Case studies, Research papers and Articles

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Research paper: How to measure IT effectiveness: the CIO's Perspective

Objective of the paper

The purpose of the research presented in the paper is to:

A) Show how businesses measure their IT effectiveness;

B) Identify the most used metrics for measuring IT effectiveness.


Background information

Definition

IT effectiveness has several definitions.

For Kurien and al. IT effectiveness is:

"A measure of how well an IT organization develops the right technology components of business solutions for its customers"

According to previous studies, IT effectiveness can be considered at two levels:

1) At an operational level, where the impact of IT is measured in terms of improvement of business operations.

2) At a strategic level, where the strategic impact of IT is referred to enterprise agility, which is the ability of a company to respond to change.


Available measurements for an IT system

Determining the effect of IT is imperative for the expansion and profitability of any business. It is a difficult task to accomplish since IT departments enable the functionality of other departments in the organization by correlating interrelated tasks. Because of that, we cannot measure the improvement made by taking each task one by one and measuring the improvement of each one singularly. Within an organization, IT effectiveness improves the efficiency of both organizational needs (for example by having a better market assessment: comparing the competitors and suppliers prices, measuring where the customers data (preferences, potential growth, etc) and personal productivity (by giving the employee a new and improved system to work with). To sum up, the increased efficiency from the IT systems can have a favorable impact on the whole organization’s effectiveness.


But how to measure the effectiveness of an IT system implementation?

Measuring the effectiveness of IT used to be about the availability of infrastructure components, but it is now more about the reliability of business services and the end user experience. Thus, the evaluation will always be subjective. Nevertheless, there are several measurements that can help a company asses their IT system:


  • IS Success:

Developed in 1992 by W. Delone and E. McLean, then “updated” in 2003 (thus a slightly old theory), the D&M IS Success Model aims at understanding and estimating the net benefit from implementing an IS system by explaining the relationships among six of the most critical dimensions of success along which information systems are commonly evaluated. Those dimensions are system quality, information quality, service quality, use, user satisfaction, and net benefits. The six variables are not independent success measures, they are interdependent. The author analyzed 15 pairwise relationships between success measures:

System quality System use
System quality User satisfaction
System quality Net benefits
Information quality System use
Information quality User satisfaction
Information quality Net benefits
Service quality System use
Service quality User satisfaction
Service quality Net benefits
System use User satisfaction
System use Net benefits
User satisfaction System use
User satisfaction Net benefits
Net benefits System use
Net benefits User satisfaction


  • User satisfaction:

Dan Remenyi and Arthur Money made some researches to measure the effectiveness of an IT system on a user level. They analyzed several gaps between the users beliefs and their actual perception on what is delivered. It came out that two key perceptual performance factors where; the effective benefits (the benefits provided by giving the user the information needed to do any task effectively and efficiently) and the system access. Furthermore, they found that the gap on effective benefits is twice as important as the gap on systems access. According to D. Remenyi and A. Money, in order to improve the efficiency of a company’s IT system we need to reduce the gap between the beliefs and the actual effective benefits for a user.


  • Systems performance, information effectiveness, and service performance:

Here, the idea was to create a functional scorecard based on a theoretical input-output model of the function an IS system as in a business process. This evaluation would have three dimensions : systems performance, information effectiveness, and service performance. Then, those dimensions would be measured throughout 18 one-dimensional factors in those specific dimensions.


  • Overall quality of service, user’s satisfaction with IT, and helpfulness of IT staff to users:

Those tree elements were used by Chebrolu and Ness; Tallon et al. to evaluate how well the IT is being used inside a company. Those measures focus mainly on how well the user’s inside of the company come along with the IT system and how much does this IT system helps them to be more performant.


  • Governance, project delivery, support and maintenance, availability, and innovation:

Here two researchers (Shields and Nolan) decided to measure IT effectiveness based on the perceived value surrounding those five key components of IT delivery. According to them an IT organization must pay attention to those five factors as well as the perceived value from each of them for the internal users. According to them, no matter how good your system is scoring on the benchmarks or other evaluation systems: if the final users does not believe there is an effective value delivered, your system is worthless.


  • Improved effectiveness, improved communications, improved decision making, improved organizational responsiveness, and information systems as a whole:

According to Gupta et al. the effectiveness of an IT system implementation in a corporation can be measured throughout the five factors state here above.


