Business Driven Software Development

A repost of my son’s blog – July 2009
http://blog.intentdriven.com/2009/07/characteristics-of-quality-software.html

As a software engineer, project proposals are available in spades. Learning how to evaluate business needs and technical needs is a basic job prerequisite (Parkinson, 2000). Parkinson explains that there is a significant difference between a technical requirement and a business requirement. An example of a technical requirement would be needing a new printer. A similar business process requirement would be needing a more efficient printing algorithm that cuts down the time spent spooling a project to the printers.Business drivers can be broken down into “chunks”. For instance, an “Expense Reduction” driver may be broken into Customer Service expenses, customer acquisition\retention, efficiency, and other expenses (Machavarapu, 2006). Once the drivers are broken down, Machavarapu recommends assigning weights to the “chunks”, such that new projects can be evaluated for priority.

Finally, there is an array of reasons for beginning an IT project which should serve as red flags for engineers and developers. Reasons such as “Wanting to stay abreast of the latest technology advances” frequently translate into “Spend money on shiny new buttons”. Unfortunately, if this desire to advance technology isn’t tempered by legitimate business needs, the project may fail, or worse, prevent legitimate projects from receiving adequate funding.

References
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
http://books.google.com/books?hl=en&lr =lang_en&id=ttaMIFv8bv8C&oi=fnd&pg=PA5&
dq=characteristics+of+quality+software& ots=yWkqT2mRFl&sig=Mj9mpFfLpWk4BkLvp4cz
M8ZhGU4 Google Books.

Machavarapu, S. (2006). Prioritizing IT Projects Based on Business Strategy – CIO.com. Retrieved July 15, 2009, from http://www.cio.com/article/22976/Prioritizing_IT_Projects_Based_on_Business_S trategy/1

Parkinson, D. (2000). Recognizing business needs can lead to new and repeat clients. Retrieved
July 15, 2009, from http://articles.techrepublic.com.com/5 100-10878_11-5027153.html

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.

Characteristics of quality software

A repost of my son’s blog – July 2009
http://blog.intentdriven.com/2009/07/characteristics-of-quality-software.html

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.

References

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.

Did they actually say that?

On occassion we should all read these quotes and realize that humor is rampant!

“Computers in the future may weigh no more than 1.5 tons.”
~Popular Mechanics, forecasting the relentless march of science, 1949

“I think there is a world market for maybe five computers.”
~Thomas Watson, chairman of IBM, 1943

“I have traveled the length and breadth of this country and talked with the best people, and I can assure you that data processing is a fad that won’t last out the year.”
~The editor in charge of business books for Prentice Hall, 1957

“But what … is it good for?”
~Engineer at the Advanced Computing Systems Division of IBM, 1968, commenting on the microchip.

“There is no reason anyone would want a computer in their home.”
~Ken Olson, president, chairman and founder of Digital Equipment Corp., 1977

“This ‘telephone’ has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us.”
~Western Union internal memo, 1876.

“The wireless music box has no imaginable commercial value. Who would pay for a message sent to nobody in particular?”
~David Sarnoff’s associates in response to his urgings for investment in the radio in the 1920s.

“The concept is interesting and well-formed, but in order to earn better than a ‘C,’ the idea must be feasible.”

~A Yale University management professor in response to Fred Smith’s paper proposing reliable overnight delivery service. (Smith went on to found Federal Express Corp.)

“Who the **** wants to hear actors talk?”
~H. M. Warner, Warner Brothers, 1927.
[When celebrities start talking politics, I find myself asking the same thing - Prof Reynolds]

“I’m just glad it’ll be Clark Gable who’s falling on his face and not Gary Cooper.”
~Gary Cooper on his decision not to take the leading role in “Gone With The Wind.”

“A cookie store is a bad idea. Besides, the market research reports say America likes crispy cookies, not soft and chewy cookies like you make.”
~Response to Debbi Fields’ idea of starting Mrs. Fields’ Cookies.

“We don’t like their sound, and guitar music is on the way out.”
~Decca Recording Co. rejecting the Beatles, 1962.

“Heavier-than-air flying machines are impossible.”
~Lord Kelvin, president, Royal Society, 1895.

“If I had thought about it, I wouldn’t have done the experiment. The literature was full of examples that said you can’t do this.”
~Spencer Silver, on the work that led to the unique adhesives for 3-M “Post-It” Notepads.

