5 Lessons Learned From The Steering Committee

Steering Comittee

The team building article is only about the lesson learned during multiple projects. Today I will discuss only the steering committee. It is not my intention to discuss the importance or the function of the steering committee.

Lesson learned.

1. Make the fast decision

2. Having the ability to investigate the reports or take the feedback from the assigned sub-team to understand the ground reality of the project

3. Open door policy

4. Set the project exit criteria

5. Set the project “Go Live” criteria

  1. Make the fast Decision:

Decision-making is a critical task for the committee. It has been observed that everybody in the team wants to play safe. Nobody wants to take the risk to decide on a time. However, it proves that the project always needs quick decisions to roll out the outcomes.

2. Investigation or the project on the ground reality

Many team members are lazy or have big egos not to ask or investigate the progress report or validate the ground reality. It may have multiple reasons why they are not doing it.

3. Exit and Success criteria

At the beginning of the project, it has to be decided and multiple factors to get out of the project if it is not viable anymore. It means include the cost, time duration, or any other factor that can contribute to it.

Similarly, the project success factors should be defined clearly early in the project charter. It isn’t easy to meet the 100% objective of any project. However, this is the steering committee’s responsibility to define or agree on the project’s success criteria.

This is not a comprehensive list of steering committee failure points, but I have limited observation on many projects. This article is not to make you agree or disagree but to inform you, which could be the case.

Kamran Ahmed

Data is The New Oil (Kamran)

data is the new oil. These simple words have two deep impacts. On the first side, it means that data is going to be one of the most valuable commodity of the 21st century. On the second side, it also means that regulators are going to have to focus on how data is being consumed, extracted and monetized by individuals and companies. Now, data is not something new and data has been around for the last few decades and whilst two thousand represented arising massive tech companies which we still see today such as the GAFAs, Google, Amazon, Facebook and Apple, those tech companies have approached data in a total different way. For example, you may have heard about the expression,

If it’s free, you’re the product.

And that sentence really means one thing. Is that the personal data that you generate is more valuable to the company and this is why they’re giving you a free product. Tech companies have understood that and because data is core to their business, they typically have Structured data, data that is searchable, index-able and that can create value more easily. But that’s not the case for everyone. Financial institution, for them data was a by-product of their business. Financial institution make money by putting a lender and a borrower together by investing your money into the stock market but they don’t necessarily make money by your data and therefore the data you provide them, whether it’s your passport or whether it’s transactional data is very much a by-product of their own business and therefore that data is so far unstructured. The difference between unstructured data and structured data is that structured data is searchable, index-able and can be more easily monetized for financial institution or tech companies. Now, data is being used in different ways in finance. For example, it can be used for decision making, new business discovery, enhancement of productivity and regulatory compliance. Let’s take an example for each of these. Startups are typically improving their product at a rapid rate because they use the data on their product and their consumer to keep on improving the quality of services they keep delivering to you. The second one is banks. Banks are using data to do better decision making. For example, credit scoring. Credit score will impact whether a bank will originate a loan to you and if so, at what interest rate. Regulators and specifically securities regulators have been using data for a long time to monitor market and check if there’s no insider trading happening. For example, Alibaba has understood that there’s a correlation between people wearing skinny jeans and breaking phones. The reason is because of the lack of pockets. And therefore Alibaba is gradually starting to sell insurance product for phone coverage to the people buying skinny jeans on their e-commerce platform. In other words, what you have is that

data has many opportunities

data has many opportunities and data will be allowing the rise of invisible banking. For the last 100 years, banks have changed the way they were operating. 70 years ago the notion of community bank where you used to know your banker and have customized service just for you happened but it was inefficient. And then the bank consolidation happened and now we have universal banks where you’re only one of a million of a customer across the globe but tomorrow you will have invisible banks. Invisible bank will create individualization of financial product just for you even though your part of a large financial conglomerate. On the back of those opportunities you also have risks. Those risk are very much the factor that data is so valuable and

the people controlling data will now control markets

the people controlling data will now control markets. And therefore regulators have to understand how this is going to be changing in the future. The regulation of data might be one of the most important part for regulators to focus on as opposed to the regulation of algorithm because we control as individual or data, the firms control the algorithm and it might be easier to control data access than data output. 

Inspired by Hong Kong University lectures.