Key learnings of the paper

  • The goal of achieving a high degree of IT effectiveness is to contribute to the profitability of the business by having better functionality in business operations.
  • According to Bradley and al. Enterprise maturity level directly influence IT effectiveness and an increase in operational IT effectiveness leads to better enterprise agility.
  • IT effectiveness is also related to how the CIO abilities (Analytical, leadership and managerial) are perceived in the organization. According to Earl and Feeny, the perception of the CIO’s abilities are based on wether IT is viewed as an asset or liability inside the firm.
  • IT effectiveness is benchmarked in the financial performance of the firm but is pragmatically assessed through metrics related to customers satisfaction and operational support.
  • Among the 121 CIO interviewed, financial performance was the leading metric to measure IT effectiveness. For the sample interviewed Effective IT systems are also cost effective.


Reference

  • DeLone, W.H. & McLean, E.R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research 3 (1), 60–95.
  • Zhang, X., Stafford, T.F., Murad, A., Risher, A., & Simmons, J. (2018). How to Measure IT Effectiveness: The CIO's Perspective. J. Inf. Technol. Manag., 29, 1-22.


Quick introduction on what Big Data actually is and what are the laws regarding it in the EU

Nowadays, almost everything that we do using a digital device leaves a digital footprint, from a simple walk that you take with your connected watch to chatting with your friends on your mobile. This leads to this new IT concept that we call Big Data. It gained momentum in the early 2000s when the amount of data we generated exploded. Big data describes the production, collection, storage and processing of large volume of unstructured and structured data that inundates our business and personal life on a day-to-day basis.

IT is usually summed up by three main characteristics known as ‘the 3 V’: Volume (the volume of data collected through various sources), Velocity (the speed at which data are processed) and Variety (the type of format, both structured and unstructured, on which data are collected)

Data can be analyzed for insight that leads to better business decisions. By analyzing data thanks to artificial intelligence and machines, companies can now find new relationships and patterns that were previously hidden so that they can have new insight into some situations. All those new indications are there to help companies build new business models by giving them a more accurate idea of our “consumption behavior” so that they can run their operations in a more efficient way.

Industry and service companies are not the only fields were big data is used; sectors such as healthcare and geology are also using it to compare and analyze a tremendous amount of information. In healthcare, medical records or images can help spot diseases earlier or develop more accurate treatments for the medical branch. In geology, sensor data can help in the prevention of natural disasters and even help rescuers to work more efficiently by giving them a pattern of the regular human behavior in these kinds of situations.

However, the European Union acknowledged that its citizens were kind of lost when it comes to personal data. That’s why the GDPR reform was decided. GDPR stand for « General Data Privacy Regulation ». It represents strong rules on data protection that allow people to have more control over their personal data and enable businesses to be on a level playing field. This regulation was definitively adopted by the European Parliament on 27 April 2016. Its provisions are directly applicable from 25 May 2018 in all Member States of the European Union. Certain disputes related to the fraudulent use of personal data has accelerated the process and the implementation of this regulation in Europe.

Now the question is how does it affect you concretely? What are your rights? Directly taken from the official website of the European Union, here are the rights that you gained with that reform and keep in mind that you didn’t have access to those things before : you now can have information about the processing of your personal data. It is possible to know what data is held on you and who owns it. You can also report incomplete or incorrect data and ask to erase it or ask for your data not to be used for marketing purposes. You also have the right to impose that your data must be analyzed and treated by humans and not only computers. All those rights apply across EU regardless of where the data is processed and where the company is established.


Three female CIOs to follow

According to the 2018 ‘Harvey Nash KPMG CIO survey’ women held just 12 per cent of senior IT roles across the globe.


