Data analysis techniques are applied to identify patterns, relations and trends that help interpret data and extract explicit and implicit information. This generation of knowledge helps meet managerial needs and support administrative processes and decision making at companies. In this article we discuss a little about the Programa MetroBI [MetroBI Program], which experimented with the application of Business Intelligence (BI) in technological services by testing concepts within CERTI itself.
The main reason to apply Business Intelligence in CERTI’s technological services is to increase competitiveness, and to position service providers using information from their own databases. This allows taking advantage of latent potential and making well founded decisions.
The MetroBI project
Various analyses were conducted within the scope of the project. The
CRISP-DM methodology helped understand the structure of a business and to identify and analyze the data available in a continuous development cycle. This allowed preparing, modeling and testing a research hypothesis to increase value by identifying patterns, relations and trends in the business.
An interesting example was the analysis of client purchasing history, which sought to improve aspects of a business and forecast income based on the hypotheses of its inputs, employees and intensity of use of machinery, for example. With this focus, a hypothesis was developed to evaluate the histories of calibration that were requested from the CERTI Foundation for different families of equipment. The hypothesis is that: Equipments from the same family have the same period for new calibration.
The work was conducted through analysis of client histories with the periodicity of recalibration of a previously calibrated or requested equipment. Data mining from CERTI Foundation records was conducted for the analysis, and the dates rounded by year. The families of equipment given priority in the study have a pattern of recalibration but were not verified on 100% of cases.
Results that drive decision making As examples, the recalibrations of spline gauges follow a clear trend of being annual, while for longitudinal measuring machines, it is not possible to draw conclusions.
Graph of periodic calibrations of spline gauges
Counter Recalibrations Spline gauge. Recalibration period(years)
Graph of periodic calibrations of longitudinal measuring machine
Counter Recalibrations Longitudinal measuring machine/Universal measuring machine. Recalibration period (years)
BUSINESS INTELLIGENCE: a new ally in service strategies
It is true that the interval of recalibration is a decision made directly by the user, considering intensity of use, criticality of the measurement or even sectoral norms and regulations.
On the other hand, the sum of the collective intelligence and data from various clients inserted in the Business Intelligence analysis can provide insights not only to the client company, but also to the manufacturer of the measuring system and directly support the sales strategies and seasonality of the technological services of a provider or laboratory.
In the scope of this project, other hypotheses were tested and, whether validated or not, have contributed to the strategic development and competitiveness of those involved.
Please contact us if you would like to learn more about the MetroBI Program and implement it for the reality of your company: [email protected]
