Is data science keeping pace with product designers' needs?


Experience design is not just like a standard advertising campaign or an online app, but rather a strategy to keep customers engaged with a brand through impactful interactions. It means that every product and service is designed to offer a delightful experience; the packaging, mobile app, web and print ads are just some of the details to consider in customer experience design.

Sounds complicated? Let’s look at Tesla. As CEO Elon Musk says, “Purchasing a Tesla should be a delightful experience,” and the brand has done everything possible to make that a reality.

It doesn’t take a rocket scientist to notice that much of the Tesla’s user experience is vastly different from the normal automobile. And the differences go beyond the fact that the car’s motor is electric.

When MotorTrend named it the 2013 Car of Year, it said “[…] all judges were impressed with the Tesla's unique user interface, courtesy of the giant touch screen in the center of the car that controls everything from the air-conditioning to the navy stem to the sound system to the car’s steering, suspension, and brake regeneration settings.”

However, the user-friendliness starts before even entering the car when the door handle that is flush with the body of the car slides out to greet you.

Another good example of a successful experience design is Philips' Wake-Up Light.

The wake-up experience created by an alarm clock substantially differs from the experience created by sunrise and happy birds. The question is whether we can create technology which understands the crucial features of sunrise and birds and which succeeds in delivering a similar experience.

Philips' Wake-Up Light is a crossing of an alarm clock and a bedside lamp. Half an hour before the set alarm, the lamp starts to brighten gradually, simulating sunrise. It reaches its maximum at the set wake-up time and then the electronic birds kick in to make sure that you really get up. It substantially changes the way one wakes up: it changes the Product Designersexperience.

Behind the scenes, product designers need data to provide insights to support good quality product innovation. The needs of product designers are increasingly sophisticated as they realize the benefits of using data and creating a solid ‘feedback loop’. But is data science keeping up with the demand?

Data, of course, does not appear out of the woodwork. Product designers have to remember that generating data is not ‘someone else’s business’. They can (and should) be stakeholders in sourcing the data that they need. This typically could include either working with an analytics team, or adopting a self-service model, by starting small and setting up a test environment that will work for them. In particular, it is important to test ideas early, and the feed the results back into the process.

IoT’s impact on data for product designers

But what about data quality? Internet of Things (IoT) is producing huge amounts of data. I have previously noted that it is changing the world of insurance. It is also being used to manage and support demand planning in retail. To my mind, using IoT data for product development is only one step further along the road: it is a matter of forecasting future products that will appeal to consumers, based on the demand for existing products.

But IoT sensors and systems often generate the so-called ‘dirty data’. In other words, before this data can be used effectively, the quality needs to be improved by complex cleaning processes. This is a key part of good data science, because ‘garbage in = garbage out’. As Erich Hugo, Business Development Director and Partner in design consultants BAS ITG says, “all data are not created equal”, going on to ask “why is there no ISO standard for data quality?”.

There are other ways to improve IoT data apart from cleaning it. In particular, it may be helpful to supplement it or contextualizing, for example, by adding geographical location information, or providing some historical trends, or even drawing on data from social media. As scientists have been pointing out for years, data from several different independent sources ‘triangulates’ findings, and makes them more reliable. And bringing together different data can provide new insights, which is why it is important to look for all data, and not just the obvious sources.

There are, of course, concerns about data security, and particularly privacy. With the impending arrival of GDPR, companies have to be very careful about how they hold and handle personal data, including anything drawn from social media. It is, therefore, very important for companies to be confident that they understand what data is being used by all teams and staff, including product designers.

It’s not what you’ve got, it’s what you do with it that counts

Once you have good quality data, what then? The next important issue is the quality of the analytics. You can have all the data you need, but if you ask the wrong questions, or use the wrong analytics processes, you will not generate insights. Companies can make a number of choices about data science at this point, and these will have an effect on outcomes.

There are two real options: self-service or via data scientists. Self-service analytics tools are likely to be extremely useful for product designers. After all, who knows better than the product designer what questions need to be asked? Simple, straightforward visualization and analytics tools can enable them to do their own analytics and feed the insights back into the product development process.

But at the same time, product designers may or may not have any expertise or understanding of data analysis. Their skill set is usually very different from that of data scientists—and rightly so. They may therefore need help choosing the right tools for the job, and also ongoing support to get the best from the data, and really understand what is available.

Many companies are finding that architecture is a key choice in determining whether data science is useful to product designers. For example, blockchain technology can be a good way of assuring IoT data quality. At the same time, with the advent of GDPR, data science experts need to be bit more hands-on to assure the quality of the data, and particularly check that it has been suitably anonymized. Self-service should not mean ‘abandoned to their own devices’ or ‘able to do anything they want’.

To answer the original question, I think that yes, data science is keeping pace with product designers’ needs. But there is no chance to rest on any laurels. Demand will only continue to grow.


About Author

Federico Alberto Pozzi

Federico Alberto Pozzi is a senior solutions specialist in IMM & Analytics at SAS Italy. The Ph.D. he obtained in Computer Science allowed him to acquire outstanding expertise on Machine Learning and Text Analytics (in particular, Sentiment Analysis) applied to Social CRM, Social Learning and Digital Media Entertainment. He also collected important international experiences: among different international research collaborations, he had a fruitful and long collaboration with Prof. Bing Liu (University of Illinois at Chicago) and Prof. Emeritus Gautam Mitra (Brunel University, London and OptiRisk Systems). Federico has published two books on Sentiment Analysis and several scientific publications in important journals and conferences.

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