Data Analytics Energy Industry Associates

What is Data Analysis?

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information suggesting conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science and social science domains.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics and database systems. It is an essential process where intelligent methods are applied to extract data patterns. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it 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. Data mining is the analysis step of the knowledge discovery in databases process or KDD. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system.

Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer?s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.