When it comes to User experience (UX), designing can be a critical process, as it takes a lot of imagination and creativity on one’s cards. In such a situation, every designer can face certain designing related challenges. Hence, working in collaboration with other designers can save you some time and effort. Collaboration allows you to interact with other designers and learn from their challenges.
User experience can be enhanced only by observing, understanding, collaborating and communicating. Collaborating with other designers in the designing process can generate better ideas for development. In addition, one must also collaborate and communicate with other teams as well. Let us look at why collaborating is such an important point for designers to consider.
1. Enables Product Thinking
When the UI/UX designers begin to design a user interface, the user requirements must be kept in mind. To solve the pain points of users, should be the key business goals. It is important that the designs are user-friendly and interactive so that customers can easily learn to use the interface and enjoy using it.
Working in collaboration with other designers helps to understand the pain points, needs, and ways to solve them. This puts the designer in a better position to enable seamless transition of deliveries.
2. Develops Understanding of Team Functionality
In order to complete and deliver a project successfully, all the teams have to contribute their fullest. The combined efforts of design, research, development, sales, and project management teams help deliver the best output in the least time. All the teams must collaborate and communicate with each other in order to execute the functions efficiently.
Working with other teams helps the designers understand the team’s functioning, and how they can work together to be more productive.
3. Helps Coping with Challenges
Designers tend to face numerous challenges and stress to deliver their best every time they are assigned a project. In order to help them unveil their true potential and work capability, designers must be respected and given equal opportunity at work. Hence, by creating a healthy collaborative work environment, we can offer the right capabilities to our designers.
Designers can take initiative and lead their design deliverables confidently. This will enhance the trust of the designers with the team and reduce fear and hesitation.
4. Improves Communication Skills
Team collaborations help to increase communication skills and other personality skills. Interacting with other team members gives clarity to thoughts and views and increases the idea generation process. Decision-making process is fastened and there is scope for constructive criticism.
5. Helps Employees to Stay intact with Company’s Vision
All the people working on the same project, work with the same vision in mind. The end result hence remains the same irrespective of individual roles and responsibilities. Efficient collaboration can help the team achieve revenue goals.
Efficient collaboration helps designers to build relationships with other designers and team members. It is a great opportunity of learning and growing; a large scope for knowledge sharing, as generally projects belong to different industries and domains. Since team contribution offers bigger results than individual contribution, collaborating is always beneficial. Healthy peer interactions, discussions, and relationship management leads to a better work culture and enhanced employee productivity.
Customer satisfaction is one aspect that is very difficult for business to meet consistently. It requires faster delivery time of products and services in order to ensure that the customers always get what they demand for. Being able to meet your customers’ demands is a means to measure the success of your business.
The traditional Software Development Lifecycle (SDLC) uses waterfall methods for testing and achieving faster quality and delivery time. However, with advancement in technology, the accuracy of the waterfall method is falling short. This is where the agile testing comes into play. In agile testing, development and testing can take place simultaneously instead of conducting it in phases.
Agile testing takes care of the requirements of end customers and testing teams. This way the customer requirements can be easily met. Instead of testing the codes after development, agile methodology conducts testing early and frequently. In addition to using agile methodology for accelerating your software release cycles, companies need to follow certain strategies for faster delivery time.
Companies often use agile testing methodology without taking care of their IT environments, workflows, culture, or architecture. This is a wrong way to go about. Faster software release cycles require better collaboration, flexibility, and transparency among the development and testing teams. The IT environment and workflows must be managed well so that teams get the right feedback and save valuable time in managing the testing methods.
Another means of saving the testing time is to automate the testing process. This way the long codes can be easily checked for mistakes and integrated with the expected outcomes. Automation prevents code defects and regressions. Automated testing helps reduce costs, compress long regression cycles and accelerate release time. Since the cost changes constantly, automated testing offers regulated feedback.
Companies face a challenge of reduced ROI due to high cost of maintenance of automated testing. The solution is to modify the automation architecture. Some companies have adopted a method to break down the large code into smaller pieces on which the teams start to work. This allows the team to properly define and maintain the interfaces.
Agile testing methodology may faster the delivery time of software release cycles; however, it comes with its own challenges and opportunities. In addition to agile testing, companies must also take care of their organizational structure, vision alignment, and team communication. Provided the company knows its end deliveries, the agile testing method can be of great help in meeting customer demands.
