Simitri Hong Kong Limited was invited to perform a Big Data & Business Analytics Workshop on 30th April 2018. I was one of participants and would like to summarize some contents of the workshop. Mr. Philip Chan (Senior Consultant, Simitri) was our trainer. The workshop objectives were to gain insight into “Big Data” and how to use to make better decisions. Mr. Philip Chan said Big Data had characteristic on Bias (偏向性) but not Absolute (絕對性). It based on past data but did not consider future trend.
Big Data could be described by 5 Vs and they were Volume, Velocity, Variety and Veracity, as well as Value.
Then Mr. Philip Chan shared customer behaviors that mainly had four hears and they were greedy (貪心), curiosity (好奇心), vanity (虛榮心) and fear (恐懼心). After that he briefed the benefits of Big Data included three main reasons included “Increase Shareholder Value”, “Increase Customer Value” and “Increase Employee Value”.
The key steps used Big Data:
iii) Manage and ControlMr. Philip Chan then briefed two types of thinking for Big Data that one is Intuition and the other is Analysis. He explained the word of “context” in Intuition thinking that related to feeling and the other similar word “content” that related to fact.
After that he mentioned the Big Data Analysis Framework as follows:
Discovery – Developing the process and collecting the data
Insights – Organizing, analyzing, and visualizing the data
Actions – Making decisions based on the data
Outcomes – Executing the decisions and measuring results
Planning sequence was Outcomes, Actions, Insights and Discovery.
Reversely, implementing sequence was Discovery, Insights, Actions and Outcomes.
During the exercise, trainer demonstrated that “When you focus on the details, you lose track of the big picture.” Indicating that understanding the Problem or Outcomes required you to see the big picture (zoom out) and focus on the details (zoom in). The six common mistakes when defining business problems, objectives and/or goals were showed as following diagram.
The three key components of an effective problem statement was introduced and they were “Object”, “Deviation” and “Evidence”. Then he briefed the goal statement using SMART (Specific, Measurable, Attainable, Relevant and Time-bound).
He also introduced Decision Matrix of Impact vs Effort for Action stage. He suggested to start from Easy to Difficult.
For Insights, four steps for collecting the data showed as follows:
Step 1: What do you really need to know in order to decide?
Step 2: What kind of data are you going to measure?
Step 3: Where will the data come from?
Step 4: What analytical software and tools will you use?And then he briefed some common tools for Big Data.
Finally, trainer explained some common mistakes when using Big Data to make decisions as follows:
“Good Data vs Bad Data”
“Correlation vs Causation”
At the end, Mr. Philip Chan presented certificate to us and we took a photo for memory.
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