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Popular Design News of the Week: May 3 2021 – May 9, 2021

Every day design fans submit incredible industry stories to our sister-site, Webdesigner News. Our colleagues sift through it, selecting the very best stories from the design, UX, tech, and development worlds and posting them live on the site.

The best way to keep up with the most important stories for web professionals is to subscribe to Webdesigner News or check out the site regularly. However, in case you missed a day this week, here’s a handy compilation of the top curated stories from the last seven days. Enjoy!

White House Launches AI Website

Become A Better Frontend Developer

Hello Weather

Sprint UI Design System Generator

How Pixar Uses Hyper-Colors to Hack Your Brain

Bootstrap 5

Microsoft’s New Font: Your Work Will Soon Take On A New Character

HTML Tips

Everything You Need to Know About UX Writing In Web Design

3 Essential Design Trends, May 2021

Source

Source de l’article sur Webdesignerdepot

9 mai 2021/par Service comm.

API Publish/Subscribe Between Zato Services

Actualités, Méthodes et organisation des process IT

One of the additions in the upcoming Zato 3.2 release is an extension to its publish/subscribe mechanism that lets services publish messages directly to other services. Let’s check how to use it and how it compares to other means of invoking one’s API services.

How Does It Work?

In your Zato service, you can publish a message to any other services as below. Simply point self.pubsub.publish to the target service by the latter’s name and it will receive your message.

Source de l’article sur DZONE

8 mai 2021/par Service comm.

How To Power Through Designer Apathy

Actualités, Actualités

Sometimes you just don’t give a damn anymore. Possibly the only thing worse than designer’s block is designer’s apathy: that sinking feeling you get when you realize that you just don’t care about this particular piece of work anymore is disheartening.

The dread of going back to it is paralyzing.

There are many reasons you can stop caring about your work. Maybe you’ve just done the same thing too many times in a row. Maybe your client is insisting on asking for things you know won’t work for them. Maybe something much more important just happened in your life, and you’ve got bigger things to worry about. You could be discouraged by the apparent ‘sameness’ of bandwagon-hopping designs.

I’ve been not caring about my work ever since I was first asked to pick up my toys

Whatever the reason, we all experience times when we know exactly what we have to do… we just don’t care.

I’m something of an expert on this phenomenon. I’ve been not caring about my work ever since I was first asked to pick up my toys. Worse, I have the attention span of a goldfish, even now.

Web design is different. When I discovered it, it was new, exciting, and I could do it on the computer. I loved it, and I still do. Writing code that makes design happen in a browser window will never get old for me.

But even so, sometimes, a particular project will make me want to throw up my hands in exasperation and play video games ‘til Judgement Day. I’d welcome Skynet with tacos and RPGs.

So what do we do about it? First, answer this question: who is the project for?

For A Client

If the project is for a client, it’s just gotta get done. There’s no way around that. You made a commitment. You’re going to follow through and give it your best possible effort because you’re a professional. Anything less would be wrong.

However, that doesn’t mean you have to just power through with only coffee and misery for company. There are things you can do to make the work easier on yourself. The less miserable you are while you work, the better quality you can deliver.

For Yourself

There are a couple of schools of thought here. The first is that it’s perfectly fine to give up on personal projects when you stop caring. I mean, it’s your free time. Why spend it on something you don’t care about?

On the other hand, is a commitment made to yourself any less important than a commitment made to someone else? Many people seem to be perfectly fine with breaking promises to themselves when they’d never willingly do that to a client. Is that wrong?

I usually buy myself a drink and forgive myself, but it’s worth thinking about.

The deciding factor for me is whether my personal project will have any sort of lasting benefit. If whatever I’m designing, writing, or making counts as a long-term investment in my career or quality of life, then it absolutely has to get done, even when I’m not feeling it. Otherwise, I call it a learning experience and move on.

How To Power Through

So, for whatever reason — whether because you have to, or you want to — you’re gonna power through. Here are five ways to do it in style:

1. Start

The hardest part of doing work you don’t care about is starting. This is when you’ll be tempted to procrastinate until the last minute. Try not to.

2. Switch To A Different Part Of The Project

If you can safely (without causing problems) work on a different aspect of the project for a while, try that. The mere variety, the break from the work in front of you before, can boost your morale.

Indeed, working on a different part of the project can give you ideas of getting the most troubling bits done faster or more easily.

3. Do Something Old In A New Way

This one has its pros and cons.

Pro: You can look at this project as a chance to try out a new grid framework, script, code editor, or another tool of some kind. Injecting the process of discovery into an otherwise boring project can make it a lot more fun and even make you look forward to working on it.

Con: You’ll need to plan for extra hours and use some version control; because bringing a new tool or process into play is almost guaranteed to make something interesting go wrong — when this happens, you probably shouldn’t bill the client for the extra hours spent on StackOverflow.

4) Make Like Aziz Ansari And Treat Yo’self

Celebrate the milestones of your project. Don’t celebrate with video games if you need to get any more work done that day. That can go very wrong. But do celebrate. Reward yourself because you’re doing something difficult.

Have a snack. Give yourself a round of applause. Whatever it takes, make yourself look forward.

5) Outsource It

As a last resort, you can always outsource the project to someone else. Just make sure it’s someone you can trust to deliver the same quality of work you would normally provide yourself. Make sure to check it over before handing it off to a client.

Alternatively, you could just outsource the bits of the work that you don’t like. Either way, this is a risky strategy because whoever you outsource to might experience delays or, ironically, not care about the project.

Conclusion

You can do it! I believe in you. The really, really boring projects can seem like huge sinkholes of sadness, but they don’t last forever.

Featured image via Pexels.

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The post How To Power Through Designer Apathy first appeared on Webdesigner Depot.

Source de l’article sur Webdesignerdepot

5 mai 2021/par Service comm.

Poll: Is Basecamp Right To Shutdown Politics At Work?

Actualités, Actualités

This week, in a move like something from a particularly eventful episode of The Office, popular project management app company Basecamp banned political and societal discussion in the company’s internal communications.

In a post that has been revised for “clarification,” the company’s co-founder Jason Fried listed six rules for employees: No societal or political discussions at work; No more ‘paternalistic’ benefits; No more committees; No more lingering on past decisions; No more 360 reviews; No forgetting what we do here.

A follow-up post from Heinemeier Hansson notes that Basecamp will still permit discussion of issues deemed central to its business like anti-trust and privacy; certain civil liberties are to be championed, while others, like racism and climate change, are not.

On the surface, it seems reasonable, Fried and co-founder David Heinemeier Hansson would like you to believe that it is. After all, people are paid to work, not soapbox, right?

So why, if they’re the ones being protected, are Basecamp’s employees angry about the move?

It turns out, multiple sources from inside Basecamp are reporting that the ‘political’ and ‘societal’ issues referred to in Fried’s public memo were, in fact, frank and open conversations about Basecamp itself.

As reported by The Verge, way back in 2009, a list of ‘funny’ customer names began circulating at the company — hardly respectful, potentially racist, and certainly inappropriate. The misalignment between co-founders and staff occurred when staff members attempted to hold discrete conversations about this and numerous other diversity and inclusivity failings at the company. Fried’s move appears to be a direct attempt to halt criticism of the status quo at Basecamp.

Basecamp itself is a highly political organization: The co-founders have written several books advocating certain societal change; they even provided a campaign headquarters and substantial donation for a candidate for Chicago mayor. Both co-founders are highly active on social media, using their business positions to elevate their personal views.

