Current Topics

Top 5 Articles of 2017

Thank you to the readers in 106 countries that read my articles in 2017. Following are the top 5 articles that you have been interested in:

  1. Is Internet a Distributed System?
  2. What is the relationship between Cloud Computing and Service Orientated Architecture (SOA)?
  3. How to select an Enterprise Architecture Framework?
  4. – Who is at fault?
  5. 5 Questions to Ask About Your Business Transformation

Following are the top 20 countries where most readers have come from:

  1. United States
  2. India
  3. Canada
  4. United Kingdom
  5. Pakistan
  6. Australia
  7. Philippines
  8. Germany
  9. Malaysia
  10. Netherlands
  11. Singapore
  12. Sweden
  13. France
  14. South Africa
  15. South Korea
  16. Saudi Arabia
  17. Brazil
  18. Indonesia
  19. Switzerland
  20. Spain

Future Considerations for Hewlett Packard Enterprise

A year ago Hewlett Packard (HP) decided that it was going to split into two companies. This decision became real last week when HP officially split into HP Inc. and Hewlett Packard Enterprise (HPE) as announced by Meg Whitman on her LinkedIn post. The main reason given for this split was focus. HP Inc. would focus on selling consumer products such as personal computer and printers. HPE would focus on selling enterprise products, enterprise software and enterprise services such as cloud computing, big data, cyber security to improve operations.

It seems that on the surface the announcement of the split of HP into HP Inc. and HPE has received a mix bag of optimism and skepticism from different corners of the tech industry. On the optimistic side, this is a good move since it would help these companies focus on their core competencies and provide focused customer service and client experience. On the skeptical side, this is a little too late since the tech industry has been moving from merely selling computer products to selling more technology software and services for at least 20 years.

If we observe the tech industry from a modern economics lens we would find that this split is not something that is novel but it is very predictable. From a modern economics lens, the ‘primary sector’ for the tech industry focused on hardware and products, the ‘secondary sector’ for the tech industry focused on software and the ‘tertiary sector’ for the tech industry focuses on technology services. What is interesting is that this split lets HP Inc. focus on the ‘primary tech sector’ for consumers while HPE focuses on both the ‘secondary tech sector’ and the ‘tertiary tech sector’ simultaneously for enterprises. Eventually though, HPE would increase their focus on the ‘tertiary tech sector’ since the margins are much better in services as compared to just products and software. In order for HPE to become a bigger player in the services market, they should consider the following:


In the Future

Who is leading the services division?


Who should be leading the services division?
What processes are being followed to provide services? What processes should be followed to provide services?
Where mix of tech and non-tech services are being provided? Where mix of tech and non-tech services should be provided?
When are services bundled with hardware and software? When should services be bundled with hardware and software?
Why standalone services are provided? Why should standalone services be provided?

HPE leadership has to realize that any organizational splits are not without consequences. These consequences could entail: (1) Stocks becoming more volatile as any budget cuts with client enterprises could affect the bottom line, (2) Competitors might be able to provide same level of service at a cheaper cost with better client experiences and (3) Lack of optimized processes with no flexibility to adjust for enterprise clients needs could reduce overall reputation of HPE.

One of the ways to address the above mentioned split issues would be to create independent mock enterprise client teams that would rate how easy or difficult it was to deal with HPE in light of changing economic conditions, client experiences and efficient and effective processes. These independent mock enterprise client teams would be used to further refine HPE and put itself in the shoes of its enterprise clients.

Organizational Changes - HPE

Future Considerations for Alphabet Inc.

A couple of weeks ago Alphabet Inc. emerged as a parent holding company of Google as announced by Larry Page on Google’s blog. The two main reasons given for this move is to make the company cleaner and more accountable. By cleaner, it means that products that are not related to each other would become separate wholly owned subsidiaries of Alphabet Inc. which includes Google, Calico, X Lab, Ventures and Capital, Fiber and Nest Labs. By becoming more accountable, it means that leaders of these wholly owned subsidies would be held to even higher standards and accountability of where money is and should be spent. This move would help Wall Street understand that Alphabet is willing and structurally capable of going into areas that are unrelated.

