Archive for the ‘Information Systems’ category

Characteristics of quality software

February 12, 2012

A repost of my son’s blog – July 2009

Software Quality

Software Quality is a concept has been discussed and defined in a number of excellent books and articles. Granular-Level specific characteristics are numerous, and the weight placed on one aspect may differ from company to company, or even from project to project. However, with the assistance of the Pfleeger and Atlee text (2006) and a text from Selby and Boehm (2009), we can examine several generic properties which are relatively universal.

  • Portability
    This is a measure of how the degree of coupling with other software or hardware. Can the software be easily installed and transferred, or is there a complicated integration with 3rd-parties (eg. SQL Server, or a special hardware key-dongle)?
  • “AS-IS” utility
    Does the software require heavy customization once it is deployed to the customer?
    (ie. Reliability, Efficiency, Human Engineering)
  • Maintainability
    In 2 years, will we be able to fix a problem or add new functionality?
    (ie. Testability, Understandability, Modifiability)

Human Factors

Bernard suggests that the most basic reason for an implementation to fail is due to inadequate training and preparation of the operators of the system. Having been involved in several different implementations of new software, I have seen both well-prepared and inadequately-prepared staff try to deal with new software. I would venture to say that Bernard is exactly right in saying that improper training is a huge reason why software does not succeed. It is my experience that users with a stake in the company don’t WANT to see software fail, but they will unintentionally sabotage the new initiative with “Well we always did it the other way” attitudes, if they don’t have a good reason to make the change.


Bernard, A. (December 26, 2003). Why Implementations Fail: The Human Factor.

Boehm, B. W. Quantitative Evaluation of Software Quality. In R. W. Selby, Ed. Software Engineering (p. 27). IEEE. Retrieved July 11, 2009, from Google Books.

Pfleeger, S. L. & Atlee, J. M. (2006). Why Software Engineering. Software Engineering Theory and Practice (3rd ed. pp. 9-11). Upper Saddle River, NJ: Pearson Prentice Hall.


Penny Rounding Problem

February 10, 2012

A computer rounding problem that I like to call “The Penny Rounding Problem” has been around for many, many years. At least two movies have been made with this problem a core element: The Office, and Superman III. The basic problem is that a column of numbers should add up to the total at the bottom. But they do not.

Mark Reynolds is currently at Southwestern Energy where he works in the Fayetteville Shale Drilling group as a Staff Drilling Data Analyst. In this position, he pulls his experiences in data processing, data analysis, and data presentation to improve Southwestern Energy’s work in the natural gas production and mid-stream market.

Recently, Mark has been working toward improved data collection, retention, and utilization in the real-time drilling environment.

For example: 1/3 is represented as .33, or even .333. But if you add .33 together 3 times, you get .99, not 1.00 – a penny off. This is why your final mortgage payment (if you ever actually paid it off) is never exactly the same as the monthly amount. Even worse, take 2/3 or .67. Multiple .66666… by 3 and you get 2.00; multiply .67 by 3 and you get 2.01.

Solving the problem is relatively simple, but requires diligence. Individual calculations must be individually rounded to the correct number of decimal places.

When I teach Excel at the college, I require the student to explicitly ROUND the answer to any mathematical operation involving

  1. possible sub-penny answers (divide by three, multiply by .0475, etc.)
  2. currency
  3. down-stream use of the answer.

Taken individually — addition of two numbers will never generate sub-penny digits, non-currency measurements (weight, speed, etc) do not bother people when the totals are off by small decimal fractions, and if the result to the mathematical calculation is never to be used then no one cares.

So when an interest equation is entered into Excel
= A3 * A4 / 12,
you should change it to be
= ROUND( A3 * A4 / 12, 2 ) so that the answer is rounded to 2 decimal places.

So can Richard Pryor get rich by taking all of the rounded, fractional pennies and putting them in his account? This is called Salami Slicing and snopes calls it a legend. But do gas stations do it with your pump price? read here for the answer

Making new IT work for the business

September 23, 2011

I found an EXCELLENT article in the Digital Energy Journal by Dutch Holland. In this article he explore different strategies for transforming operational requirements into successful initiatives.

Without stealing too much of his well articulated article, the five approaches normally used are:

  • The by-the-book business analyst
  • The business-experienced analyst”
  • The businessman CIO
  • The IT expert Inside the business
  • The operations-led interface

I encourage anyone attempting to implement a operations-centric technological solution to read his article.

