining unless all those who pay for it, or otherwise invest time and resources in it, can see the business value. For business value, read money. If governance depends on personalities or events to drive it, change will erode it. If it has a clear dollars and cents (substitute your favored currency) business case, it will survive.

So how do we figure out the value? Not the soft values, but the numbers? I’m thinking there’s a typical 2×2 matrix that will serve as a good model. Crudely, the choice is “do governance, or don’t do governance”. Each option has cost and value, and that gives the four quadrants of my matrix.

The “cost” of not doing governance is simple. It’s zero! To be clear, by “costs” I mean the investment of time, money and people. In those terms, doing nothing costs nothing. Many of the things often described as costs are, to my mind, negative entries in the value quadrant.

The “value” of not doing governance really amounts to penalties. The explicit penalties of regulatory failures, the measurable penalties of wasted resources and the (perhaps) less measurable penalties of wasted time in decision making and delivery of product to market.

The costs of governance are the costs of people, processes and resources. Supporting the processes and resources may require tool investments and will certainly require systems analysis and design.

The positive values of governance are primarily the benefits accruing from better value from information.

So far I’ve presented this as an “either / or” … But of course it isn’t. The costs are disbursed, and the values accrue, over time. Understanding your governance economics implies predicting and tracking the costs and values over time. I’m prepared to say that if you have nothing in place, there will definitely be a positive impact to getting started. I suspect (but don’t have proof) that there are escalating costs to manage more and more information, and diminishing returns from the process. That would imply that there is a “Zone” where there is a very positive return on governance, and that managing explicitly to stay within the zone is a key to long-term support for the governance program.

Can you have big data with airtight personal data privacy? In the age of ubiquitous over sharing and zealous big-data-driven hyper-analysis, digital privacy has become elusive and hard to attain.

With the proliferation of internet usage, and the reliance on internet search engines such as Google for everyday needs from looking up a location to online shopping, it is safe to say privacy concerns are rooted deep in the heart of the online experience, which thrives on freewheeling give-and-take. We should put privacy considerations at the core of our social business initiatives before customers demand it or the courts, regulatory bodies, and legislators decide to force our hands.

That is why businesses should embrace the concept of “privacy by design” – a systematic approach that takes privacy into account from the start – instead of trying to add protection after the fact. My IBM colleague Jeff Jonas authored a paper to help organizations understand how to incorporate privacy technologies from the ground-up, while preserving functionality.

Balancing the potential of modern data technology with the privacy and security of employees, clients, customers, and citizens in general, is something organizations must take very seriously. And privacy-protection best practices have taken hold in businesses everywhere. But here’s the rub. As a business IT professional, even if you could harmonize  disparate privacy regulations the world over and abide by every one to the nth degree, it can prove difficult to figure out how to legitimately access personal data to solve problems. You still have to be careful about how you conduct data sourcing, content aggregation, entity resolution, de-identification, segmentation analysis, and target marketing efforts.

Additionally, under any regulatory system, there is no law stopping IT professionals from using legitimate analytic tools and techniques to make smarter inferences about peoples’ behaviors, propensities, proclivities, and the like. For instance, a CMO being able to turn 12 terabytes of tweets created each day into a better understanding of his or her customers.

And while online entities are becoming more transparent in terms of explaining to their users what, if anything, is being tracked and why, businesses simply need to become smarter about how the data is used to provide a personalized experience.  For example, IBM is collaborating with the Future of Privacy Forum and other firms to establish a consumer trust seal to reassure consumers that their smart grid information is being accessed and used appropriately.

While big data and personal information have become an essential resource for solving some of society’s most pressing problems, it is imperative organizations protect and respect personal privacy when analyzing data to uncover insights.