The truth is that the solo entrepreneur is an almost mythical beast. Successful startups require contributions from a range of skills and experience beyond any one individual. Jason Fried may be the frontman, strutting up and down the stage in spandex pants, with David Heinemeier Hansson playing lead guitar with his teeth, but behind them, there’s a drummer keeping time, and behind them all, there’s a crew of roadies without whom none of the equipment will arrive, let alone sound good.

Basecamp’s founders argue that the company has a mission, and that mission is to create apps that streamline the workplace. But how can you develop a product that is inclusive if staff cannot discuss what inclusive means? The answer is, you can’t.

Discussing racial bias in advertising or the impact of company wastage, climate change, or gender pay gaps in HR meetings are all political and societal and lead to a healthier, more united company.

As designers, we often say that you cannot not communicate; every decision is a design decision; there is no such thing as “adesign.” Likewise, choosing to be apolitical is itself a political choice. The only way it is feasible to run a company like this is to treat employees like robots (in the word’s original sense).

If employees feel the need to discuss exclusionary policies in the workplace, do the company founders, who benefit from those policies (or they would not be in place), have a moral or legal right to restrict those discussions?

Although it is the first point in Fried’s list that has drawn most ire, it is the fourth item on the list that is most telling: “No more lingering or dwelling on past decisions.” Like a parent answering, “Because I said so,” Fried’s attitude to his staff is laid bare in one statement.

It turns out two wealthy white men would rather their employees not try to change the world or even their workplace.

When Coinbase announced a similar move last year, it lost 5% of its staff. If Basecamp suffered the same loss, it would amount to three people. Hardly a disaster. The question for the founders — who, judging by the number of follow-ups and clarifications they’ve published, are aware the ice they’re on is perilously thin — is whether this kind of controversy creates irreparable reputational damage.

Featured image via Pexels.

Source

The post Poll: Is Basecamp Right To Shutdown Politics At Work? first appeared on Webdesigner Depot.

Source de l’article sur Webdesignerdepot

29 avril 2021/par Service comm.

10 Elements of Landing Pages That Convert

Actualités, Actualités

Landing pages are central to successful marketing campaigns; they allow you to target particular customers with particular solutions to particular problems.

It’s easy to confuse what a landing page is because users “land” on many pages. When we talk about landing pages, we mean a page that is entirely dedicated to a particular type of customer. In fact, if we could create a unique landing page for each individual user, that would be awesome.

You might think your homepage is a landing page, but it’s not; users reach your landing page in various ways — directly, via organic search, or backlink. A landing page is normally dedicated to a specific marketing campaign. It is accessed from a link in an email, via social media, or most often via a PPC (Pay Per Click) advert.

Here are 10 elements of landing pages that are proven to convert successfully:

1. Use A Single Call To Action

Your potential customers must know how to move forward with your product or service as early in the experience as possible.

Are they signing up for a free trial? Are they signing up for your newsletter? Are they buying a product? Are they contacting you? Whatever you need them to do, make it clear.

The Hick-Hyman law of UX says the more choices you give a user, the less likely they are to make any choice at all; conversely, the fewer choices, the greater the likelihood that they’ll move forward.

Give the user one choice: click the button, or don’t click the button. A single CTA will out-perform multiple options.

2. Keep Forms Simple

Often, your landing page will need a potential customer’s information. They might be creating an account, setting up a trial, or just joining your newsletter.

If the potential customer is signing up for a trial, by all means, ask for their email address. But you don’t need their cell number, their mother’s name, the street they grew up on, their birthday, or any of that other junk that’s used to profile users.

Whatever the purpose, keep your form ultra-simple. That means as few fields as possible. If you really want it, give the user the option to fill it in later as part of an onboarding process — when they’re already invested — but not on the landing page.

3. Make the Headline Punchy

The first thing your potential customer sees on your landing page is the headline, so make it count.

Half a dozen words are usually more than enough. Your goal is to keep it short enough that the potential customer has read the headline before they realize it.

Often, you’ll want to clarify the statement with more information. That’s fine as a sub-heading after you’ve grabbed their interest, but make sure you grab their attention first.

The headline “Coyote Anvils” is best followed by the sub-heading “You’ll be eating roadrunner for dinner!”

Your goal for your headline is to explain your product or service in 2–3 seconds.

4. Center Your Content Around Your Value Proposition

What makes your product or service stand out? What makes it better than the competition? If you’re not sure, spend some time checking out companies in your space.

Creating a value proposition can be one of the toughest challenges a business faces because you need to put yourself in your potential customer’s shoes. But if you get this right, it will carry your marketing. You need to find the benefits within your product or service, not the features.

Value propositions are best when backed by facts. The “World’s Most Accurate Anvils” is best backed by proof: “9/10 coyotes said they were more likely to hit their target than themselves when using our patented AccuAnvil.”

5. Lists, Lists, and More Lists

You’ve got seconds to engage your potential customer, perhaps even less. One way to grab them is with a great headline, but you have to keep them interested beyond the headline.

One great way is bullet lists with short entries. Short-item lists naturally pull our eyes down the page because our eyes take in the whole line in one glance; we don’t need to read to absorb the information.

The longer you can keep someone on the page, the greater the likelihood they’ll keep looking, so pulling them down the page with lists is a great tactic.

6. Exploit the Zeigarnik Effect

The Zeigarnik Effect says that people remember incomplete experiences better than they do completed ones. This is because when a task is seen as completed, it can be filed away as a memory, but if it’s incomplete, then it remains at the front of your mind.

This is a boon for designers creating landing pages because we can create a situation where the potential customer begins an onboarding process and is aware that it hasn’t been completed — they might need to verify their email address, for example.

The lack of completion keeps the landing page and the product or service fresh in the potential customer’s mind. So when they see that onboarding email, they’ll use it.

7. Proof

Anyone can put up a website. It’s easy. And as a result, potential customers don’t necessarily trust you.

One way you can combat this is with some form of proof. That may be in the form of official certifications, or featured testimonials, or just independent reviews.

It rarely occurs to potential customers that you’re cherry-picking the testimonials and reviews you’re choosing to display, so even if only some of your reviews are good, it’s worth including them.

But be careful not to sound too good. If you post nothing but 5* reviews, people will smell a rat; that 3* review may actually do you a favor by making the 5*s seem more genuine.

8. Predictive Images

Potential customers lack imagination, they don’t have all the facts, and unless your product or service is very basic, they may not fully understand what the product does for them.

Use images to quickly show them what life may be like using your product or service. Paint an appealing picture. If they can see themselves in the image, they’ll grant you a little more time to persuade them in the form of further content.

9. Continuity

How did the potential customer arrive at your landing page? Chances are it was via a PPC link, or if you were lucky an organic search link. However they arrived, they were in a certain frame of mind, with a certain problem they wanted to solve; they aren’t going to take kindly to being diverted onto a different train of thought.

Your landing page has to match the tone, style, and value proposition of your adverts. The potential customer’s experience of your organization begins with the advert, not the landing page, so make sure that you don’t break the spell. If your landing page doesn’t match your advert, you could lose the potential customer altogether — and increase your bounce rate while you’re at it.

Remember: the customer was attracted by something in your advert, so give them the same attractive qualities on your landing page.

10. Drop the Nav

Most sites have a single main menu and a rich footer with links to customer service, contact pages, and so forth. These are detrimental on a landing page because you’ll leak traffic to other, less-focused parts of your site.

Your landing page is a streamlined selling machine. The only link you want on the page is your CTA.

It’s fine to keep legal text and even links to privacy policies — users rarely click those anyway. You can also link to your homepage using your logo. But don’t add any navigation that invites a click, or you’ll dilute all the work you’ve put in.

