Standards of Data Analyze

The standards of data analyze follow the steps of CRISP-DM (CRoss Industry Standard Process for Data Mining)

• Business understanding
• Data Understanding
• Data Preparation
• Data Modeling
• Evaluation
• Deployment

Business understanding

To achieve your objectives, NeoVision undertakes a pivotal step by comprehensively recognizing and analyzing the specific requirements of your business, ensuring a thorough understanding of your implemented procedures

NeoVision's comprehensive understanding of the process enables us to accurately identify the required data and determine the most effective methods for analyzing and deriving insights from it

Data Understanding

With the necessary data identified, our next phase involves collecting the initial data, exploring it based on the questions posed, and carefully examining the collection

Examination of data sets is to:

• categorize them based on being Quantitative or Qualitive
• recognize the relations among them
• discover how clean or dirty are they

after the examination, the quality of data sets can be verified

Data Preparation

It can be said that this stage is one of the most important and time-consuming parts of any project; In this stage, the main focus is on turning the information that is useless for modeling and analysis into useful information;

The final outcome of this step provides us with the necessary datasets for modeling; This process involves four straightforward and essential steps:

• Data Selection: In this step, we identify and select all the datasets that will be used in the subsequent stages
• Data Cleaning: To ensure the integrity of our analysis, we proactively identify and address uncertain data, outliers, and duplicate entries; If any of these elements lack meaningful values, they are promptly removed, safeguarding against biases in our findings
• Data Construction: In certain scenarios, a series of features may not be given directly, which can be extracted by using other data; For instance, if provided with the price and sales amount of a product, we can calculate the total sales revenue
• Data Integration: In a business context, information from various sources may need to be brought together to create a unified view; For instance, sales data and financial information may reside in separate databases; We consolidate this data to provide a comprehensive view of the business

Data Modeling

To effectively align the project with your business goals, we employ a range of diverse techniques; Each method operates within its own framework, offering distinct approaches to problem-solving and gathering information

We utilize different techniques to model the information and thoroughly analyze the results in order to establish an optimal model. This step is closely linked to the previous one, and it may be determined through testing multiple models that certain information preparation issues need to be addressed in order to achieve desired outcomes

Evaluation

At this point, we test the selected models in practice to see if they can meet the main targets of the project; The results of the experiments that are carried out can help to improve the model in reality so that points that were neglected in the initial modeling can be identified to help finalyze the model; Finally, the output of this level will be used if this model can lead to business decision making

Deployment

Creation of the model is generally not the end of the project; Usually, the knowledge gained will need to be organized and presented in a way that the customer can use it; Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process

In many cases it will be the user, not the data analyst, who will carry out the deployment steps; In any case, it is important to understand up front what actions will need to be carried out in order to actually make use of the created models

A model is not particularly useful unless the customer can access its results

The complexity of this phase varies widely; This final phase has four tasks :

• Plan deployment: Develop and document a plan for deploying the model
• Plan monitoring and maintenance: Establish a thorough monitoring and maintenance program to prevent problems during the operational phase (or post-project phase) of a model
• Produce final report: The project team documents a summary of the project which might include a final presentation of data mining results
• Review project: Conduct a project retrospective about what went well, what could have been better, and how to improve in the future

Your organization’s work might not end there...

As a project framework, CRISP-DM does not outline what to do after the project (also known as “operations”); But if the model is going to production, we must be sure to maintain the model in production. Constant monitoring and occasional model tuning is often required