So, then, is personal privacy a lost cause in the age of big data? Not necessarily. Despite increased privacy concerns that may be raised in regards to the use of analytics, there are many benefits that this increased visibility into data offers, and they far outweigh the infringement on privacy – from increased public safety to better traffic systems, and reduced fraud, etc. Every day, data is being used to make the world smarter, safer, more efficient and more customized. So while data privacy is a major concern, we as a community should grow to better understand the balanced risks and rewards of data analysis.

I talk about data all day and every day. After all it’s my job, my passion and what brings in my salary. So sometimes I feel like talking about something else – and today I just want to talk about stuff.

Those who know me know that I am a biker. By biker I mean I ride a motorcycle – not for commuting to work and back but for the pleasure of it. I have two bikes – one is a Harley – heavy, big, noisy and black. The other one is a small 600cc superbike. I ride them for different reasons, but generally these days, when I go out it is on the Harley. And I choose to go cruising along, spending time enjoying everything around me.

However, on occasion (when I am feeling at rest with the world) I get on the back of my hubby’s Harley (yes – he has one too AND a superbike – a big, mean beast of a speedster) and become a pillion for the ride. Being a pillion is actually quite difficult – since you are stuck behind someone so can’t always see what is going on up ahead and you have to sit still otherwise you can upset the smooth riding or give the person you are riding with a big fright. Don’t want that to happen – road rash (the nasty stuff that scrapes your leathers off your skin as you skid along the tar) is really painful.

I digress – the idea was to set the scene – me on the back of hubby’s “hog”, us taking a really please cruise through to the monthly bikers council meeting. It’s 6pm in the evening – just on the very edge of summer, but moving into autumn. Another explanation is in order here – in South Africa the seasons are not as distinct as in Europe or the US for instance. Season change is more of a dip or increase in temperature and having sunrise and sunset at different times. By the time one realizes that we are in the middle of winter, we are almost out of winter and into spring again.

So here I am – sitting still (here is where people who know me fall on the floor laughing at the thought I can sit still) and just enjoying the scenery. This specific evening, the moon is already rising and I can see we are in for a full moon. The sun is setting in glorious style almost in front of us as we are riding along and the lights of the city are beginning to twinkle as people are getting home and starting their evenings. It’s such a magical time of day – the air around me is slightly cool but still has pockets of warm air that washes over my exposed face as we ride along. I can smell that unique smell that is Africa, I can hear the cadence and music that is also distinctly African in nature – above the roar of the Harley of course. And I feel at peace all of a sudden. I relax and sit back against the sissy bar (a really neat attachment that stops me flying off the back of the bike as hubby hits the gas again in an effort to avoid what he calls “moving chicanes” – but are more commonly known as motor vehicles).

As we ride along I start to think about how lucky I am. I consider how amazing is this planet that we live on. Yes – I can see the smog on the horizon – turning the sunset spectacularly reddish in colour. I can see the smoke from the township fires smudging the sky with greys and blacks. I watch the people in the cars around us – some are smiling and talking to each other; some are on their mobile phones, gesturing and ignoring the rest of the world; some are alone in their car, frowning in concentration as they thread their weary way home through long queues of cars.  We are flying down the highway right now – the buildings on either side flashing past blurry with the speed. We lean through a long sweeping corner and pick up speed as we approach the next intersection where we have to get off. As quick as a flash we are turning down a darkened road where there no overhead lights and trees on either side block any light from the houses and shops from helping us along the way. But it’s cool with me – the bike slows down and we are back in cruising mode. I can look around me once again, look up at the sky and seem some stars begin to show. We reach a stop street and slip past the queue right to the front. Amazingly tonight the first driver is in a good mood and waves us through. We lift our hands in a wave of thanks and surge ahead. This part of the road is all about bends – left, right, left again and hubby just loves this. I lean tight into him and we ride through like pro’s. Thank goodness I have come to terms with the way he hugs the tar as we take these bends. I no longer scream into my helmet as I have learnt to trust.

