DevOps and Big Data
In the era of big data and big code numerous open source tools, libraries, frameworks and code repositories allow IT experts to follow the DevOps approach, where time consuming developing tasks such as deployment, installation, configuration and the set up are automatized.
“Big Code” and open-source Tools
Code repositories offer pre-built, refactorable and reusable working examples for the easy usage within the enterprise architecture. A large set of standardized models, methods, techniques and practices allow developers and testers to perform mathematical operations and analysis such as forecasting and AI with an ease on large amounts of data. The magic lies in the huge number of free usable open source tools and libraries with to access to the APIs of numerous providers of data and code.
PROQNOSTIX enables companies to perform analysis on data, driven by a business goal to predict future developments based on the data, most appropriate regarding the business goals from the perspective of predictive analytics.
Discipline of Econometrics
Thus PROQNOSTIX offers products and services in the discipline of econometrics, defined as “the forecasting of macroeconomic variables, such as interest rates, inflation rates, gross domestic product (GDP) and a collection of methods for the forecasting of economic time series and the prediction of economic theories.” (cf. Wooldridge 2009, p. 1f)
Data can be structured as time series or as cross-sectionnal data.
Time Series Data
Time series data consist of observations on stock prices, money supply, consumer price index, annual homecide rates, automobile sales etc. (cf. Wooldridge 2009, p. 8)
“Cross-sectional data consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. Sometimes, the data on all units do not correspond to precisely the same time period. For example, several families may be surveyed during different weeks within a year, In a pure cross-sectional analysis, we would ignore any minor timing differences in collecting the data. If a set of families was surveyed during different weeks of the same year, we would still view this as a cross-sectional data set.” (cf. Wooldridge 2009, p. 5)
Wooldridge, Jeffrey, M. (2009): Introductory Econometrics. A modern approach. South-Western. Cengage Learning. Fourth Edition.