Click here to close now.


Industrial IoT Authors: Harry Trott, Xenia von Wedel, Elizabeth White, Kevin Jackson, Chris Fleck

Related Topics: Microservices Expo, Industrial IoT

Microservices Expo: Article

$3 Trillion Problem: Three Best Practices for Today's Dirty Data Pandemic

Maybe your software is healthy, but is your data terminally ill?

In survey after survey, about half of IT executives consistently agree that data quality and data consistency is one of the biggest roadblocks to them getting full value from their data.

This has been consistently true all since the Chinese invented the abacus. I suspect it will be true long after quantum computing has solved every other problem that humanity faces.


Incorrect, inconsistent, fraudulent and redundant data cost the U.S. economy over $3 Trillion a year - an astounding figure that is over twice the amount of the 2011 Federal Deficit.

Similarly, many experts estimate that HALF the money spent on developers goes towards "software repair". So we're living in a world of sick software and dirty data. And the cost of all this is staggering.


I've long been a proponent of healthy software - but healthy software can only function properly in the presence of healthy data. Does quality software even matter if the underlying data are defective? Agreed - that's pushing the point to the extreme.

The rapid, iterative, continuous testing model has measurably improved the quality of software development. Evangelists such as Kent Beck have had a huge impact on this. I recently posted a freely downloadable white paper on this topic. But where are the evangelists for data quality? Where is an open source "JUnit for Data" and if it's out there, why isn't everyone using it?

The Cost of Bad Data
Anyone care to make a guess at how much money is wasted every year due to dirty or duplicate / redundant data? I'll start by presenting one common user story - one you probably have also recently experienced. And then expand on it.

Recently, I went to my mailbox and waiting for me was yet another invitation from a major bank to join their credit card program.

This shouldn't come as a surprise, as people everywhere are deluged by credit card offers. Except that I already have the particular card in question. Not only that, but because the particular bank in question has managed to acquire a number of other banks and credit card lines of business, between my personal and my corporation, I believe I now have five Visa cards from this particular bank.

I also occasionally get mail from them offering me cash bonuses to open up a checking account at their bank. Probably wasted postage, as I already have two checking accounts there. I suppose I could open up a third, just to get the $100.

Every month, I get a significant number of expensive looking direct mail offers from this bank, often with slightly different variations on my name, which I promptly throw away. Aside from the impact on the environment and the wasted direct mail expense, it's a bit irritating to me. I hate junk mail, and I feel compelled to shred things like credit card offers. So they've burdened me (an existing customer) with yet another "thing to do". So they've spent money, hurt the environment, irritated an existing customer, and now I get to make fun of them online. Bad investment on their part.

QAS (an Experian company) estimates that the average company wastes $180,000 per year simply on direct mail that does not reach the intended recipient because of inaccurate data. But this is just one miniscule slice of the data quality issue. In fact it's only one small part of the "direct mail" data quality issue. A lot more money is wasted in "inappropriate offers" and "duplicate offers" such as the ones my bank sends. I also get offers from several companies that are convinced that I'm married to the previous owner of my house. Those offers reach me, yet are immediately shredded. No sense opening them. So the "big picture" just for direct mail is much larger than what QAS shows.

None of this accounts for the "irritation" factor - what is the cost of annoying existing customers (or potential customers) with badly targeted offers?

Yet direct mail and all other forms of advertising together add up to a tiny slice of the bad-data pie.

Fraud Is a Bad Data Problem
Some time back, the US Attorney General's office stated that they believed that 14 percent of health care dollars are wasted in fraud or inaccurate billing.

Why do I lump fraud in with "bad data"? Bad data comes in two forms - accidentally created bad data and intentionally created bad data (for example, fraudulent billing). Either way, it's bad data. It doesn't matter how it got there, it's defective. And a lot of it could be detected and remediated "at the point of entry".

Healthcare accounts for over 16% of the U.S. GDP (Canada is 10%, Australia is 9% as a comparison). The U.S. GDP is currently approximately $14 Trillion - therefore healthcare spending in the U.S. amounts to $2.25 trillion. And the cost of bad data in Healthcare- $314 Billion.

