In order to succeed in the clients project it is vital to understand the business objectives, goal and its operations to extract the significant factors involved within its functionality. This helps in ensuring the effectiveness of the outcomes and avoiding errors and false recommendations and answers. To achieve this, the data analyst should extract the primary business objectives and the relevant questions the business would like to put forth.
A good data analyst always tries to measure the success. Success can be measured in various ways such as looking in depth and getting an awareness of the customer base and taking necessary actions accordingly based on what the customers want and what they are looking for. The factors that influence the needs of the customers play a significant role in the success. The data analysis should be done in a way that it sets achievable goals and objectives to make sure that the success criterion refers to one of the objectives which are set up.
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Moving on to the next step of assessing the situation the data analyst briefs and mentions the outcomes and the resources that are required in the employee perspective and the software perspective to achieve the data mining project. It is vital to identify what kind of data is at hand to meet the business goal. After identifying that the data analyst should come up with outlines of what assumptions are made and the data analyst puts forth the potential risks that might come across during the project period and how to overcome those risks by certain recommendations and a cost benefit analysis is done.
Determining the Data mining Goals
The data mining goals describes the main motives of the project in business perspective based on demographic and statistical data available. Success is predicted accurately based on the assumptions and the data available. The business goals should be translated effectively ese it might lead to uncertain consequences.
A project plan propose the outline of what and how the data mining is dose the required time line and the procedure and the sources of data mining. It is a typical representation of the plan of action. Various tools and techniques which are going to be used are described and mentioned in the project plan. A project plan plays key role in initiating the process which puts forth the objectives and the activities of the project.
The second phase starts with the understanding of data. This step involves the observation and collection of the required data and getting an awareness of it. Familiarizing with the data and the potential problems involved are a part of this phase. This phase involves four steps a. Collection of initial Data b. description of data c. exploration of data and d. verification of the quality of data.
Collection of data
The data analyst first gathers the appropriate and required data and integrates and loads the data according to the necessity. The data analyst should be able to have an insight into the problems that might be faced during the collection of data as some of the data might be outdated and may not be appropriate. This could be overcome with the help of the suggestions and recommendation that are made earlier in the project plan to avoise sucj kind of problems in collecting data
Description of Data
This step involves the data analyst in examining the gross or surface properties of the obtained data and reporting is done accordingly on different issues such as the type and format of the data obtained, the quality and the quantity of the data. Certain questions are made after acquiring data such as, Is the data appropriate and relevant to the requirement?
Exploring the data
Various activities are involved in the task of exploring the data such as visualization, questioning and addressing. The data analyst should take proper care and explore the data to find out errors of fraud that might have been involved in the data.
Verify data quality
Always on Time
Marked to Standard
The quality of the data obtained, described and explored should be double checked before proceeding for the accuracy of the data. This helps in getting the clean content that is required for the project and the data mining process.
The phase of data preparation helps in covering all the operations to build the last data that are required to be injected into the modeling tools from the prior collected data which is at large. There are different stages involved in the data preparation. They are as follows
- Select Data
- Clean Data
- Construct Data
- Integrate Data
- Format Data
Data should be prioritized and deleted according to the relevance and requirement based on the view that the data will be put for analysis and is determined based on the data mining goals.
The data analyst should take proper care to make sure that the data acquired is clean and appropriate, various methods and techniques are used by the analyst to refine the required data through the analysis of modeling. The verification of data which is done previously will help in addressing the issues faced earlier.
Construction of data plays an important role in the phase of Data preparation. New records are prepared based on the ones they have earlier. It is just like making new and dummy copies of records for ex size width and length. It helps in getting intense data rather than genralising data.
The process of integrating data involves mixture of content from various sources, tables or records. Information related to the same thing which are scattered are gathered. These records can be added together to make a new table or record. It is nothing but combination of data related to the same object.
Formatting data involves restructuring or redesigning the data in a systematic and logical The changes would not be to complicated in fact makes te data more clear and understandable. The changes in the data should sometime put forth the data mining questions.
In the process of modeling different types of modeling techniques are used and applied to obtain the accurate values. Based on the form of data some techniques requirements change.The various kinds of stages involved in the process of modeling are
Selecting the modeling Technique:
In this first stage different modeling techniques are selected and used for application. Things like decision tree building and others should be used fr analysis. If certain assumptions are made those are to be carefully recorded.
Generating the design for the test:
After the designing of a model and using techniques the data analyst should check the reliability, accuracy, quality and validity to check the strength of the model. Usually the train and set process is used to determine the model and its working.
Build the model:
Building of the model involves the data analyst in running the model and checking its capability on the prepared data to make more models and enable them.
Assessing the Model:
The data analyst determine the models based on his idea on domains and the criterion for the success of the models is evaluated. He later then judges the reliability and success of the models based on his technical knowledge. But it is always a good suggestion to contact the business analyst as well to get a clear picture and idea and to interpret the problems in the data in business context.
Before going ahead with the models designed and checked by the data analyst it is good to double check and evaluate the models and see if its performance is helping in achieving business objectives. Every business issue should be thoroughly covered if not in that contect the model should be re assessed and evaluated. These are the stages in the evaluation process
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By evaluating the results it determines whether if the model helps in meeting the business objectives or not. If not why is it deficient it doing that is evaluated. Certain main aspects such as if time and budget constraints permit or not should be looked into and considered. This also helps in identifying future challenges and information for further planning.
Stringent process is involved in data mining and this process shows how care is taken to make sure that it is done effectively. A proper review is done again to assure that the models and the data are accurate and perfect. This also helps in identifying if any aspects or things have been overlooked.
Determine further steps:
At this peak stage the manager of the project should take a decision whether to go ahead and finish the project and move further to deployment of the project or to start new data mining projects.
The job is not just done by designing a model. The actual task also involves in helping the customers and making sure that they are able to use it. Proper presentation of the working mechanism should be explained and put forth. Based on the requirement and the intensity of the requirements the deployment can be harder or easier with the level of complexity.
The following are some of the stages in Deployment process
To be able to put forth and come up with the data mining results and make sure that they align with the business helps in developing a strategy for deployment
Plan Monitoring and Maintenance:
Proper planning should be done in order to maintain and monitor the data mining results as they are the day to day part of the business nd its environment.
Produce Final Report:
The data analyst should come up with a detailed final report which carries out the summary of the whole project and the experiences that are faced all the way till the completion. This report puts forth all the deliveries and summaries and organizes the results.
Review the project:
The data analyst reviews the project describing the potential ares of development and improvement. This also includes the summary of interviews that were taken during the process and the individual participants. The views and experiences of the members involved in the project are also mentioned in the report explain the various experiences and difficulties faced.