We are not far from our destination and we slow down to take the final two corners. Once again that feeling of peace descends on me and I consider how totally amazing the world is. In just one hour I have seen the earth show its most beautiful sunset; I’ve seen the good and bad of people; I’ve been treated to the joy of riding and I have been shown how much I have to be thankful for.

And all this is not over. I still have the ride back to look forward to.

I know this is not about data. It wasn’t meant to be. I guess really this was actually just a celebration. I’ll be back writing about data soon – but I do hope this tiny glimpse into my life was worth the read!

Til next time ….

This is a follow up to the original article on RAID vs. Drive Rebuilds. Several questions came up since the original posting, and Part 2 will attempt to address those questions.

One of our readers noted:  “In your first example, parity is missing from stripe 4. You didn’t mention how that stripe can get rebuilt if there’s no parity.”

The parity is missing from stripe 4 because it should be on the missing or damaged drive.  In other words, in a healthy array it would be at the top of stripe 4.  As to how it is rebuilt, in this example all of the data is intact and the parity sector on HDD 1 in Stripe 4 would be rebuilt by XOR’ing the data from drives 2-4 (P4 = XOR (D9, D8, D7).  See below for a picture of the rebuilt drive.

Another question that came up multiple times since the original post relates to other ways data can be damaged or lost.

One of our readers asked:  “Your second example shows how the data can be lost if the wrong type of rebuild is done, “such as” rebuild parity. Is that the only case? “Such as” kind of implies you could do other rebuilds that would get you in trouble.”

There are several types of rebuilds that can happen where data can be lost. Below is a list of some of the types of rebuilds that can cause data loss.

1. Rebuild parity with zeroed drive (parity overwritten)
2. Rebuild parity with degraded drive (forced online and parity overwritten)
3. Rebuild parity with drives out of order (parity and data overwritten)
4. Rebuild RAID with missing drive (parity and data overwritten)
5. Rebuild RAID with different stripe size (parity and data overwritten)
6. Rebuild RAID with different configuration (parity and data overwritten)

As an example, one of the most common data loss cases we see is when parity is updated with a zeroed disk in the RAID configuration (RAID rebuild instead of HDD rebuild). This type of rebuild effectively destroys the original parity and prevents a drive rebuild.  Once the parity is overwritten, the missing user data from the damaged or missing HDD cannot be recreated.

Another scenario where data could be lost is a disordered RAID array, especially during a RAID rebuild. Parity rebuilds on drives that are out of order can end up overwriting good user data.

In the example above, the data that was originally on HDD 3 on stripe 1 is now overwritten with new parity. The parity that is on HDD 4 in stripe 1 is now treated like user data instead of parity causing logical corruption. Furthermore, the data that is on HDD2 in stripe 1 is skewed, also contributing to the logical volume corruption. All of the areas marked in red would be damaged.

Even if a parity rebuild is not done, there would still be logical volume corruption. This logical corruption often triggers volume repair tools to run (CHKDSK, FSCK, etc.). These repair utilities will try to “fix” the logical corruption when the damage is really at the RAID level, causing even more damage such as deleting metadata and making the system unrecoverable.

Another scenario is where a RAID is rebuilt after a two-drive failure using a degraded drive that has been forced online and a new drive. This rebuild with this combination will overwrite the “good” parity with new “bad” parity, often making the system unrecoverable or the data unusable.

The final example to illustrate is where the RAID configuration changes and parity and data areas are overwritten with the new configuration.

Let’s assume for this example that we have a RAID 5 array with a stripe size of 64K. The OS will read the data from the stripes starting with HDD1 and the data represented by M1. Then, it will proceed to M2 and then to D1 and so on.

If the array controller loses the configuration and the user forces the wrong configuration, damage will occur. In our example, the user has forced a new configuration with a 32K stripe size, effectively splitting the data in half.

