The issues are broad, and their solutions require expertise from several domains in computer science, mathematics and statistics. One must deal with data storage, and data transport across networks and through the storage hierarchy from tertiary storage to disk to main memory. Mapping from native data into feature vectors can explode the number of features of each object, and potentially lose information in the process. High algorithmic complexity - Algorithms in this field vary dramatically. D is the average domain size for the variables.
More complex algorithms are exponential.
This last issue is important, but rarely mentioned. Data mining tools are decision making aids. Data mining tools generate models that have built in biases, and are often the product of heuristic, non-exhaustive searches through a problem space. For example, build intelligent interfaces between DBMS systems and tertiary storage. Develop algorithms that work with non-traditional data types such as time sequences, protein sequences, or 3D structures.