That's just for fraud or inaccurate billing. What about other areas in healthcare (e.g. lost data, "bad patient outcomes", duplicate patient testing, manual rework, etc.)?  Even if we round down, we're still taking about $500 Billion for one industry alone.  If I extrapolate that out to the entire U.S. economy, we're talking about a $3.1 Trillion problem.  No matter how far off my estimate is (on the high side or the low side), it's a problem of astonishing proportions.

Cost of Bad Data to Business and IT
A classic but very worthwhile book from information governance expert Larry English posits that the business cost of nonquality data may be as high as 10-25% of an organization's revenue, and that as much as 50% of the typical IT budget may be spent in "information scrap and rework".  If that is the case, then my $3.1 estimate is not out of line.

In the introduction to his book, English states "With this proliferation of information, the challenge of managing data and providing quality information has never been more important or complex."

That was in 1999. With so much more data today, and a surprising lack of attention to the data quality issue, I can only imagine the total economic impact of things today. I do not doubt that the cost of bad data has risen.

Dealing with bad data at the I.T. level is expensive. But if I.T. doesn't deal with the bad data problem, then the cost gets pushed downstream to the "business", where the business costs are geometrically higher. The model is not that different from that of "healthy software", where it costs $1 to uncover a defect during developer/unit testing, but $100 to fix that defect if the software is released to the end-users.

"Low Hanging Fruit" - Best Practices for Bad Data Avoidance
I am not saying that there are any easy fixes to the bad data problem. Even something as relatively simple as cleaning, standardizing and de-duping a mailing list with 10,000,000 entries is essentially impossible to get completely right no matter how much effort is put into it. Yet there are some relatively easy things that can be done to substantially improve the quality of our data.  As with so many other problems in life, the some version of the 80/20 rule applies to this as well.

Best Practice #1: When integrating data, fix the quality problem during integration
As data are added or integrated, data should be tested. Profiling is a simple, fast, relatively easily implemented and highly effective way for eliminating significant volumes of defective data.

When developers write a new application for the input of some new data, it's normal for input fields to be "validated" - a simple "hard coded" form of profiling. Month number needs to be between 1-12. 13 is never correct.  Not rocket science. And it's universally done.

Yet people have far fewer reservations about integrating data from here, there and everywhere - often not checking for even the most egregious data errors, and thereby polluting the organizational drinking water (i.e. all the data and applications downstream).

I strongly suspect that's why I get so many offers from my current mega-bank. Since the banking implosion, this particular bank has purchased every other bank around. And their credit card businesses. And their marketing databases. And (apparently) smashed them together. So I get offers for Hollis Tibbetts, Hollis W. Tibbetts, Hollis Winslow Tibbetts, Hollis Tibbets, Hollis Tibbitts and so on.

Integration of data isn't necessarily just a "big bang" event - like when one company acquires another and smashes all the data together, or when two divisional customer applications get merged. It can be more insidious and more when you have "trickle" integration - the slow feed of new data from one system into another (either within the organization or from customers/suppliers/partners).  This is the class of integration that is causing a lot of the problems previously discussed with healthcare fraud.

Either way, FIX IT before integrating it. Once the poison enters the corporate drinking water, it's a lot harder to get out (not just technically, but especially politically/organizationally).

Best Practice #2: When migrating data, fix the data problem as PART of the migration project
Spending $1 billion to upgrade your Seibel system like the US Government is doing? Sounds like a great time to fix your data quality problem.

If you're doing something like migrating your customer data from Seibel to Netsuite or, data quality should be a major element in your project plan (and budget). Fixing the problems during the migration are easier than fixing them later:

  1. You probably already possess a lot of knowledge about the existing legacy systems, the types of problems in the data. But your new system is relatively unknown to you. So it's likely to be easier to fix data issues from a technical perspective BEFORE they get loaded into the new system.
  2. As part of the data migration process, you can export the data to a staging platform (On Prem or Cloud), leverage any number of data quality tools/engines, and then import the data into the the application platform.  This approach may partially pay for itself in an easier/smoother upgrade to the new application, but that's a rounding error in the overall scheme of things.
  3. Organizationally and politically, companies are much more likely to spend money to clean data if it's part of a project like "upgrade the CRM system". I'd hate to be the CIO that spends a mountain of money to upgrade the CRM system and then goes back to the board asking for another mountain of money to fix all the bad data that just got loaded into the CRM system. That's how CIO's become ex-CIOs.