The OS will read the first half of the first section of metadata represented as M1.1. Then, the OS will jump to the next disk in the stripe and read the first half of the next section of metadata represented as M2.1. This will cause logical corruption, making the data unusable. Often this will trigger volume repair tools to run and “repair” the logical damage, which in turn can cause additional damage and even make the volume unrecoverable.

So how do you protect yourself in the event you run into a situation like this?  Here are some tips on how to safely recover from this type of data loss:

1. Image the drives before attempting a rebuild. That way if the rebuild is unsuccessful, your data is protected.  Make sure the imaging program you choose allows for a forensic or sector/block- level image of the disk.
2. Restore backups to a different volume. This ensures that all important files on the backup are good before possibly overwriting data on the active volume.
3. If there is a RAID problem, test the backup by restoring it to a different location or image each drive from the RAID before attempting a rebuild. Sometimes a RAID rebuild does not work correctly and can make the problem worse.
4. Do not create any new files on the disk requiring recovery or continue to run applications until the important data is recovered. New files can overwrite the files that need recovery.
5. Do not run FSCK or CHKDSK file system repair tools on a virtual disk unless a good backup has been validated by restoring it to a different volume. These repair tools assume that there is a good backup of the data and can overwrite file pointers to make a file system consistent. If desired, these tools can be run in read-only mode to find any major corruption before repairs are made.
6. Do not delete any additional files prior to a data recovery of deleted data.   Deleting files includes moving files from the source to another volume.  A move is simply a copy then delete.    If you need a copy of the data from the source, make sure to copy it and not move it.  Additional deleted files can complicate the data recovery.
7. Do not try recovery software unless you are sure it will not write anything to the disk that needs recovery. Some recovery software will attempt to write to the source disk and could damage later recovery attempts.
8. Contact a data recovery professional before attempting the recovery on your own.  A professional can outline the possible impacts your plan will have on the recoverability of the data and offer suggestions for self-recovery.

Do you recognize the famous quote that inspired the title of this blog? It is frequently referenced when people are talking about their careers. And although I don’t often think about Woody Allen when discussing Data Governance, it seems to me that when he stated “80% of Life is Showing Up,” he pretty much hit the Data Governance nail on the head.

Woody’s statement is deceptively simple – especially when we look at it through the lens of Data Governance. We all understand the spirit of his statement, but it is easy for many people to under estimate the effort it takes to “show up” for Data Governance. We can’t simply walk into a room of leaders and spark their interest in our efforts with ill prepared presentations or informal conversation. Rather, I believe it’s about doing the hard work, and sometimes the thankless work, to be prepared to “show up” for Data Governance.

We “show up” for Data Governance in many ways – meetings, conference calls, hallway conversations, emails, instant messages, casual lunch conversations, etc. But how do we prepare ourselves to “show up” at these events? Are we ready for all of the questions and conversations?” Are we ready to creatively think of ways for Data Governance to partner with an area or a project? Do we have enough people with the breadth of knowledge about Data Governance to help our team “show up” in a unified approach?

Things to consider to successfully “show up” for Data Governance:

1. Are you actively seeking out ways for people in your company to tap into Data Governance? Are you looking and listening for not only the obvious partnerships, but the ones that may be a little disguised?

Lessons Learned: Do we have Data Governance representatives who actively “show up” at meetings? Or, as a former colleague of mine once said, “the Data Governance representative is a potted plant in our meetings.” Imagine hearing that about your peer! My reaction to this was quite obvious. Ouch! Are you and your team present and engaged in conversations? Do Data Governance representatives check emails when they should be paying attention to the meeting? Yes, we are all guilty of trying to multi-task at times, but we need to “show up” and represent Data Governance well at every opportunity. For myself, I rarely take my smart phone to a meeting any longer. Even if I could multi-task, should I? Data Governance needs to be present and engaged so we can uncover opportunities.

2. Do you share what you know about Data Governance? Do you mentor people or do you expect that they already understand your effort?

