Data mining is the process of extracting and 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 of extracting information from a data set and transforming 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.
In computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed and loaded into an output data container. The data can be collated from one or more sources and it can also be output to one or more destinations. ETL processing is typically executed using software applications but it can also be done manually by system operators. ETL software typically automates the entire process and can be run manually or on reoccurring schedules either as single jobs or aggregated into a batch of jobs.
Quantitative marketing research is the application of quantitative research techniques to the field of marketing research. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion.
In science and engineering, root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. It is widely used in IT operations, manufacturing, telecommunications, industrial process control, accident analysis, medicine, healthcare industry, etc. Root cause analysis is a form of inductive and deductive inference.
In systems engineering, information systems and software engineering, the systems development life cycle (SDLC), also referred to as the application development life cycle, is a process for planning, creating, testing, and deploying an information system. The SDLC concept applies to a range of hardware and software configurations, as a system can be composed of hardware only, software only, or a combination of both. There are usually six stages in this cycle: requirement analysis, design, development and testing, implementation, documentation, and evaluation.
In North America, a wrangler is someone employed to professionally handle animals, especially horses and cattle, but sometimes other types of animals as well. The word "wrangler" is derived from the Low German "wrangeln" meaning "to dispute" or "to wrestle". It was first documented in 1377. Its use as a noun was first recorded in 1547. Its reference to a "person in charge of horses or cattle" or "herder" was first recorded in 1888.
In computer science, data validation is the process of ensuring data has undergone data cleansing to confirm they have data quality, that is, that they are both correct and useful. It uses routines, often called "validation rules", "validation constraints", or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system. The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic of the computer and its application.
A camera raw image file contains unprocessed or minimally processed data from the image sensor of either a digital camera, a motion picture film scanner, or other image scanner. Raw files are so named because they are not yet processed, and contain large amounts of potentially redundant data. Normally, the image is processed by a raw converter, in a wide-gamut internal color space where precise adjustments can be made before conversion to a viewable file format such as JPEG or PNG for storage, printing, or further manipulation. There are dozens of raw formats in use by different manufacturers of digital image capture equipment.
Data cleansing or data cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall.
DMAIC or define, measure, analyze, improve and control refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. The DMAIC improvement cycle is the core tool used to drive Six Sigma projects. However, DMAIC is not exclusive to Six Sigma and can be used as the framework for other improvement applications.
Telecommunications mediation is a process that converts call data to pre-defined layouts that can be imported by a specific billing system or other OSS applications.
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration.
Raw data, also known as primary data, are data collected from a source. In the context of examinations, the raw data might be described as a raw score.
Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.
Technology Intelligence (TI) is an activity that enables companies to identify the technological opportunities and threats that could affect the future growth and survival of their business. It aims to capture and disseminate the technological information needed for strategic planning and decision making. As technology life cycles shorten and business become more globalized having effective TI capabilities is becoming increasingly important.
Trifacta is a privately owned software company headquartered in San Francisco with offices in Bengaluru, Boston, Berlin and London. The company was founded in October 2012 and primarily develops data wrangling software for data exploration and self-service data preparation on cloud and on-premises data platforms.
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data.
Data lineage includes the data origin, what happens to it, and where it moves over time. Data lineage provides visibility and simplifies tracing errors back to the root cause in a data analytics process.
Data preparation is the act of manipulating raw data into a form that can readily and accurately be analysed, e.g. for business purposes.
Sean Kandel is Trifacta's Chief Technical Officer and Co-founder, along with Joseph M. Hellerstein and Jeffrey Heer. He is known for the development of new tools for data transformation and discovery and is the co-developed of Data Wrangler, an interactive tool for data cleaning and transformation.