Several companies look for talented resources that possess both UI and UX design skills. The major reason is that having both the skills proves to be an attractive combo for the employer. Although having both UI and UX design skills may prove to be beneficial, there are certain unique differences between the two. In this blog, we will understand the differences between UI and UX Design.
UI stands for User Interface, which is a series of specific assets users interact with in order to experience a product or service. For example: screen, pages, and other visual design elements such as colors and typography, button, icons, etc.
UX stands for User experience which deals with the interaction and experience users have with a company’s internal products and services. Based on a user’s experience, the interaction patterns can be modified and made better.
Both these terminologies may seem to be similar, but they are not. While a good UI design helps to attract users, a good UX design helps to sell the products or services. While UI caters to only interfaces, UX designing caters to products and services in addition to interfaces.
UI designers are responsible for creating an attractive product appearance which results in branding and graphic development, customer analysis, and creating user guides or storylines. They work on developing UI prototypes and implementing it.
The UX designer is responsible for content strategy, customer analysis, and product strategy. They work on prototyping, testing, development and planning of overall user experience for company’s products and services.
3.Colors in Use
This is a unique difference between both the designers. UI designers tend to design the prototypes in full color. On the other hand, UX designers use only three colors in the prototype design – Black, White, and Gray.
This difference can be prominently seen in their designing styles specially in the usage of assets like icons, buttons, pages, images, drop down lists, text fields, checkboxes, etc.
The functioning of the two roles differ because of the different tools used by the UI and UX designers.
For UI designers, designing images is of utmost importance. They tend to use the best tools for creating images such as, Flinto and Principle. Both these tools offer the ability to sketch, which comes handy for developing images.
UX Designers look for tools that help them modify and improvise user experience from time to time. This means, they must be able to test and preview projects from time to time. Mockplus is one such prototyping tool that is helpful during the testing process.
Both the roles may be distinct, but they complement each other. However, it is important to understand the differences between the two roles in order to use them wisely. In conclusion, let us summarize all the differences.
Takes care of how things look
Takes care of how things work
UI elements include icons, drop down lists, text fields, buttons, and more.
UX elements include visual design, usability, interactive patterns, and more
Uses full colors for prototyping
Uses White, Black, and Gray colors for prototyping
The world is digital more than it was a year ago, with Covid-19 pushing most human activities online. There is a huge surge in the demand for information online. Web pages, email, science journals, e- books, social media websites, news feeds provide a lot of data. In order to sort the data into information and make sure that it reaches the target audience fast is what text classification is all about.
According to IBM, 80 % of all information is unstructured and companies have hard time extracting required information from textual data with analyzing, understanding, organizing and sorting taking a lot of time.
As the CEO and President of Amazon, said in his annual shareholder’s letter, over the past decades that computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques make it easier to do the tasks for which tracing the precise rules is much harder. – Jeff Bezos
This is where auto-classification comes in, as the name implies it is classification of text into categories. Tasks are automated using machine learning making the whole process super-fast and efficient. Artificial Intelligence applies machine learning, deep learning and other techniques that make tasks faster. AI has enabled IoT that uses technology to make smart Televisions to Flasks.
Reasons for Leveraging Text Classification with Machine Learning
Automating the process of analyzing and organizing data which is in the form of text results in much faster and efficient results. Reading and restructuring each text is time consuming for the human mind’s.Machine learning enables analyzing millions of texts at a fraction of cost.
Companies could use real – time analysis for critical situations to take immediate action. Text classifiers with machine learning can make accurate predictions in real time that can be used to make decisions right away.
Machine learning with text classifications outputs accurate results consistently. Humans make errors due to fatigue, boredom and distractions that are overcome by text classifications.
Applications of Text Classification
It involves an automated process of scanning texts for positive, negative or neutral emotions. It is also called sentimental analysis. Emotion Analysis covers a range of applications like product analytics, brand monitoring, customer support, market research, workforce analytics, and much more.
The topic is studied carefully for clubbed for related subjects. It involves rearranging of data according to the related topic, for ex: sorting out the latest news of the hours, organizing customer reviews by its topic or clubbing together
Language detection is an important element of text classification; it is the process of classifying text according to its language. These text classifiers are used for routing purposes (e.g. route the related customers to according to the services they are looking for).
Text classifiers are used for detecting the purpose of customers from their conversations like phone calls, email, chat and social media posts that is used to promoted customized products or for product analytics
For example, the following classifier was trained for detecting the intent from replies in customer’s chats. The classifier tags the customers as Interested, Not Interested, Unsubscribe, Wrong Person, Email Bounce, andAuto Responder etc.
