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SAP récompensée pour son soutien aux startups et son programme d’accélération

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Paris – Le 20 décembre 2021 – Pour la deuxième fois consécutive, SAP SE (NYSE : SAP) a été récompensée par le prix annuel Corporate Startup Stars Award 2021 en tant que l’une des 25 meilleures entreprises à encourager l’innovation des startups.

SAP a également été de nouveau récompensée par le Corporate Startup Accelerator Award quant à ses efforts d’accélération et de collaboration avec les startups. Le 16 décembre, la Chambre de Commerce Internationale et la société de conseil en innovation Mind the Bridge ont annoncé les lauréats de cette année lors de la cérémonie de remise des prix à Paris.

SAP adopte une approche holistique de l’innovation en encourageant une innovation organique et qui provient des startups afin de rester un acteur résilient sur le marché mondial, capable d’offrir de nouvelles innovations à ses clients. Avec son unité commerciale stratégique SAP.iO, SAP soutient les jeunes entreprises pour accélérer les nouvelles idées au sein et en dehors des structures.

“Nous nous considérons comme des intermédiaires entre les jeunes entreprises d’une part et les clients de SAP d’autre part”, a déclaré Alexa Gorman, Vice-Présidente senior et Responsable Mondiale de SAP.iO Foundries & Intrapreneurship. « Cette reconnaissance valide notre approche d’innovation ouverte pour travailler en étroite collaboration avec des startups prometteuses et diverses et apporter des innovations supplémentaires à nos clients. »

Depuis 2016, les Corporate Startup Stars Awards célèbrent les entreprises qui sont des modèles en matière d’innovation et qui s’engagent fortement à travailler avec les startups. Par conséquent, les startups désignent elles-mêmes les entreprises qu’elles estiment être les plus actives en termes d’innovation ouverte.

« Au cours des dernières années, SAP a constamment confirmé qu’elle faisait partie des entreprises mondiales les plus efficaces pour transformer l’innovation ouverte en résultats« , a déclaré Alberto Onetti, président de Mind the Bridge. « SAP s’engage auprès des startups mondiales de multiples façons, allant de l’accélération et des partenariats aux investissements et aux acquisitions. Nous avons particulièrement apprécié l’approche de SAP et notamment ses évolutions récentes. Le programme SAP.iO Intrapreneurship et SAP.iO Venture Studio constituent un point de référence intéressant, ainsi que la manière dont SAP a évolué et optimisé son accélérateur de startups d’entreprise. »

The post SAP récompensée pour son soutien aux startups et son programme d’accélération appeared first on SAP France News.

Source de l’article sur sap.com

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Qu’est-ce que l’analytique augmentée ?

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La définition la plus simple de l’analytique augmentée ? C’est une analytique qui est « améliorée » par des technologies d’intelligence artificielle (IA), notamment par le machine learning et le traitement du langage naturel (NLP).

Le machine learning automatise les processus analytiques complexes, comme la préparation des données et la génération d’informations. Le traitement par le langage naturel permet à tout utilisateur, même non formé, de poser des questions sur ses données et d’obtenir des réponses de manière simple sous forme de phrases.

Le terme « Augmented Analytics » a été inventé par Gartner en 2017 et est désormais largement considéré comme l’avenir de la business intelligence (BI) et de l’analyse de données – y compris l’analyse prédictive.

Pourquoi l’analytique augmentée est-elle importante ?

Exploiter les possibilités offertes par le Big Data

Les données représentent la plus grande opportunité de l’économie moderne. Grâce à elles, les entreprises peuvent savoir quoi produire et quand, à qui s’adresser, comment évoluer, et bien plus encore. Mais le volume de données est aujourd’hui trop important pour que les collaborateurs puissent les interpréter seuls – ou sans parti pris – et l’exigence de réponses immédiates est tout simplement impossible à satisfaire. Des technologies comme l’IA et l’apprentissage automatique sont nécessaires pour découvrir des informations significatives dans un océan de Big Data. C’est l’une des raisons pour lesquelles les analyses augmentées sont si importantes : elles combinent la datascience et l’intelligence artificielle pour aider les entreprises à analyser des ensembles de données massifs en temps réel.