“When trying to connect technology innovation with business, an intelligent interface between the two is required. It must be able to translate business opportunity into technical requirements; innovate, test and evaluate; and seamlessly implement new technology into the business.” ~Dutch Holland

The Big Crew Change

May 17, 2011

“The Big Crew Change” is an approaching event within the oil and gas industry when the mantle of leadership will move from the “calculators and memos” generation to the “connected and Skype” generation. In a blog 4 years ago, Rembrandt observes:

“The retirement of the workforce in the industry is normally referred to as “the big crew change”. People in this sector normally retire at the age of 55. Since the average age of an employee working at a major oil company or service company is 46 to 49 years old, there will be a huge change in personnel in the coming ten years, hence the “big crew change”. This age distribution is a result of the oil crises in ‘70s and ‘80s as shown in chart 1 & 2 below. The rising oil price led to a significant increase in the inflow of petroleum geology students which waned as prices decreased.”

Furthermore, a Society of Petroleum Engineers study found:

“There are insufficient personnel or ‘mid-carrers’ between 30 and 45 with the experience to make autonomous decisions on critical projects across the key areas of our business: exploration, development and production. This fact slows the potential for a safe increase in production considerably”

A study undertaken by Texas Tech University make several points about the state of education and the employability of graduates during this crew change:

  • Employment levels at historic lows
  • 50% of current workers will retire in 6 years
  • Job prospects: ~100% placement for the past 12 years
  • Salaries: Highest major in engineering for new hires

The big challenge: Knowledge Harvesting. “The loss of experienced personnel combined with the influx of young employees is creating unprecedented knowledge retention and transfer problems that threaten companies’ capabilities for operational excellence, growth, and innovation.” (Case Study: Knowledge Harvesting During the Big Crew Change).

In a blog by Otto Plowman, “Retaining knowledge through the Big Crew Change”, we see that

“Finding a way to capture the knowledge of experienced employees is critical, to prevent “terminal leakage” of insight into decisions about operational processes, best practices, and so on. Using of optimization technology is one way that producers can capture and apply this knowledge.When the retiring workforce fail to convey the important (critical) lessons learned, the gap is filled by data warehouses, knowledge systems, adaptive intelligence, and innovation.”

When the retiring workforce fail to convey the important (critical) lessons learned, the gap is filled by data warehouses, knowledge systems, adaptive intelligence, and innovation. Perhaps the biggest challenge is innovation. Innovation will drive the industry through the next several years. Proactive intelligence, coupled with terabyte upon terabyte of data will form the basis.

The future: the nerds will take over from the wildcatter.

The Data-Information Hierarchy

January 31, 2011

(originally posted on blogspot January 29, 2010)

I see much internet attention given to the data –> information –> knowledge –> understanding –> wisdom tree. Most will omit the step of understanding. Many will overlook data or wisdom. But all five stages are required to move from total oblivion to becoming the true productive member of society that pushes us forward. To paraphrase, “it is a process”.

“The first sign of wisdom is to get wisdom; go, give all you have to get true knowledge.” ( This central and key verse in proverbs rings true today. Wisdom is the objective, but the progression to wisdom must begin with assimilating the data and information into knowledge. And from knowledge comes understanding, from understanding comes wisdom.

In keeping with the phylosophical / religious examination of knowledge and wisdom, explains it as:

Wisdom is the principal thing – the most important matter in life. Wisdom is the power of right judgment – the ability to choose the correct solution for any situation. It is knowing how to think, speak, and act to please both God and men. It is the basis for victorious living. Without wisdom, men make choices that bring them pain, poverty, trouble, and even death. With it, men make choices that bring them health, peace, prosperity, and life.

Understanding is connected to wisdom, and it is also an important goal. Understanding is the power of discernment – to see beyond what meets the eye and recognize the inherent faults or merits of a thing. Without understanding, men are easily deceived and led astray. Without it, men are confused and perplexed. With it, men can see what others miss, and they can avoid the snares and traps of seducing sins. With it, life’s difficulties are simple.

As great as this biblical basis is, for the effort of business and scientific endeavours require all five steps. But, as Cliff Stoll said, “Data is not information, Information is not knowledge, Knowledge is not understanding, Understanding is not wisdom.” Data is sought, information is desired, but wisdom is the objective.

“We collect and organize data to achive information; we process information to absorbe knowledge; we untilize the knowledge to gain understanding; and we apply understanding to achieve wisdom.” (Mark Reynolds)

Russell Ackoff provides a definition of the five stages

  1. Data: symbols
  2. Information: data that are processed to be useful; provides answers to “who”, “what”, “where”, and “when” questions
  3. Knowledge: application of data and information; answers “how” questions
  4. Understanding: appreciation of “why”
  5. Wisdom: evaluated understanding.