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The post 10 Elements of Landing Pages That Convert first appeared on Webdesigner Depot.

Source de l’article sur Webdesignerdepot

28 avril 2021/par Service comm.

20 Best New Websites, April 2021

Actualités, Actualités

This month’s collection contains a combination of big and bold, and clean and minimal. Although basic minimalism is still trendy, with lots of white space and greyscale type, we are seeing it softened with color. This is implemented differently, ranging from hints of off-whites in images to gentle pastels as section backgrounds.

Playing around with type and using typefaces with a few characteristic quirks is another way minimalism is being tempered without negating the overall effect. Plus, we’ve got some strong examples of type rules being deliberately broken to good effect. Enjoy!

Crusta C

The new website for seafood company Crusta C makes clever use of the company’s simple logo mark ‘C’ with a cutout video effect.

How Many Plants

How Many Plants is a guide to house plants and how to look after them. A good combination of illustration and space gives a friendly but efficient feel.

Out of the Valley

Out of the Valley, make bespoke and prefabricated cabins focusing on natural materials and traditional craft. The subtle changes in background color add warmth to the minimal layout.

Saskia Wilson

Portfolio site for photographer Saskia Wilson. This is absolute simplicity, with a clear grid and nice, bold type to bare minimum text.

Made Thought

Design studio Made Thought has some pretty prestigious clients; for a designer, it doesn’t get more prestigious than creating a new brand identity for MoMA. Their bold aesthetic and approach explain their success.

The Great Lake

For-fun sites like The Great Lake are a great way for web creatives to show their skills. This one from designer and front-end developer Anna Sherruble is visually appealing and has some informative content.

Acayaba + Rosenberg

Architects Acayaba + Rosenberg use carefully curated photography and subtle scrolling animation to pull the user in and create a pleasing browsing experience.

Omexco

Soft colors and a well-ordered grid recreate the feel of a mood board that prevents this site for Omexco from appearing cluttered and overly busy while showcasing multiple products.

Johan Belin

For his own site, digital creator Johan Belin has opted to show off his skills by creating this single-page site instead of simply showing work. This can be a risky tactic, but it works here.

La Nouvelle

A combination of contrasting and complementary color combinations creates freshness in this site for digital agency La Nouvelle.

Found

Found Studio’s website uses a very basic grid layout to allow the work to stand out; varying the typeface, weight, and style within sections of text creates individuality.

CKMS

CKMS is a design and build company. Their site is minimal but with a few nice little touches, like the background color change button in the bottom right corner.

Slow

Slow is a collective of people–largely artists, designers, artisans–aiming to implement and live by the slow movement principles. The design of their site reflects these aims, creating a sense of calm and deliberation.

Anne Frank House

Practical information for visiting the Anne Frank House and museum is combined with historical information and educational resources in this thoughtfully structured and visually engaging site.

Runway

Runway is a platform for publishing open-source, pre-trained machine learning models, as well as for training your own models aimed at artists and filmmakers. If this site aims to make the user want to try Runway, it succeeds.

Fat Free

Fat Free video branding agency add warmth to their minimal site with soft color and occasional illustration.

Pinch

The furniture and other interior products produced by Pinch Design aim for a quiet, elegant aesthetic, and their website reflects that with pale grey and generous spacing.

Sentempo

Digital studio Sentempo manages to achieve glossy without being overdone. The star dividers are a nice detail.

One Year

Many companies, including creative agencies, have come up with ‘what we did/achieved in the last year’ microsites. This one from Context Creative succeeds as a good advert for them.

GT Super

This single-page intro to GT Super font has a certain drama in keeping with the font itself and allows you to play around with the size, weight, and style of the font in most sections of the text.

Source

The post 20 Best New Websites, April 2021 first appeared on Webdesigner Depot.

Source de l’article sur Webdesignerdepot

26 avril 2021/par Service comm.

No-Code: »It’s a Trap! »

Actualités, Méthodes et organisation des process IT

Gartner predicts that by 2023, over 50% of medium to large enterprises will have adopted a Low-code/No-code application as part of their platform development.

The proliferation of Low-code/No-code tooling can be partially attributed to the COVID-19 pandemic, which has put pressure on businesses around the world to rapidly implement digital solutions. However, adoption of these tools — while indeed accelerated by the pandemic — would have occurred either way.

Even before the pandemic, the largest, richest companies had already formed an oligopsony around the best tech talent and most advanced development tools. Low-Code/No-code, therefore, is an attractive solution for small and mid-sized organizations to level the playing field, and it does so by giving these smaller players the power to do more with their existing resources.

While these benefits are often realized in the short term, the long-term effect of these tools is often shockingly different. The promise of faster and cheaper delivery is the catch — or lure — inside this organizational mousetrap, whereas backlogs, vendor contracts, technical debts, and constant updates are the hammer.

So, what exactly is the No-Code trap, and how can we avoid it?

What is a No-Code Tool?

First, let’s make sure we clear up any confusion regarding naming. So far I have referred Low-Code and No-Code as if they were one term. It’s certainly easy to confuse them — even large analyst firms seem to have a hard time differentiating between the two — and in the broader context of this article, both can lead to the same set of development pitfalls.

Under the magnifying glass, however, there are lots of small details and capabilities that differentiate Low-code and No-code solutions. Most of them aren’t apparent at the UI level, leading to much of the confusion between where the two come from.

In this section, I will spend a little bit of time exploring the important differences between those two, but only to show that when it comes to the central premise of this article they are virtually equivalent.

Low-Code vs. No-Code Tools

The goal behind Low-Code is to minimize the amount of coding necessary for complex tasks through a visual interface (such as Drag ‘N’ Drop) that integrates existing blocks of code into a workflow.

Skilled professionals have the potential to work smarter and faster with Low-Code tools because repetitive coding or duplicating work is streamlined. Through this, they can spend less time on the 80% of work that builds the foundation and focuses more on optimizing the 20% that makes it different. It, therefore, takes on the role of an entry-level employee doing the grunt work for more senior developers/engineers.

No-Code has a very similar look and feel to Low-Code, but is different in one very important dimension. Where Low-Code is meant to optimize the productivity of developers or engineers that already know how to code (even if just a little), No-Code is built for business and product managers that may not know any actual programming languages. It is meant to equip non-technical workers with the tools they need to create applications without formal development training.

No-Code applications need to be self-contained and everything the No-Code vendor thinks the user may need is already built into the tool.

As a result, No-Code applications create a lot of restrictions for the long-term in exchange for quick results in the short-term. This is a great example of a ‘deliberate-prudent’ scenario in the context of the Technical Debt Quadrant, but more on this later.

Advantages of No-Code Solutions

The appeal of both Low-Code and No-Code is pretty obvious. By removing code organizations can remove those that write it — developers — because they are expensive, in short supply, and fundamentally don’t produce things quickly.

The benefits of these two forms of applications in their best forms can be pretty substantial:

Resources: Human Capital is becoming increasingly scarce — and therefore expensive. This can stop a lot of ambitious projects dead in their tracks. Low-Code and No-Code tools minimize the amount of specialized technical skills needed to get an application of the ground, which means things can get done more quickly and at a lower cost.
Low R isk/H igh ROI: Security processes, data integrations, and cross-platform support are all built into Low-Code and No-Code tools, meaning less risk and more time to focus on your business goals.
Moving to Production: Similarly, for both types of tools a single click is all it takes to send or deploy a model or application you built to production.

