How to Kill Bad Projects

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It is an open secret how hard to kill projects in development. In the Harvard Business Review article “Why Bad Projects Are So Hard to Kill“, professor Isabelle Royer says that many projects are hard to kill because of a “fervent and widespread belief among managers in the inevitability of their projects’s ultimate success.” The desire to believe in something is primal. The excitement and exuberance associated with a project typically originate with the project champion, whose unyielding conviction that the project will succeed is often based on a hunch rather than on strong evidence. The champion’s exuberance spreads because others also want to believe, especially if the champion is charismatic and well networked within the company.

Even worse, when a project is going monumentally off the rails, people and organizations keep adding resources to the project despite all the evidence of impending disaster. Such an escalation of commitment that throws good money after bad is known as sunk cost fallacy.

The million dollar question is how to find out if your team is victim to the subtle development of entrapment. Rita Gunther McGrath and Ian C. MacMillan offer a simple way in their “Discovery-Driven Growth” methodology. According to them, each team member should answer the following “yes” or “no” questions:

  • I fell we will lose the respect of others if this project is shutdown — nobody respects a failure.
  • Giving up now would just be an admission of weakness.
  • Stopping this project would have a negative effect on my career: bonus, raise, promotion, or position.
  • Stopping this project would have a negative effect on the rest of the team’s careers: bonus, raise, promotion, or position.
  • We made a public commitment to this project.
  • It will destroy our record of past success.
  • We have had some good results — it would be premature to stop the project now.
  • There will be a big payoff if we succeed in the end.
  • We’re nearly at a turning point; it would be a shame to stop now, when we are so close.
  • We have already spent a lot of time and money, which would be wasted if we stopped now.
  • It would cost us more to stop now than it would to finish the project.
  • We won’t get anything back if we close the project now.
  • Our part of the business is counting on us to succeed.
  • People who want us to fail (rivals, enemies, competitors) will gloat.
  • A lot of people are depending on us to succeed here.
  • A lot of people left steady, secure positions to join this project.
  • We’ve made commitments to outside parties that depend on the success of the project: investors, suppliers, distributors, customers.
  • We’ve made commitments to inside parties to continue with the project: the board, top management, other divisions, employees.
  • The firm’s reputation with banks and investment analysts has been staked on the success of this project.
  • The firm’s reputation with regional, national or foreign government officials has been staked on the success of this project.

If you have a third or more “yes” answers, your team is at risk of escalated commitment. Each of these questions reflects a reason why people have consciously or unconsciously continued committing their talent and resources to projects that reasonably should have been shut down. If these subtle pressures are overcoming the better judgment, we have to make a tough call.

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The Future Business Model of Payroll

 ADP Paypal
Money Movement  $1.7 trillion  $354 billion
Revenue  $12.21 billion  $11.27 billion
Profit  $1.75 billion  $1.42 billion
Market Cap  $43.3 billion  $59.7 billion
  • ADP revenue includes full HCM services besides payroll.

Notice something here? ADP moves a lot of money than Paypal, but makes less revenue on money movement (less the revenue from other HCM services). It has a smaller market cap too. Why? Well, ADP is in the business of solution shop and value add process while Paypal is a facilitated network.

There are three general types of business models: solution shops, value-adding process businesses, and facilitated networks. Solution shops are institutions structured to diagnose and recommend solutions to unstructured problems. Almost always, solutions shops charge their clients on a fee-for-service basis. The value-adding process transform inputs of resources into outputs of higher value. Because value-adding process organization tend to do their work in repetitive ways, the capability to deliver value tends to be embedded in processes and equipment. The facilitated networks operate systems in which customers buy and sell, and deliver and receive things from other participants. Much of consumer banking is a network business in which customers will make deposits and withdrawals from a collective pool.

When on boarding clients, ADP is in the mode of solution shops where a heavy team executes a time-consuming and highly customized process for each major client. Once a client is on board, ADP performs the repetitive payroll service with computers in every pay cycle. In return, clients pay ADP service fees. No matter how big or small the paycheck is, for CEO or for average Joe, the service fee is the same. In contrast, Paypal is a facilitated network and the service fee is proportional to the transaction size just like credit card services.

Given its dominance in payroll business, it is very challenging for ADP to achieve high growth in this area by grabbing more market share. But high growth is still possible by changing the business model with the above analysis. That is, ADP should become a facilitated network, more specifically a bank!

It sounds ridiculous but ADP has a unique advantage to be a great bank by managing the risk well. The open secret is its massive payroll and HR data. By knowing the incomes in advance, work history, performance metrics, time management data, etc., ADP can reduce the risk a lot with data science. Another great news is that there is a huge market. Many households try to make a go of it week to week, paycheck to paycheck, expense to expense. In fact, 63% Of Americans don’t have enough savings to cover a $500 emergency. Often they have to pay a very high borrow rate to meet a small financial need. With the good risk management based on its data, ADP can potentially help us with much lower rate. It is a win-win for everyone.

To learn more how this works, please check out Payroll: An Overlooked Area in Fintech.

 

Choosing The Best Tools

India is one of few nations that can buy military equipment from both western world and Russia. When building their destroyers, India does take this advantage to install best sensors from multiple countries to their ships. However, this choosing-best-tools-for-each-problem approach is an engineering nightmare. It is extremely challenging to make sensors from Russia, Italy, France, India, etc. work smoothly together due to various compatibility issues.

The issues are not in each module itself. Essentially every large engineering project is an integration work. We can easily lose the big picture when we focus on the performance attributes of each module. So be careful next time when your architect shows you a system architecture like the below.

Disruptive Innovation: When and Where?