CUNTHIA STODDARD
"As senior vice president and chief information officer of Adobe, Cynthia Stoddard oversees Adobe’s global Information Technology and Cloud Operations teams. In her leadership role, Cynthia spearheads a global strategy for delivering services and operations that form the mission-critical backbone for the company. She has 25-plus years of business experience and IT expertise leading large global organizations including Adobe, Netapp, Safeway, and APL Limited in supply chain, retail, and technology development." [1]

"People will take small projects [and] experiment, put them on display and show off what they have -- and many of their ideas have turned into some great things we use in our production environment. I give [employees] the time to innovate, think and experiment and let them fail a little bit and come back with those ideas and be able to present and have the ideas recognized. It becomes a major culture change within the organization’’' [2]
Her twitter @StoddardCA


DORIEN WEIJTS
SVP & CIO at Blue Yonder (formerly JDA Software), Dorien Weijt helps lead IT and technology solutions for one of the biggest technology companies in Phoenix, Arizona. Dorien has built an impressive 15-year career at Blue Yonder, scaling the ranks to become the top IT leader within the company. From a degree in hotel management to implementing new internal applications, upgrading and enhancing tools, her career is remarkable.

"Through the years, I’ve learned what questions to ask, what information to look for, and what kind of reports or analyst analysis we need to review to make the best decisions for the organization". [3]


JACQUELINE GUICHELAAR
Jacqueline Guichelaar joined Cisco in February 2019 as Senior Vice President and Group Chief Information Officer. She is spearheading the creation of a digital architecture to accelerate Cisco’s digital enterprise transformation. She oversees a $1B+ organization that is responsible for running and transforming the technology infrastructure, digital platforms, security operations, and business applications that enable Cisco’s global workforce to excel and be productive at their jobs. During her 30-year career, Guichelaar has witnessed and shaped several significant industry trends. She attributes her success to the talent she surrounds herself with and is passionate about developing individuals to reach their potential.

“Since I was a young girl my philosophy has always been about diversity of thought"' [4]
Her twitter @jacquiGF12


A Ministry of digital for Belgium ?

A Belgian entrepreneur, Xavier Damman has recently written an open letter to Belgium’s first minister, Alexander De Croo denouncing the absence of a Belgian digital ministry while digital has an enormous impact on our society and is omnipresent in our lives. Moreover, he explains that the current pandemic underlines the necessity to understand and use correclty digital tools. Digital is used greatly in the health sector but also in the everyday life of people (online classes...). He also stresses that digital is not only a positive thing as he writes about the negative impact social media can have on people. Therefore, digital has to be supervised and taken into account in a significant way.

Xavier Damman uses Taiwan has an example. Indeed, Audrey Tang, a Taiwanese free software programmer has been assigned minister of digital in 2016. Consequently, according to Damman, a more participative and open digital environment has been implemented in Taiwan. He also notes that it is thus not surprising that Taiwan is one of the countries that has best handled the current coronavirus crisis...

Xavier Damman’s letter has been signed by more than 60 people working in the digital field. And you, why do you think? Do you believe our country needs a ministry of digital? Don’t hesitate to have a look at Damman’s letter which is available on the openletter.earth website.


Hacks through the history

Play Station Network hack
The 7 May 2011, the Sony’s Playstation network system. Playstation had to cut their system for 20 days and had lost $171 million. Lot of data and personal information of users have been lost. The personal information of 77 million accounts has been exposed. It is one of the biggest data security breach in history.


Comodo Hack
Comodo is a company that provides the security certificates that let you know that you are surfing on a secured website. In 2011 they were hacked by an Iranian programmer who has created fake security certificates. The hacker has collected e-mail and personal information of thousands of people.


MafiaBoy
Michael Calce a.k.a. MafiaBoy, a 15 years old, has unleashed an DDOs attacks on a number of high-profile commercial websites including Amazon, eBay and Yahoo!. Total estimated costs : $1.2 Billion. He didn’t go to jail but writes a book ‘’Mafiaboy : A portrait of the hacker as a young man’’ [5]


Anonymous
February 2020 : Anonymous hacked the United Nation's website and created a page for Taiwan, a country which has not had a seat at the UN since 1971. The hacked page featured the Flag of Taiwan, the KMT emblem, a Taiwan Independence flag, the Anonymous logo, and embedded YouTube videos such as the Taiwanese national anthem and the closing score for the 2019 film Avengers: Endgame titled "It's Been a Long, Long Time", along with a caption. The hacked server belonged to the United Nations Department of Economic and Social Affairs. [6]


ICT addressing environmental challenges

In 2012 (admitted, a bit outdated) the International Institute for Sustainable Development (IISD) published an article addressing the impact of information and communication technology (ICT) on the environment. The main question being: is the impact positive or negative? This is an important perspective on ICT in view of the necessity of sustainable development incorporated throughout all business processes towards the near future. The objective of IISD for publishing this article is to stimulate debate around the subject. In the following paragraphs a summary of some concepts will be made to provide a basic knowledge to get readers of the wiki prepared to discuss the topic.