It seems that on the surface the announcement of creating Alphabet Inc. has deemed to be a good move as many pundits and professors have pointed out ever since its emergence. The reasons of cleanliness and accountability are great for internal purposes. However, if we dig a little deeper we would find that there are external purposes that are at play here as well. Firstly, due to Alphabet Inc.’s cleaner approach, mergers and acquisitions of unrelated industries would become much easier and thus accountability of each wholly owned subsidiaries would be justifiable to Wall Street. Secondly, Alphabet Inc. would now be able to enter into industries or create new industries altogether. This move could mean that Alphabet Inc. could also be the next big 3D manufacturer of electronic equipment or even the next Big Bank that finally removes paper-based transactions. While both of these examples are interesting and achievable due to Alphabet Inc.’s deep pockets. In order for Alphabet Inc. to really disrupt or create new industries, strategic consideration should be taken into the following:


In the Future

Who is leading the organization(s)?


Who should lead the organization(s)?
What processes are being followed? What processes should be followed?
Where are products and services being deployed? Where products and services should be deployed?


When do people, process, technologies, products and services disrupt/create markets? When should people, process, technologies, products and services disrupt/create markets?
Why already bought companies make sense? Why companies should be bought?

Alphabet Inc. leadership also has to realize that any organizational structural changes are not without consequences. These consequences could entail: (1) Stocks could become more volatile as even any slightly negative news concerning the wholly owned subsidiaries could affect Alphabet Inc. stocks, (2) Due to autonomy and fiefdom creation, collaboration across people, process, technologies, products and services among the wholly owned subsidiaries could be compromised and (3) There could be rise of duplicative functional teams (e.g., HR, Finance etc.) across all wholly owned subsidiaries thus taking resources away from core business pursuits.

One of the ways to address the above mentioned conglomerate issues would be to create a task force with enough teeth within Alphabet Inc., and cross-organizational teams across all wholly owned subsidiaries who can help find and remedy these issues. This task force and its teams could be similar to internal consultants whose lessons learned and methodologies could help Alphabet Inc. become more efficient and effective. Perhaps these practices could also open the door for Alphabet Inc. to dominate the Management Consulting industry as well.

Organizational Changes

Understanding and Applying Predictive Analytics

Executive Summary

This article proposes looking at Predictive Analytics from a conceptual standpoint before jumping into the technological execution considerations. For the implementation aspect, organizations need to assess the following keeping in mind the contextual variances:




  • Top Down
  • Bottom Up
  • Hybrid
  • Organizational Maturity
  • Change Management
  • Training
  • Practical Implications
  • Pros and Cons of Technology Infrastructure
  • Providing Enabling Tools to Users
  • Best Practices

Describing Predictive Analytics

Predictive Analytics is a branch of data mining that helps predict probabilities and trends. It is a broad term describing a variety of statistical and analytical techniques used to develop models that predict future events or behaviors. The form of these predictive models varies, depending on the behavior or event that they are predicting. Due to the massive amount of data organizations are collecting, they are turning towards Predictive Analytics to find patterns in this data that could be used to predict future trends. While no data is perfect in predicting what the future may hold there are certain areas where organizations are utilizing statistical techniques supported by information systems at strategic, tactical and operational levels to change their organizations. Some examples of where Predictive Analytics is leveraged include customer attrition, recruitment and supply chain management.

Gartner describes Predictive Analytics as any approach to data mining with four attributes:

  1. An emphasis on prediction (rather than description, classification or clustering)
  2. Rapid analysis measured in hours or days (rather than stereotypical months of traditional data mining)
  3. An emphasis on the business relevance of the resulting insights (no ivory tower analyses)
  4. An (increasing) emphasis on ease of use, thus making tools accessible to business users

The above description highlights some important aspects for organizations to consider namely:

  1. More focus on prediction rather than just information collection and organization. Sometimes in organizations it is observed that information collection becomes the end goal rather than using that information to make decisions.
  2. Timeliness is important otherwise organizations might be making decisions on information that is already obsolete.
  3. Understanding of the end goal is crucial by asking why Predictive Analytics is being pursued and what value it brings to the organization.
  4. Keeping in mind that if the tools are more accessible to business users then they would have a higher degree of appreciation of what Predictive Analytics could help them achieve.

Relationship of Predictive Analytics with Decision Support Systems or Business Intelligence

University of Pittsburg describes Decision Support Systems as interactive, computer-based systems that aid users in judgment and choice activities. They provide data storage and retrieval but enhance the traditional information access and retrieval functions with support for model building and model-based reasoning. They support framing, modeling and problem solving. While Business Intelligence according to Gartner is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. These descriptions point to the fact that Decision Support Systems or Business Intelligence are used for decision making within organization.

