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December 2020

Mohammad Anas Wahaj | 28 dec 2020

Logos are a brief visual representaion of the organizational identity and help differentiate them from each other. They assist to instantly recognize brands and over a period of time can become one of the most important component of their identity. Traditionally, organizations utilize the services of graphic designers to get their logos and the process has artistic and creative orientation. But now powered with technologies like artificial intelligence (AI), there are online logo design software tools that can design logos instantly once some specifications are submitted. These tools also provide editing and customization features. Technology is transforming the creative field of logo design into a more scientific one. Research paper, 'Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design' (SSRN, 25 nov 2019) (Authors: Ryan Dew of Wharton School at the University of Pennsylvania, Asim M. Ansari of Columbia Business School at the Columbia University, Olivier Toubia of Columbia Business School at the Columbia University), proposes a more data-driven approach to logo design in which the authors developed a 'logo feature extraction algorithm' that uses modern image processing tools to break a company's logo into many visual constituent parts like font, color scheme, and many other meaningful features, and a multiview representation learning framework that links the visual components to text that describes the company like industry, value propositions etc. Researchers then applied this framework to a large amount of data available on companies to predict their logo features. Prof. Ryan Dew explains, 'There are things that data and models can say about the design process that can help firms develop brand identities - visual brand identities that are doing the right things for them...we looked at hundreds of different logos, and we also looked at a bunch of textual data describing these firms - taken mostly from the firms' websites. And we also got consumers to react to these logos and the textual descriptions by rating these firms according to what's called a 'brand personality scale'...we developed an algorithm that lets us work with logos as a source of data. We call this our 'logo feature extraction algorithm'...and then we also have all this text, which can be anything...It conveys what the firm does and what their brand is...The idea is, we want to link these two domains to try to get the words to describe what the logo is trying to say. Let the logo speak. Conversely, this is actually how the design process works. You start with a textual blurb describing - 'This is what my brand is. This is what my firm does'. And then you go from that to a logo — to a logo template. This is where the concept of data-driven design comes in. We both, in the first sense, are able to use text to understand logos, but in the second sense, we're able to go from text to new logo templates that will let firms develop logos that are consistent with their brand identities...a more fundamental thing that the current paper can address is this idea of coming up with the 'right template' to convey what you want to convey visually. That is, in some sense, firms should be a little cautious when they're designing logos...understanding these templates and having this model of data-driven design can help with the creative process, to come up with new redesigns or new logos that will excel.' Read on...

Knowledge@Wharton: Why a Data-driven Approach Can Enhance the Art of Logo Design
Author: NA


Mohammad Anas Wahaj | 20 dec 2020

Organizations now have large amount of data available to them, but the challenge is to obtain actionable insights by using right data analytics tools and processes that help in making right organizational decisions. Data-driven decision-making has become a common practice with organizations trying to find purpose for the data. But it is not necessary that all analytics processes answer the right questions and it's also not a safeguard against the influence of preexisting beliefs and incentives. Prof. Bart de Langhe of Esade - Ramon Llull University (Spain) and Prof. Stefano Puntoni of Rotterdam School of Management at Erasmus University (Netherlands) propose a new approach termed as 'decision-driven data analytics' - 'Find data for a purpose, instead of finding a purpose for data.' They explain, 'Data-driven decision-making anchors on available data. This often leads decision makers to focus on the wrong question. Decision-driven data analytics starts from a proper definition of the decision that needs to be made and the data that is needed to make that decision...Data-driven decision-making empowers data providers and data scientists. The risk is that decision makers take data that is consistent with their preexisting beliefs at face value.' Elaborating their approach, they say, 'To move to a decision-driven data analytics approach, a company must start by identifying the business’s key decisions and the people who make them, and finding data for a purpose rather than finding a purpose for the data at hand.' Data-driven Data Analytics (Anchor on data that is available; Find a purpose for data; Start from what is known; Empower data scientists). Decision-driven Data Analytics (Anchor on a decision to be made; Find data for a purpose; Start from what is unknown; Empower decision makers). To allay fears of executives who might confuse decision-driven approach with preference-driven data analytics (where decision makers use data to support a decision that has already been made and fall prey to confirmation bias), authors suggest leaders to take three important steps - Step I: Responsibility of decision makers to form a narrow consideration set of alternative courses of action. Step II: Joint responsibility of decision makers and data scientists to identify the data needed to figure out which course of action is best. Step III: Choose the best course of action. Read on...

MIT Sloan Management Review: Leading With Decision-Driven Data Analytics
Authors: Bart de Langhe, Stefano Puntoni



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