Wednesday, April 26, 2017

The Scientific High-level Strategic Planning on Big Data Province

The article “Bring Science to The Art of Strategy” systematically lays out the entire steps when designing the new innovative strategies based on the hypothesis-driven methodology. The strategists should move the focus from the facial issues to the root causes and different solution choices with various possibilities. By listing the conditions and barriers, companies can evaluate trade-offs and then set the priorities to scientifically verify the conditions through all sorts of tests before making the final decision. “In this way, this approach takes the strategy-making process from the merely rigorous (or unrealistically creative) to the truly scientific.”

All the steps not only align with the tool kits and methodology that consulting firms apply for each strategy project, but also provide me with many details and thinking-process I have never though before. It is very interesting to find the interconnection of those key takeaways with my past experience of designing high-level big data development strategy proposal for a local Chinese province.

The client is a local Chinese province located in inner land China. The province already had massive big data infrastructure such data center but needed a large amount of high-level big data companies to enter the market and become a real big data province. The local government had no realistic plan, industry exposure or global networking resources, but only the ultimate goal. It was our job that to come up with a realistic development five-year plan after two-month project period.

The early stage of the project was all about high-level strategic planning exactly like the article. Several details match with the principles and steps discussed in the article. For example:

  • The brainstorming session last for a week which was long enough for us to reach a strategic and creative consensus that big data industry can only be built on other “physical” industries, or it would have no value and sustainability. Then the choice was which traditional industries are suitable for big data transformation.
  • We then examined client’s supporting industries to see which possibilities we had. We paid special attention to both traditional strong industries such as white wine and agriculture industries, and relatively new arenas such as traveling and education.
  • After having the available industry list, the manager led us to list the conditions for a successful big data transformation. For example, the industry shall be growing and have potential value for big data technologies and applications to add on. The industry also needed lead players to quickly cooperate with big data companies.
  • We identified several major potential barriers such as lack of qualified big data workers, difficulty for people to apply big data model and long payoff period for some particular industries.
  • We refined our market research by further connecting conditions with barriers to see how they interacted. Our research team helped us to design survey and interview questions to conduct the on-site tests before deciding which industries to dig deeper to find the detailed action plan.

This scientific methodology worked very well in that project as it provided a nice framework to reach a proved decision. It was also interesting to see many senior consultants play with this framework based on their rich project experience. For example, when speaking of the test of workforce shortage, one of my colleague quickly responded that we should take a look at how railway runs between this province and near costal big cities where the large human capital locates. Overall, I am very glad to understand this big picture better.

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