The cross-industry standard process for data mining, otherwise referred to as CRISP-DM, is the most popular and most used data analytics process model that describes experts’ traditional data mining approaches. Birthed sometime in 1996, CRISP-DM was sponsored by the European Union (EU) and spearheaded by five companies; Teradata, Daimler AG, Integral Solutions Ltd (ISL), OHRA, and NCR Corporation. Since then, CRISP-DM has gone on to become one of the most loved data mining models.
In2015, IBM released a modified version called Analytics Solutions Unified Method for Data Mining/Predictive Analytics (ASUM-DM).
In the space of twenty-five years, the relevance of CRISP-DM in the data mining industry has only become more pronounced and is even more advantageous in 2021. Thanks to the timely modifications applied to the model, it has become an industry favorite for obvious reasons. It has notably solved many pre-existing data mining industry problems and continues to function as a solution to new challenges in the data-mining world.
Data mining as a phenomenon has been a handy tool in the AI and tech world generally. However, some of its challenges lie in its implementation stage, with discovered faults in the data pool. Issues like complex or incomplete data, the data privacy argument, and the distribution of data, among others, could pose a threat to the smooth running of the data-mining process. That is where CRISP-DM comes in.
CRISP-DM, as a data-mining model, consists of six phases which make the process a seamless one;
- Business understanding: In ensuring conversion to analytic problems, the business requirements must be fully comprehended. This aids ease of strategy in solving issues discovered. Understanding the business requirement is a requisite phase of the CRISP-DM process whose relevance cannot be overemphasized. Solid foundational knowledge in what is needed will help avoid unnecessary waste of energy, time, and resources on a wrong problem statement. This phase could be made possible through steps like; establishing the actual business goals clearly, correctly assessing the situation, weighing the details, determining the data analysis objectives, and producing a viable project plan.
All of these go into the ‘business understanding’ phase of the CRISP-DM process.
- Data understanding: A spill-over from the business understanding phase, the data understanding phase comprises all the activities that involve scrutiny, assessing, assembly of received data sets which ultimately aid the success of the entire project. Data understanding can be materialized with the aid of a few sub-phases such as the collection of relevant data, detailed description of collected data (including data volume, availability of the data attributes, data types, correlations, range, etc.), proper exploration/query of data and delicately verifying the data.
- Data preparation: The data preparation phase of the CRISP-DM model encompasses all activities involved in producing the final data sets necessary for the modeling phase. Also referred to as ‘data wrangling,’ data preparation entails specific tasks geared towards creating data sets to develop the raw data into the final data sets. For best results, this phase consists of five tasks: data selection, data cleaning, data construction, data integration, and data formatting. Data miners incorporate these tasks into the data preparation stage to eliminate any possibility of inaccurate data or information production.
- Modeling: During the execution of the modeling phase of the CRISP-DM model, the first and most crucial step is the selection of the required modeling technique (confirming specific algorithms to be experimented on), generating the test design, actual building of the model, and then assessing the developed model. The role of the data scientist or miner during this phase is to initiate the modeling process and then interpret achieved results using parameters such as test design, expected success criteria, and general domain knowledge.
- Evaluation: After the modeling phase has been executed, the model is adequately evaluated and assessed. All previous phases are re-run and checked for errors, omissions, and confirmation that the model achieves business goals set at the initial stage of the process.
During this phase, cross-checking is thoroughly executed to ensure the effective achievement of all business issues, confirming that none has been left out in the overall consideration.
As is typical with previous phases, the evaluation phase comprises three major tasks: a thorough evaluation of results, properly reviewing the entire process, and ascertaining subsequent steps to be taken. At this stage, the data scientist determines how feasible deployment can be for each obtained result.
- Deployment: This phase involves the actual resourcefulness of the produced model as the entire process is seemingly a waste of time and investment if the product cannot be applied by the clients or target market.
Complexities lie in this process, especially with the broad range of what may be described as ‘deployment,’ based on the specific requirements. A deployment phase may just include informing the client on usage or involving the actual re-mining of data.
Whatever the case, the deployment phase involves four major tasks which aid a conclusive result. They are:
- Plan deployment (everything that entails developing the deployment plan, taking into cognizance user experience).
- Arrangements for monitoring and maintenance (maintenance tools can be arranged to ensure glitches during the operational phase).
- Production of a final report and a proper project review.
Although these phases can be sequential, tasks can be performed randomly, especially in urgency and without necessary regard to order, as adjustments can be made by backtracking previous stages and editing actions.
CRISP-DM could be considered as a data miners’ favorite and not without compelling reasons. It is a pocket-friendly methodology as it employs several processes that ensure the avoidance of costly errors. It is primarily fire-proof.
CRISP-DM is a model that makes for a unified framework and enables the implementation of project replication and best practices. Now more than ever, this thorough, advanced methodology is of great relevance as the tech world continues to produce innovative ideas and results necessary for a better future.