Interestingly, it seems like Predictive Analytics is the underlying engine for Decision Support Systems or Business Intelligence. What this means is the predictive models that result in Predictive Analytics could be under the hood of Decision Support Systems or Business Intelligence. It should be noted that organizations should proceed with caution with regards to the Decision Support Systems or Business Intelligence since if the underlying assumption are incorrect in making the predictive models then the decision making tools would be more harmful then helpful. A balanced approach would be to create expert systems where Decision Support Systems or Business Intelligence is augmented by human judgment and the underlying models are checked and verified periodically.

Implementation Considerations for Predictive Analytics

As the descriptions above have indicated that the aim of Predictive Analytics is to recognize patterns and trends that can be utilized to transform the organization. This requires organizations to firstly educate themselves on what value they want and what can be derived from Predictive Analytics. Predictive Analytics is about business transformation and it needs to show what value it brings to the organization. In this regard, we have to assess people, processes and technologies of the organization in terms of current state (where the organization is right now) and future state (where the organization wants to be). Typically, this revolves around Strategies, Politics, Innovation, Culture and Execution (SPICE) as shown below.

SPICE Factors

SPICE Factors

The assessment of people for Predictive Analytics means to understand what users will be leveraging Predictive Analytics and if they understand that simply relying on Predictive Analytics is not enough but in order to have an effective system they need to be part of the system. This means that the analytics insights need to be augmented by human expertise to make intelligent decisions. The assessment of processes for Predictive Analytics entails looking at how organizations make decisions right now and how future decisions would be made if Predictive Analytics is put into place. This includes having appropriate governance structures in place. The assessment of technology entails looking at what technologies exist within the organization and if they could be leveraged for Predictive Analytics. If not then looking at what Predictive Analytics products are in the market that would work for the organization and are they flexible enough in case the underlying assumptions for the predictive models change and when predictive models become obsolete.

The advanced techniques mentioned in the book, Seven Methods for Transforming Corporate Data into Business Intelligence would be applicable to Predictive Analytics. These methods are:

  1. Data-driven decision support
  2. Genetic Algorithms
  3. Neural Networks
  4. Rule-Based Systems
  5. Fuzzy Logic
  6. Case-Based Reasoning
  7. Machine Learning

Technologies Used for Predictive Analytics

Gartner has been publishing their Magic Quadrant on Business Intelligence and Analytics Platforms since 2006. Due to the increased importance of Predictive Analytics in the marketplace, Gartner decided to create a separate Magic Quadrant for Advanced Analytics Platforms which focuses on Predictive Analytics and published its first version on February 2014. Since it is the first version of the Magic Quadrant, all vendors listed are new and no vendors were dropped.


Gartner's Magic Quadrant for Advanced Analytics Platforms

Gartner’s Magic Quadrant for Advanced Analytics Platforms

As we can see from this Magic Quadrant that it includes well-known vendors but also vendors that are not as big or as well-known. It is interesting to note that open-source vendors such as RapidMiner (a Chicago company) and Knime (a European company) are in the same Leaders Quadrant as well-established vendors such as SAS and IBM. While there are some issues with these open-source vendors as stated in the report but perhaps this Magic Quadrant is also an indication of where the next generation of analytics would come from. Due to the very nature of open-source, there are more opportunities for cheaper customization which would give the organizations the flexiblity to be as granular as they want to be. Ofcourse code stablity and lack of proper documentation are issues that organizations need to be cognizant about. Organizations may also want to “try out” these open source tools before they make a big commitment to propertary software to see if Predictive Analytics is something they want to invest heavily in.

Using Predictive Analytics in Specific Industries

There are many industries that utilize Predictive Analytics. The organizations in these industries either use Predictive Analytics to transform their business and/or to address certain areas that they would like to improve upon. Following is a list of some of the industries that utilize Predictive Analytics:

Industry How is Predictive Analytics used?
  • Customer Retention
  • Inventory Optimization
  • Low-Cost Promotions
Oil and Gas
  • Well and Field Asset Surveillance
  • Production Optimization
  • Equipment Reliability and Maintenance
  • Adjust production schedules
  • Tweak marketing campaigns
  • Minimize Inventory
  • Human Resources Allocation
  • Supply Chain Optimization
  • Electronic Health Records
  • Nation-wide Blood Levels
Social Media
  • New Business Models

While there are many examples of industries that have embraced Predictive Analytics but there are other industries that have not fully accepted it as a new reality. These industries have many excuses for not considering Predictive Analytics but typically revolve around scope, quality, cost and fear of the known. However, the tide might be changing for these industries as well since industry bloggers are beginning to insist how Predictive Analytics could be leveraged for competitive advantages.