Lessons Learned: Those of us working in the discipline of Data Governance can sometimes forget that not everybody works in Data Governance. We need to make sure to “show up” by presenting and speaking the right language to the right audience. Nothing will turn off people faster than speaking in our industry jargon. How did you learn about Data Governance? Who mentored you? More than likely there was somebody in our work history that helped us understand these concepts. Personally, I always think back to my fantastic mentors and how they helped me learn and understand Data Governance. It only takes a little extra time to explain new concepts to people, so I am an opportunistic mentor whenever possible. And not only does this help people understand the questions they have at that time, it makes them comfortable to come back to you for their next question. That’s how I try to “show up” in a day-by-day fashion.

3. Do you have all of the collateral you need to withstand drastic changes in your organization? What happens if your biggest advocate leaves your company?

Lessons Learned: There is one thing you can count on in your Data Governance career and that is change. Your leadership will change. Your organizational support and structure will change. Your team members will change. Your peers will change. Many things will change and we have to “show up” for every one of them. Therefore, we must make sure to have all of our collateral up-to-date and ready to go at the drop of a hat. The best thing you can do for yourself and your team is to have your Data Governance collateral in excellent condition. With Data Governance it is never “one and done.” If your Data Governance collateral hasn’t been updated in two or three years I would say the team is not ready to “show up.” If you are executing toward your Data Governance goals, how could the collateral remain stagnant for that long? If the collateral is stale, so too is your message. Constantly improving and updating our collateral is key to “showing up” at every opportunity.

4. Does your company understand the benefits your efforts bring? Do people understand more about your processes or do they understand how you will assist their efforts?

Lessons Learned: When we are working to stand up our Data Governance effort, we sometimes get stuck in the processes, best practices, and industry standards. We sometimes forget to talk about why we are doing all of this because we are focused on how we are doing our jobs. I sometimes find that people think we perform Data Governance just to perform Data Governance. We need to “show up” as early as possible in our efforts and inform people how our discipline will benefit the company, its people, and projects.

So, do you think 80% of Data Governance is about showing up? Showing up sounds much easier than it is; we all know it’s a more difficult challenge than just physically being there. It’s about being very prepared and extremely engaged. We don’t want to be potted plants, we want to Govern Data. Therefore, I think we need to remember that Data Governance “shows up” in many ways and we need to be ready for all of them.

In two of my recent posts, I discussed the potential impact big data can have on energy management and business, explaining how customer and device data yield results that can provide an organization with a strategic advantage, either in service or offerings, over its competitors. As we now know, big data is the newest and most valuable natural resource to any organization, no matter which industry. This is particularly true in healthcare, where patient data analysis can very likely be a matter of life and death.

Not unlike typical business applications, where the ultimate goal is to provide improved customer service, big data technology is playing a large role in healthcare institutions to analyze enormous volumes of patient data and ensure a higher level of personalized care. Through the use of analytics tools that collect, synthesize and analyze historical and real-time data, physicians and healthcare providers not only obtain a more holistic view of patient health, but are also given the opportunity to monitor patient condition more closely, and conduct in depth research into diseases and drugs more efficiently.

For instance, using big data technologies, providers can now consider a number of factors, including test results, past visits and environmental elements, when addressing specific patient needs and determining a course of care. The Premier healthcare alliance is using a big data platform to gain insights on, measure and improve patient health and safety while also reducing the overuse of procedures, readmissions, unnecessary ER visits and hospital-acquired conditions. Through this work, patients have a greater certainty they will receive the most effective treatment possible and that their care will follow nationwide best practices.

At institutions like UCLA Ronald Reagan Medical Center doctors are using real-time sensor data to prevent and react to sudden changes in condition for patients suffering from traumatic brain injuries (TBIs).  The technology targets rapid rises in brain pressure to predict potentially dangerous changes and alert caregivers. Similarly, at the University of Ontario Institute of Technology in conjunction with Toronto’s Hospital for Sick Children, neonatal intensive care specialists are using data from monitoring equipment to track the condition of premature babies. Doctors and nurses are now, for the first time, able to spot and prevent potentially life-threatening infections up to 24 hours earlier.