Réduire la dépendance à l’égard des data scientists

Le processus d’analyse est une série d’étapes manuelles et chronophages, si compliquées qu’en général seuls les data scientists peuvent les réaliser. Ces analystes professionnels doivent :

  1. Collecter des données à partir de sources multiples
  2. Les préparer pour l’analyse
  3. Effectuer l’analyse
  4. Trouver des insights utiles
  5. Visualiser les résultats
  6. Partager les résultats d’une manière convaincante
  7. Créer un plan d’action

Le problème, c’est qu’il y a une grande pénurie de data scientists dans le monde – et les embaucher coûte cher. Si l’analytique augmentée ne remplace pas ces professionnels, elle peut réduire votre dépendance à leur égard en automatisant des processus tels que la collecte, la préparation, le nettoyage et l’analyse des données.

En plus de libérer le temps des data scientists pour des tâches plus importantes, comme l’interprétation des résultats, l’analytique augmentée peut améliorer la valeur que ces analystes apportent à votre organisation. Les analyses optimisées par l’IA et l’apprentissage automatique les aident à établir des liens qu’ils auraient autrement manqués – et à trouver des informations pertinentes en moins de temps. Ces technologies peuvent également aider des collaborateurs qui occupent d’autres fonctions analytiques – des analystes commerciaux aux analystes métier – en améliorant leurs connaissances et en les aidant à faire le travail qui était auparavant réservé aux data scientists experts.

D’ici 2025, la rareté des data scientists ne sera plus un frein à l’adoption de la science des données et du machine learning dans les organisations.

Gartner, 2018

Démocratiser l’analytique pour les utilisateurs non formés

Une autre raison pour laquelle l’analytique augmentée est si importante est qu’elle permet aux « explorateurs de données » non formés d’entrer en jeu. En automatisant les processus analytiques complexes et en permettant aux utilisateurs d’interroger les données simplement en posant des questions, les collaborateurs qui n’ont pas de compétences en datascience peuvent quand même tirer parti des analyses avancées. L’apprentissage automatique peut guider ces explorateurs de données en leur proposant des questions/réponses pré remplies – et en leur suggérant où creuser davantage.

Avec l’analytique augmentée, les réponses aux requêtes se présentent sous la forme de visuels prêts à l’emploi, comme des diagrammes, des graphiques et des cartes, de sorte que les utilisateurs n’ont pas à les créer eux-mêmes. Ces visualisations peuvent être analysées à l’aide de commandes simples, rassemblées dans des récits de données et facilement partagées avec d’autres équipes et la direction.

L’évolution de l’analytique

L’Analytique et la Business Intelligence ont beaucoup évolué ces dernières années, passant d’outils sophistiqués destinés aux professionnels des données et de l’analyse à des outils optimisés par le machine learning que tout le monde peut utiliser.

1. Analytique traditionnelle

  • Impulsée par l’IT
  • Autonomie de l’utilisateur limitée
  • Des outils sophistiqués pour les professionnels des données et de l’analyse
  • Se focalise sur le reporting à grande échelle

2. Analytique en libre-service

  • Impulsée par les métiers
  • Plus d’autonomie pour les utilisateurs
  • Interface conviviale
  • Se focalise sur la découverte par les utilisateurs

3. Analytique augmentée

  • Impulsée par l’IA et le machine learning
  • Une véritable autonomie des utilisateurs
  • Outils d’IA et processus guidés
  • Se focalise sur des informations rapides, profondes et précédemment cachées.