My Opinion

Predictive Analytics can come in handy in making organizations analytical and becoming a better version of themselves. However, Predictive Analytics can be a deal-breaker if organizations have attempted and failed in the past and for this very reason Predictive Analytics should start as a discussion first. This discussion should revolve around asking which areas need improvements and among other things determine if Predictive Analytics could be something that could help. After a successful Predictive Analytics initiative other areas could be potential candidates as well.

An important thing to note is that Predictive Analytics is an organization-wide initiative that has touch points across the organization and thus the maturity of the organization has to be seriously considered prior to going on a Predictive Analytics journey. No matter how good Predictive Analytics can be for the organization but if the organization is not mature enough and it does not have the right governance, processes and feedback mechanisms in place then it might turn out to be another attempt at glory but nothing to show for it.


  1. Predictive Analytics for Dummies
  2. Seven Methods for Transforming Corporate Data Into Business Intelligence
  3. IBM Journal Paper on A Business Intelligence System by H.P. Luhn
  4. Gartner report (G00258011) Magic Quadrant for Advanced Analytics
  5. Gartner IT Glossary on Predictive Analytics
  6. Gartner IT Glossary on Business Intelligence
  7. SAP Predictive Analytics
  8. Decision Support Systems by Marek J. Druzdzel and Roger R. Flynn
  9. 5 Questions to Ask About Predictive Analytics
  10. 5 Factors for Business Transformation

Identifying Organizational Maturity for Data Management

The maturity of an organization is determined by how that organization can collect, manage and exploit data. This is a continuous improvement process where data is used to make strategic decisions and strategic decisions are made to collect data that creates competitive advantages. But in order to create strategic advantages through data, an organization needs to have data management and related processes in place to discover, integrate, bring insight and disseminate data within the entire organization. In terms of data, organizations need to understand where they are currently and where they want to be in the future and thus they need to ask the following questions:


In the Future

Who receives the data? Who should received the data?
What happens to data? What should happen to data?
Where does data come from? Where should data come from?
When is the data being shared? When should data be shared?
Why data is collected? Why should data be collected?

After an organization understands and documents the above then they need to develop metrics to measure the relevance of their data as it pertains to the entire organization. Since being a data-driven organization is a continuous improvement journey, organizations can use the following adaptation of the Capability Maturity Model (CMM) to determine their maturity of data management and related processes:

Data Management Maturity Levels

Data Management Maturity Levels

Additionally, organizations can have governance and processes that can help them assemble, deploy, manage and model data at each level of CMM as shown below:





  1. Khan, Arsalan. “5 Questions to Ask About Your Information.” Arsalan Khan., 16 May 2014. Web.

What is the relationship between Cloud Computing and Service Orientated Architecture (SOA)?

According to the publication from Mitre, Cloud Computing and Service Orientated Architecture (SOA), cloud computing has many services that can be viewed as a stack of service categories. These service categories include Infrastructure-as-a-Service (IaaS, Platform-as-a-Service (PaaS), Storage-as-a-Service, Components-as-a-Service, Software-as-a-Service (SaaS) and Cloud Clients. The following figure shows the service categories stack as depicted in the Mitre publication:

Mitre's Cloud Stack

Mitre’s Cloud Stack

SOA is a framework that allows business processes to be highlighted to deliver interoperability and rapid delivery of functionality. It helps system-to-system integration by creating loosely coupled services that can be reused for multiple purposes. The concept of SOA is similar to Object-Orientated Programming where objects are generalized so that they can be reused for multiple purposes.

Now that we have an understanding of the various types of Cloud Computing services and SOA, lets explore how Cloud Computing and SOA are similar and different.