Looking at these advantages, it is no wonder that both Low-Code and No-Code have been taking industries by storm recently. While being distinctly different in terms of users, they serve the same goal — that is to say, faster, safer and cheaper deployment. Given these similarities, both terms will be grouped together under the ‘No-Code’ term for the rest of this article unless otherwise specified.

List of No-Code Data Tools

So far, we have covered the applications of No-Code in a very general way, but for the rest of this article, I would like to focus on data modeling. No-Code tools are prevalent in software development, but have also, in particular, started to take hold in this space, and some applications even claim to be an alternative to SQL and other querying languages (crazy, right?!). My reasons for focusing on this are two-fold:

Firstly, there is a lot of existing analysis around this problem for software development and very little for data modeling. Secondly, this is also the area in which I have the most expertise.

Now let’s take a look at some of the vendors that provide No-Code solutions in this space. These in no way constitute a complete list and are, for the most part, not exclusively built for data modeling.

1. No-Code Data Modeling in Power BI

Power BI was created by Microsoft and aims to provide interactive visualizations and business intelligence capabilities to all types of business users. Their simple interface is meant to allow end-users to create their own reports and dashboards through a number of features, including data mapping, transformation, and visualization through dashboards. Power BI does support some R coding capabilities for visualization, but when it comes to data modeling, it is a true No-Code tool.

2. Alteryx as a Low-Code Alternative

Alteryx is meant to make advanced analytics accessible to any data worker. To achieve this, it offers several data analytics solutions. Alteryx specializes in self-service analytics with an intuitive UI. Their offerings can be used as Extract, Transform, Load (ETL) Tools within their own framework. Alteryx allows data workers to organize their data pipelines through their custom features and SQL code blocks. As such, they are easily identified as a Low-Code solution.

3. Is Tableau a No-Code Data Modeling Solution?

Tableau is a visual analytics platform and a direct competitor to Power BI. They were recently acquired by Salesforce which is now hoping to ‘transform the way we use data to solve problems—empowering people and organizations to make the most of their data.’ It is also a pretty obvious No-Code platform that is supposed to appeal to all types of end-users. As of now, it offers fewer tools for data modeling than Power BI, but that is likely to change in the future.

4. Looker is a No-Code Alternative to SQL

Looker is a business intelligence software and big data analytics platform that promises to help you explore, analyze, and share real-time business analytics easily. Very much in line with Tableau and Power BI, it aims to make non-technical end-users proficient in a variety of data tasks such as transformation, modeling, and visualization.

You might be wondering why I am including so many BI/Visualization platforms when talking about potential alternatives to SQL. After all, these tools are only set up to address an organization’s reporting needs, which constitute only one of the use cases for data queries and SQL. This is certainly a valid point, so allow me to clarify my reasoning a bit more.

While it is true that reporting is only one of many potential uses for SQL, it is nevertheless an extremely important one. There is a good reason why there are so many No-Code BI tools in the market—to address heightening demand from enterprises around the world — and therefore, it is worth taking a closer look at their almost inevitable shortcomings.

Source de l’article sur DZONE

23 avril 2021/par Service comm.

What Is The Independent Web And Does It Matter In 2021?

Actualités, Actualités

Ten years ago, people began talking about the “Independent Web.” Although we don’t commonly use the term anymore, that doesn’t mean that it’s not still as vital a topic of discussion today as it was a decade ago.

Today, I want to look at where the term came from, what it refers to today, and why it’s something that all of us in business, marketing, and web design should be thinking about.

What Is The Independent Web?

The Independent Web is a term that was coined back in 2010 by John Battelle.

In “Identity and The Independent Web,” Battelle broaches the subject of internet users losing control of their data, privacy, and decision-making to the likes of social media and search engines.

“When we’re ‘on’ Facebook, Google, or Twitter, we’re plugged into an infrastructure that locks onto us, serving us content and commerce in an automated but increasingly sophisticated fashion. Sure, we navigate around, in control of our experience, but the fact is, the choices provided to us as we navigate are increasingly driven by algorithms modeled on the service’s understanding of our identity.”

That’s the Dependent Web.

This is how Battelle explains the Independent Web:

“There is another part of the web, one where I can stroll a bit more at my own pace, and discover new territory, rather than have territory matched to a presumed identity. And that is the land of the Independent Web.”

In 2010, this referred to websites, search engines, and apps where users and their activity were not tracked. But a lot has changed since then, and many websites that were once safe to peruse without interference or manipulation are no longer.

What Happens When the Dependent Web Takes Over?

Nothing good.

I take that back. It’s not fair to make a blanket statement about Dependent Web platforms and sites. Users can certainly benefit from sharing some of their data with them.

Take Facebook, for instance. Since its creation, it’s enabled people to connect with long-lost friends, stay in touch with distant relatives, enable freelance professionals like ourselves to find like-minded communities, etc.

The same goes for websites and apps that track and use visitor data. Consumers are more than willing to share relevant data with companies so long as they benefit from the resulting personalized experiences.

But the Dependent Web also has a darker side. There are many ways that the Dependent Web costs consumers and businesses control over important things like:

Behavior

If you’ve seen The Social Dilemma, then you know that platforms like Facebook and Google profit from selling their users to advertisers.

That’s right. They’re not just selling user data. They’re selling users themselves. If the algorithms can change the way users behave, these platforms and their advertisers get to cash in big time.

Many websites and apps are also guilty of using manipulation to force users to behave how they want them to.

Personal Data

This one is well-known thanks to the GDPR in the EU and the CCPA in California. Despite these initiatives to protect user data and privacy, the exploitation of personal data on the web remains a huge public concern in recent years.

Content and Branding

This isn’t relevant to websites so much as it is to social media platforms and Google.

Dependent Web platforms ultimately dictate who sees your content and when. And while they’re more than happy to benefit from the traffic and engagement this content brings to their platforms, they’re just as happy to censor or pull down content as they please, just as Skillshare did in 2019 when it deleted half of its courses without telling its course creators.

What’s more, while social media and search engines have become the place to market our businesses, some of our branding gets lost when entering such oversaturated environments.

Income

When algorithms get updated, many businesses often feel the negative effects almost immediately.

For example, Facebook updated its algorithm in 2018 to prioritize “meaningful content.” This pushed out organic business content and pulled regular user content to the top of the heap.

This, in turn, forced businesses to have to pay-to-play if they wanted to use Facebook as a viable marketing platform.

Access

The Dependent Web doesn’t just impact individuals’ experiences. It can have far-reaching effects when one company provides a critical service to a large portion of the population.

We saw this happen in November when AWS went down.

When Amazon Web Services burps and half the Internet goes down maybe just maybe it’s not a great idea to have a single company with so much control over what has essentially become our society’s critical infrastructure?

— Evan Greer (@evan_greer) November 25, 2020

It wasn’t just Amazon’s servers that went down, though. It took out apps and sites like:

1Password
Adobe Spark
Capital Gazette
Coinbase
Glassdoor
Roku
The Washington Post

And there’s absolutely nothing that these businesses or their users could do but sit around and wait… because Amazon hosts a substantial portion of the web.

Innovation

When consumers and businesses become dependent on platforms that predominantly control the way we live and work, it’s difficult for us to stand up for the little guys trying to carve out innovative pathways.

And that’s exactly what we see happen time and time again with Big Tech’s buy-and-kill tactics.

As a result, we really lose the option to choose what we use to improve our lives and our businesses. And innovative thinkers lose the ability to bring much-needed changes to the world because Big Tech wants to own the vast majority of data and users.

How Can We Take Back Control From The Dependent Web?