Avantages de l’analytique augmentée

L’analytique augmentée offre de nombreux avantages similaires à ceux de la business intelligence, comme l’amélioration du reporting et de la prise de décision, mais elle offre également un niveau de rapidité et de précision impossible à atteindre sans intelligence artificielle et apprentissage automatique. Voici quelques avantages spécifiques à l’analytique augmentée :

  • Préparation plus rapide des données : Les analystes passent environ 80 % de leur temps à préparer les données pour l’analyse. Ils exportent de grands ensembles de données et les combinent, les nettoient et les structurent avant que l’analyse ne puisse commencer. L’apprentissage automatique de l’analytique augmentée automatise ce processus, libérant les analystes pour des activités plus utiles et réduisant les erreurs par la même occasion.
  • Analyse automatisée : Les modèles d’apprentissage automatique peuvent automatiser des analyses complexes qui, autrement, prendraient des semaines aux data scientists. Les réponses et les visualisations de données sont immédiatement générées et disponibles pour les utilisateurs, qui peuvent ainsi passer moins de temps à creuser dans les données et plus de temps à interpréter les informations, à raconter des histoires de données aux dirigeants et à provoquer le changement.

Libérer la valeur des données : l’analytique augmentée fait le travail pour vous

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Libérer la valeur des données : l'analytique augmentée fait le travail pour vous

Découvrez comment l’analytique augmentée fournit automatiquement des réponses aux requêtes, afin que les utilisateurs passent moins de temps à explorer les données et plus de temps à agir.

  • Des insights profonds : Les machines peuvent examiner les données d’une manière qui serait impossible pour les humains. Elles peuvent examiner des ensembles de données beaucoup plus vastes sous plus d’angles et trouver des corrélations, des relations -via des modèles statistiques- invisibles à l’œil humain. Les machines peuvent comprendre les données rapidement et à grande échelle, renforcer l’intelligence humaine par des informations impartiales et indiquer aux utilisateurs où porter leur attention.
  • L’analyse conversationnelle : Le traitement du langage naturel – la même technologie d’IA conversationnelle qui équipe des assistants numériques comme Siri et Alexa – permet aux utilisateurs professionnels n’ayant aucune connaissance des langages de requête ou du code de poser des questions de manière conversationnelle. Et la génération de langage naturel (NLG) leur donne des réponses sous forme de phrases complètes, écrites ou orales, qui résument ou expliquent les résultats.
  • Contexte instantané : Les informations sans contexte n’ont aucun sens. En tenant compte de l’intention et des comportements des utilisateurs, les algorithmes d’apprentissage automatique peuvent fournir des informations contextuelles prêtes à être utilisées. En outre, en démocratisant l’analytique, les cadres et les employés expérimentés peuvent enrichir les informations grâce à leurs connaissances et à leur compréhension approfondie des business models et des opérations.

Libérer la valeur des données et de l’analytique : la valeur ajoutée de l’analytique augmentée

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Libérer la valeur des données et de l'analytique : la valeur ajoutée de l'analytique augmentée

Hyoun Park, PDG et analyste principal chez Amalgam Insights, explique comment l’analytique augmentée fournit un contexte, afin que vous sachiez réellement ce que vos données contiennent.

Cas d’utilisation de l’analytique augmentée

L’analytique augmentée a le pouvoir de révolutionner les processus d’entreprise, mais à quoi cela ressemble-t-il dans le monde réel ? Voici quelques exemples de cas d’utilisation de l’analytique augmentée dans les domaines de la finance, des ventes et du marketing, de la production, des ressources humaines et du recouvrement.

L’analytique augmentée pour la finance
Un Analyste peut utiliser l’analytique augmentée pour prévoir et contrôler facilement les frais de voyage et de représentation (T&E) dans différents départements.

L’analytique augmentée pour le recouvrement
Les responsables du recouvrement peuvent utiliser l’apprentissage automatique dans l’analytique augmentée pour anticiper les retards de paiement, déterminer la bonne stratégie de recouvrement et maîtriser les flux de trésorerie.

L’analytique augmentée pour les ventes et le marketing
Les équipes de vente et de marketing disposent d’une meilleure connaissance des clients – et d’une identification rapide des opportunités de ventes croisées et incitatives – grâce à l’analytique augmentée.

L’analytique augmentée pour l’industrie manufacturière
Un analyste d’un fabricant d’acier peut utiliser l’analytique augmentée pour prévoir, surveiller et contrôler les dépenses dans différentes usines.