Relevance
How exactly could ICT improve sustainable development? ICT applications can reduce inputs per unit of output by making core processes such as transport systems, manufacturing and energy more efficient. However, the advantages have to be laid next to the disadvantages. For example, ICT might reduce greenhouse gas emission through applications that improve energy efficiency in buildings, but on the other hand an increase in emissions can result from their development, production and their uncontrolled disposal and it exhausts scarce resource.


Green ICTs
Green ICTs are ICT applications that have a positive impact on environmental performance and ecosystems. This can be achieved by direct and indirect factors. Direct factors involve reduction of physical and energy inputs in production, use, disposal and recycling. Indirect factors involve wider application and use of ICTs in several equipments and systems.


Three levels of ICT impact on environment
Interaction between ICTs and the environment can be classified into the following 3 levels:

Direct impacts:
This type of impact is linked to the ICT goods and services. It comes from the manufacturing process of ICT. It depends on the materials used, the life time and the recycling process.

Enabling impacts:
The way ICTs work and their design affects how other compatible products are designed, produced and disposed. Enabling impacts come from environmental impacts outside the ICT manufacturing sector but linked with ICT application. There are four ways enabling impacts can occur: optimisation, dematerialisation and substitution, induce increased demand in other products and degradation when ICTs in other products lead to difficulties in disposal management.

Systemic impacts:
The last impact covers the behavioural change ICTs can provoke. This comes from the ability of ICTs to provide and disclose information, to enable dynamic pricing and enhancing real-time price sensitivity, a change of technology that can impact consumer behaviour and to trigger rebound effects.


Governmental influence
In general, governments are slow on directing use of ICT applications for tackling environmental issues. Their focus lies more on direct impacts, which has improved. Governmental instruments such as policies can promote lowering all three kinds of environmental impacts earlier discussed.


Reference
Vickery, G. (2012). Smarter and Greener? Information Technology and the Environment: Positive or negative impacts?. Changing Our Understanding of Sustainability: The Impact of ICTs and the Internet. Winnipeg: IISD, 1-7.


Our Digital Carbon Footprint: an interesting article

If you are the type of person concerned about environmental and more generally speaking, societal issues, then this article discussing the carbon footprint of our digital world is made for you! We know that we cannot stop progress (fortunately, it does help a lot to improve our everyday live), but this should be done in respect to people and the planet. Indeed, due to their intangible nature, we tend to forget that data also have their carbon footprint and that it may actually be very polluting since it consumes significant and endlessly growing amount of energy which in turn emits CO2. Also, at the end of their life, the disposal of electronics devices is not well managed and they end up as toxic electronic waste. Another issue discussed in this article is the impact of data centres and potential solutions already implemented in some areas around the globe.

To quote the authors of this interesting article, "This poses a very important question: will digitalisation be able to help us on the way to a greener and fairer world, or will our growing reliance on digital tools ultimately prove to be an accelerator for climate change and the destruction of the planet?"

Personally, I think that this question deserves some thought. That is why I highly recommend you to read this article. I am deliberately not summarising this article because it really deserves your full attention in order to form your opinion on the subject and make you aware of the problem we are currently facing (even if I guess that many of you (I hope) are already aware of it).

Of course, (spoiler alert), reading it will not provide you with a black and white answer to the question, but this will be only the beginning… of your reflexion on this issue, of debates about it with your friends and relatives, the beginning of a ‘’’change’’’.


Reference
Sheena Stolz and Sarah-Indra Jungblut 2019. ‘’’Our Digital Carbon Footprint: What's the Environmental Impact of the Online World?’’’ [Online] (Published August 2019)

Available at: https://en.reset.org/knowledge/our-digital-carbon-footprint-whats-the-environmental-impact-online-world-12302019 [Accessed 13 December 2020].