Many things are happening right now that are trying to push consumers and businesses towards a more Independent Web:

Consumer Privacy Protection: GDPR and CCPA empower consumers to control where their data goes and what it’s used for.

Big Tech Regulations: The Senate held tech regulation hearings with Facebook’s and Twitters’s CEOs.

Public Awareness Initiatives: Films like The Social Dilemma bring greater awareness to what’s happening on social media.

Ad Blocker Adoption: Adblocker usage is at an all-time high.

Private Search Engine Usage: Although Google dominates search engine market share, people are starting to use private search engines like Duck Duck Go.

Private Browsing Growth: Over 60% of the global population is aware of what private browsing is (i.e., incognito mode), and roughly 35% use it when surfing the web.

Self-hosted and Open Source CMS Popularity: The IndieWeb community encourages people to move away from Dependent platforms and build their own websites and communities. This is something that Matt Mullenweg, the founder of WordPress, talked about back in 2012.

“The Internet needs a strong, independent platform for those of us who don’t want to be at the mercy of someone else’s domain. I like to think that if we didn’t create WordPress something else that looks a lot like it would exist. I think Open Source is kind of like our Bill of Rights. It’s our Constitution. If we’re not true to that, nothing else matters.”

As web designers, this is something that should really speak to you, especially if you’ve ever met a lead or client who didn’t understand why they needed a website when they could just advertise on Facebook or Instagram.

A Decentralized Web: Perhaps the most promising of all these initiatives are Solid and Inrupt, which were launched in 2018 by the creator of the Web, Tim Berners-Lee.

As Berners-Lee explained on the Inrupt blog in 2020:

”The Web was always meant to be a platform for creativity, collaboration, and free invention — but that’s not what we are seeing today. Today, business transformation is hampered by different parts of one’s life being managed by different silos, each of which looks after one vertical slice of life, but where the users and teams can’t get the insight from connecting that data. Meanwhile, that data is exploited by the silo in question, leading to increasing, very reasonable, public skepticism about how personal data is being misused. That in turn has led to increasingly complex data regulations.”

This is something we should all keep a close eye on. Consumers and businesses alike are becoming wary of the Dependent Web.

Who better than the creator of the web to lead us towards the Independent Web where we can protect our data and better control our experience?

Featured Image via Pexels.

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21 avril 2021/par Service comm.

25 Exciting New Tools For Designers, April 2021

Actualités, Actualités

Rather than spring cleaning, do some spring “shopping” for tools that will make your design life easier. Packed with free options this month, this list is crammed full of tools and elements that you can use in your work every day.

Here’s what new for designers this month:

April’s Top Picks

Charts.css

Charts.css makes creating beautiful online charts that much easier. It’s a modern CSS framework that uses CSS utility classes to style HTML elements as charts. It’s accessible, customizable, responsive, and open source. There’s a quick start option and available source code to work with.

Haikei SVG Generator

Haikei is a web app that helps you generate SVG shapes, backgrounds, and patterns in all types of shapes to use in projects. Everything can be exported into the tools you are already using for easy integration, and every element is customizable. The tool is free right now – no credit card needed – and you get access to 15 generators and can export in SVG and PNG format. A premium option is on the way, and you can sign up to get notified for access.

Fluid Space Calculator

Fluid Space Calculator helps you create a related space system and export the CSS to implement it. The calculator allows you to add space value pairs and multipliers and see the impact on the screen before snagging the related code. It’s great for determining how things will look in different viewports and for creating custom space pairs.

Night Eye WordPress Plugin

Night Eye WordPress Plugin helps you create a dark mode option for your WordPress website with ease. It’s completely customizable, schedulable, and one of those things that users are starting to expect. The plugin has free and paid versions – the only difference is a link to credit the developer.

3 Productivity Boosters

Macro

Macro is a supercharged checklist app for recurring processes. It’s designed to help teams document, assign, track, and automate for maximum efficiency. Now is the time to test this tool because it is free in public beta.

Writex.io

Writex.io is a free writing app that uses AI and smart features to help you write more efficiently. It can check readability as you write, make suggestions, check spelling, and allows you to work with versioning. All the settings are customizable, so you can get help and suggestions when you want them and avoid things you don’t want.

Taloflow

Taloflow, which is in beta, is a tool that helps you find the top cloud and dev tools for your use case. This is designed to be a time-saving solution to finding the right infrastructure and API products for your work.

8 Kits with Illustrations and User Interface Elements

Skribbl

Skribbl is a collection of free, hand-drawn illustrations in a light and fun style. The black and white sketches are friendly, and the collection keeps growing. Plus, the illustrators are allowing them to be used free for any use.

Mobile Chat Kit

Mobile Chat Kit is a free starter kit for building apps in Figma, Sketch, and Adobe XD. It includes more than 50 screen options with mapped-out flows for a quick-start project.

Flowchart.fun

Flowchart.fun is exactly what the name implies. The app allows you to type, create nodes, and link elements to develop simple flow charts quickly. Then you can alter shape and size with drag and drop. Export it for use as an SVG, JPG, or PNG.

Shuffle

Shuffle is a marketplace packed with UI libraries to help you with a variety of digital projects. There are more than 1,500 pre-built components to choose from with professional designs. This premium tool comes with a monthly subscription or lifetime license.

Cryptocurrency 3D Pack

Cryptocurrency 3D Pack is a set of icons with fun colors in three-dimensional shapes that you can use to represent different crypto elements. The pack includes 55 #D icons in PNG and BLEND formats.

Stratum UI Kit for Figma

Stratum UI Kit for Figma includes nine free screens that are ready to use. Options include API documentation, Kanban, document, data dashboard, ecommerce product list, ecommerce product options, payments spreadsheet, cloud storage, and newsfeed.

Conic.css

Conic.css is a collection of simple gradients that you can browse and then click to copy the code into your CSS to use them in projects. It’s quick and easy while using trendy color options.

Artify Illustrations

Artify Illustrations is a Figma plugin that allows you to access more than 5,000 SVG and PNG illustrations within the app. It’s got a built-in search feature, everything is high-resolution, and the huge library includes various styles.

2 Tutorials

A Complete Guide to Accessible Front-End Components

A Complete Guide to Accessible Front-End Components is an amazingly comprehensive guide from Smashing Magazine with everything you need to know about accessible components. From tabs to tables to toggles to tooltips, you’ll find it all here and learn how to use it the right way.

Grid CheatSheet in 2021

Grid CheatSheet in 2021 is a useful guide of everything you can do with CSS Grid. Plus, it has plenty of fun illustrations and an accompanying video.

8 Fresh and Fun Fonts

Athina

Athina is a modern display serif with beautiful connector strokes. The free version is a demo, and there’s a full family that you can buy.

Brique

Brique is a free (personal and commercial) display font with a wide stance and uppercase character set. The letters have a lot of personality and a readable configuration.

Code Next

Code Next is a great geometric sans serif with a full family of styles. Including two variable fonts. It’s highly readable and would work for almost any application.

Inter

Inter is a simple and functional sense serif family with everything from extra light to heavy weights. The extra character personality makes this a fun and functional font option.

Nothing Clean

Nothing Clean is a fun grunge-type option. It’s an all uppercase character set with alternates.

Playout

Playout is a fun, hand-drawn style typeface with interesting glyphs and alternate characters. The most fun feature might be the pawprint characters in the demo set.

Rockford Sans

Rockford Sans is a geometric typeface with subtly rounded edges. It has eight weights and italics. With its large x-height and round features, it’s legible and friendly. It’s suited to cover a wide variety of tasks from editorial to brand design and advertising.