L’analytique augmentée pour les RH
Les responsables RH peuvent prédire le turn-over des collaborateurs, en comprendre les raisons et prendre des mesures correctives pour conserver les meilleurs éléments – tout cela grâce à l’analyse de l’IA.

 

Pictogramme d'un graphique pour représenter l'analytique augmentée

Découvrez SAP Analytics Cloud

Exploitez la Business Intelligence, l’analytique augmentée et la planification pilotées par l’IA dans une solution unique et facile à utiliser.

En savoir plus

 

The post Qu’est-ce que l’analytique augmentée ? appeared first on SAP France News.

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Integrating Alexa With Microsoft LUIS

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Alexa has a very good Natural Language Processing engine. However, there are other NLP engines in the market that can be used and those are including more and more capabilities.

  • Integrating Alexa with Microsoft LUIS
    • Prerequisites
    • Preface
    • Setting up our Alexa Skill
    • Creating Azure Cognitive Services
    • Creating MS LUIS App
    • Calling MS LUIS from Alexa Skill
    • Final Result
    • Resources
    • Conclusion

Prerequisites

Here you have the technologies used in this project

Source de l’article sur DZONE

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Designing UI for the Voice Era

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The world of web design is incredibly dynamic. Every year, new trends and opportunities emerge, primarily driven by the arrival of modern technology. 

In recent years, we’ve seen various updates to the web design landscape, such as the arrival of AR and VR solutions for making mixed media. Video content has increased in quality, while the demand for inclusivity and usability has transformed the way that we build everything from websites to apps. 

Yet, for the most part, web design trends have continued to focus on the visual. 

When we hear the word “interface,” we often think of the graphical user interface – the ultimate way to connect users with sites. However, now we have a new, more natural way for customers to interact with their digital tools… The era of voice is here. 

Designing for the Age of Voice

The technology sector has made incredible progress in the development of things like Automated Speech Recognition and Natural Language Understanding. 

Thanks to updates in the way that machines process and understand human language, voice recognition accuracy is now at 90% and above. More than ever before, users can speak to a smart assistant, speaker, or phone-based application, and get the results that they’re looking for without error. 

The simplicity of communicating with technology via voice means that users have adopted this technology at an incredible pace. Half of all searches will be made with voice by the end of this year.

We’re standing on the edge of a fundamental shift in the way that we interact with computers and critical tools. As designers and developers, we need to be ready to embrace this new medium. 

With that in mind, here’s what designers need to think about when designing for voice UI. 

1. Decide How to Experiment with Voice

There are various steps involved in making a website more “conversational.” One of the first steps for any designer or developer is to think about the kind of voice-based interactions they’re going to enable for an app or website. 

For instance, rather than embedding voice technology into a website, you might decide to create a separate Amazon Alexa “Skill” for devices like the Echo. Companies like Capital One have already invested in this technology so that users can ask their smart speaker about their balance, rather than opening a laptop and logging into the site. 

To determine what kind of voice experiences you should be creating for your client, work with them on a customer journey map. Using this map of interactions that the customer has with your client on a regular basis, you can highlight areas where voice interactions might fit into the user flow. 

For instance, if customers are constantly asking questions about a brand or its service, an FAQ page that’s equipped with a bot that can respond to voice queries could be an excellent choice. 

UI design should always solve problems. Examining the frictions and frustrations that your client’s end-users encounter during their journey will help you to decide which direction to take with your voice UI experience. 

