Models often used by operators (3): Ability model

Pyramid principle

The pyramid principle follows four basic principles for work summary.

1. Conclusion: Express a central idea and placed in the forefront.

2, the above is unified: Each argument is summarized in the next level argument.

3, classification group: Every group is the same logical category.

4, logic progress: Each group is arranged in a certain logic order.

3W gold circle rule

Thinking mode is in the outermost layer, know what "what" you have to do, but rarely think about how to do it better.

People in the middle layer know how to "How" do better completion of tasks and goals, but rarely think about the reason for this.

Only people in the top circle are clear why "Why" do this. Why is the core nature of this matter, others are all around this center.

PDCA cycle

The meaning of the PDCA cycle is to divide quality management into four stages, namely:

P: Program (Plan) includes the determination of the principles and objectives, as well as the development of activity planning.

D: Execute (DO) to design specific action plans according to known information; then perform specific actions according to the plan.

C: Check (Check) summary results, clear results, find out the problem.

A: Processing (ACT) processes the results and problems of the inspection, and there is no solution to the next PDCA loop.

Kiss restraint

KISS is not a English word, but a scientific project replica method to promote the next activity to be better launched.

Keep (can be maintained): The reap-on activity is good, follow-up activities can continue to maintain.

Improve (requires improved): What links / factors lead to unsatisfactory activities, and it is necessary to improve in subsequent activities.

START (requires start): Which links are not implemented in this event, and follow-up need to start doing.

STOP (need to stop): Which behavior is unfavorable to the activity and needs to stop.

The KISS principle refers to the principle of simplicity in design. Summarizing the experience in the design process, most systems should be kept simplicity and simple, without incorporating unnecessary complexity, such system operation results will be optimal; therefore, the simplicity should be designed The key target is to avoid unnecessary complexity.

Data analysis six-step method

Data analysis also requires certain skills, don’t indulge in the ocean of data, data is tool, we should use tools.

1. Propose a question: What is the first question of our solution?

2. Make a hypothesis: On this issue, what is our pre-hypothesis?

3. Data Acquisition: According to this assumption, start collecting data.

4, data processing: machining the collected raw data, including cleaning, packet, retrieval, extraction, etc. of data.

5. Data analysis: After the data is finished, the data needs to be integrated and cross-analyzed.

6. The result is presented: visual data, and the specific concluding information is obtained.

SMART principle

Everyone has the experience of developing goals, it seems simple, but if it is rising to the technology level, you must learn and master the SMART principle.

The goal must be specifically (Specific) and cannot be generally available.

The goal must be measurable, which is quantified.

The goal must be achieved (ATTAINABLE), but it is not much low.

The goal must be associated with other goals (Relevant), forming ductility, and ultimately achieving higher objectives.

The target must have a clear deadline, reached within a specified time, and finally determine whether the target is achieved with the deadline.

SCQA model

The SCQA model is a "structured expression" tool, which is made in the McKinsey Consultant Barbara.

S (Situation) scenario– Introduced by the scenarios familiar with everyone.

CMPLICATION conflict– The actual situation is often conflict with our requirements.

A (ANSWER) answer– Our solution is …

Postscript: The model is just a model, the theory is just theory. These can only become tools for us to use, but they cannot be a shackle that constrains our thinking. This time the theoretical model is just the first version, and we will also increase these commonly used theoretical models, and even some theoretical models will be explained alone.

Remarks: Some images come from the network, and the data is mainly collected in Wikipedia.

[Information] a little talk | intelligent data-driven rise of

CalledData-driven wisdomThe concept is new energy model new perspective collect insights from large amounts of data. This article intelligent model data-driven intelligence and more familiar with the application-driven comparison.

One of the earliest IT name is "data processing", it includes demand and processing of the data, which focus on dealing with or compute-centric dominated. Data from birth (created) to death (deletion), are within the control of most of the data in the application. Of course, after the data analysis of the data to create applications already exist (such as business intelligence process), but these applications only a fraction of the actual use of IT.

In the ‘remodeling discovery ", author Michael Nielsen discusses the data-driven intelligence, and were compared with artificial intelligence and human intelligence. He will define intelligent data-driven ability to extract meaning from the data for the computer. He will distinguish it with artificial intelligence, artificial intelligence, he said implementation of the human good at tasks designed to mimic or improve human performance (such as chess) and human intelligence (for example, our ability to process visual information). According to Nielsen’s statement, data-driven intelligence by solving different types of problems to supplement human intelligence.

Let’s IT perspective to study its meaning. Application-driven intelligence tend to create, read, update and delete data in order to achieve the initial purpose, such as managing order processing, shipping and workflow processes receivables. In contrast, the conventional data driving intelligent data (manually or machine-generated) for a secondary or additional purposes, such as performing analyzes or electronic discovery using external information collected from the network email large data files to increase sell or cross-sell customers. First create sensory information (such as a meter) or a machine / computer-generated information (e.g., logs), and then the downstream processes (which may be real-time) analysis (as appropriate).

From an IT perspective, the skill set of application development and developer may vary; From an operational perspective, service level agreements (SLA), such as performance and recoverability of data, you may have a different way planning. Resources (servers, network, storage) planning must also be different. It is familiar with intelligence applications based on application-driven, but must know how to deal with more intelligent data-driven applications, such as large data.

The world is nothing new. Data-driven intelligence (for example, using regression analysis, linear programming and simulation modelingMachine learning techniquesThe statistical analysis) has been around a long time. Later, there have been new concepts, including data warehousing, online analytical processing and data mining. The problem is that advanced analytics, business intelligence and big data companies and other terms are regarded as valuable, but they are as isolated IT silos exist. However, to see these isolated (or at most overlapping) work and consider this work from the perspective of intelligent data-driven, you can combine them in order to emphasize the importance of data-centric focus.

Yes, the concept can be mixed applications. Data-driven intelligence can be inserted into an operating system, such as retail credit card to check if there is fraud, or inserted into various points in the supply chain.

Data-driven intelligenceIs an additional point of view, it broadens our understanding, and not a substitute for application-driven intelligence. letIntelligent SoftwareContinues to grow exponentially, and increase our understanding of the value we derive.