- 6 Sections
- 14 Lessons
- 52 Weeks
- Data analytics7
- 1.1Activate power pivot15 Minutes
- 1.2Data sets5 Minutes
- 1.3Module 1: Converting excel spreadsheets into the database
- 1.4Module 2: Changing Excel into a database – another way
- 1.5Module 3: Build database relationships
- 1.6Module 4: Creating a basic pivot table, graph and slicers
- 1.7Power pivot – Youtube video31 Minutes
- The basics5
- Role players in the design of the dashboard1
- Sources of data - input, process and output phases1
- Documenting the process1
- Multiple financial analysis1
Benefits and limitations
Benefits of Data Mining
Data mining ensures a company is collecting and analyzing reliable data. It is often a more rigid, structured process that formally identifies a problem, gathers data related to the problem, and strives to formulate a solution. Therefore, data mining helps a business become more profitable, efficient, or operationally stronger.
Data mining can look very different across applications, but the overall process can be used with almost any new or legacy application. Essentially any type of data can be gathered and analyzed, and almost every business problem that relies on qualifiable evidence can be tackled using data mining.
The end goal of data mining is to take raw bits of information and determine if there is cohesion or correlation among the data. This benefit of data mining allows a company to create value with the information they have on hand that would otherwise not be overly apparent. Though data models can be complex, they can also yield fascinating results, unearth hidden trends, and suggest unique strategies.
Limitations of Data Mining
This complexity of data mining is one of the largest disadvantages to the process. Data analytics often requires technical skillsets and certain software tools. Some smaller companies may find this to be a barrier of entry too difficult to overcome.
Data mining doesn't always guarantee results. A company may perform statistical analysis, make conclusions based on strong data, implement changes, and not reap any benefits. Through inaccurate findings, market changes, model errors, or inappropriate data populations, data mining can only guide decisions and not ensure outcomes.
There is also a cost component to data mining. Data tools may require ongoing costly subscriptions, and some bits of data may be expensive to obtain. Security and privacy concerns can be pacified, though additional IT infrastructure may be costly as well. Data mining may also be most effective when using huge data sets; however, these data sets must be stored and require heavy computational power to analyze.