Below are the stages in the Agile Software Development Life Cycle (SDLC) to help you find out if this process will work according to your team’s needs.
1) Strategy Meeting
A new Agile project starts by defining a clear goal or business need that your project wants to address. Why are you doing what you’re planning to accomplish? Look at the big picture–this serves as the core belief you’ll keep referring back to throughout the development process. Continue reading
Architecture Trade-Off Analysis Method (ATAM), Architecture Review of Intermediate Design (ARID) and Software Architecture Analysis Method (SAAM) are some of the well known methods for architecture evaluation.
These methods can also be customized based on the context and the need of the stakeholders.
Architect is already part of the project so why do we need another architect for architecture evaluation.
Architecture is not yet complete so wait for the completion. [some times in some of the agile project, if it is incremental then that “completion point” never comes or forgotten.] Continue reading
Data Mining – An introduction
By: Vineeta Tawney
Data mining can be described as the process of improving decision-making by identifying useful patterns and insights from data. Data mining is particularly useful for revealing hidden patterns and providing insights during analysis, for example, understanding how many people will be impacted by specific changes. It involves examining large volumes of data from varying viewpoints and summarising the data so that useful patterns and connections can be established. It may involve the use of dashboard and reports that facilitate visual communication of results. The main challenge with data mining usually lies in securing the right type, volume and quality of data that is necessary to draw insights.
highlights 3 variants of data mining outcomes.
Descriptive: This involves the use of clustering to display patterns within a set of data, for example, similarities between suppliers can be displayed visually.
Diagnostic: With this approach, techniques such as decision trees and segmentation can be employed to show why a pattern or relationship exists within the data set. An example here is identifying the attributes of the most successful suppliers within a region.
Predictive: This approach involves the use of techniques such as regression to show the probability of an event occurring in the future.
If you are an analyst charged with a data mining exercise, ensure the following steps are followed at the minimum:
Define goal and extent of the data mining exercise. What questions are to be answered?
Prepare the data set to be used as basis for analysis. Is the data sufficient and accurate?
Analyse the data using a variety of statistical measures and visualisation tools so that observations can be made around how data values are distributed and missing data identified. Examples of data mining techniques that can be employed include linear regression, decision tree analysis, predictive scorecards, etc.