Similarities between Cloud Computing and SOA:

  • Reuse – Conceptually speaking, the idea of reuse is inherent both in Cloud Computing and SOA.
  • As needed basis – In Cloud Computing, the services are provided to the users on demand and as needed. SOA is similar to this since the system-to-system services are on demand and as needed as well.
  • Network Dependency – Cloud Computing and SOA both require an available and reliable network. If a network does not exist then the cloud services provided over the Internet would not be possible. Similarly, if a network does not exist then the communications between systems would not be possible. Thus, both Cloud Computing and SOA are dependent on a network.
  • Cloud Contracts – In Cloud Computing, contracts entail the mutual agreement between an organization and cloud service providers. In cloud contracts, there is a cloud service provider and a cloud service consumer (the organization). In the case of SOA, contracts are important and can be either external (e.g., Yahoo! Pipes) and/or internal (e.g., organizational system integration). In SOA contracts, there are service producer(s) and service consumer(s) that are conceptually similar with cloud contracts.

Differences between Cloud Computing and SOA:

Despite the similarities between Cloud Computing and SOA, they are not the same. Following are some of the differences between them:

  • Outcome vs. Technology – In Cloud Computing, we are paying for the outcome but in SOA we are paying for technology.
  • External vs. External and/or Internal Point-of-View – In Cloud Computing, the services that organizations get are from external organization but in SOA these services can be either from external organizations (e.g., Yahoo! Pipes) and/or internally (e.g., system-to-system integration between two or more systems).
  • IaaS, PaaS, SaaS vs. Software Components – In Cloud Computing, the services provided can go up and down the stack but in SOA the services are software components.


  1. Raines, Geoffrey. “Cloud Computing and SOA.” The MITRE Corporation. The MITRE Corporation, Oct. 2009. Web.
  2. Gedda, Rodney. “Don’t Confuse SOA with Cloud.” CIO. CIO Magazine, 28 July 2010. Web.



People who have not heard the term “gamification” before perceive it to be about games but this is inaccurate. In order to address this misperception, Deterding and his team researched the various uses of gamification and came up with a definition that states gamification as “the use of game design elements in non-game contexts.” (Deterding et al.) We can see from this definition that gamification distinguishes itself from games by implying that while games are for fun without real world implication but gamification has implications in the real world. Broadly speaking, gamification continues to be applied to various areas of business, technology and society.

For this research paper, the focus is on the business and technology aspects of gamification. This leads to the definition by Gartner that states gamification as “the use of game mechanics and experience design to digitally engage and motivate people to achieve their goals.” In this definition, game mechanics refers to the points, badges and leaderboards that are applied to computers, smartphones and wearables and experience design refers to game play, play space and story line(s) to motivate people to change behavior. What this means is that gamification is used to change the norms, attitudes and habits of people and organizations from a current state to a future state through today’s technologies.

For organizations, gamification is looked at from a strategic perspective in the following ways:

  1. External Purposes: Organizations use gamification externally for customer engagement. The idea is that if customers are more engaged then this can create customer loyalty. This customer loyalty can lead to brand awareness and consequently more products and services being sold. In this sense, organizations use gamification as a sales and marketing tool that is often applied using websites, online communities, mobile devices and other digital devices.
  2. Internal Purposes: Organizations use gamification internally for employee engagement. The idea is that if employees are more engaged then this can help achieve various organizational objectives. Thus, in this sense, organizations use gamification in hiring, training, product enhancements, innovation, performance improvement and change management that is often applied through technologies in the organizations.

Initially, when gamification started to pick up steam in 2010, organizations were only interested in its external uses up until 2013. However, Gartner states that this started to change in 2014 where now organizations are interested in using gamification for external and internal purposes. This change is using gamification is reflected in Gartner’s Emerging Technologies Hype Cycle where in 2014 gamification moved to Trough of Disillusionment from 2013’s Peak of Inflated Expectations. By simply looking at the 2013 and 2014 Hype Cycles one might think that gamification’s days are numbered. But a closer look reveals that organizations are beginning to understand how gamification can be used to not only increase the bottom line but also how it can help them transform themselves.

Thus, in a nutshell for our purposes, gamification for organizations is the use of game design thinking to change behaviors internally and externally through technology.