2. Examine the Anatomy of Voice Commands

Before designers can create a dialog flow for their voice UI, they need to understand how voice commands work. The key to success in a successful design for voice is understanding the objective of the interaction. A voice consists of three crucial factors for designers to consider:

  • Intent: Intent represents the subject and context of the voice command. A high utility interaction involves a request for a specific task. For instance, your users might request that your app gives them a list of five-star hotels in a specific area. Designing for these requests is often straightforward because what the voice algorithm needs to do is clear. However, low-utility requests can be harder to decipher, such as “hotels near me,” because there’s less specificity for the bot to work with. 
  • Utterance: Utterance refers to how a user phrases a command. For instance, in the case of looking for five-star hotels in Amsterdam, the customer might say “show me hotels,” or they might ask for “places to stay”. Designers must consider every variation of an utterance for their voice command UI. 
  • Optional variables: This refers to the extra filters that your voice UI needs to be aware of. In the case of five-star hotels in Amsterdam, the descriptor “five stars” is optional. The optional input needs to overwrite default values and bring more detail to the search. 

SideChef, for instance, is a voice-activated recipe app that offers narrated guidance to users and allows customers to search for recipes based on their specific needs. The app comes with a wide range of variables built-in, allowing users to customize their searches according to descriptors like “vegetarian” or “quick” meals. 

3. Learn How to Prototype with Dialog Flows

Learning how to leverage a complex UI strategy like VUI takes time and practice. Prototyping designers will often have to think like scriptwriters, designing various dialog flows to suit the different needs of customers, and the numerous interactions they might face. 

Dialog flows will outline:

  • Keywords that lead to the interaction
  • Branches that represent where the conversation might lead
  • Example dialogs for the user and the voice assistant. 

Practicing your dialog flows with scripts that illustrate the back-and-forth between the voice assistant and user will help designers and developers to understand the various nuances that can appear in a customer to robot interaction. 

Remember, while a crucial part of good voice UI design is keeping the communication conversational and straightforward, you will need to ensure that there is a dialog flow in place for every discussion that may occur between end-users and their apps, website, or digital tools. Users don’t want to feel overloaded and overwhelmed, but they need to ensure that they can complete their tasks too.

Consider the voice-based game RuneScape: One Piercing Note, for instance. 

The developers behind this app allowed players to speak with other in-game characters and use commands like “pull the lever” or “open the chest.” In designing the playable components of the game, the designers needed to think about every possible interaction that a player might have with different parts of the story while ensuring that users didn’t stray off track. 

A Few Tips for Voice UI Design 

Voice UI design can be very complex, mainly if you’ve never created something using voice as your only input before. However, once you get used to creating dialog flows, the whole process starts to feel a lot easier. 

As you’re designing, remember to:

  • Always confirm when a task is complete: When designing a checkout flow for an eCommerce page, one of the most crucial screens for a designer is the confirmation page. It shows the customer that the transaction has successfully been completed and stops them from worrying whether they’ve done the right thing. The same concept applies to Voice UI design. If your client’s end-user asks a voice-activated app to book an appointment with their therapist, for instance, they want to know that the appointment has been successfully booked and added to their calendar. Determine how you’re going to deliver the peace of mind your customers need. 
  • Create a strong strategy for errors: Designers and developers are still in the very early stages of experimentation with voice UI. This means that there’s a good chance that something could go wrong with your applications and tools from time to time. Having a strong error strategy in place is crucial. Always design a dialog flow scenario that allows the assistant to respond if they don’t understand a request, or don’t hear anything at all. You can also implement analytics into these situations to identify misinterpretations and improve usability in the future.  
  • Add extra layers of security: Various Voice UI solutions like Google Assistant and Alexa can now recognize individual voices. This is a kind of biometric security that’s similar to face or touch ID. As voice recognition continues to improve, it’s essential to ensure that you’re adhering to the latest guidelines in security. Additional authentication may be required for some companies. For instance, passwords, face recognition, or fingerprints might be needed for things that require payments and transactions. For instance, the Duer voice assistant uses face recognition to both approve payments, and make meal recommendations based on previous purchases. 

Are You Ready for the Voice UI Revolution?

Voice-based user interfaces are here to stay.

In the years to come, the chances are that developers and designers will need to learn how to use voice more consistently as part of their interface strategies. 

The good news is that although voice takes some getting used to as a design tool, it’s easy enough to make sure that your projects are moving in the right direction. Just like any other kind of design, implementing voice means thinking about whether the interactions and experiences that you’re delivering to end-users are seamless, effective, and valuable. 

