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What Is Quality in the Business World? AKA “You Can’t Add a Pound of Quality to a Process”

Let me start this article with a quote from one of my favorite books I read many years ago:

“Quality, value, creates the subjects and objects in the world. The facts do not exist until value has created them. If your values are rigid, you can’t really learn new facts… If you’re plagued with value rigidity, you can fail to see the real answer even when it’s staring you right in the face because you can’t see the new answer’s importance.”

Robert Pirsig
Zen and the Art of Motorcycle Maintenance
An Inquiry into Values

Quality in Business

Is it just me or do you find that quality in customer satisfaction is just not at the top of the list in many organizations today? Whenever I mention to a friend a problem I’ve had trying to fix a billing problem with an organization they always come up with a more serious problem they’ve had. Maybe that’s human nature and people just making conversation; maybe not.

Throughout my career I’ve been involved in developing and teaching how to build quality Information Systems and lately how the same concepts apply to human systems and management. The good news is there’s now an accepted movement towards quality conscious management where we recognize that we should approach “zero defect” systems using such things as continuous business improvement and bug-free software development.

As far as the Information Systems development profession is concerned it has finally evolved to a point where we can now build quality systems that have zero defects (i.e., no bugs) that do not deteriorate during production and modification (i.e. they can last as long as the business they support). That’s a good thing because Information Systems are becoming more complex. Lives are dependent on them in such areas as medical systems and air traffic control systems and commerce is very dependent on them. Bad systems can bring down an organization and put many people out of work.

Quality in Systems

The bad news is judging by the number of defects in systems and products there aren’t many systems development “professionals” around and the vast majority of systems in production today don’t even come close to zero defects. The phrase “computer system bug” is a recognized common phrase. Even my wife knows what a computer “bug” is, and she can’t read a line of computer code.

Many information systems suffer from hidden bugs that cause them to fail frequently and are so difficult to modify that even the people who built them expect them to blow up and have short production lives.

It’s not uncommon to have computer systems last as little as 2-3 years even though the business they support hasn’t changed (notice I say the business hasn’t changed – but the technology may well have). So many existing computer systems were not built for flexibility and maintainability. In fact, systems professionals at many companies are familiar with the monolithic, difficult-to-change computer system that “bites back” whenever they attempt to make a change to it.

Information Systems developers who are accustomed to this kind of system and don’t know a better way to develop systems, will generally tend to create new systems in the same style thus perpetuating the same undesirable system characteristics. I believe Information Systems developers should not make an organization be at the mercy of technology, allowing it to dictate the lifetime characteristics of business systems. I should also point out that manual systems that support a business tend to have an equal number of reasons for obsolescence and also suffer from unnecessary defects.

A Working Definition of Quality

Having been involved in teaching engineering disciplines for systems development for decades, I’ve often pondered what quality really is. It’s always seemed to me to be an ethereal concept, a feeling maybe.

For many years I felt that quality was “attention to detail.” But that didn’t seem to ring true when I found myself in my personal life building something in my garage like a set of shelves and literally putting them together with some blocks and a couple of boards. I knew that even though I hadn’t paid too much attention to detail, I was quite satisfied with the resultant shelves. The result actually served my needs and I felt satisfied with the product.

One of the books I read that was fairly significant to me in this area of quality was “Zen and the Art of Motorcycle Maintenance” by Robert Pirsig (I started this article with a quote from that book). I found it interesting that in one of the chapters of the book, Pirsig thought that the divorce of art from engineering was an “archaeological wrong turn” and quite unnatural. That seemed to satisfy my inner feelings that there was some sort of “pride” that went into my work which had more affinity with art than it did with an engineering discipline. However, this feeling wasn’t an easy thing to teach in my seminars. It’s quite difficult to instill a “feeling of quality” into one’s student.

In my search for a definition of quality I was lucky to come across a book called “Quality Is Free” by Philip Crosby which summed up something that I could actually identify with, define and teach. That something was that quality is simply “conformance to requirements.” If a product satisfies our requirements we believe it to be a quality product. This means it has the necessary level of quality we want – it’s that simple.

