November 25, 2023

Building a Successful Data Quality Program

Assessing Data Quality often includes establishing a standard of acceptable Data Quality, using data profiling and analysis techniques, and using statistical methods to identify and correct any Data Quality issues. The key features (often called “dimensions”) that should be examined and measured are: Completeness:- Data should not be missing or have incomplete values.?Uniqueness:- Locate and eliminate copies to ensure the information in the organization’s data files is free of duplication. Validity:- This refers to how useful the data is, and how well the data conforms to the organization’s standards.?Timeliness:- Old information that is often no longer true or accurate needs to be removed. Data can be measured using its relevance and freshness. Out-of-date data should be eliminated, so as not to cause confusion. Accuracy:- This is the precision of data, and how accurately it represents the real-world information.?Consistency:- When data is copied, the information should be consistent and accurate. The need for a single source of accurate in-house data provides a good argument for the use of master data and its best practices.

Building brand trust in a new era of data privacy

Emily emphasized the importance of anonymizing data to utilize it in aggregate without compromising individual privacy, a task that requires close collaboration between technical and marketing departments. Anita introduced the intriguing concept of a Chief Trust Officer, a role highlighted by Deloitte, which spans data, business, and marketing, safeguarding all aspects of compliance and privacy. The idea of having such a partner resonated with her, underlining the multifaceted nature of trust in business operations. Jake echoed the sentiment, stressing the need for understanding the types of data at hand and leveraging them without violating regulations - a balance that is critical yet challenging to achieve. These insights from the panelists underscore a common theme: building brand trust in the digital age is a multifaceted challenge that requires a blend of transparency, consistency, and compliance. As we continue to delve into this topic, it's clear that the role of data privacy is not just a technical issue but a cornerstone of the customer-brand relationship.

How Does Technical Debt Affect QA Testers

How many times have your testers been caught off guard at the last minute when the delivery manager abruptly appeared and said, “Guys, we need to launch our product in a week, and we are very sorry for not communicating this sooner? Please complete all test tasks ASAP so that we can begin the demo.” Simply put, any missing tests or “fix it later” attitude can result in a tech debt problem. Lack of test coverage, excessive user stories, short sprints, and other forms of “cutting corners” due to time constraints all contribute significantly to the building of technical debt in QA practice. When the complexity of the testing mesh began to grow with each new sprint, a US-based online retailer with a strong presence across various websites and mobile apps found itself in a real-world “technical debt” dilemma. ... Most QA managers mistakenly believe that tech debt is a legitimate result of putting all of your work on the current sprint alone, which leads to completing test coverage manually and completely ignoring automation. According to agile principles, we should see the tech debt problem as an inability to maintain and meet QA benchmarks.

How digital twins will enable the next generation of precision agriculture

Digital twins are digital representations of physical objects, people or processes. They aid decision-making through high-fidelity simulations of the twinned physical system in real time and are often equipped with autonomous control capabilities. In precision agriculture, digital twins are typically used for monitoring and controlling environmental conditions to stimulate crop growth at an optimal and sustainable rate. Digital twins provide a live dashboard to observe the environmental conditions in the growing area, and with varying autonomy, digital twins can control the environment directly. ... Agriculture is among the lowest-digitalized sectors, and digital maturity is an absolute prerequisite to adopting digital twins. As a consequence, costs related to digital maturity often overshadow technical costs in smart agriculture. A company undergoing the early stages of digitalization will have to think about choosing a cloud provider, establishing a data strategy and acquiring an array of software licences, to name just a few critical challenges.

What are Software Design Patterns?

Software design patterns are an essential aspect of software development that helps developers and engineers create reusable and scalable code. These patterns provide solutions to commonly occurring problems in software design, enabling developers to solve these problems efficiently and effectively. In essence, a software design pattern is a general solution to a recurring problem in software design that has been proven to be effective. It's like a blueprint for a specific type of problem that developers can use to create software systems that are reliable, maintainable, and scalable. Software design patterns have been around for a long time and are widely used in the software development industry. They are considered to be a best practice in software design because they provide a standardized approach to solving common problems, making it easier for developers to communicate and collaborate with one another. In this blog, we will explore what software design patterns are, the different types of software design patterns, and the benefits of using them in software development.?

Examples of The Observer Pattern in C# – How to Simplify Event Management

The observer pattern is an essential software design pattern used in event-driven programming and user interface development. It is composed of three primary elements: the subject, observer, and concrete observers. The subject class is responsible for keeping track of the observer objects and notifying them of changes in the subject’s state. On the other hand, the observer is the object that wishes to be notified when the state of the subject changes. Finally, the concrete observer is an implementation of the observer interface. One of the observer pattern’s significant advantages is its capability to facilitate efficient event management in software development. By leveraging this ability, developers can trigger related events without the need for tightly coupling the pieces of code leading to the events. The observer pattern also ensures that the code continues to be free from changes that would cause a ripple effect or the chain reaction of changes. The observer pattern’s primary components are the Subject, Observer, and Concrete Observer. The subject defines the interface for attaching and detaching observers from the subject object.?

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