Information is the coupling of data and the knowledge obtained by analyzing such data. Data typically consists of text documents or photographic/video video-graphic information.
Company activities are heavily reliant on information, using it as the basis for most, if not all, important management decisions. If management fails to consider such information, the chance of making wrong decisions greatly increases.
It is difficult for management to assimilate the increasingly large volumes information that is constantly being generated, particularly since it tends to be scattered over numerous staff computer systems or presents as many differing kinds of printed material.
The Question & The Answer
This raises the question; given the large volumes of data generated on a daily basis, how can managers find information relevant to the decisions they need to make to sucessfully run their businesses? For any company serious about managing their data assets in a usable and useful way, data science is the answer. Data science (sometimes known as ‘data discovery’) is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue and/or cuts costs.
Data science consists of five major elements:
- Extraction, transformation, and loading of transaction data onto a data warehouse system.
- Storage and management of the data in a multidimensional database system.
- The provision of data access to business analysts and data science professionals.
- Analysis of the data by the appropriate application software.
- Presentation of the data in useful formats, such as graphs, tables, or text.
Many Levels Of Analysis
The application of data science techniques to general business problem analysis is made possible by the increased availability of data and inexpensive storage and processing power.
Some of the tools used for data science are:
Data Analytics: Examination of raw data with the purpose of drawing conclusions from the information.
Genetic Algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
Decision Trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.
Nearest Neighbor: A technique that classifies each record in a dataset based on a combination of the classes of the record(s) most similar to it in a historical dataset.
Rule induction: The extraction of useful if-then rules from data based on statistical significance.
Data visualization: The visual interpretation of complex relationships in multidimensional data.