Quality as Conformance to Requirements

What I consider to be quality in a product or service is the consistent satisfaction of my requirements. The requirements and their satisfaction must both be measurable. Based on what I’ve just said, here’s my definition of Quality – building on Crosby’s definition.

Quality is recognized in a product or service when it satisfies both the ethical and measurable requirements of the requester. It is accomplished with pride of ownership on the part of everybody involved in satisfying those requirements.

This definition solved why I felt comfortable after having built a set of shelves that I knew hadn’t had too much attention to detail paid to them. The shelves satisfied what I was trying to accomplish and was something that would satisfy my requirements for a storage place. (Notice that I was the requester, not my wife – identifying the real customer is important.)

Identifying the Customer

Although I do still believe that there’s something to do with art as well as engineering that’s brought together to form pride in ones work, that just might have something to do with my acquired internal value systems. However, I hope we can all agree that quality is “conformance to requirements”, especially when you look on requirements as having many different aspects.

What I mean by that is we can have requirements for the system to build a system or product. That’s my definition of a systems development methodology – a System to build Systems. So we should have requirements for the methodology we follow as well as requirements for the system or product that results from it. In other words, you can have quality built into the practices and the step-by-step procedures for building a house as well as for the resultant house that’s produced from using that methodology.

Obviously the requirements for a methodology change depending on the product being produced; for example, the methodology for building a skyscraper is different from one for building a log cabin. It follows that we can also have requirements for each activity or phase within a methodology; for the analysis, design, and implementation activities. So we have business requester requirements in analysis, system designer requirements in the solution, and implementer requirements for the implemented system/product. Having measureable requirements is at the heart of any profession.

By building in the necessary quality in each one of these activities, we can produce a system (human or computer) that has quality – at least the level of quality expected by a customer.

Now having said all that, maybe an organization that utilizes poor or “good-enough” systems simply expects and accepts that level of quality. I guess that’s fine if that’s a stated goal in their mission or charter.

Some History of the Quality Movement

There were a few pioneers that I’d like to mention in the evolution of attaining quality in processes and systems.

In 1930 a Professor Shewart working at Bell Laboratories identified that most of the problems resulting from a system were in the system itself and not in its implementation. He recognized that we could measure a process that produced some product or service by focusing on the process’ inputs and outputs. The areas of his study were the tasks and functions that took place in the office environment. One of his ideas was to use those measurements of the input into and the output from a process to eliminate the variations in the quality of a final product that came out of that process. This approach was called Process Control and its goal was to ensure an acceptable range of quality in some finished product.

Then, an individual by the name of Dr. W. Edwards Deming advocated the principles of quality control/management in production to put over the idea that we should eliminate after-the-fact inspections and improve the quality of the process itself. Deming introduced the concept of being aware of the customer as the person who ultimately had to be satisfied with the results of a process. He also recognized the quality added value to a product or service. His approach therefore extended the ideas of Process Control to include the customer who up to that point was an entity totally external to the organization that produced the product or service.

He questioned the value of after-the-fact inspections because he realized that the only thing you can do if an inspector finds a bad product is to throw the product away. Deming was a revolutionary when he said that we shouldn’t produce the bad product in the first place. Many people attribute Deming with the turnaround in product quality in Japan in the late 1940s and 1950s.

Dr. Deming and another person by the name of Dr. Joseph M. Juran looked at the principles of statistical control of quality and process built-in quality. They introduced some of the basic graphical models that we now almost take for granted as a means of representing processes.

Both Deming and Juran were given medals by the Emperor of Japan for their work. An award was created, called the Deming Prize, which is still sought today by companies looking to be associated with quality products and services.

In the 80s we saw the idea of “Quality Circles” as put forward by Professor Ishikawa. This idea centered on a process where teams would manage themselves and introduce continuous improvements in quality on their own (and not be managed by a hierarchical system). The idea here was that the best people to come up with quality suggestions were the people who were doing the work.

Of course there are many other approaches for obtaining quality such as Six Sigma which again looks at process improvement, and the International Organization for Standardization, ISO recommending standards for business and government worldwide.

In the United States the US government issues the Malcolm Baldrige Quality Award which recognizes performance excellence in both public and private organizations.

The Deming Prize and Baldrige Award (and others) are aimed at recognizing the introduction and ongoing improvement of quality in organizations.

