How The CRISP-DM Method Can Help Manage Your Next Data Science Project
Data science takes a highly top-down, solution-oriented approach to problems. As the name suggests, it is a ‘science’ and benefits heavily from having a systematic approach to the issue. Keeping in mind that data science is a more established field, there have been standards established for the area. One of the most popular and prominent methods used until today is the CRISP-DM method. CRISP-DM stands for CRoss-Industry Standard Process for Data Mining and was developed in 1996 under the ESPRIT initiative. It has been a favourite for business analysts and data scientists alike owing to its easily adaptable model. What Is The CRISP-DM Process? CRISP-DM is one of the more structured approaches to solving a problem that requires data science. More precisely, CRISP-DM focuses on the data mining part of the operation and features a 6-step process. It is worth noting that CRISP-DM has slowly begun to fall to the side, with company-based approaches taking the lead in a data science-first business world. However, the basics are still strong and flexible enough for changing technology and methodologies. IBM is one of the major practitioners of the style, and have integrated it into their data mining and text analyzer software known as SPSS Modeler. Step 1 – Business Understanding The first step of the CRISP-DM process is business understanding. This is one of the big reasons it is popular among business intelligence practitioners; a BI-first approach. This step includes the basic groundwork for the rest of the project, such as determining goals and objectives, producing a plan and planning out business success criteria. It is also important to gain an understanding of the workings of the situation, requiring a deep assessment of the situation. As the process requires data mining, it is also important to determine which features to explore and which to eliminate. The goals of the data mining procedure must also be established. This will enable the project to have a much more focused view of things, leading to less time mining data which will not be used. Along with determining where the business needs improvement, this step also shows the pain points of the organization. Knowing the company inside out is important for deriving actionable insights. Step 2 – Data Understanding One of the biggest parts of data science is, of course, handling data. A well-managed set of data sources and collection of data marks the difference between a successful project and a confusing mess. The second step of CRISP-DM involves acquiring the data listed in the project. All data relevant to the project goals must be collected, with reports being made at every stage. After collection, efforts must be made to explore the data using methods such as querying, data visualization and more. It is also important to keep track of the quality of the data in order to ensure that unclean data doesn’t hamper the results. Moreover, there should be a back-and-forth with the business understanding step for a truly flexible approach. Step 3 – Data Preparation Data preparation is the step where data to be used is determined. This makes the difference between looking in the wrong place and finding a solution that works. Data mining goals must be solidified, along with data cleaning and integration processes. Records must be kept at every step in order to operate within the constraints of the project. The technical constraints and other factors determining the data must also be pinned down to eliminate bias and derive insights more easily. Step 4 – Modeling This is where most of the work is done, with the modeling method being integral to the kind of problem to be solved. If the wrong method is used, the results obtained will not be comparable to results gained when the method is right. Narrow down the technique and set the stage for it to be used effectively. This includes taking care of the assumptions and preparing the data for use with the model. A test model must also be designed for proof-of-concept and suitability tests. The model should also be fitted for the problem, with testing and backpropagation being important parts if the model used is a neural network. The approach must also be tailored with respect to the goals and the business and data understanding in order to create a good fit for the problem. In this manner, the model should be assessed. Step 5 – Evaluation This step will be for evaluating factors such as the accuracy and generality of the model. In addition to this, the process must also be put through a fine-combed inspection to ensure that there are no errors. A revision sub-step is also present in this, as a way to fine-tune the solution offered by this process. This includes going back to the business understanding roots and seeing if the process makes sense in a sustainable and scalable fashion. A report must also be compiled for documentation. In addition to this, any possible issues must be ironed out before the next step. Step 6 – Deployment This step will differ depending on the kind of problem that the organization is facing. However, the basics remain mostly the same. The first thing to do is to summarize how the solution will be deployed in an organized manner. The solution also needs to future-proofed to ensure that it can be used easily for an extended period of time. Factors such as monitoring and maintenance should also be taken care of, along with a final report and review of the solution. In Conclusion The CRISP-DM, even today, remains as a dependable method to develop data science solutions for enterprise problems. Its BI-first approach also enables better sourcing of insights and other such data knowledge. The flexible and iterative approach of the CRISP-DM also makes it a future-proof alternative for anyone looking to solve data science problems. Even as it is important to develop a unique method, it should also be kept in mind that using methods such as CRISP-DM bring an element of professionalism and uniformity to operational procedures.
Data science takes a highly top-down, solution-oriented approach to problems. As the name suggests, it is a ‘science’ and benefits heavily from having a systematic approach to the issue. Keeping in mind that data science is a more established field, there have been standards established for the area. One of the most popular and prominent […]
Data science takes a highly top-down, solution-oriented approach to problems. As the name suggests, it is a 'science' and benefits heavily from having a
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