CRISP-DM stands for cross-industry process for data mining. The CRISP-DM methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology. We do not claim any ownership over it. We did not invent it. We are however evangelists of its powerful practicality, its flexibility and its usefulness when using analytics to solve thorny business issues. It is the golden thread than runs through almost every client engagement. The CRISP-DM model is shown on the right. Show
This model is an idealised sequence of events. In practice many of the tasks can be performed in a different order and it will often be necessary to backtrack to previous tasks and repeat certain actions. The model does not try to capture all possible routes through the data mining process. You can jump to more information about each phase of the process here: We have other CRISP DM resource available to help you with your data mining projects. You can download our free guide to using CRISP DM to evaluate data mining tools or you can watch the recording of our introduction to CRISP DM webinar. STAGE ONE – DETERMINE BUSINESS OBJECTIVESThe first stage of the CRISP-DM process is to understand what you want to accomplish from a business perspective. Your organisation may have competing objectives and constraints that must be properly balanced. The goal of this stage of the process is to uncover important factors that could influence the outcome of the project. Neglecting this step can mean that a great deal of effort is put into producing the right answers to the wrong questions. What are the desired outputs of the project?
Assess the current situationThis involves more detailed fact-finding about all of the resources, constraints, assumptions and other factors that you’ll need to consider when determining your data analysis goal and project plan.
Determine data mining goalsA business goal states objectives in business terminology. A data mining goal states project objectives in technical terms. For example, the business goal might be “Increase catalogue sales to existing customers.” A data mining goal might be “Predict how many widgets a customer will buy, given their purchases over the past three years, demographic information (age, salary, city, etc.), and the price of the item.”
Produce project planDescribe the intended plan for achieving the data mining goals and thereby achieving the business goals. Your plan should specify the steps to be performed during the rest of the project, including the initial selection of tools and techniques.
STAGE TWO – DATA UNDERSTANDINGThe second stage of the CRISP-DM process requires you to acquire the data listed in the project resources. This initial collection includes data loading, if this is necessary for data understanding. For example, if you use a specific tool for data understanding, it makes perfect sense to load your data into this tool. If you acquire multiple data sources then you need to consider how and when you’re going to integrate these.
Describe dataExamine the “gross” or “surface” properties of the acquired data and report on the results.
Explore dataDuring this stage you’ll address data mining questions using querying, data visualization and reporting techniques. These may include:
These analyses may directly address your data mining goals. They may also contribute to or refine the data description and quality reports, and feed into the transformation and other data preparation steps needed for further analysis.
Verify data qualityExamine the quality of the data, addressing questions such as:
Data quality reportList the results of the data quality verification. If quality problems exist, suggest possible solutions. Solutions to data quality problems generally depend heavily on both data and business knowledge. STAGE THREE – DATA PREPARATIONSelect your dataThis is the stage of the project where you decide on the data that you’re going to use for analysis. The criteria you might use to make this decision include the relevance of the data to your data mining goals, the quality of the data, and also technical constraints such as limits on data volume or data types. Note that data selection covers selection of attributes (columns) as well as selection of records (rows) in a table.
Clean your dataThis task involves raise the data quality to the level required by the analysis techniques that you’ve selected. This may involve selecting clean subsets of the data, the insertion of suitable defaults, or more ambitious techniques such as the estimation of missing data by modelling.
Construct required dataThis task includes constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes.
Integrate dataThese are methods whereby information is combined from multiple databases, tables or records to create new records or values.
STAGE FOUR – MODELLINGSelect modeling techniqueAs the first step in modelling, you’ll select the actual modelling technique that you’ll be using. Although you may have already selected a tool during the business understanding phase, at this stage you’ll be selecting the specific modelling technique e.g. decision-tree building with C5.0, or neural network generation with back propagation. If multiple techniques are applied, perform this task separately for each technique.
Generate test designBefore you actually build a model you need to generate a procedure or mechanism to test the model’s quality and validity. For example, in supervised data mining tasks such as classification, it is common to use error rates as quality measures for data mining models. Therefore, you typically separate the dataset into train and test sets, build the model on the train set, and estimate its quality on the separate test set.
Build modelRun the modelling tool on the prepared dataset to create one or more models.
Assess modelInterpret the models according to your domain knowledge, your data mining success criteria and your desired test design. Judge the success of the application of modelling and discovery techniques technically, then contact business analysts and domain experts later in order to discuss the data mining results in the business context. This task only considers models, whereas the evaluation phase also takes into account all other results that were produced in the course of the project. At this stage you should rank the models and assess them according to the evaluation criteria. You should take the business objectives and business success criteria into account as far as you can here. In most data mining projects a single technique is applied more than once and data mining results are generated with several different techniques.
STAGE FIVE – EVALUATIONEvaluate your resultsPrevious evaluation steps dealt with factors such as the accuracy and generality of the model. During this step you’ll assesses the degree to which the model meets your business objectives and seek to determine if there is some business reason why this model is deficient. Another option is to test the model(s) on test applications in the real application, if time and budget constraints permit. The evaluation phase also involves assessing any other data mining results you’ve generated. Data mining results involve models that are necessarily related to the original business objectives and all other findings that are not necessarily related to the original business objectives, but might also unveil additional challenges, information, or hints for future directions.
Review processAt this point, the resulting models appear to be satisfactory and to satisfy business needs. It is now appropriate for you to do a more thorough review of the data mining engagement in order to determine if there is any important factor or task that has somehow been overlooked. This review also covers quality assurance issues—for example: did we correctly build the model? Did we use only the attributes that we are allowed to use and that are available for future analyses?
Determine next stepsDepending on the results of the assessment and the process review, you now decide how to proceed.Do you finish this project and move on to deployment, initiate further iterations, or set up new data mining projects? You should also take stock of your remaining resources and budget as this may influence your decisions.
STAGE SIX – DEPLOYMENTPlan deploymentIn the deployment stage you’ll take your evaluation results and determine a strategy for their deployment. If a general procedure has been identified to create the relevant model(s), this procedure is documented here for later deployment. It makes sense to consider the ways and means of deployment during the business understanding phase as well, because deployment is absolutely crucial to the success of the project. This is where predictive analytics really helps to improve the operational side of your business.
Plan monitoring and maintenanceMonitoring and maintenance are important issues if the data mining result becomes part of the day-to-day business and its environment. The careful preparation of a maintenance strategy helps to avoid unnecessarily long periods of incorrect usage of data mining results. In order to monitor the deployment of the data mining result(s), the project needs a detailed monitoring process plan. This plan takes into account the specific type of deployment.
Produce final reportAt the end of the project you will write up a final report. Depending on the deployment plan, this report may be only a summary of the project and its experiences (if they have not already been documented as an ongoing activity) or it may be a final and comprehensive presentation of the data mining result(s).
Review projectAssess what went right and what went wrong, what was done well and what needs to be improved.
There’s more advice on how to manage the deployment phase of the data mining process in this post on our blog. |