“So we went to Atari and said, ‘Hey, we’ve got this amazing thing, even built with some of your parts, and what do you think about funding us? Or we’ll give it to you. We just want to do it. Pay our salary, we’ll come work for you.’ And they said, ‘No.’ So then we went to Hewlett-Packard, and they said, ‘Hey, we don’t need you. You haven’t got through college yet.’”
~Apple Computer Inc. founder Steve Jobs on attempts to get Atari and H-P interested in his and Steve Wozniak’s personal computer.

“Professor Goddard does not know the relation between action and reaction and the need to have something better than a vacuum against which to react. He seems to lack the basic knowledge ladled out daily in high schools.”
~1921 New York Times editorial about Robert Goddard’s revolutionary rocket work.

“Drill for oil? You mean drill into the ground to try and find oil? You’re crazy.”
~Drillers who Edwin L. Drake tried to enlist to his project to drill for oil in 1859.

“Stocks have reached what looks like a permanently high plateau.”
~Irving Fisher, Professor of Economics, Yale University, 1929.

“Airplanes are interesting toys but of no military value.”
~Marechal Ferdinand Foch, Professor of Strategy, Ecole Superieure deGuerre.

“Everything that can be invented has been invented.”
~Charles H. Duell, Commissioner, U.S. Office of Patents, 1899.
[Possibly my favorite - Prof Reynolds]

“Louis Pasteur’s theory of germs is ridiculous fiction”.
~Pierre Pachet, Professor of Physiology at Toulouse, 1872 p>

“The abdomen, the chest, and the brain will forever be shut from the intrusion of the wise and humane surgeon”.
~Sir John Eric Ericksen, British surgeon, appointed Surgeon-Extraordinary to Queen Victoria 1873.

“640K ought to be enough for anybody.”
~Bill Gates, 1981

Penny Rounding Problem

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.

www.ProfReynolds.com

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

Artificial Intelligence vs Algorithms

I first considered aspects of artificial intelligence (AI) in the 1980s while working for General Dynamics as an Avionics Systems Engineer on the F-16. Over the following 3 decades, I continued to follow the concept until I made a realization – AI is just an algorithm. Certainly the goals of AI will one day be reached, but the manifestation metric of AI is not well defined.

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.

www.ProfReynolds.com

Consider the Denver International Airport. The baggage handling system was state of the art, touted as AI based and caused the delay of the opening by 16 months and cost $560M to fix. (more – click here) In the end, the entire system was replaced with a more stable system based not on a learning or deductive system, but upon much more basic routing and planning algorithms which may be deterministically designed and tested.

Consider the Houston traffic light system. Mayors have been elected on the promise to apply state of the art computer intelligence. Interconnected traffic lights, traffic prediction, automatic traffic redirection. Yet the AI desired results in identifiable computer algorithms with definitive behavior and expectations. Certainly an improvement, but not a thinking machine. The closest thing to automation is the remote triggering features used by the commuter rail and emergency vehicles.

So algorithms form the basis for computer advancement. And these algorithms may be applied with human interaction to learn the new lessons so necessary to achieving behavioral improvement with the computers. Toward this objective, distinct fields of study are untangling interrelated elements – clustering, neural networks, case based reasoning, and predictive analytics are just a few.

When AI can be achieved, it will be revolutionary. But until that time, deterministic algorithms, data mining, and predictive analytics will be at the core of qualitative and quantitative advancement.

Making of a Fly

While watching a TED video about algorithms, mention was made of an unrealistic price on Amazon. Apparently two retailers had an out-of-control computer feedback loop.

One company, with lots of good customer points, is in the habit of selling products a little higher than the competition. Anyone’s guess why, but facts are facts – they routinely price merchandise about 25% higher than the competition (and rely on the customer experience points to pull customers away?).

Well, the competition routinely prices merchandise a little lower than the highest priced competitor: about 1% less.

So these computer programs began a game of one-upmanship. A $10.00 product was listed for $12.70 by the first company. Later in the day, the second company’s computer listed the same product for 1% less – $12.57. So the process repeated:  $15.96 and $15.80. Then $20.07 and $1987. The process continued until the book was listed for $23,698,655.93, plus shipping. (all numbers illustrative)

This story illustrates one of the challenges to automated feedback loops. An engineering instructor once explained it – if the gain feedback is a positive value greater than 1, the feedback will either oscillate, or latch-up.

More on feedback controls for real systems another day.

Read more here: https://www.google.com/#q=making+of+a+fly

Making new IT work for the business

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.

http://www.findingpetroleum.com/n/Making_new_IT_work_for_the_business/d1a1861b.aspx

“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

Predictive Analytics

Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields (Wikipedia). But operations – manufacturing, processing, etc., have been a little slower to encompass the concept. A drilling engineer friend of mine says “just put my hand on the brake lever and I’ll drill that well”. He probably can, but few of the rest of us can, or want to.

