TRILLIUM SOFTWARE INSIGHTS

The Reasonable Test

David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

In all of the projects that I’ve been involved with over the years, I can’t ever recall seeing a requirement from the customer specifying that the overall results comply with what I feel is the final data requirement – do the results pass the “reasonable” test.  Sure there have been plenty of projects where the customer has come up with target percentages or variance levels for the test cases but rarely do I recall a customer specifically asking to check if the end results appear reasonable. 

The reason that I’m bringing this up is that I have seen plenty of projects where we’ve been called in late in the implementation phase or shortly after going live where the customer is in a panic and making statements to the effect that “these results don’t appear reasonable”.  So is this panic a byproduct of not setting proper expectations or is it because the reality of the data’s quality has changed those expectations?  I venture to say that, in most cases, it’s likely been due to both.

Approximately Correct

David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

Way back in 1989, I was once at an executive conference up in Boston put on by the Cambridge Technology Group.  While the subject of that conference – the rapid implementation of the concept of surrounding your systems (applications) with a UNIX based  “umbrella” application thereby integrating  the disparate data into meaningful information has surprisingly stayed with me all these years, it was a brief exchange between  two MIT and Harvard  professors that has me writing this blog today.

Basically, the exchange was a light hearted “ribbing” about the relative strengths of their two respective institutions and it went like this:  “You Harvard people call it a square, aren’t you educated enough to know that it’s really a triangle?”  To which the Harvard professor replied: “As opposed to MIT, we at Harvard have always believed that it’s better to be approximately correct than totally wrong!”  Of course that response was greeted by loads of laughter and nobody thought much more of it for the remainder of the week.  But, fortunately, that response has stayed with me over the years and has been continuingly reinforced the longer I’ve stayed in the IT industry. 

Assets Aren’t Assets unless the Information you have on them is Accurate

Ed Wrazen, Vice President Marketing International, Harte-Hanks Trillium Software

Over the last few years, more and more water, gas and electricity companies have established data governance programmes. From my discussions with several firms, one of the key business drivers for this trend is the need to ensure cost-effective and safe governance of assets in a challenging and complex environment. Speaking recently with the head of information management at a major UK utility, he told me they have £8 billion worth of physical assets such as pipes, meters, pumping stations and other items, which all need proper status monitoring, servicing, repairs and eventual replacement.

The accuracy of all the data held on those assets is key to ensuring their safe operation and that business processes are optimised for efficiency and performance. It’s essential to ensuring pumps and other mechanical devices are serviced at appropriate intervals – helping reduce risk of failure for example (which could mean extra costs as well as customer inconvenience and possible health and safety breaches, leaks and environmental damage). Trusted data also means being able to avoid having to send out engineers to survey exactly what assets are held, their location and their specification before planning an upgrade to services to an area.

1202

David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

Around this time of year and this year in particular with the Space Shuttle having made its final flights, I get to reminiscing about some of the events in the space program from the past 40 plus years and what we’ve all learned from them.  Today I’m thinking about the Apollo 11 moon landing – that glorious time when man landed on the moon and the realization that most of the people I work with these days either were very young then or not yet even born.

So, because many of you may be younger than I and not know the story; the first manned lunar landing almost was aborted and software was right in the middle of it.  What happened was that when the lunar lander got about to about 6,000 feet above the surface, the Lunar Module Guidance computer commanded attention of the crew when it started to display unexpected “1202” and “1201” error codes.  Fortunately, Mission Control was monitoring situation and Computer Engineer Jack Garmin quickly analyzed the situation identifying that the errors meant that the program alarms indicated "executive overflows" - meaning the guidance computer could not complete all of its tasks in real time and that it had to postpone some of them.  Jack quickly notified the ground guidance commander and the message that it was safe to continue was quickly relayed to the crew.  The world took a breath and man successfully landed (and returned) from the moon.

Data –the Fourth Factor of Production?

Ed Wrazen, Vice President, Product Marketing, Harte-Hanks Trillium Software

I recently read a new Economist Intelligence Unit report commissioned by Capgemini. Surveying 607 business leaders around the globe, one of the headline findings is that 75% of those surveyed said they believe their organisations to be data-driven. Paul Nannetti, a director at Capgemini proposes, “For our [survey] respondents, data is now the fourth factor of production, as essential as land, labour and capital.”