Unfortunately, it’s rather difficult for a computer program to improve itself once it’s been written and installed in a system. A production computer system typically keeps the same level of quality for its life cycle as it had on day-one of its installation (or its quality could even deteriorate due to poor system maintenance and modification practices).

An implemented computer program has no feelings (not yet anyway). It doesn’t care about satisfying customer needs, but a human being that requested it and developed it should. The basic idea here is that as a human being in an organization, you should no longer look upon the procedure that is put in place today as cast in concrete. In fact, the focus of an organization should be on the question: “Do our systems efficiently satisfy our customer’s needs?” And, when one doesn’t, we need to constantly change that system and/or its individual processes.

From an implementation point of view this involves everyone. It involves the people who conduct the customer interaction, the people inside the system who may never see a customer (but who nevertheless are satisfying the customer’s needs), the information systems and the people who are the leaders and facilitators helping/facilitating the systems.

My own modest contributions to the evolution of ensuring quality in response to a customer’s need in an organization are the introduction of Business Event partitioning in the 1980s (which I now see reflected in the concepts of Event Stream Processing, Use Cases, Services, and Event Driven Architecture etc.). As well as the need to focus on six aspects of an organizations reaction to those customer Events: the Source, the Stimulus, the Processing, the Memory theResponse and the Recipient. I have also advocated for many years the creation of “seamless” business systems as a response to customer needs along these Business Event lines (e.g. ignoring traditional departmental or other organizational boundaries).

Quality is Free

The ironic thing about this discussion on quality is captured in the title of Philip Crosby’s book, “Quality Is Free.” You see, actually producing a quality product ends up being cheaper than not producing a quality product. By the time you include customer satisfaction (i.e. not losing customers), the cost of discarding a poor product and all of the costs of installing inspections and testing mechanisms in systems, and, of course, the costs of fixing problems in development and “putting out fires” in production and recalls after a product is sold, then a non-quality product easily exceeds the cost of building quality into a product or system.

So I would even go further than Philip Crosby’s statement that “Quality is Free” and claim that quality saves you money in development and makes you money in customer satisfaction.

In my own profession I see way too many information system developers believe in testing errors out instead of not putting them in in the first place. Testing often just brings to light the presence of a poor development process. If an error is found when testing something it means there are probably more errors to be found.

In my technical workshop seminars I would set up an exercise where teams would attempt to find errors in another team’s deliverable. I always liked it when a deliverable, after being reviewed by four other teams resulted in the comment “We can’t find anything wrong with this”. There wasn’t a reward for finding errors there was a reward for the team that produced the error-free deliverable.

Now there’s a realistic phrase “You don’t know what you don’t know”. I bring this up here because I’m not saying that we can eliminate all errors and defects in systems or products, however the errors that get through will be unknown errors, not ones that we can predict in advance. In other words the methodology should eliminate all known errors from making their way into a product. And of course the unknown error now becomes a known error that is used to improve the methodology.

So build quality into the methodology which will result in quality in the finished product.

I believe there’s a mind-set that goes with quality. You decide the level of quality you want for your organization’s systems and products – because your customers certainly will.

Small Business Accounting Explained

Below we give you some tips on how to make small business accounting more bearable for you.

First, you should make a list of all accounting tasks to perform in your business. Once you have your list, small business accounting is less stressful and takes less time. You will only have to perform one or more tasks at regular intervals (daily, weekly, monthly …).

We also recommend that if do not know a lot about small business accounting, learn the basics.

Small business accounting is a specific jargon and all sorts of words and concepts reserved for the experts. Do not be discouraged if you do not understand them all. Get used to the most basic concepts directly related to successfully improving your small business accounting. Ultimately, the goal is that you increase profit. It is the primary goal of any business. Request help from a professional if you want, but do not miss the basic concepts.

In contrast, it is a mistake to become a super fan of small business accounting. The company does not come down to accounting. There are firms and professionals who do this very well. This is not the job of an entrepreneur to be a know-it-all of Management or Accounting.

The third golden advice is to separate your personal finances from your business finances.