We want to see operating parameters, performance metrics, and process trends. All this because we want to have the necessary information and knowledge to assimilate understanding and invoke our skill set (wisdom). In this scenario, we are responding to stimulus, we are applying “reactive analytics”. But systems get more complex, operations becomes more intertwined, performance expectations become razor-thin. And with this complexity grows demand for better assistance from technology. In this case, the software performs the integrated analysis and the results is “predictive analytics”. And with predictive analytics comes the close cousin: decision models and decision trees.

Sullivan McIntyre, in his article From Reactive to Predictive Analytics, makes an observation about predictive analytics in social media that is mirrored in operations:

There are three key criteria for making social data useful for making predictive inferences:

• Is it real-time? (Or as close to real-time as possible)
• Is it metadata rich?
• Is it integrated?

Having established these criteria, the nature of the real-time data and the migration of historical data into real-time, predictive analytics becomes achievable.

What is Content?

Several internet articles and blogs address the meaning of content from an internet perspective. From this perspective, content is the (meaningful) stuff on a page, the presentation of information to the seeker.

But content within an operations-centric perspective is entirely different. And the databases and operational tools must be content data reflecting the desired information being sought in the pursuit of knowledge. Thus, paraphrasing Scottie Claiborne (http://www.successful-sites.com/articles/content-claiborne-content1.php), “content is the stuff in your operations system;  good content is useful information”.

Therefore, content is the meaningful data and the presentation of this data as information.

Content can, and should be, redundant. Not redundant from a back-up perspective; redundant from an information theory perspective – data that is inter-related and inter-correlated. (Data that is directly calculated need not be stored, however, the method of calculation may change and therefore the original calculation may prove useful.) Data that is inter-correlated may be thought of in terms of weather: wind speed, temperature, pressure, humidity, etc. are individual, measurable values but the inter-relate and perfectly valid inferences may be made in the absence of one or more of these datums. When the historical (temporal) and adjacent (geospatially) datums are brought into the content, then, according to information theory, more and more redundancy exists within the dataset.

Having identified the basis of content, the operations system designer should perform content analysis. Content analysis is both qualitative and quantitative. But careful attention to systems design and systems management will permit increased quantification of the results. What is content analysis in its most base form: the designer asking the questions “What is the purpose of the data? What outcomes are expected from the data? How will the data be imparted to produce the desired behavior?”

So how do we quantify the importance of specific data / content? How do we choose which data / content to retain? This question is so difficult to answer, the normal response is to save everything, forever. And since data not retained is data lost, and lost forever, this approach seems reasonable in a world of diminishing data storage costs. But, then, the cost and complexity of information retrieval becomes more difficult.

The concept and complexity of data retrieval is left for another day…

The Value of Real-Time Data, Part 2

Previously, predictive analytics was summarized as “system anticipates” (http://profreynolds.wordpress.com/2011/08/31/the-value-of-real-time-data/). But that left a lot unsaid. Predictive analytics is a combination of statistical analysis, behaviour clustering, and system modeling. No one piece of predictive analytics can exist in a vacuum; the real-time system must be statistically analyzed, its behaviour grouped or clustered, and finally a system modeled that can use real-time data to anticipate the future – near term and longer.

Examples of predictive analytics in everyday life include credit scores, hurricane forecasts, etc. In each case, past events are analyzed, clustered, and then predicted.

The result of predictive analytics is, therefore, a decision tool. And the decision tree will, to some degree, take into account a predictive analysis.

The output of Predictive Analytics will be descriptive or analytic – subjective or objective. Both outputs are reasonable and viable. Looking at the hurricane predictions, there are analytical computer models (including the so-called spaghetti models) that seek to propose a definitive resulting behaviour; then there are descriptive models that seek to produce a visualization and comprehension of the discrete calculations. By extension, one can generalize that descriptive predictions must be the result of multiple analytic predictions. Perhaps this is true.

Returning to the idea that predictive analytics is comprised of statistical analysis, clustering analysis, and finally system modelling, we see that a sub-field of analytics could be considered: reactive analytics. Reactive analytics seeks to understand the statistical analysis, and even the clustering analysis, with an eye to adapt processes and procedures – but not in real-time. Reactive Analytics is, therefore, the Understanding portion of the Data-Information hierarchy (http://profreynolds.wordpress.com/2011/08/31/the-data-information-hierarcy-part-3/). Predictive Analytics is, therefore, the Wisdom portion of the Data-Information hierarchy.