It is very much interesting to say Data is the new Oil but we definitely have to consider the reasons at which this statement appears to be true because without the right usage of data it is absolutely useless.
It is very much interesting to say Data is the new Oil but we definitely have to consider the reasons at which this statement appears to be true because without the right usage of data it is absolutely useless.
The huge amounts and abundance of data are totally insubstantial if it can’t be visualized or interpreted which brings me to cull up some histories on data; The idea of hiring a dedicated resource to maneuver and extract meaning from data only started in 2001, when the term ‘data science’ was first used in a paper by Cleveland (2001; Pollack 2012). This ushered the way for colleges to begin promoting the field of data science by opening data science institutions and centers to formally teach data science as a profession, examples of some are the Institute for Data Science at Berkley, Centre for Doctoral Training in Data Science at Edinburgh University, and the Centre for Data Science at New York University, amongst so many others. Leaving the past and taking a hop into a decade later, data science has become essential in every industry with numerous authors referring to data as the “currency of the future” and even going as far as comparing it to gold (Pollack, 2012; Johnson, 2012) making data scientists become integral to the success of this new currency. Bakhshi & Mateos-Garcia (2014) define data scientists as “experts who use analytical techniques from statistics, computing and other disciplines to create value from new (‘big’) data.”
As a very data-centric human, I get very intrigued to see the applications and implementations of Data in innovations or even the everyday usage of products in general. This article would be solely focused on three major capitals that make innovations or everyday products appealing to use.
It is also important to note that I’m more of a Data Scientist (in this context) who has helped businesses build products based on data-driven designs and experiences and that’s why I feel I have atleast a little knowledge on how this works + as a researcher I’ve also made loads of research into understanding how this works using real world examples as use-cases :)
Data science can be defined as analyzing to extract meaning and insights from data with the overlapping areas of statistics, scientific methods, and machine learning models. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.
Design can be seen as a solid plan that enables us to reach our desired outcome, whether that outcome be a product, service, process, or even strategy. A design is considered a “Good design” when it’s goal-oriented and based on insight from data and not some random guesswork.
Designers basically make use of all accesible information when trying to structure an accurate understanding of the situation with the aim to identify the challenges that need to be solved so that the desired outcome can be reached. Designers specialize in using qualitative research methods to understand human needs and behavior.
You can be data-driven if you’ve got done the work of knowing specifically what your downside is, what your goal is, and you have got a awfully precise and unambiguous question that you just wish to grasp. This additionally assumes that your methodology and measurements square measure sound which the kind of question you wish to answer is one that knowledge will confirm. It depends on a keen understanding of the kinds of pitfalls that knowledge will bring, and taking steps to correct those pitfalls
Designers planning to perceive the data-driven style method is a very important career ability as plunging into user analysis and testing process, and understanding what data analytics is and the way it works, provides designers further tools to urge support for their concepts. It additionally permits them to make the most effective doable product, with the data to backup that assertion.
Data-driven UI design is both art and science. Understanding the way to collect and analyze data and implement designs supported on it’s a very important ability for beginner and skilled designers alike.
The need for any field to become more “data-driven” is a need that is increasingly familiar to people. Organizations are relying more strongly on data to aid their decision-making, including decisions about design and user experience. Although the term “data-driven design” is something that has become popular in the literature, King et al. (2017) discuss three different ways to think about data and how it is used within the design process. They discuss the familiar terms data-driven and data-informed and have additionally coined the term data-aware.
The following outlines the definition of those terms culled from the book Designing with Data:
I also recently discovered one very interesting approach from an article on implementing a better user experience design called:
I believe that applying data in the design process is definitively not about replacing the things that design processes and designers do well already. It’s about helping designers extend the value of those things by offering another way to look at the impact of the design work on users. A/B testing can’t answer all questions, but it can answer certain kinds of questions that other methodologies and practices cannot. In fact, we think you’ll find that working with A/B testing is actually quite similar to other evaluative design processes. When applied correctly, it is creative, iterative, and empowering.
Failing to consider data (or using data in an ineffective way) can have serious implications for the success of a project. If you rely solely on instinct or best practices to make decisions without performing any data-driven investigation, you risk wasting money on changes to design choices that are ineffective (or even harmful).
Using data effectively can lead directly to improved business outcomes. Research by MIT’s Center for Digital Business found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.”
There are many examples of cases where data-driven UX techniques have delivered a tangible improvement on ROI. For example, in 2014, airline Virgin America used A/B testing to redesign a new, responsive website. This led to:
Another interesting example comes from the e-commerce website Music & Arts, which used usability testing and heuristic evaluation to inform a website redesign. Upon the conclusion of the project, their online sales increased about 30% year over year.
User-centered design and data applied to understanding behavior are both focused on establishing effective, rewarding, and replicable user experiences for the intended and current user base of a business or product. I believe that data capture, management, and analysis is the best way to bridge between design, user experience, and business relevance. Based on my previous descriptions, this is why I believe a data-aware approach to designing great user experiences is a better description of what we aspire to rather than the more commonly used data-driven terminology, as I believe data feeds into a creative design process, and is itself varied and creative, providing many possible ways to approach a problem.
But first, let’s review some of the assumptions we are making about which kind of designer you are:
I believe that experimental methods and the data they yield can help hone your craft, improve your products, and concretely measure your impact on users and, ultimately, on the business. In sum, I believe that becoming familiar with the ways in which experiments carried out on the internet with large numbers of users where you can gather large amounts of disparate data types — that is, experiments at scale — can help you in your design practice. and lastly I believe that great design and smart data practices are key to strategic impact in any business.
After getting to understand what data-driven design is and it’s processes there’s actually a dark side to using data for your designs.
A lot of designers fall into the trap of always trying to optimize the data while forgetting a broader picture that this may lead to a worse user experience and damage to a brand image in the long term because maintaining a healthy balance between your instinct and empirical evidence. You’ve to subconsciously learn the subtle art of knowing when to rely on which. So in basic terms, when data does not give you a very clear answer, or you need to stitch many different pieces in one harmonious ensemble, you can always feel free to trust your expertise, gut feeling and intuition.
Be informed by numbers but not a slave to them.
After all, data can inform, but the designer needs to add that secret ingredient and bring the design to life. This human factor is what drives real innovation, while numbers can only inspire or give some useful insights.
If you eventually got this end, this is a disclaimer that this article was a pure experiment of combining human and AI (GPT-3) to creating articles like this, so if you read any passage that felt a little ‘robotic’ that’s definitely something I’m still working on to perfect but if not, you can applaud my model for a job well done :)
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