It is a bad idea to mix your personal account and your business account. Separate them completely: even if you’re the sole shareholder in the company, even if only your money is put into the pot. This separation enables you to plan, predict, without confusing personal cash and professional cash. This allows a lucid view of the true accounts of the company.

Finally, you must be consistent.

It may be hard to get used to at first but we recommend that once you create an accounting system for you, you should stick to it no matter what. Remember to register everything, forget nothing, and be consistent. If you are not consistent you will start doubting your own system later which will not help you when you want to evaluate your business performance at the end of the year. It is a good idea to meet with your accountant to have him or her verify the information. 

Data Denial and Business Intelligence – How to Achieve Data Quality

The greatest battle you may face inside the organization will be to get management to the point where they agree that data quality is a goal even worth considering.

Everybody talks about data, but many often confuse it with information and knowledge. Basically, data is a core corporate asset that must be synthesized into information before it can serve as the basis for knowledge within the organization. Nevertheless, data is ubiquitous – it is used to support every aspect of the business, and is an integral component of every key business process. However, incorrect data cannot generate useful information, and knowledge built on invalid information can lead organizations into catastrophic situations. As such, the usefulness of the data is only as good as the data itself – and this is where many organizations run into trouble.

Many organizations neither recognize nor accept the bad quality status of their data, and try instead to divert the attention to supposed faults within their respective systems or processes. To these organizations data denial has practically become an art form, where particularly daunting corporate barriers have been built – typically over long periods of time – to avoid the call to embark on any “real” Data Quality improvement initiatives.

However, we have found that the best way to measure the extent to which your organization may be dealing with data denial is to ask the following key questions:

  • Are you aware of any Data Quality issues within your company?
  • Are there existing processes that are not working as originally designed?
  • Are people circumventing, the system in order to get their work completed?
  • Have you ever been forced to deny a business request for information due to an issue of Data Quality?
  • If the system was functioning properly, would this information have been readily available?
  • Has a business case been made outlining the economic impact of this issue? And, if so, has it ever been addressed with the organization’s leadership?
  • What was the response to these issues? And if there was no response, what is stifling this process?
  • What causes these “gaps” in Data Quality?
  • How are these issues affecting the responsiveness of your organization (i.e., to customers, stockholders, employees, etc.)?
  • If these issues were to be addressed and corrected, what strategic value would be added or enhanced?
  • Who bears the responsibility for addressing these issues within your organization?
  • What can be done to address these issues in the future?
  • What support is needed to implement a Data Quality strategy?

Depending on the answers to these questions, your organization may already be facing significant barriers to attaining Data Quality, each of which will need to be identified, assessed, prioritized and corrected. According to William K. Pollock, president of the Westtown, PA-based services consulting firm, Strategies For GrowthSM, “Most companies already know what data they do not have – and for them, this is a significant problem. However, the same companies are probably not aware that some of the data they do have may be faulty, incomplete or inaccurate – and if they use this faulty data to make important business decisions, that becomes an even bigger problem”.

Common Problems with Corporate Data

Research has shown that the amount of data and information acquired by companies has close to tripled in the past four years, while an estimated 10 to 30 percent of it may be categorized as being of “poor quality” (i.e., inaccurate, inconsistent, poorly formatted, entered incorrectly, etc.). The common problems with corporate data are many, but typically fall into the following five major areas:

  • Data Definition – typically manifesting itself through inconsistent definitions within a company’s corporate infrastructure.
  • Initial Data Entry – caused by incorrect values entered by employees (or vendors) into the corporate database; typos and/or intentional errors; poor training and/or monitoring of data input; poor data input templates; poor (or nonexistent) edits/proofs of data values; etc.
  • Decay – causing the data to become inaccurate over time (e.g., customer address, telephone, contact info; asset values; sales/purchase volumes; etc.).
  • Data Movement – caused by poor extract, transform and load (ETL) processes that lead to the creation of data warehouses often comprised of more inaccurate information than the original legacy sources, or excluding data that is mistakenly identified as inaccurate; inability to mine data in the source structure; or poor transformation of data.
  • Data Use – or the incorrect application of data to specific information objects, such as spreadsheets, queries, reports, portals, etc.