Now if only top management could truly recognise that since their businesses are now data-driven, it’s become their responsibility to make certain their business data is actually good data.

With business analytics, business intelligence, CRM, and many other enterprise applications in place, it’s perhaps too easy for executives to believe that data quality has been taken care of. However, many data managers I meet, say that their company’s data is not nearly as good as business managers assume it is, if those managers even think about it at all.

I Can See (My Data Quality) Clearly Now

David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

A few weeks ago, I had my annual eye examination and somehow my aging eyesight got me thinking about my data quality topic today. Read on and perhaps you’ll see why.

In health care (according to our friends at Wikipedia), Optometry is defined as a health care profession concerned with the health of the eyes and related structures, as well as vision, visual systems, and vision information processing in humans. Optometrists (also known as Ophthalmic Opticians outside of North America) are the licensed medical professionals trained to prescribe and fit lenses to improve vision, and to diagnose and treat various eye diseases.

As usual during these exams, the Optometrist goes through the task of updating your personal information, and sooner or later the question of “what type of work do you do?” always comes up. Now I could have taken the easy way out and said something generic like “I work with computers,” but, I had to be exact and say that I work with data quality.

Don’t be Chicken Little - Monetize the Value of Data Quality

Len Dubois, Sr. Vice President, Marketing & Sales Support, Harte-Hanks Trillium Software

Recently, I read and commented on Jim Harris’ excellent blog post titled “Data Quality and Chicken Little Syndrome.” The point he makes is that the general sales pitch for the value of data quality focuses on the negative aspects of not investing in data quality (the sky is falling), when in reality, data quality software providers really should be focusing on the tangible and quantifiable business benefits of establishing better data quality processes. I wholeheartedly agree with Jim.

And, I wanted to expand on his use of the allegory of Chicken Little and the Sky is Falling. The sky isn't falling, it never has and never will. The sky, like data quality, is all around us.

It is rare when I run into someone who doesn't understand that poor data quality manifests itself within business processes all the time. Data Quality, like many disciplines, requires subject matter experts who know how important it is to ensure that the context of data meets the business needs of any application.

Data Obesity

David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

Recently, by chance, I happened to view a Mary Meeker slide presentation dealing with the current state of the web. The presentation was fairly lengthy but, fortunately, it was interesting enough to hold my attention through the 100+ slides and as with all presentations, there was much to agree and disagree with.
What really caught my attention was a slide that I still don’t normally see these days – a slide that mentioned data quality.

That got me thinking about the “SOON...” side of slide and how critical data quality will be in the “Re-Imagination” of data and how much impact “Data Obesity” will have on all of us sooner or later.

Now it’s no secret that many of us have been dealing with a form a data obesity for quite some time – after all data de-duplication has been a key component of data quality since the keypunch days. In a traditional sense, data de-duplication has always been thought of more along the lines of customer and household identification and de-duplication. 

Flip that Data!

By David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

I’m sure many of you have seen, or at least heard about, those popular television shows that all focus around people buying old dilapidated houses; quickly fixing them up and selling them for huge profits. With help in part from the name of a certain A&E show, this renovation-to-sale process got tagged with the name “flipping.”

While some people end up being fairly successful in their efforts, many others end up being caught in a quagmire of unforeseen problems and costly renovation budget expense line items. While all of that may make for good (OK, entertaining) TV, in the real world, those of us who have ever done renovation projects know how painful they can be when things don’t go as planned.

But why am I talking about flipping houses in a blog devoted to data quality? Well, there are a whole lot of similarities between house flipping and the impact of data quality in today’s business world.

For example, in flipping, the premise is to find an inexpensive home to renovate. How many times in the past half-decade have you been asked to find some inexpensive way to improve sales, lower costs or increase profitability?  Have you ever thought of some of your existing data stores and applications from the point of “how dilapidated is the data” and how much could we profit from “renovating” it? I’ll bet you that many of your business users can identify with these issues.

