Welcome!

Industrial IoT Authors: Pat Romanski, Liz McMillan, Elizabeth White, Stackify Blog, Yeshim Deniz

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 Salesforce.com, 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.

Conclusion
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 – linkedin.com/in/SoftwareHollis. 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 HollisTibbetts.com

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

@ThingsExpo Stories
In an era of historic innovation fueled by unprecedented access to data and technology, the low cost and risk of entering new markets has leveled the playing field for business. Today, any ambitious innovator can easily introduce a new application or product that can reinvent business models and transform the client experience. In their Day 2 Keynote at 19th Cloud Expo, Mercer Rowe, IBM Vice President of Strategic Alliances, and Raejeanne Skillern, Intel Vice President of Data Center Group and ...
DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the 22nd International CloudEXPO | DXWorldEXPO "Early Bird Registration" is now open. Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)
"We are a well-established player in the application life cycle management market and we also have a very strong version control product," stated Flint Brenton, CEO of CollabNet,, in this SYS-CON.tv interview at 18th Cloud Expo at the Javits Center in New York City, NY.
In his session at @ThingsExpo, Arvind Radhakrishnen discussed how IoT offers new business models in banking and financial services organizations with the capability to revolutionize products, payments, channels, business processes and asset management built on strong architectural foundation. The following topics were covered: How IoT stands to impact various business parameters including customer experience, cost and risk management within BFS organizations.
Here are the Top 20 Twitter Influencers of the month as determined by the Kcore algorithm, in a range of current topics of interest from #IoT to #DeepLearning. To run a real-time search of a given term in our website and see the current top influencers, click on the topic name. Among the top 20 IoT influencers, ThingsEXPO ranked #14 and CloudEXPO ranked #17.
While the focus and objectives of IoT initiatives are many and diverse, they all share a few common attributes, and one of those is the network. Commonly, that network includes the Internet, over which there isn't any real control for performance and availability. Or is there? The current state of the art for Big Data analytics, as applied to network telemetry, offers new opportunities for improving and assuring operational integrity. In his session at @ThingsExpo, Jim Frey, Vice President of S...
With the introduction of IoT and Smart Living in every aspect of our lives, one question has become relevant: What are the security implications? To answer this, first we have to look and explore the security models of the technologies that IoT is founded upon. In his session at @ThingsExpo, Nevi Kaja, a Research Engineer at Ford Motor Company, discussed some of the security challenges of the IoT infrastructure and related how these aspects impact Smart Living. The material was delivered interac...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
Amazon started as an online bookseller 20 years ago. Since then, it has evolved into a technology juggernaut that has disrupted multiple markets and industries and touches many aspects of our lives. It is a relentless technology and business model innovator driving disruption throughout numerous ecosystems. Amazon’s AWS revenues alone are approaching $16B a year making it one of the largest IT companies in the world. With dominant offerings in Cloud, IoT, eCommerce, Big Data, AI, Digital Assista...
In his session at Cloud Expo, Alan Winters, U.S. Head of Business Development at MobiDev, presented a success story of an entrepreneur who has both suffered through and benefited from offshore development across multiple businesses: The smart choice, or how to select the right offshore development partner Warning signs, or how to minimize chances of making the wrong choice Collaboration, or how to establish the most effective work processes Budget control, or how to maximize project result...
The Founder of NostaLab and a member of the Google Health Advisory Board, John is a unique combination of strategic thinker, marketer and entrepreneur. His career was built on the "science of advertising" combining strategy, creativity and marketing for industry-leading results. Combined with his ability to communicate complicated scientific concepts in a way that consumers and scientists alike can appreciate, John is a sought-after speaker for conferences on the forefront of healthcare science,...
In his keynote at 19th Cloud Expo, Sheng Liang, co-founder and CEO of Rancher Labs, discussed the technological advances and new business opportunities created by the rapid adoption of containers. With the success of Amazon Web Services (AWS) and various open source technologies used to build private clouds, cloud computing has become an essential component of IT strategy. However, users continue to face challenges in implementing clouds, as older technologies evolve and newer ones like Docker c...
When shopping for a new data processing platform for IoT solutions, many development teams want to be able to test-drive options before making a choice. Yet when evaluating an IoT solution, it’s simply not feasible to do so at scale with physical devices. Building a sensor simulator is the next best choice; however, generating a realistic simulation at very high TPS with ease of configurability is a formidable challenge. When dealing with multiple application or transport protocols, you would be...
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
Personalization has long been the holy grail of marketing. Simply stated, communicate the most relevant offer to the right person and you will increase sales. To achieve this, you must understand the individual. Consequently, digital marketers developed many ways to gather and leverage customer information to deliver targeted experiences. In his session at @ThingsExpo, Lou Casal, Founder and Principal Consultant at Practicala, discussed how the Internet of Things (IoT) has accelerated our abilit...
Detecting internal user threats in the Big Data eco-system is challenging and cumbersome. Many organizations monitor internal usage of the Big Data eco-system using a set of alerts. This is not a scalable process given the increase in the number of alerts with the accelerating growth in data volume and user base. Organizations are increasingly leveraging machine learning to monitor only those data elements that are sensitive and critical, autonomously establish monitoring policies, and to detect...
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
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 sessio...
Recently, REAN Cloud built a digital concierge for a North Carolina hospital that had observed that most patient call button questions were repetitive. In addition, the paper-based process used to measure patient health metrics was laborious, not in real-time and sometimes error-prone. In their session at 21st Cloud Expo, Sean Finnerty, Executive Director, Practice Lead, Health Care & Life Science at REAN Cloud, and Dr. S.P.T. Krishnan, Principal Architect at REAN Cloud, discussed how they built...
In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settl...