Digital Economy and Society Index (DESI) 2020 - Belgium

DESI is the Digital Economy and Society Index.
It is a report that enables the European Commission to monitor the progress of each member state in the digital field. Indeed, the COVID-19 pandemic has shown us how important digital resources are for our economy. They allow the continuation of work, the monitoring of the spread of the virus, etc.

Belgium is in 9th place in 2020 out of the 28 EU member states, ahead of France which is in 16th place and behind Finland which is in first place. This index relating to the digital economy and society is divided into 5 parts :

1) Connectivity
2) Human capital
3) The use of internet services
4) The intergration of digital technology
5) Digital utilities

1) Connectivity :
The different member states are rated according to the rate of households covered by fixed networks, the number of individuals with 4G, etc.
Belgium has an overall score of 52.0 and ranks 13th among EU countries. The country has not yet reached all its connectivity targets but it is likely that they will be reached soon thanks to the national plan "Digital Belgium" (Plan for a very high speed internet 2015-2020)

2) Human capital :
Belgium is in 12th place with an overall score of 50.4. The human capital includes the level of basic and advanced digital skills, graduates in ICT .

3) Use of internet services :
Belgium is in 10th place with an overall score of 61.2. The use of internet services includes internet users, also those who have never used the internet, social networks, online entertainment (news, music, videos, games and video-on-demand services), video calls... Overall, Belgium has a higher use of internet services than the EU average (58.0).

4) Integration of digital technology
Belgium has very good results in the integration of digital technology. Indeed, its overall score is 65.9 and the EU average is 41.4 in 2020. The integration of digital technology includes the use of social networks, online sales, electronic exchange of information, etc.

5) Digital public services
Belgium's overall rating is 71.7 in 2020. This last part of the DESI includes the digitized public services offered to companies, online administration services, the use of pre-filled forms, etc.


Disruptive technology: How Kodak missed the digital photography

Rise and fall of Kodak

Kodak was founded in 1880 and created the first snapshot camera in 1888.
They continued to invest and put effort in this technology. Thus, they were very successful and even did not let go their dominance on the market when the color photography was introduced. Kodak was the indisputable leader in the mid seventies by having respectively 90% and 85% of the film and camera market shares in the US.
In 1986, they have invented the first digital camera. But then, the trouble started. From perpetual restructuring and bad choices, they could not manage to reinvent themselves. Despite the fact that George Fisher, a "digital man" was introduced as the new CEO in 1993. Unlike the expected changes, he obstinately refocused the group on normal photography.

The coming of new digital challengers really disrupted the photography incumbents in particular Kodak who did not believe in this change.


Analysis of Kodak's response to digital photography

Kodak seemed to ignore the customers and clearly underestimate the speed of adoption of the digital photography by the public. Obviously,they started to invest into a digital strategy but it was already to late. Even more, the culture was not fitted for such change. Indeed, the middle managers rejected this disruptive technology. The cause of this rigid mindset was the strong market shares, they became sort of "lazy".

Reference

  • Henry C. Lucas, Jie Mein Goh, Disruptive technology: How Kodak missed the digital photography revolution, The Journal of Strategic Information Systems,Volume 18, Issue 1,2009, Pages 46-55


The 8 essentials of innovation by McKinsey

Do you really innovate?

ASPIRE: Do you regard innovation led-growth as critical, and do you have cascaded targets that reflect this?

CHOOSE: Do you invest in a coherent , time- and risk-balanced portfolio of initiatives with sufficient resources to win?

DISCOVER: Do you have differentiated business , market and technology insights that translate into winning value propositions?

EVOLVE: Do you create new business models that provide defensible and scalable profit sources?

ACCELERATE: Do you beat the competitors by developing and launching innovations quickly and effectively?

SCALE: Do you launch innovations at the right scale in the relevant market and segments ?

EXTEND: Do you win by creating and capitalising on external networks ?

MOBILISE: Are your people motivated, rewarded and organised to innovate repeatedly?

Chatbots : Revolution for the customer service?

Chatbots use AI to simulate human conversation. Nowadays almost every e-commerce website has one of those cutting-edge computer programs. It might seem kind of scary as that would mean that chatbots would literally replace humans.

It is true to say that this AI technology has several advantages, in order to boost customer engagement and the customer interaction process.

