Data migration
, "nearly 40 percent of data migration projects were over time, over budget, or failed entirely." As such, to achieve an effective data migration, proper planning is critical. While the specifics of a data migration plan may vary—sometimes significantly—from project to project, the computing company IBM suggests there are three main phases to most any data migration project: planning, migration, and post-migration. Each of those phases has its own steps. During planning, dependencies and requirements are analyzed, migration scenarios get developed and tested, and a project plan that incorporates the prior information is created. During the migration phase, the plan is enacted, and during post-migration, the completeness and thoroughness of the migration is validated, documented, closed out, including any necessary decommissioning of legacy systems. For applications of moderate to high complexity, these data migration phases may be repeated several times before the new system is considered to be fully validated and deployed.
Data mining
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign, in contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data.