Each of these areas represents a potential problem to any business; both in their existence within the organization, as well as the ability of the organization to even recognize that the problem exists. In any case, these are classic symptoms of “data denial” – one of the most costly economic drains on the well-being of businesses today.

Data Quality Maturity Levels

There are five key status indicators that can be used to measure the existing levels of Data Quality maturity in an organization, each with its own set of distinct corporate – and human – attributes. However, it is at the mature level where you will want your organization to be positioned.

  1. Embryonic – this level is the least beneficial place to be, as Data Quality does not even appear on the organization’s radar screen; there is extensive finger-pointing with respect to data-associated blame, generally leading to cover-ups and CYAs; and there is no formal Data Quality organization in place. As far as the humans involved in the process are concerned – they are totally “clueless”.
  2. Infancy– this level is not that much better, although the organization has begun to consider looking into Data Quality; various ad hoc groups may have been established to search for “answers”; and Data Quality has been positioned as a subset of corporate IT. This typically occurs as the human element begins to show an emerging interest.
  3. Adolescence – this level is one of mixed Data Quality accomplishments where most of the pain points have already been identified and the strategy team has shifted into a crisis-driven “full court press” managed by formal Data Quality teams that are populated and coordinated by both IT and the Business. However, this is also the point where alternating periods of panic and frenzy typically set in.
  4. Young Adult – by the time the organization reaches this level, there begins to be some semblance of an evolving Data Quality structure, where the entire organization is involved; one where both IT and the Business have begun to work as partners toward a common goal. Accordingly, the human attribute has also become much more “stabilizing”.
  5. Mature – once the organization has attained the this level, it has finally reached the point where it has implemented an effective Data Quality structure, characterized by collaborative efforts and Data Quality/Center of Excellence (DQCE), as well as the ability to measure and track customer value over time. As such, the organization has been able to attain a “controlled” environment, where all of the personnel involved – on both the supply and demand sides – are comfortable that the desired levels of Data Quality have been achieved.

Moving Toward Data Quality

Data Quality is the desired state where an organization’s data assets reflect the following attributes:

  • Clear definition or meaning;
  • Correct values;
  • Understandable presentation format (as represented to a knowledge worker); and
  • Usefulness in supporting targeted business processes.

However, regardless of the state of the organization’s data assets, there must still be a balance of data, process and systems in order to meet the company’s stated business objectives, which generally focus on things like:

  • Increasing revenues and margins;
  • Increasing market share; and/or
  • Increasing customer satisfaction.

In today’s economy, companies tend to focus their investments more on packaged systems and business process optimization, rather than on Data Quality. As a result, investment in corporate Data Quality is often overlooked – and this can very easily lead to a significant reduction in the organization’s ability to effectively answer critical business questions, such as:

  • Who is our customer?
  • Are we missing sales opportunities?
  • Is the customer’s product entitled to service?
  • Are inaccuracies causing customer dissatisfaction?
  • What should we spare; how many; where?
  • Are our service functions efficient; is our decision support timely and reliable?
  • How is our product defined?
  • Is our billing accurate and timely?

The inability to answer these critical business questions leads to data quality issues such as:

  • Inconsistent or incomplete product structure and service data
  • Inability to uniquely identify entitled versus non-entitled equipment
  • Incomplete or non-existent configuration data on entitled products
  • Duplication and redundancy

But, it gets even worse! Poor Data Quality eventually stunts operational efficiency in virtually every area of the organization, as otherwise valuable resources (i.e., personnel, dollars, time, etc.) are required to spend an inordinate – and unnecessary – amount of extra effort:

  • Searching for missing data;
  • Correcting inaccurate information;
  • Creating temporary, or permanent, workarounds;
  • Laboring to assemble information from disparate data bases; and
  • Resolving data-related customer complaints.