2013 Emerging Technologies Hype Cycle

Figure1: 2013 Emerging Technologies Hype Cycle

2014 Emerging Technologies Hype Cycle

Figure 2: 2014 Emerging Technologies Hype Cycle


The idea of using gamification for external and internal purposes is not new. One of the earlier applications of gamification can be attributed to the Cracker Jack company who more than 100 years ago introduced the idea of having a toy prize in its boxes. While basic but it worked and soon other organizations started to follow the same route. However, currently gamification implies making real-world activities more game-like through the use of technological advancements. In order to understand the current view on gamification, we have to look at a few significant events that have occurred over the last 40 years. These include:

  • In the 1970s, the invention of the microprocessor that led to the PC revolution
  • In 1980s, the development of the first massive multiuser computer game called Multi-User Dungeon (MUD) created by Richard Bartle at University of Essex in England
  • In 1984, Charles Coonradt explored why people were more engaged at sports and recreation activities that at work which led to the following 5 principles of making work more game-like:
    • Clearly defined goals
    • Better scorekeeping and scorecards
    • Frequent feedback
    • A higher degree of personal choice of methods
    • Constant coaching
  • In 2002, the Serious Games Initiative was launched that brought together the private sectors, academia and military to develop battlefield simulations for training
  • In 2010, Jesse Schell’s presentation goes viral and starts to resonate with the business community.

Today, the video games industry is a $70 billion industry. Over these last 40 years, it has figured out how to keep people engaged and motivated for longer periods on time. During this time we have seen the explosion of computer devices being used and the creation of the Internet that has given rise to video games that can be played simultaneously online with many users. From a business perspective, some of Coonradt’s principles have been used for creating performance reviews and loyalty programs. As time passes we will see technology and business fields coming together for gamification purposes.


The Good Examples:

Gamification can be beneficial for organizations that know how to correctly and thoughtfully use it for internal and external purposes. Following are some examples of where gamification was used to produce positive results for organizations:

  • Increasing System Usage and Engagement: Salesforce sells Software-as-a-Service (SaaS) subscription services to organizations. One of these subscription services is the Customer Relationship Management (CRM) service in the cloud that is mostly used by the customer’s sales staff. In order to increase the adoption of the CRM services with the customer’s sales staff, Salesforce turned to gamification by creating a game called Salesforce’s User Hunt for Chicken. In this game, game mechanics such as status changes were used to motivate sales staff to use more of the CRM services features. In one case, the customer’s sales staff compliance in using the CRM service increased by 40%. This example shows how gamification was used to create a win-win-win scenario where (1) the customers’ sales staff wins since they become more aware of how to utilize the CRM service to its full potential by using its features, (2) the customer wins since their sales staff is using the features that they have already paid for and (3) Salesforce wins since it creates opportunities for itself to stay competitive by keeping the customer’s sales staff reliant on their CRM services.
  • Increasing Customer Interaction: Dodgeball was the predecessor to Foursquare that used check-ins for events. It was sold to Google in 2005 but after four years of getting very little traction, Google decided to shut it down. Foursquare on the other hand took the check-in concept and used gamification to gain traction. They did this by using game mechanics such as badges and perks to incentivize users to check-in. Today, Foursquare is one of the most popular applications in the world. It has 50 million registered users, 1.9 million listed businesses and it has crossed 6 billion check-ins. This example shows how gamification was used to enter and dominate a market by creating value for the users in the forms of perks.

The Bad Examples:

Gamification can be detrimental for organizations that don’t know how to correctly and thoughtfully use it for internal and external purposes. Following are some examples of where gamification produced negative results for organizations:

  • No Value for Users: Google’s social network is called Google+. Google+ has 300 million users who spend an average of 7 minutes per month on the social network. In order to increase the time spent on Google+, Google decided to use gamification. Thus, Google decided to use game mechanics such as badges to give to Google+ users who read Google News. The problem with this approach was that it was not clear what value these badges provided to the users since they were private and added no value to the search being conducted. This example shows how a half-baked attempt at gamification resulted in creating something that users did not find appealing.
  • Lack of Proper Cost Benefit Analysis: Marriot was interested in recruiting and training associates for its management program. In order to do this they turned to gamification by creating a game called My Marriot Hotel. While the initial idea was not bad but as Kevin Kleinberg states in his article, the amount of money spent in creating this game versus the number of people who would actually become managers at Marriot seems to be miscalculated. This example shows gamification is not a remedy for management’s lack of foresight into the actual user base versus the amount of money spent on gamification.