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In the theory of disruptive innovation, Clayton Christensen argues that the incumbent companies introduce new and improved products year-by-year with the sustaining innovations, which eventually overshoot the performance that some customers can use because companies innovate faster than customers’ lives change. Overshooting creates opportunities for firms to change the basis of competition in order to earn above-average profits. After functionality and reliability have become goo enough, for example, the next competition dimensions could be convenience, customization, and price, etc.

This theory has achieve tremendous success with strong supports in many business cases. Few academic management theories have had as much influence in the business world as the theory of disruptive innovation. However, the tricky part is how to find out when the overshoot happens and where the new competition dimension is. Even Christensen himself and masters like Andy Grove made mistakes on them.

As an early adopter and supporter, Andy Grove credits the theory of disruptive innovation as having been the main impetus for Intel introducing the Celeron processor in 1998. However, overshooting didn’t really happen in desktop computing in 1990s and early 2000s. AMD never posted strong challenges to Intel with cheaper and lower-performance CPUs. On the other hand, AMD really threaten Intel’s dominance in 2003 with their Opteron processor, which has superior performance to Intel’s Pentium 4 and Xeon. AMD missed the chance of overtaking Intel then because of their limited manufacture capability.

In the book Seeing What’s Next (2004), Christensen argued that customization and convenience would be the new competition frontier in semiconductor industry. He took Tensilica as an example. Tensilica allows engineers to customize their own systems-on-a-chip on a website.  Xilinx is another example that lets users to decide what specific functionality they need. However, convenience and customization have not become the decisive factor for customers to choose processors.

The true threat to Intel is mobile ARM processors. With the introduction of iPhone, ARM has become the king of personal/mobile computing due to its energy efficiency. More than 95 billion ARM-based chips have been shipped to date. Recently, ARM-based server chips are introduced by industry giant Qualcomm and several startups, which may significantly lower the utility bill of data centers. If ARM finally gets into data center successfully, Intel will lose its last hold.

The competition dimension of processors did change as Christensen predicted. However, it is not because of overshooting but because of the shift of computing paradigm. Since processors are not used by the end users directly but are only a module of computing devices, we should not try to find the new competition dimension by only looking at their attributes but have to see the big picture of ecosystem.

Risk Aversion and Sunk Cost Fallacy

In his book Misbehaving, Richard H. Thaler tells an interesting story. In a class on decision-making to a group of executives from a company in the print media industry, Thaler puts the executives to a scenario: Suppose you were offered an investment opportunity for your division that will yield one of two payoffs. After the investment is made, there is a 50% chance it will make a profit of $2 million, and a 50% chance it will lose $1 million. When Thaler asked who would take on this project, only three of twenty-three executives would do it. Then he asked the CEO how many of the projects would he want to undertake (suppose all projects were independent, that is the success of one was unrelated to others), the answer is all of them! Continue reading

Agile Software Development: China Navy

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The best demonstration of agile software development is probably the modernization of China Navy. Following a “Run Swiftly in Small Steps” strategy, China Navy has undergone a stunning modernization push that puts it near parity with the US. Look below how China Navy has steadily improved each class of their destroyers in gradually shorter and shorter time. They are the grand master of agile development. Continue reading

Payroll: An Overlooked Area in FinTech

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payroll-image

A lot of brain power and money have been poured into FinTech, especially lending and payment areas. These are indeed exciting areas with new business models and technologies. On the other hand, people rarely associate the sexy FinTech with payroll services. Although it may sound boring, payroll is actually an overlooked gold mine for innovators. Traditionally, payroll service companies make money by service fees. New HCM service companies such as Zenefits work as insurance brokers while providing free payroll and HR services. But if we lean under the hood and look at the process, there is an interesting opportunity. Continue reading

Smile 1.2.0 Released!

Dear Smilers,

We are proud to announce the release of Smile 1.2.0.

The key features of the 1.2.0 release are:

  • Headless plot. Smile’s plot functions depends on Java Swing. In server applications, it is needed to generate plots without creating Swing windows. With headless plot (enabled by -Djava.awt.headless=true JVM options), we can create plots as follows:
    val canvas = ScatterPlot.plot(x, '.')
    
    val headless = new Headless(canvas);
    headless.pack();
    headless.setVisible(true);
    
    canvas.save(new java.io.File("zone.png"))
    
  • All classification and regression models can be serialized by
    write(model) // Java serialization
    

    or

    write.xstream(model) // XStream serialization
    
  • Refactor of smile.io Scala API.
    • Parsers are in smile.read object.
    • Parse JDBC ResultSet to AttributeDataset.
    • Model serialization methods in smile.write object.
  • Platt scaling for SVM
  • Smile NLP tokenizers are unicode-aware.
  • Least squares can handle rank deficient now.
  • Various code improvements.

Unicorn 2.0 is Released!

unicorn

There are a lot of NoSQL databases out there. We have used or tried out many of them. We love a lot of cool features they offer. However, we also face many unique challenges in a highly regulated HCM SaaS business. So we have kept looking for the unicorn database to meet our requirements. Unfortunately, none of existing solutions fully address all of our challenges. So we asked ourselves two years ago if we can build our own solution. It was how Unicorn database was born. Unicorn is built on top of BigTable-like storage engines such as Cassandra, HBase, or Accumulo. With different storage engine, we can achieve different strategies on consistency, replication, etc. Beyond the plain abstraction of BigTable data model, Unicorn provides the easy-to-use document data model and MongoDB-like API. Moreover, Unicorn supports directed property multigraphs and documents can just be vertices in a graph. With the built-in document and graph data models, developers can focus on the business logic rather than work with tedious key-value pair manipulations. Of course, developers are still free to use key-value pairs for flexibility in some special cases.

During the past two years, we have learned a lot and made a lot of improvements, which resulted in Unicorn 2.0, which we are excited to open source to the community. Continue reading