It's easy to make simple mistakes in data analysis. But these little mistakes can result in rework, errors, and—in the worst case—incorrect conclusions that lead you down the wrong path. Making small process changes can help you steer clear of these mistakes and end up having a real impact, both in the amount of time you spend and in your results. Here are some tips for avoiding rookie mistakes in data analytics.
Rapid changes in data availability and analysis tools are leading to evolving expectations for the data analyst role. We can do much more than just generate reports; we have the opportunity to not only process data, but convert it into understandable information and use the knowledge revealed by our work to help change happen.
There’s a bit of hype in terms such as business intelligence, data analytics, and data mining. In testing terms, though, it means working with scripts and databases, often without traditional GUI interaction. But core testing skills—analysis, synthesis, modeling, observation, and risk assessment—will still help you go far in business intelligence testing.
In this interview, Geoff Meyer, a test architect in the Dell EMC infrastructure solutions group, explains how test teams can succeed by emulating sports teams in how they collect and interpret data. Geoff explains how analytics can better prepare you for the changing nature of software.
In this interview, Daria Mehra, the director of quality engineering at Quid, explains how people can use machine learning to better contextualize data, details the complexity of test automation and how to be sure you have enough test coverage, and defines the term “artisanal testing.”
In this interview, Alon Eizenman, the CTO and cofounder at SeaLights Technologies, discusses his many experiences with startup companies, how software teams are adapting to the current demand for speed, and why you need data before you take testing actions.
In this interview, Cher Fox, of Fox Consulting, explains why test automation is essential for agile data teams' success. However, there are many other items to consider and address before implementing test automation. You may be able to get started with tools you already have.
Data is the most valuable commodity in the world, and testers generate the most valuable data in the product development organization. When effectively tracked and presented, that data can inform release schedules, aid in decision-making, and shape the direction of the product.
This paper sets out to illustrate some of the ways that data can influence the test process, and will show that testing can be improved by a careful choice of input data. In doing this, the paper will concentrate most on data-heavy applications; those which use databases or are heavily influenced by the data they hold. The paper will focus on input data, rather than output data or the transitional states the data passes through during processing, as input data has the greatest influence on functional testing and is the simplest to manipulate. The paper will not consider areas where data is important to non-functional testing, such as operational profiles, massive datasets and environmental tuning.