Succeeding in voice UI isn’t just about adding the capacity for voice into your designs. It’s a matter of learning how to make user’s lives easier with the power of voice.

 

Featured image via Unsplash.

Source

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Alexa and Kubernetes: Deploying the Alexa Skill on Google Kubernetes Engine (IX)

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Now, we have everything prepared and ready to go to a Kubernetes Cluster in a cloud provider. It is a fact that creating a cluster in any cloud provider manually is a difficult task. Moreover, if we want to automate this deployment, we need something that helps us in this tedious task. In this article, we will see how to create a Kubernetes Cluster and all of its required objects, deploying our Alexa Skill with Terraform using Google Kubernetes Engine.

Pre-Requisites

Here, you have the technologies used in this project:

Source de l’article sur DZONE

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Generating Business Insights Using Advanced Analytics

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Machine learning-based applications have seen significant commercial success in several mainstream consumer applications in the recent past. Self-driving cars, stock-trading bots, robo-advisors, Amazon’s Alexa, and Apple’s Deep Fusion and Siri are some of the renowned examples of commercial success with artificial intelligence and machine learning. AI has also made our lives easier by improving the customer experience of the products we use. Google’s text generation software, Netflix’s recommendation engine, and Facebook and Twitter’s fake news detection are other prime examples. In fact, every single technology company uses AI in its mainstream applications either directly or indirectly. Non-technology companies are also using AI to improve customer experience, improve efficiency, and generate new revenue streams. Chatbots, robo-advisors, systems that predict system failures, and products that generate efficient supply chain routes are some of the prominent ways in which non-technology companies use AI. This is leads to a popular belief that AI and ML are primarily used by technology companies or they are being used by non-tech companies to build AI-based products.

This popular perception is not true. There are plenty of avenues in which AI/ ML is being used or can be used by non-tech and non-product-based groups to generate insights. In this article, I am going to share with you four ways in which you can augment advanced analytics into your analytics strategy to generate insights.

Source de l’article sur DZONE

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The Quest for Convenient and Efficient Speech Recognition

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Introduction

A few years ago, speech recognition technology was a punchline in many a sitcom’s jokes. Understandably, the technology was in its infancy prone to errors. Now, reaching new levels of maturity and wide acceptance, Amazon’s Alexa is just one example of this, the technology is now being implemented in novel ways. The need for developing APIs has now evolved from making the technology work to how it can be done conveniently and efficiently.

Accessibility

Moving beyond the technology’s threat to privacy one of the greatest virtues of speech recognition technology is its accessibility. In providing the disabled with a technology that allows interaction and interfacing with other technologies, speech recognition has become a technology at the forefront of providing accessibility and promoting inclusivity. For the workplace, the benefits range from more productive employees to promoting greater diversity amongst the workforce.

Source de l’article sur DZONE

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Should We Be Designing For Voice?

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Voice is one aspect of technology that is getting bigger and bigger, and showing little sign of relenting. In fact, 2019 data revealed that 22% of UK households owned a voice-controlled digital home assistant device such as an Amazon Echo or Google home. This is double the figure recorded in 2017 and it is predicted that over the next five years nearly 50% of all homes will have one. Smart home adoption rates are increasing, and it shows how voice control is something we are all becoming more accustomed to.

With these high figures, does it follow that voice should be something web designers build into sites? Or is it merely a gimmick that will die out and render sites with hardware and complex design issues? You only have to look at the failed introduction of Google glass to see that certain technological advancements don’t always have the outcome that might be expected.

Multiple Voices

One of the first issues with voice is establishing whether you want sites to recognise everyone’s voices, or just those who have registered. If you’re using the site in a crowded room will it pick up on snippets of conversation from others and think these are instructions? Google Home has a feature whereby you have to register your voice with its app to use more personalised features such as the shopping list. Is this the sort of thing websites would need?

Accents

The implementation of voice is complex, not only does it need to understand certain languages (such as English), but all the accents and variations too. With 160 English dialects alone, that is a lot that the technology needs to understand – not including mispronunciations, slang, and colloquialisms. Also, if a site is used all over the world (which many are) how many languages will it need to know?