Best Practice #3: Data profiling and data de-duplication engines
Data profiling engines are a great technology for quickly improving the quality of data as it is integrated from one system into another. At the highest level, they are an engine that scans data, and applies certain easily definable rules to data elements, such as formats, ranges, allowable values and can evaluate relationships between different fields.

Furthermore, these engines can also be used to analyze existing data stores very rapidly and generate "exceptions files" for manual, or semi-automated remediation (if anyone can find a totally automated data remediation system, I'd love to know about it). So they can be used in "continuous testing" or "batch testing" mode.  In batch mode, they're ideal for application migrations or big-bang integrations, as they're easiest to use them if you have your data in something like a staging database.  But they can also be used to test data as it is "trickle integrated" into production systems.

De-duping engines generally fit into the same category. I haven't seen them be as effective as data profiling engines, yet I believe they're essential. The technology for de-duping is considerably more sophisticated - with a large number of different algorithms and tunable thresholds and such. It's a harder class of technology to implement. More manual effort is involved. And, unlike profiling (where there is NEVER a month "13"), de-duping can "get it wrong", so the technology needs to be applied more selectively.

I've never understood why these engines haven't been more popular. There is no "JUnit for data" as far as I know. But commercial solutions are available - they're not terribly expensive and rapidly pay for themselves.

On the other hand, I've never understood why organizations are so tolerant of bad, dirty data. They waste millions and millions directly because of it (and untold quantities of money in "wasted opportunities"), but are reluctant to spend $15,000 on a data quality engine to help fix a significant portion of the problem.

More Stories By Hollis Tibbetts

Hollis Tibbetts, or @SoftwareHollis as his 50,000+ followers know him on Twitter, is listed on various “top 100 expert lists” for a variety of topics – ranging from Cloud to Technology Marketing, Hollis is by day Evangelist & Software Technology Director at Dell Software. By night and weekends he is a commentator, speaker and all-round communicator about Software, Data and Cloud in their myriad aspects. You can also reach Hollis on LinkedIn – His latest online venture is OnlineBackupNews - a free reference site to help organizations protect their data, applications and systems from threats. Every year IT Downtime Costs $26.5 Billion In Lost Revenue. Even with such high costs, 56% of enterprises in North America and 30% in Europe don’t have a good disaster recovery plan. Online Backup News aims to make sure you all have the news and tips needed to keep your IT Costs down and your information safe by providing best practices, technology insights, strategies, real-world examples and various tips and techniques from a variety of industry experts.

Hollis is a regularly featured blogger at ebizQ, a venue focused on enterprise technologies, with over 100,000 subscribers. He is also an author on Social Media Today "The World's Best Thinkers on Social Media", and maintains a blog focused on protecting data: Online Backup News.
He tweets actively as @SoftwareHollis

Additional information is available at

All opinions expressed in the author's articles are his own personal opinions vs. those of his employer.