This technology is used in applications such as:
Social media monitoring
Voice of customer
Resources for Text Classification
Dataset to provide examples for training the classifier – We need training data that will guide your text classifier. An efficient classifier depends on the right data that best represents the outcome that you are looking for. Gathering the right data is the key. E.g.: you want to predict the intent from particular data sets like chats on social media, you need to identify and gather such data exchanges that represent different intents so as to predict the outcome. If you feed your algorithm with another type of data, it is not going to give the desired result.
Training data can be found internally and externally. Internal data generated from apps and tools that we use everyday such as CRM, chat apps, help desk software, survey tools etc. External data include data available publicly on the internet, on social media sites or public data sets.
Some publicly available datasets that you can use for building text classifier
Reuter’s news dataset
It contains 21,578 news articles from Reuters labeled with 135 categories with varied topic, such as Politics, Economics, Sports, and Business
20 Newsgroups: It is a popular, widely accessed dataset that consists of 20,000 documents across 20 different topics.
Datasets for Sentiment Analysis
Amazon Product Reviews: A well-known dataset that contains around 143 million reviews and star ratings (1 to 5 stars) spanning from May 1996 – July 2014.
IMDB reviews: It is much smaller dataset with 25,000 movie reviews labeled as positive and negative from the Internet Movie Database (IMDB)
Twitter Airline Sentiment: With around 15,000 tweets about airlines that is labeled as
Labeled as positive, neutral, and negative, this dataset is very handy
Other Popular Datasets
Spambase: This dataset consists of 4,601 emails labeled as spam and not spam
SMS Spam Collection:spam detection dataset that consists of 5,574 SMS messages tagged as spam or legitimate.
A tool for generating and consuming the classifier- Once the classification categories are defined, the labeled data is fed into the machine learning algorithm and it is called supervised classification. The algorithm is set up to take on the labeled dataset, making sure that it generates the desired output. Example of supervised classification is spam filtering where the incoming email is automatically categorized based on its content. Other examples are Emotion Analysis, Topic Labeling, Purpose Detection, Identifying emergency situations by analyzing online information etc.
Some of the resources used in the different phases of the process, that is transforming texts into vectors, training machine learning algorithms and using the model to make predictions are:
Open Source libraries
Open source libraries are available for developers interested in applying text classification. Python, Java, and R offer a wide selection of machine learning libraries that are actively developed with a diverse set of features, performance, and capabilities.
SaaS APIs for Text Classification
Software as a Service (SaaS) for text classification is for people without any knowledge in machine language. SaaS don’t require machine learning experience and even people who don’t know how to code can use and experience the power of text classifiers. Some of the SaaS solutions and APIs for text classification include:
Google Cloud NLP
Supervised Classification is where the computer imitates human actions. The classifier has to be trained to identify emergency situations with accuracy from millions of text lines which could be from email text or online conversations.
It uses functions, sampling techniques and methods like building a stack of multiple classifiers in a step by step result oriented process. Algorithms are given a set of data called the train data which generate AI models that are given untagged data that are automatically classified.
Unsupervised Text Classification
Unsupervised classification does not depend on external information for the process. The algorithms are formulated to discover natural structure in data. Natural structure is not what we think of as logical division. Similar patterns and structures data points are identified and grouped into clusters by the algorithms. Data is classified based on the clusters formed. An example is Google search. Here the algorithm makes clusters based on the search sequence that the user requests and outputs them as results to the user.
Every data point is embedded into the hyperspace. The data exploration helps to find similar data points based on textual similarity. Similar data points form a cluster of nearest neighbors. Unsupervised classification enables generating quality insights from textual data and is language agnostic since it is customizable as no tagging is required and can operate on any textual data without the need of training and tagging it.
Custom Text Classification
A lot of the time, the biggest barrier to Machine learning is the unavailability of a data-set. Businesses and individuals are looking to apply AI for categorizing data but the necessity of a data-set is giving rise to a situation similar to a chicken-egg problem. That is where Custom text classification comes in; it is one of the best ways to build your own text classifier without any data set.
Altius has come up with unique methods for text classification using algorithm structures that are able to identify customer emotions on a large dataset and come up with new categories or dataset. This allows for the algorithm to create its own data set which is used to work against the data clusters. This training methodology is used in multiple neural network algorithms to get better results from different datasets. It brings down the cost and time takes to build a text classification model, since no training data is needed.