Data itself is not able to be absorbed by the human. It is individual quantums of substance, neither understandable nor desirable.

Information is the recognizable and cognitive presentation of the data. The reason that the Excel chart is so popular is that it allows the manipulation of data but the perception of information. Information is processable by the human, but is only a historical concept.

Knowledge is the appropriate collection of information, such that it’s intent is to be useful. Knowledge is a deterministic process. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level… understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge. (

Understanding an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between “learning” and “memorizing”. (

Wisdom is the only part of the five stages that is future based, forward looking. Systems Engineering courses have wisdom as the basis for hteir existance, whether they regognize it or not. An alternate definition of Systems Engineering could be the effort to put knowledge and understanding to use. And as such, a large part of the Systems Engineering course work is to teach the acquisition of knowledge and understanding while pressing for the student’s mind to explore its meaning and function.

Neil Fleming observes: (

  • A collection of data is not information.
  • A collection of information is not knowledge.
  • A collection of knowledge is not wisdom.
  • A collection of wisdom is not truth.

Where have we come from, what are we doing here, where are we going? These questions look like philosophical questions. But they form the basis for the data, information, knowledge, understanding, wisdom tree. And once the level of wisdom is achieved in any study, the question of where are we going is effectively answered.

Protecting Digital Identities, Part 1

January 31, 2011

A Digital Identity is the mechanism used to identify an individual to computers, networks, the internet, and social media. In a general case, digital identity is the digital fingerprint of an individual – or of an entity other than an individual – in either case, it is generically called the Digital Subject. But whatever it is, it consists of properties, relationships, attributes and authentication.

Properties are the characteristics of the digital subject. Within Facebook, properties may include name, age, marital status. Within a corporate network, the properties may include employment date, withholding exemptions, supervisor.

Relationships are the correlation between digital subjects. Within Facebook, relationships include friends, family, schools, employers, and special interests. Within the corporate environment, relationships refer to directory access rights, functional groups, etc.

Attributes are special characteristics of the digital subject and are not too different from properties. An attribute includes login name, password, home server. Generally, attributes are not shared outside the digital authority.

Authentication is the process for verifying the legitimacy of the digital subject. Generally username and password is the first line of defense. But authentication includes:

  • what you know (password)
  • what you have (passkey)
  • who you are (fingerprint, retina)
  • what you can do (this is relatively new and is generally seen in the form of captcha)

The protection of digital identity must address many facets. And the laws, ethics, and policies surrounding these protections do not encompass all aspects nor do they form a seamless shield.

As the digital identity becomes more and more integral to the existence of people in moderns societies, the protection and reliability of the digital identity becomes paramount.

Protecting the authentication. Authentication protection is the responsibility of both the digital subject and the central account store. And this responsibility is frequently substandard. Obviously the digital subject has shown laziness and disregard toward passwords in numerous scenarios. People tend to only use a couple passwords making their entire digital life accessible once a single account store has been violated. But within the central account store passwords may be kept in unencrypted form, they may be encrypted in a breakable two-direction cypher, or they may be broken through simple, brute-force dictionary comparisons. By far, the best solution is many passwords that use a combination of lowercase, uppercase, numbers, and symbols. But these are nearly impossible to remember.

Protecting the data. All of the protection of the authentication is meaningless if the digital data itself is unprotected. Unencrypted social security numbers, addresses, credit card numbers remain pervasive throughout the commercial industries. Remarkably, the medical community is making significant progress toward true information security. This progress is accomplished through the disappearance of paper records and the integration of digital-only records. The significance of this is that any view of the records requires 1) and authenticated user and 2) tracking of all access. (Three hospital employees were fired for improperly accessing the shooting victims in Arizona.)

Ensuring reliability. Safe and authenticated data is meaningless if not accurate. And accuracy has not received the level of attention as authentication and protection. Mistyped court records, un-updated address and employment records are the examples. Invalid properties, relationships, and attributes will cost money, cost jobs, cost relationships, cost productivity, etc. And typically, no one is held responsible. But the inaccuracies affect all of us.

Summary. Digital identities require a multifaceted oversight. failure of any level of protection, accountability, of reliability will render the records useless and affect the lives of many people. As the inventiveness of the nefarious groups improve, so must the determination of the shepherds of the data.

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