Going further, there is also an increase in the use of big data within the medical research community, as it provides researchers and scientists with more insights into unstructured and structured data they wouldn’t have normally been able to access and analyze. This in turn allows them to generate a better understanding of what causes diseases and what can treat or cure them. An example of this is the work currently being undertaken at the State University of New York (SUNY) at Buffalo. Using analytics technology, researchers at SUNY are studying the more than 2,000 genetic and environmental factors that may contribute to multiple sclerosis, a chronic neurological disease that affects approximately 400,000 people in the US. Through big data, the scientists are aggregating and analyzing medical records, lab results, MRI scans and patient surveys to develop algorithms containing genomic datasets to uncover critical factors that speed up the disease’s progression in patients. The insights gained will help doctors better target individual treatments.

Regardless of your organization’s role – energy management, finance, marketing, healthcare – understanding and making the most of available data is essential to success, both on a business level and a personal level.

It was Tony Shaw’s vision to develop a Trade Journal that would provide year-round education on Data Management to compliment the conferences he was doing under the Wilshire Conferences, Inc. brand.  He had this new brand name he was anxious to use as it better depicted the mission: DATAVERSITY. So we built a website and folded into it the old “Enterprise Data Journal.” It gave us our initial content and Tony brought on Angela Guess, our fabulous Data Daily writer to generate a few news summaries each weekday.

And oh my! How far we have come since then! The number of people visiting our site grew and continues to grow daily. It is so, so fun to watch. The total number of unique visitors in that first month, April 2011, to our biggest month yet has increased just shy of 4,300%. And yes. That’s a four thousand three hundred percent increase.

The success of the site of course has everything to do with the incredible people we work with to help provide us valuable, educational content and our amazing subscribers. And we wouldn’t be able to provide the site and all the free content without the Advertisers and Sponsors who fully support the mission of providing valuable education.

We now have almost 20 active bloggers and many more occasional guest bloggers who are all Industry Leaders with 15 to 20+ years experience in Data Management. Not to mention all of the incredible webinar speakers we’ve had grace our presence over the last couple of years. Also on that list are five fantastic freelance writers who work so hard every week to do the deep research, find the hottest trends and news, and write the articles, white papers, and research papers of which we are so proud.

All are amazing people, and we cannot thank every single contributor enough for generating education in Data Management. People like Karen Lopez (@datachick) who was one of our first regular bloggers. Not only does she still blog for us, but is moderating a monthly webinar panel: “Big Challenges in Data Modeling.” Peter Aiken likewise brought us our first monthly webinar series, “DataEd Online” reviewing sections from the Data Management Body of Knowledge. Bob Seiner accepted our request for him to present the monthly webinar series, “Real-World Data Governance,” and now he is co-authoring our third research paper on starting a Data Governance program.

But none of the content means anything if there are no readers. It’s our continuing growing number of subscribers who impress me every single day. I get so excited to see content being shared via social media channels and when comments are added to blogs and articles. If you’ve attended one of our webinars, you know how much I love it when the chat is on fire throughout and people are really engaging with the presentation. Discussion is so good and so essential to moving any industry forward. We at DATAVERSITY are so, so blessed to have subscribers that are so passionate and so engaged.

So THANK YOU everyone for aiding in a great two year anniversary. And CONGRATULATIONS to you on that success. I hope we continue to enable a community you are proud to be of part.

So keep the feedback coming! Shoot me an email anytime with what you think we should take on next. And if you’re going to be in San Diego, CA for the Enterprise Data World 2013 Expo and Conference, stop by the DATAVERSITY booth and say hello!

Regards,

Shannon Kempe

Executive Editor

DATAVERSITY.net