Gamification as an emerging technology has its risks and challenges as any new technology would have but since it is heavily dependent upon people either as players/users or game designers, it needs to consider areas of human psychology and motivation as well. Following are some of the risks and challenges of gamification:

  • Points Obsession by Players: The competitive nature of gamification in organizations can result in players being too obsessed with the points, badges, statuses rather than understanding why the game is being played in the first place (e.g., training, change management, innovation etc.). The main question to ask here is, “How do you prevent players from becoming obsessed with just collecting points?”
  • Manipulation: Gamification has elements of manipulation in it either by the game designers and/or by the players themselves. These are discussed below:
    • Game Design: A game with a predefined agenda to change behaviors can be considered by some to be manipulation by the game designers. The main question to ask here is, “How do you avoid manipulation baked into gamification that is based on the game designer(s) biases?”
    • Players: The competitive nature of players can result in them cheating the system that is supposed to be equal for everyone. The main question to ask here is, “How do you prevent players gaming the system?”
  • Regulatory and Legal Issues: Depending upon the nature of gamification in the organization, there might be laws and regulations that the organization needs to abide by. If careful attention is not paid, this can result in fees and fines. The two main questions to ask here is, “What are the federal, state and local regulatory concerns that need to be considered?” and “Are there any laws/rules that are applicable nationally and internationally?”

While the above questions are not a comprehensive list but it is a starting point when organizations are thinking about how gamification can be used externally and internally.


Based on research done on gamification, its examples, its risks and challenges, I recommend the following:

  • General Recommendations: Following are some general recommendations that should be considered for both external and internal uses of gamification:
    • Clearly defined goals: For organizations and players, it should be clearly stated what the gamification initiative is intended to accomplish.
    • Not a Panacea: While there is great promise in gamification for organizations but it should not be considered an answer for everything. Blindly applying gamification without thinking through organizational repercussions can be a recipe for disaster.
    • Measure, Measure, Measure: Gamification is used for organizational improvements whether it is used for external purposes or internal purposes. Since it is about organizational improvements, its progress needs to be measured, feedback should be obtained from its users and should be updated as needed.
  • External Use Recommendations:
    • It is about Values: When using gamification, organizations need to understand that it is not a one-way street but a multi-way street where value needs to be created for everyone involved.
  • Internal Use Recommendations: When using gamification, organizations need to create a balanced approach between intrinsic considerations and extrinsic rewards. This balanced approach should include understanding the organization in terms of Strategies, Politics, Innovation, Culture and Execution (SPICE) factors as shown below:
SPICE Factors

SPICE Factors


As we have seen throughout this research paper, gamification has implications across many different aspects of an organization whether it is applied externally and/or internally. Due to the continuous improvements in technology and our desire to be better, there will always be new business models where gamification can prove to be useful. Here organizations have a choice of ignoring it as a passing fad or getting ahead of it to understand how it can help them transform themselves. Thus, gamification will continue to be an emerging technology regardless of where it stands in Gartner’s Hype Cycle.


  1. Sebastian Deterding, Dan Dixon, Rilla Khaled, and Lennart Nacke. 2011. From game design elements to gamefulness: defining “gamification”. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments (MindTrek ’11). ACM, New York, NY, USA, 9-15. DOI=10.1145/2181037.2181040
  2. “Gamification – Gartner IT Glossary.” Gartner IT Glossary. Gartner, n.d. Web.
  3. “Gartner’s 2013 Hype Cycle for Emerging Technologies Maps Out Evolving Relationship Between Humans and Machines.” Gartner’s 2013 Hype Cycle for Emerging Technologies Maps Out Evolving Relationship Between Humans and Machines. Gartner, 19 Aug. 2013. Web.
  4. “Gartner’s 2014 Hype Cycle for Emerging Technologies Maps the Journey to Digital Business.” Gartner’s 2014 Hype Cycle for Emerging Technologies Maps the Journey to Digital Business. Gartner, 11 Aug. 2014. Web.
  5. “About Us.” About. Foursquare, n.d. Web.
  6. Shaw, Elizabeth. “Elizabeth Shaw’s Blog.” Gamification: Defining A Shiny New Thing. Forrester Research, n.d. Web.
  7. Werbach, Kevin. “Coursera – Gamification.” Coursera. Coursera, n.d. Web.
  8. Krogue, Kevin. “5 Gamification Rules From The Grandfather Of Gamification.” Forbes. Forbes Magazine, n.d. Web.
  9. “Getting past the Hype of Gamification.” PwC. PwC, n.d. Web.
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