SpaceType

SpaceType is a fun and funky typeface in regular and expanded styles. The stretched letterforms make interesting alternates for display purposes.

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19 avril 2021/par Service comm.

Qu’est-ce que le Big Data ?

Actualités, ERP & Applications, SAP

Le Big Data est le flot d’informations dans lequel nous nous trouvons tous les jours (des zettaoctets de données provenant de nos ordinateurs, des terminaux mobiles et des capteurs). Ces données sont utilisées par les entreprises pour orienter la prise de décisions, améliorer les processus et les stratégies, et créer des produits, des services et des expériences centrés sur le client.

Le Big Data désigne non seulement de gros volumes de données, mais aussi des données de nature variée et complexe. Il dépasse généralement la capacité des bases de données traditionnelles à capturer, gérer et traiter ce type de données. De plus, le Big Data peut provenir de n’importe où et de tout ce que nous sommes en mesure de surveiller numériquement. Les satellites, les appareils IoT (Internet des Objets), les radars et les tendances des réseaux sociaux ne sont que quelques exemples parmi la multitude de sources de données explorées et analysées pour rendre les entreprises plus résilientes et compétitives.

L’importance de l’analyse du Big Data

La véritable valeur du Big Data se mesure d’après votre capacité à l’analyser et à le comprendre. L’intelligence artificielle (IA), le machine learning et les technologies de base de données modernes permettent de visualiser et d’analyser le Big Data pour fournir des informations exploitables en temps réel. L’analyse du Big Data aide les entreprises à exploiter leurs données en vue de saisir de nouvelles opportunités et de créer de nouveaux modèles de gestion. Comme l’a si bien dit Geoffrey Moore, auteur et analyste de gestion, « sans analyse du Big Data, les entreprises sont aveugles et sourdes, errant sur le Web comme des cerfs sur une autoroute ».

How does Big Data and Analytics work? Simply Explained

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How does Big Data and Analytics work? Simply Explained

L’évolution du Big Data

Aussi inconcevable que cela puisse paraître aujourd’hui, l’Apollo Guidance Computer a emmené l’homme sur la lune avec moins de 80 kilo-octets de mémoire. Depuis, la technologie informatique s’est développée à un rythme exponentiel, de même que la génération de données. La capacité technologique mondiale à stocker des données a doublé tous les trois ans depuis les années 1980. Il y a un peu plus de 50 ans, lors du lancement d’Apollo 11, la quantité de données numériques générées dans le monde aurait pu tenir dans un ordinateur portable. Aujourd’hui, l’IDC estime ce chiffre à 44 zettaoctets (soit 44 000 milliards de gigaoctets) et prévoit qu’il atteindra 163 zettaoctets en 2025.

44 zettaoctets de données numériques aujourd’hui, IDC

163 zettaoctets de données numériques en 2025, IDC

Plus les logiciels et la technologie se développent, moins les systèmes non numériques sont viables. Le traitement des données générées et collectées numériquement requiert des systèmes de data management plus avancés. En outre, la croissance exponentielle des plates-formes de réseaux sociaux, des technologies pour smartphones et des appareils IoT connectés numériquement ont contribué à l’émergence du Big Data.

Types de Big Data : que sont les données structurées et non structurées ?

Les ensembles de données sont généralement catégorisés en trois types, selon leur structure et la complexité de leur indexation.

Données structurées : ce type de données est le plus simple à organiser et à rechercher. Il peut inclure des données financières, des machine logs et des détails démographiques. Une feuille de calcul Microsoft Excel, avec sa mise en forme de colonnes et de lignes prédéfinies, offre un moyen efficace de visualiser les données structurées. Ses composants peuvent facilement être catégorisés, ce qui permet aux concepteurs et administrateurs de bases de données de définir des algorithmes simples pour la recherche et l’analyse. Même lorsque les données structurées sont très volumineuses, elles ne sont pas nécessairement qualifiées de Big Data, car elles sont relativement simples à gérer et ne répondent donc pas aux critères qui définissent le Big Data. Traditionnellement, les bases de données utilisent un langage de programmation appelé SQL (Structured Query Language) pour gérer les données structurées. SQL a été développé par IBM dans les années 1970 pour permettre aux développeurs de créer et gérer des bases de données relationnelles (de type feuille de calcul) qui commençaient à émerger à l’époque.
Données non structurées : cette catégorie de données peut inclure des publications sur les réseaux sociaux, des fichiers audio, des images et des commentaires client ouverts. Ces données ne peuvent pas être facilement capturées dans les bases de données relationnelles standard en lignes et colonnes. Auparavant, les entreprises qui voulaient rechercher, gérer ou analyser de grandes quantités de données non structurées devaient utiliser des processus manuels laborieux. La valeur potentielle liée à l’analyse et à la compréhension de ces données ne faisait aucun doute, mais le coût associé était souvent trop exorbitant pour en valoir la peine. Compte tenu du temps nécessaire, les résultats étaient souvent obsolètes avant même d’être générés. Contrairement aux feuilles de calcul ou aux bases de données relationnelles, les données non structurées sont généralement stockées dans des lacs de données, des entrepôts de données et des bases de données NoSQL.
Données semi-structurées : comme leur nom l’indique, les données semi-structurées intègrent à la fois des données structurées et non structurées. Les e-mails en sont un bon exemple, car ils incluent des données non structurées dans le corps du message, ainsi que d’autres propriétés organisationnelles telles que l’expéditeur, le destinataire, l’objet et la date. Les dispositifs qui utilisent le marquage géographique, les horodatages ou les balises sémantiques peuvent également fournir des données structurées avec un contenu non structuré. Une image de smartphone non identifiée, par exemple, peut indiquer qu’il s’agit d’un selfie et préciser l’heure et l’endroit où il a été pris. Une base de données moderne exécutant une technologie d’IA peut non seulement identifier instantanément différents types de données, mais aussi générer des algorithmes en temps réel pour gérer et analyser efficacement les ensembles de données disparates.

Les sources du Big Data

Les objets générateurs de données se développent à un rythme spectaculaire, depuis les drones jusqu’aux grille-pains. Toutefois, à des fins de catégorisation, les sources de données sont généralement divisées en trois types :

Données sociales

Comme leur nom l’indique, les données sociales sont générées par les réseaux sociaux : commentaires, publications, images et, de plus en plus, vidéos. En outre, compte tenu de l’ubiquité croissante des réseaux 4G et 5G, on estime que le nombre de personnes dans le monde qui regardent régulièrement des contenus vidéo sur leur smartphone atteindra 2,72 milliards en 2023. Bien que les tendances concernant les réseaux sociaux et leur utilisation évoluent rapidement et de manière imprévisible, leur progression en tant que générateurs de données numériques est incontestable.

Données machine

Les machines et appareils IoT sont équipés de capteurs et ont la capacité d’envoyer et de recevoir des données numériques. Les capteurs IoT aident les entreprises à collecter et traiter les données machine provenant des appareils, des véhicules et des équipements. Globalement, le nombre d’objets générateurs de données augmente rapidement, des capteurs météorologiques et de trafic jusqu’à la surveillance de la sécurité. Selon l’IDC, il y aura plus de 40 milliards d’appareils IoT en 2025, générant près de la moitié des données numériques mondiales.

Données altérables

Il s’agit des données parmi les plus évolutives au monde. Par exemple, un détaillant international traite plus d’un million de transactions client par heure. Si l’on ajoute à cela les transactions d’achat et bancaires au niveau mondial, on comprend mieux le volume phénoménal de données générées. En outre, les données altérables contiennent de plus en plus de données semi-structurées, y compris des images et des commentaires, ce qui les rend d’autant plus complexes à gérer et à traiter.