@ThingsExpo Stories
We are rapidly moving to a brave new world of interconnected smart homes, cars, offices and factories known as the Internet of Things (IoT). Sensors and monitoring devices will touch every part of our lives. Let's take a closer look at the Internet of Things. The Internet of Things is a worldwide network of objects and devices connected to the Internet. They are electronics, sensors, software and more. These objects connect to the Internet and can be controlled remotely via apps and programs. Because they can be accessed via the Internet, these devices create a tremendous opportunity to inte...
Today air travel is a minefield of delays, hassles and customer disappointment. Airlines struggle to revitalize the experience. GE and M2Mi will demonstrate practical examples of how IoT solutions are helping airlines bring back personalization, reduce trip time and improve reliability. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Dr. Sarah Cooper, M2Mi’s VP Business Development and Engineering, explored the IoT cloud-based platform technologies driving this change including privacy controls, data transparency and integration of real time context with p...
We all know that data growth is exploding and storage budgets are shrinking. Instead of showing you charts on about how much data there is, in his General Session at 17th Cloud Expo, Scott Cleland, Senior Director of Product Marketing at HGST, showed how to capture all of your data in one place. After you have your data under control, you can then analyze it in one place, saving time and resources.
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data shows "less than 10 percent of IoT developers are making enough to support a reasonably sized team....
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, rich desktop and tuned mobile experiences can now be created with a single codebase – without compromising functionality, performance or usability. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, demonstrated examples of com...
As organizations realize the scope of the Internet of Things, gaining key insights from Big Data, through the use of advanced analytics, becomes crucial. However, IoT also creates the need for petabyte scale storage of data from millions of devices. A new type of Storage is required which seamlessly integrates robust data analytics with massive scale. These storage systems will act as “smart systems” provide in-place analytics that speed discovery and enable businesses to quickly derive meaningful and actionable insights. In his session at @ThingsExpo, Paul Turner, Chief Marketing Officer at...
In his keynote at @ThingsExpo, Chris Matthieu, Director of IoT Engineering at Citrix and co-founder and CTO of Octoblu, focused on building an IoT platform and company. He provided a behind-the-scenes look at Octoblu’s platform, business, and pivots along the way (including the Citrix acquisition of Octoblu).
In his General Session at 17th Cloud Expo, Bruce Swann, Senior Product Marketing Manager for Adobe Campaign, explored the key ingredients of cross-channel marketing in a digital world. Learn how the Adobe Marketing Cloud can help marketers embrace opportunities for personalized, relevant and real-time customer engagement across offline (direct mail, point of sale, call center) and digital (email, website, SMS, mobile apps, social networks, connected objects).
The Internet of Everything is re-shaping technology trends–moving away from “request/response” architecture to an “always-on” Streaming Web where data is in constant motion and secure, reliable communication is an absolute necessity. As more and more THINGS go online, the challenges that developers will need to address will only increase exponentially. In his session at @ThingsExpo, Todd Greene, Founder & CEO of PubNub, exploreed the current state of IoT connectivity and review key trends and technology requirements that will drive the Internet of Things from hype to reality.
Two weeks ago (November 3-5), I attended the Cloud Expo Silicon Valley as a speaker, where I presented on the security and privacy due diligence requirements for cloud solutions. Cloud security is a topical issue for every CIO, CISO, and technology buyer. Decision-makers are always looking for insights on how to mitigate the security risks of implementing and using cloud solutions. Based on the presentation topics covered at the conference, as well as the general discussions heard between sessions, I wanted to share some of my observations on emerging trends. As cyber security serves as a fou...
Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
With all the incredible momentum behind the Internet of Things (IoT) industry, it is easy to forget that not a single CEO wakes up and wonders if “my IoT is broken.” What they wonder is if they are making the right decisions to do all they can to increase revenue, decrease costs, and improve customer experience – effectively the same challenges they have always had in growing their business. The exciting thing about the IoT industry is now these decisions can be better, faster, and smarter. Now all corporate assets – people, objects, and spaces – can share information about themselves and thei...
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists discussed what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
Growth hacking is common for startups to make unheard-of progress in building their business. Career Hacks can help Geek Girls and those who support them (yes, that's you too, Dad!) to excel in this typically male-dominated world. Get ready to learn the facts: Is there a bias against women in the tech / developer communities? Why are women 50% of the workforce, but hold only 24% of the STEM or IT positions? Some beginnings of what to do about it! In her Day 2 Keynote at 17th Cloud Expo, Sandy Carter, IBM General Manager Cloud Ecosystem and Developers, and a Social Business Evangelist, wil...
PubNub has announced the release of BLOCKS, a set of customizable microservices that give developers a simple way to add code and deploy features for realtime apps.PubNub BLOCKS executes business logic directly on the data streaming through PubNub’s network without splitting it off to an intermediary server controlled by the customer. This revolutionary approach streamlines app development, reduces endpoint-to-endpoint latency, and allows apps to better leverage the enormous scalability of PubNub’s Data Stream Network.
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true change and transformation possible.
Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem" in this scenario: microservice A (releases daily) depends on a couple of additions to backend B (re...
I recently attended and was a speaker at the 4th International Internet of @ThingsExpo at the Santa Clara Convention Center. I also had the opportunity to attend this event last year and I wrote a blog from that show talking about how the “Enterprise Impact of IoT” was a key theme of last year’s show. I was curious to see if the same theme would still resonate 365 days later and what, if any, changes I would see in the content presented.