Well let’s say that we’ve found an old house that needs work…uh, I mean an existing dilapidated data store. Just consider it the house we want to potentially flip.

Ok, we’ve found our potential house for flipping but first we need to determine if the house has any major flaws that will either increase the renovation costs or prevent us from completing the renovation.  In flipping, you get an expert to perform a home inspection (there’s a whole other show highlighting the pitfalls of those people who fail to get it inspected) and research any applicable local building codes. Those of us in data quality are experienced enough to use data quality tools to perform the analysis necessary to determine if the renovation is possible, if the data is truly dilapidated and if we can really get any benefits from it. I definitely would not be shy about bringing in an expert to review an area where I was unsure of myself.

Then you buy the house – or as we say in the business world “get buy-in from management.”

The next main premise of flipping is, because holding costs lower your profits, you need to quickly perform the renovation. Again, that’s where tools come in – we need to quickly identify and renovate the areas of the “house” where we will get the most return for our money. 

Flipping houses also shows us the true value of having contractors experienced in renovations who can quickly and efficiently renovate the property. Time and time again on those shows we see the false economy of trying to cut corners and shoddy workmanship that was performed on the original house and how it ends up costing us when we try to renovate. I don’t think that it’s necessary to give any examples from the data quality side as we’ve all seen those scenarios before. You need experts in there.

If you are experienced in flipping you will know (or will have identified) where you can get the most bang for the buck. Be it a new kitchen, landscaping or wood floors in home flipping – or something like customer, account, or other information.

Finally, after usually more work than expected (tell me where I’m wrong here), the flip is completed.  Competent flippers move on to the next house and the others you never see on the show again. If we’ve done our jobs professionally and correctly in a data flip, management lets us find another “house” to renovate and perhaps you’ve just discovered a new data strategy. 

But we must warn the unskilled and uninformed data flippers out there that there’s a huge difference between house flipping and the business world – in house flipping the property gets sold and the flipper is done with it.  If the flip was done poorly, the paint peels, the roof leaks and people get sued. In data flipping, you will likely be living with the results for a long time and you are just not putting a coat of paint on the data that will peel off six months later. 

With the economy the way it is and the home flipping phenomena being a contributor to its downfall, we must always keep in mind that there are no shortcuts for quality work and creating ongoing and sustainable efficiencies and profits. Flipping can be profitable short term, but we’re all in it for the long run.

See you all the next time you decide to flip your data.

 

A Data Quality Revolution is Required to Achieve Microsoft’s Vision

 By Michele Goetz, Vice President of Product Marketing, Harte-Hanks Trillium Software

While attending the Microsoft Convergence event earlier this year, the most poignant phrase I heard was “…moving from a system of record to a system of engagement…” during the keynote. Music to my ears.

Why is this so important? It’s not just because it illustrates Microsoft’s vision of a user experience across their platform of applications and operating system. No, more critical is the fact that the phrase actually gets to the heart of what we as data quality managers really need to do in order to help our users gain the most out value from their data quality user experience – by providing the right information and insight in an actionable, dynamic and real-time way.

In our world, the system of record is the data source – the master data that is the bedrock. Yet, the notion of engagement is much more fluid, alive…and value producing. The data is ever-changing and constantly being updated, so it has to be accurate and on-target too.

This drastically alters how we provide data quality programs within the Microsoft Dynamics CRM system for example. We need to expect that users are always actively and frequently engaged with their customers, partners, and vendors. Context is king.

The data must be there in real time to educate instantly, helping users discover opportunities and support value in every interaction. Validating and improving data on the fly needs to be much more in line with the context of use and communication, and not just relegated to form and fit of data elements of an entity or record. Data quality is the foundation of an ever-evolving profile to generate a 360º view of every engagement on the fly and in real time.

Data has primarily been thought of as the by-product of a business that has to be housed and filed for some future retrieval. The retrieval of data is often a secondary thought to how the information provides value to the business. In this day and age, that perspective and approach is out-dated. 

A data quality revolution needs to occur that puts business value first, and then can be translated into the actual processes and programs that make data not just fit for purpose, but “fit for engagement.”