Over time, poor data quality significantly decreases an organization’s revenue-generating opportunities. Lost revenue can exist is the following:

  • Lost Maintenance Contract Revenue – products that should be under contract are not captured and billing revenue is understated.
  • Lost T&M Revenue – Non-entitled products that should be serviced under T&M are serviced under contract
  • Lost Product Upgrade Opportunities – Inability to identify customer need for product and software upgrades
  • Incorrect Maintenance Charges – Incorrect contract pricing since product configurations cannot be accurately identified.
  • Lost Customer – Lost customers and revenue due to dissatisfaction with poor asset management and cumbersome reconciliations.
  • Delayed Contract Renewals – lost renewal revenue and increased admin costs due to delays in new contract initiation.
  • Overlooked Cross-sell & Up-sell Opportunities – missed opportunity to sell complementary or advanced solutions die to inaccurate records

Poor data quality also significantly increases its operating costs and, may in fact, lead to a reduction of customer satisfaction. Increased operating costs can exist in the following areas:

  • Sales Team – more time is required to manage new opportunities and create quotes, less time is spent selling and and quoting new maintenance contracts becomes inaccurate.
  • Customer Care Center – T&M billing disputes increase, the cost of contract dispute resolution is higher and there is a decreased accuracy and timeliness of invoices with increased dispute losses.
  • Contract Management – the effectiveness and timeliness of renewal activity is decreased.
  • Logistics – stocking locations become sub-optimized by an over/under stocking of spare parts.
  • Finance – data for decision support and performance reporting becomes incomplete and/or inaccurate.
  • Service Delivery – tech on-calls are doubled dispatched due to the wrong part, service level commitments are missed and trouble call handling is degraded.
  • Product Management – the product lifecycle position is inaccurately identified and inaccurate service history affects service offering decisions.
  • Services Marketing – the ability to develop pricing programs is hindered, marketing programs are not deployed effectively and there is an increased burden/time for data collection.

How to Achieve Data Quality

Arguably, the greatest battle you may face inside the organization will be to get management to the point where they agree that data quality is a goal even worth considering. To do this, every organization must have a champion to help find ways for removing barriers and changing existing perceptions. The primary focus of the champion should be on:

  • Assisting in making data quality a strategic priority;
  • Assuring that data quality will be used to enable business processes; and
  • Find – and communicate – compelling ways to make data quality attractive.

In our own experiences, Bardess Group has assisted many organizations to achieve data quality by applying the most effective methodology for accelerating the data cleansing and control processes.

Finding Success

Many organizations can achieve data quality by applying the most effective methodology for accelerating the data cleansing and control processes.

The seven major steps that must be taken to achieve Data Quality are:

  1. Acknowledge the problem, and identify the root causes;
  2. Determine the scope of the problem by prioritizing data importance and performing the necessary data assessments;
  3. Estimate the anticipated ROI, focusing on the difference between the cost of improving Data Quality vs. the cost of doing nothing;
  4. Establish a single owner of Data Quality with accountability (e.g., make it a senior management role, such as a Data Officer/DQ COE);
  5. Create a Data Quality vision and strategy, and identify the key change drivers;
  6. Develop a formal Data Quality improvement program based on specific tools wherever possible (e.g., First Logic, Trillium, IBM Ascential, Data Flux, Group 1), and use a value-driven approach for large projects; and
  7. Make it a priority to move your organization up through the levels of the Data Maturity model!

Achieving Data Quality is critical, but getting there is often a complex process. Data Quality requires commitments from all business functions, as well as from the top-down. Quick fixes typically do not work and generally only end up creating frustration. For many organizations, it may have taken years to create and foster a culture of data denial, and it will require rigorous processes to:

  • First, identify the problem before it can be fixed and;
  • Second, recognize – and accept – the full extent of the potential benefits that can ultimately be realized.

However, for many business enterprises, the numbers speak for themselves, where the implementation of a Data Quality initiative can ultimately lead to:

Reductions ranging from:

  • 10 – 20% of corporate budgets,
  • 40 – 50% of the IT budget, and
  • 40% of operating costs;
  • And increases of:
  • 15 – 20 % in revenues, and
  • 20 – 40% in sales

The application of Data Quality can provide an organization with the opportunity to capitalize on its cumulative information and knowledge assets. Knowledge that was previously unknown – or unavailable – such as cross-referenced customer buying patterns, profiles of potential buyers, or specific patterns of product/service usage may be uncovered and put into practical use for the first time. The end result can lead to anything ranging from improvements in operational efficiency, more accurate sales forecasting, more effective target marketing, and improved levels of customer service and support – all based on a strong foundation of Data Quality.