Les cinq V du Big Data

Ce n’est pas parce qu’un ensemble de données est volumineux qu’il s’agit nécessairement de Big Data. Pour être qualifiées en tant que telles, les données doivent posséder au minimum les cinq caractéristiques suivantes :

Volume : même si le volume n’est pas le seul composant qui constitue le Big Data, il s’agit d’une de ses caractéristiques principales. Pour gérer et exploiter pleinement le Big Data, des algorithmes avancés et des analyses pilotées par l’IA sont nécessaires. Mais avant tout cela, il doit exister un moyen fiable et sécurisé de stocker, d’organiser et d’extraire les téraoctets de données détenus par les grandes entreprises.
Vitesse : auparavant, les données générées devaient ensuite être saisies dans un système de base de données traditionnel (souvent manuellement) avant de pouvoir être analysées ou extraites. Aujourd’hui, grâce à la technologie du Big Data, les bases de données sont capables de traiter, d’analyser et de configurer les données lorsqu’elles sont générées, parfois en l’espace de quelques millisecondes. Pour les entreprises, cela signifie que les données en temps réel peuvent être exploitées pour saisir des opportunités financières, répondre aux besoins des clients, prévenir la fraude et exécuter toute autre activité pour laquelle la rapidité est un facteur clé.
Variété : les ensembles de données contenant uniquement des données structurées ne relèvent pas nécessairement du Big Data, quel que soit leur volume. Le Big Data comprend généralement des combinaisons de données structurées, non structurées et semi-structurées. Les solutions de gestion des données et les bases de données traditionnelles n’offrent pas la flexibilité et le périmètre nécessaires pour gérer les ensembles de données complexes et disparates qui constituent le Big Data.
Véracité : bien que les bases de données modernes permettent aux entreprises d’accumuler et d’identifier des volumes considérables de Big Data de différents types, elles ne sont utiles que si elles sont précises, pertinentes et opportunes. S’agissant des bases de données traditionnelles alimentées uniquement avec des données structurées, le manque de précision des données était souvent dû à des erreurs syntaxiques et des fautes de frappe. Les données non structurées présentent toute une série de nouvelles difficultés en matière de véracité. Les préjugés humains, le « bruit social » et les problèmes liés à la provenance des données peuvent avoir un impact sur la qualité des données.
Valeur : les résultats de l’analyse du Big Data sont souvent fascinants et inattendus. Mais pour les entreprises, l’analyse du Big Data doit fournir une visibilité qui les aident à gagner en compétitivité et en résilience, et à mieux servir leurs clients. Les technologies modernes du Big Data offrent la possibilité de collecter et d’extraire des données susceptibles de procurer un avantage mesurable à la fois en termes de résultats et de résilience opérationnelle.

Avantages du Big Data

Les solutions modernes de gestion du Big Data permettent aux entreprises de transformer leurs données brutes en informations pertinentes avec une rapidité et une précision sans précédent.

Développement de produits et de services :l’analyse du Big Data permet aux développeurs de produits d’analyser les données non structurées, telles que les témoignages clients et les tendances culturelles, et de réagir rapidement.
Maintenance prédictive : dans le cadre d’uneenquête internationale, McKinsey a constaté que l’analyse du Big Data émanant des machines IoT pouvait réduire les coûts de maintenance des équipements jusqu’à 40 %.
Expérience client :dans le cadre d’une enquête réalisée en 2020 auprès de responsables d’entreprises du monde entier, Gartner a déterminé que « les entreprises en croissance collectent plus activement des données sur l’expérience client que les entreprises à croissance nulle ». L’analyse du Big Data permet aux entreprises d’améliorer et de personnaliser l’expérience de leurs clients avec leur marque.
Gestion de la résilience et des risques :la pandémie de COVID-19 a été une véritable prise de conscience pour de nombreux dirigeants d’entreprise qui se sont rendu compte à quel point leur activité était vulnérable. La visibilité offerte par le Big Data peut aider les entreprises à anticiper les risques et à se préparer aux imprévus.
Économies et efficacité accrue : lorsque les entreprises effectuent une analyse avancée du Big Data pour tous les processus de l’organisation, elles peuvent non seulement détecter les inefficacités, mais aussi déployer des solutions rapides et efficaces.
Amélioration de la compétitivité : les informations obtenues grâce au Big Data peuvent aider les entreprises à réaliser des économies, à satisfaire leurs clients, à concevoir de meilleurs produits et à innover dans les opérations de gestion.

IA et Big Data

La gestion du Big Data repose sur des systèmes capables de traiter et d’analyser efficacement de gros volumes d’informations disparates et complexes. À cet égard, le Big Data et l’IA ont une relation de réciprocité. Sans l’IA pour l’organiser et l’analyser, le Big Data n’aurait pas grande utilité. Et pour que l’IA puisse générer des analyses suffisamment fiables pour être exploitables, le Big Data doit contenir des ensembles de données suffisamment étendus. Comme l’indique Brandon Purcell, analyste chez Forrester Research, « les données sont au cœur de l’intelligence artificielle. Un système d’IA doit apprendre des données pour remplir sa fonction ».

« Les données sont au cœur de l’intelligence artificielle. Un système d’IA doit apprendre des données pour remplir sa fonction ».

Brandon Purcell, analyste, Forrester Research

Machine learning et Big Data

Les algorithmes de machine learning définissent les données entrantes et identifient des modèles associés. Ces informations permettent de prendre des décisions avisées et d’automatiser les processus. Le machine learning se nourrit du Big Data, car plus les ensembles de données analysés sont fiables, plus le système est susceptible d’apprendre, de faire évoluer et d’adapter ses processus en continu.

Technologies du Big Data

Architecture du Big Data

À l’instar de l’architecture du bâtiment, l’architecture du Big Data fournit un modèle pour la structure de base déterminant la manière dont les entreprises gèrent et analysent leurs données. L’architecture du Big Data mappe les processus requis pour gérer le Big Data à travers quatre « couches » de base, des sources de données au stockage des données, puis à l’analyse du Big Data, et enfin via la couche de consommation dans laquelle les résultats analysés sont présentés en tant que Business Intelligence.

‍Analyse du Big Data

Ce processus permet de visualiser les données de manière pertinente grâce à l’utilisation de la modélisation des données et d’algorithmes spécifiques aux caractéristiques du Big Data. Dans le cadre d’une étude approfondie et d’une enquête de la MIT Sloan School of Management, plus de 2 000 dirigeants d’entreprise ont été interrogés sur leur expérience en matière d’analyse du Big Data. Comme on pouvait s’y attendre, ceux qui s’étaient impliqués dans le développement de stratégies de gestion du Big Data ont obtenu les résultats les plus significatifs.

Big Data et Apache Hadoop

Imaginez une grande boîte contenant 10 pièces de 10 centimes et 100 pièces de 5 centimes. Puis imaginez 10 boîtes plus petites, côte à côte, contenant chacune 10 pièces de 5 centimes et une seule pièce de 10 centimes. Dans quel scénario sera-t-il plus facile de repérer les pièces de 10 centimes ? Hadoop fonctionne sur ce principe. Il s’agit d’une structure en open source permettant de gérer le traitement du Big Data distribué sur un réseau constitué de nombreux ordinateurs connectés. Ainsi, au lieu d’utiliser un gros ordinateur pour stocker et traiter toutes les données, Hadoop regroupe plusieurs ordinateurs sur un réseau pouvant évoluer presque à l’infini et analyse les données en parallèle. Ce processus utilise généralement un modèle de programmation appelé MapReduce, qui coordonne le traitement du Big Data en regroupant les ordinateurs distribués.