Privacy Issues

if there are clips of your voice out there on the web…it can easily be imitated

If a website involves a feature such as online shopping or other functions which require sensitive details to be input, this could put people off using voice. Users need to know where this saved data is being stored, how it will be used and if it is secure. In 2018, HMRC had signed up about 6.7 million people to its voice ID service and HSBC said over 10,000 were registering each week. This shows many trust the service, but experts say that if there are clips of your voice out there on the web (such as in a podcast) it can easily be imitated. Bringing with it security and privacy issues.

According to futurologist Dr Ian Pearson, who invented the text message back in 1991, it won’t be long until we can complete a financial transaction with just a few words and a gesture. This can be a time-saver for things such as online shopping, but we need to ensure there are the correct security steps in place.

Users Don’t Talk The Way They Type

When speaking we tend to use shortened and more colloquial language as opposed to when we type. The voice function on a website will need to be able to adapt for this. One example is if you are filling in a form or comment box by voice for a website, you will need to tell it what to punctuate, letting it know where to add a comma, exclamation mark etc.

Website Processes Need to be Simpler

With the web as we use it now, we often browse through pages, reading other snippets of information before clicking through to the page we want. With voice recognition it will cut out these middle steps. For example, if you are looking for a recipe of something specific, you will just say the command “Show me the … recipe” and it will take you straight there. This direct access to what we are looking for could lead to a simplification of websites.

Regular Updates

With websites as they are now, they need updates semi-regularly, depending on how they are built, how complex they are, and what features we have built into them. A voice-based site will need updating regularly, whether to add new words or processes or to keep up with the fast-adapting technology. It might end up being quite a complex process.

Mistrust

While there are more of us now than ever using voice control via tech such as Alexa, Google, and Siri, there is still a level of mistrust over it. It’s still not quite clear where data is being stored, if it is being stored, and how easy it could be to abuse.

Larger Storage and Bandwidth

If a site is built for voice, will it utilise a ready-built plugin or will it have its own software built by developers? Will this feature require a greater amount of storage and bandwidth to cope with it? These are further factors to consider when thinking of the future of implementing voice to websites.

We Still Don’t Know Where It Will Go

Voice technology while working in some respects, is still a bit of a grey area when it comes to future use. Will it be the next big thing as many have predicted, or will it simply die down?

Look at Google Glass – highlighted as the big new technology, they soon died down and were eventually discontinued. Smart watches were another thing. You can see their initial downfall by reading an article published in 2017 about smartwatches – how major smartwatch makers such as Apple and Samsung rushed into the market before the technology was ready and they subsequently failed. Motorola exited the smartwatch market, Pebble and Jawbone shut down and Fitbit sold 2.3 million fewer devices than in their previous quarter. It was perceived as being a fad. However, fast forward to 2020 and more people than ever are wearing and using smartwatches. The smartwatch market was valued at shipment volumes of 47.34 million in 2019 and is expected to reach 117.51 by 2025, reaching a growth of 15.4 over the next five years.

Will voice follow a similar trend?

No More Impulse Buying

People enjoy browsing websites and many businesses rely on user’s impulse buying and ask their websites to be designed to reflect this. With voice taking you directly to the page’s users want to find, will they bypass these potential selling traps and just buy what they want – rather than added extras? Will it end up being a negative for businesses and see users not as satisfied for the experience?

Voiceless Still Matters

You will also have to remember that not all devices might work with voice, or people might be browsing somewhere where voice cannot be used. This means in the design process it needs to work both for voice instruction and manual use. It needs to work just as well for both to ensure the customer journey isn’t affected.

There are many ways voice can affect how we design websites in the upcoming future. It’s important to take note of market trends and usage – seeing how people use voice and thinking of the customer journey. It’s vital we don’t forget the end goals of websites – whether it’s to inform or to sell, the implementation of voice needs to assist this process not make it harder.

 

Featured image via Unsplash.

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