With the increase of people using apps such as Facebook Messenger or Instagram, it is undeniable that companies have to catch up and adapt. In this case, chatbots are a huge gain of efficiency. They indeed allow a programmed bot to instantly handle frequent and repetitive questions and help customers with their issues. They are also a huge gain of time as chatbots are available 24/7. It is also cost efficient as companies don’t need as many employees at the customer service department, according to BI intelligence, chatbots contribute to cutting customer care costs by up to 29%.


5G and health : any risk ?

5G relies on signals carried by radio waves transmitted between antenna and your phone. It is using higher frequency waves than earlier mobile network. Unfortunately, it needs more transmitter masts because it can travel shorter distance.

Is there any concern ?
More than 240 scientists have published reviewed and researches on the health effect of exposure of radio frequency radiation (RFR). 500 studies have found harmful biologic effects from RFR exposures. [7]

A toxicology research has been conducted by the US Department of Health in 2018 here are the context and the results: Rats’ bodies were exposed to radiation from mobile phones for nine hours a day every day for two years. No cancer has been found in female rats but actually they were living longer than rats that haven’t been exposed. However, male rats exposed to high doses of frequency have developed cancer and the tumor was develop in their heart.

Unfortunately, exposure used in the studies cannot be compared to the exposure that humans experience every day. It has been concluded in the UK that ‘’although some of the research suggests a statistical possibility of increased cancer risks for heavy users, the evidence to date for a causal relation is not sufficiently convincing to suggest the need for precautionary action’’ [8]

A simple Framework for Building Predictive Models

Definition

Predictive modelling is a commonly used statistical technique to predict behavior. It is a form of data mining technology that works by analyzing historical and current data and generating a model to help predict futur outcome. Predictive models are created whenever data is used to train a predictive modelling technique.

Business usage of Predictive Models

Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

Modelling Process

Plan the Model

To build a predictive model, you first need to assemble the datasets that will be used for training. Clear objectives must be formulated. Each model is developed for its own specific purpose and can’t be used effectively in another situation. An example of clearly defined model objectiv contains the action that the model has to predict and the period when it is most likely to happen. Data must be cleansed and organized. The variables that aren’t included in the dataset will not form part of the prediction. Both technical and business people need to be involved in the decision relating to the contents of the datasets.

Build the Model

The model code needs to be written. The score calculated and the data validated. The model will be build using a sample from the dataset created. The resulting model will contain a subset of the origianl list of variables considered for the model.

Implement the Model

The model is deployed and applied. The model performance estimated, assessed and monitored. The model will be built on a subset of data. Once it is completed and validated. It will be run over the customer, product or case base. After that, you will have to understand the performance of your model. You will have to monitor the performance of the model on a continous basis.

Type of Predictive Model

Classification and Decision Trees

A decision tree is a decision support tool that use a model of decisions and their possible consequences.

Naives Bayes

In machine learning, naive Bayes classifiers are a family of simple probalistic classifiers based on applying Bayes’ theorem with strong independence assumptions between the features. The technique construct classifiers : models that assign class labels to problem instances, represented as vectors of feature values.

Linear Regression

In statistics, regression analysis is a statistical process for estimating the relationships among variables. Linear regression is an approach for modelling the raltionship netween a sclara dependant variable Y and or explanatory variables X.

Logistic Regression

Logistic regression is regression model where the dependant variable is binary.

Neural Network

Is an information processing paradigm that is inspired by the way biological nervous system process information.

Machine Learning

Is a type of AI that provides computers with the ability to learn without being explicitly programmed.

Support Vector Machine

In Machine Learning, a support vector machine is a supervised learning model with associated learning algorithm that analyze data used for classification and régressions analysis.

Natural Language Processing

Content analytics feed predictive prescriptive analytics. Natural Language processing extraction can be used with descriptive statistical formula to make use of the wealth of unstructured data.

Supervised and Unsupervised

Support Vector Machine, Decision Trees, Natural Language Processing and regression models use supervised learning to create mapping function between a set of input data fields and a target variable. Unsupervised learning requires no target. Clustering techniques fall into this category. Data points are simply grouped together based on their similarity. Black-box is a term used to identify certain predictive modelling techniques that are not capable of explaining their reasoning because you can’t really know what is ahppening in the black-box.

Source

Halechosky, H."A Simple Framework of Building Predictive Models". 2016