Lacs de données, entrepôts de données et NoSQL

Les bases de données traditionnelles de type feuille de calcul SQL servent à stocker les données structurées. Le Big Data non structuré et semi-structuré nécessite des modèles de stockage et de traitement uniques, car il ne peut pas être indexé et catégorisé. Les lacs de données, les entrepôts de données et les bases de données NoSQL sont des référentiels de données capables de gérer les ensembles de données non traditionnels. Un lac de données est un vaste pool de données brutes qui n’ont pas encore été traitées. Un entrepôt de données est un référentiel de données qui ont déjà été traitées à des fins spécifiques. Les bases de données NoSQL fournissent un schéma flexible qui peut être modifié en fonction de la nature des données à traiter. Ces systèmes présentent chacun des avantages et des inconvénients, c’est pourquoi de nombreuses entreprises utilisent plutôt une combinaison de ces référentiels de données pour répondre au mieux à leurs besoins.

Bases de données in-memory

Les bases de données traditionnelles sur disque ont été conçues pour SQL et les bases de données relationnelles. Bien qu’elles soient capables de traiter de gros volumes de données structurées, elles ne sont pas adaptées au stockage et au traitement des données non structurées. Dans le cas des bases de données in-memory, le traitement et l’analyse se font entièrement dans la RAM, pour ne pas avoir à extraire les données d’un système sur disque. Les bases de données in-memory reposent également sur des architectures distribuées. Cela signifie qu’elles peuvent atteindre des vitesses beaucoup plus élevées en utilisant le traitement parallèle, par rapport aux modèles de base de données sur disque à un seul nœud.

Fonctionnement du Big Data

Le Big Data remplit ses fonctions lorsque son analyse fournit des informations pertinentes et exploitables qui améliorent l’activité de manière significative. Pour se préparer à la transition vers le Big Data, les entreprises doivent s’assurer que leurs systèmes et processus sont en mesure de collecter, de stocker et d’analyser le Big Data.

Collecter le Big Data.Une grande partie du Big Data est constituée d’énormes ensembles de données non structurées qui émanent de sources disparates et incohérentes. Les bases de données traditionnelles sur disque et les mécanismes d’intégration des données ne sont pas suffisamment performants pour les gérer. La gestion du Big Data requiert des solutions de base de données in-memory et des solutions logicielles spécifiques de l’acquisition de ce type de données.
Stocker le Big Data.Comme son nom l’indique, le Big Data est volumineux. De nombreuses entreprises utilisent des solutions de stockage sur site pour leurs données existantes et espèrent réaliser des économies en réutilisant ces référentiels pour traiter le Big Data. Toutefois, le Big Data est plus performant lorsqu’il n’est pas soumis à des contraintes de taille et de mémoire. Les entreprises qui n’intègrent pas dès le départ des solutions de stockage Cloud dans leurs modèles de Big Data le regrettent souvent quelques mois plus tard.
Analyser le Big Data. Il est impossible d’exploiter pleinement le potentiel du Big Data sans utiliser les technologies d’IA et de machine learning pour l’analyser. L’un des cinq V du Big Data est la « vitesse ». Pour être utiles et exploitables, les informations du Big Data doivent être générées rapidement. Les processus d’analyse doivent s’auto-optimiser et tirer régulièrement profit de l’expérience, un objectif qui ne peut être atteint qu’avec l’IA et les technologies modernes de bases de données.

Applications du Big Data

La visibilité offerte par le Big Data est bénéfique à la plupart des entreprises ou secteurs d’activité. Cependant, ce sont les grandes entreprises aux missions opérationnelles complexes qui en tirent souvent le meilleur parti.

Finance	Dans le Journal of Big Data, une étude de 2020 souligne que le Big Data « joue un rôle important dans l’évolution du secteur des services financiers, en particulier dans le commerce et les investissements, la réforme fiscale, la détection et les enquêtes en matière de fraude, l’analyse des risques et l’automatisation ». Le Big Data a également contribué à transformer le secteur financier en analysant les données et les commentaires des clients pour obtenir les informations nécessaires à l’amélioration de la satisfaction et de l’expérience client. Les ensembles de données altérables figurent parmi les plus importants et les plus évolutifs au monde. L’adoption croissante de solutions avancées de gestion du Big Data permettra aux banques et aux établissements financiers de protéger ces données et de les utiliser d’une manière qui bénéficie à la fois au client et à l’entreprise.
Hygiène et santé publique	L’analyse du Big Data permet aux professionnels de santé d’établir des diagnostics plus précis, fondés sur des données avérées. De plus, le Big Data aide les administrateurs d’hôpitaux à identifier les tendances, à gérer les risques et à limiter les dépenses inutiles, afin de consacrer le maximum de fonds aux soins des patients et à la recherche. En cette période de pandémie, les chercheurs du monde entier s’efforcent de traiter et de gérer au mieux la COVID-19, et le Big Data joue un rôle fondamental dans ce processus. Un article de juillet 2020 paru dans The Scientist explique comment des équipes médicales ont pu collaborer et analyser le Big Data afin de lutter contre le coronavirus : « Nous pourrions transformer la science clinique en exploitant les outils et les ressources du Big Data et de la science des données d’une manière que nous pensions impossible ».
Transport et logistique	L’« effet Amazon » est un terme qui définit la manière dont Amazon a fait de la livraison en un jour la nouvelle norme, les clients exigeant désormais la même vitesse d’expédition pour tout ce qu’ils commandent en ligne. Le magazine Entrepreneur souligne qu’en raison de l’effet Amazon, « la course logistique au dernier kilomètre ne fera que s’intensifier ». Les entreprises du secteur s’appuient de plus en plus sur l’analyse du Big Data pour optimiser la planification des itinéraires, la consolidation des charges et les mesures d’efficacité énergétique.
Éducation	Depuis l’apparition de la pandémie, les établissements d’enseignement du monde entier ont dû réinventer leurs programmes d’études et leurs méthodes d’enseignement afin de faciliter l’apprentissage à distance. L’un des principaux défis a été de trouver des moyens fiables d’analyser et d’évaluer la performance des étudiants et l’efficacité globale des méthodes d’enseignement en ligne. Un article paru en 2020 au sujet de l’impact du Big Data sur la formation et l’apprentissage en ligne indique, au sujet des enseignants, que « le Big Data les aide à gagner en confiance pour personnaliser l’enseignement, développer l’apprentissage mixte, transformer les systèmes d’évaluation et promouvoir l’apprentissage continu ».
Énergie et services publics	Selon le U.S. Bureau of Labor Statistics, le service public consacre plus de 1,4 milliard de dollars aux relevés de compteurs et s’appuie généralement sur des compteurs analogiques et des lectures manuelles peu fréquentes. Les relevés de compteurs intelligents fournissent des données numériques plusieurs fois par jour et, grâce à l’analyse du Big Data, ces informations permettent d’accroître l’efficacité de la consommation énergétique, ainsi que la précision des prix et des prévisions. En outre, lorsque les agents n’ont plus à se charger des relevés de compteurs, la saisie et l’analyse des données peuvent permettre de les réaffecter plus rapidement là où les réparations et les mises à niveau sont les plus urgentes.

Publié en anglais sur insights.sap.com

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Source de l’article sur sap.com

15 avril 2021/par Service comm.