Trending February 2024 # Lastpass Vs 1Password Vs Dashlane # Suggested March 2024 # Top 8 Popular

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Lugging around a notebook full of important passwords and information can be both time consuming and inconvenient. The days of remembering dozens of complex passwords is over when you have a good password manager.

The programs listed below will not only remember your list of passwords for you — all you have to know is the master password to get in — but also secure them through encryption so that even if someone were to get their hands on the raw data, they wouldn’t be able to read your passwords.

Table of Contents

All of these password manager programs have software for Windows, macOS, Android, and iOS, but each one is also unique from the others. You might find one with a feature another doesn’t have, or one that includes a free feature that costs in another password manager.

Before deciding on the best password manager for you, read through the features below to get a feel for what each one offers. Below these short reviews of the top three password managers is our take on which one is the best.


LastPass includes a wide variety of features and configurations. The free version of LastPass synchronizes with an unlimited number of devices and offers a few extra perks to its paid subscription. What makes this program stand out is its ability to sync passwords and information across all existing devices, which is rare for any “free to use” password manager.

An added bonus to LastPass is the wide range of availability. LastPass doesn’t require a separate download, instead offering a plugin to all major browsers with a full-featured

LastPass also has offline access, the ability to auto-save newly created accounts directly into your LastPass database, can generate answers to security questions, and boasts a really awesome password generator with lots of customizations.

You’d think the features would stop now for free users, but the LastPass free subscription can also typing time.

The automatic password changer is another huge feature in LastPass that might sway you into choosing this password manager over the others. The feature doesn’t work for all websites, but for the ones it does work with, the tool will take control of your mouse and literally change the password for you to something strong, in real time as you watch.

One last thing to remember when making the decision to use LastPass vs 1Password or Dashlane is its ability to hand over your passwords in the event of your death (which is a good thing). You can set up emergency contacts that can request access to your LastPass data, and if you don’t respond to the request before it expires, LastPass will give them access to your account. This is great for families.

You can get LastPass on Windows, Mac, Linux, or your mobile device. It also runs directly from various popular web browsers like Edge, Firefox, and Chrome.


1Password has a nice, modern design that’s really easy to use from a computer or mobile device. This password manager started off being an Apple only application but now works with Windows and Android.

Like most password managers, you can expect unlimited password syncing across all your devices, account access for both online and offline, security alerts, email support, 1 GB of secure online storage, and a password generator.

The family plan for 1Password adds the sharing of passwords, documents, permission controls, and an account-recovery tool. When sharing passwords with family members, you create a shared vault that they have access to from their own account, much like a shared folder of passwords. It’s really handy if multiple people always need access to the same websites from their own computers or phones.

What separates this manager from the others is its Travel Mode feature. When turned on, 1Password will remove any vaults marked as Not Safe for Travel on any device. Once you’ve turned off this mode, all vaults and applications will be restored. This feature protects sensitive data from unauthorized users.

The Windows version works with all major web browsers. However, you can also use 1Password on non-Windows computers via the browser-only extension for Chrome, Firefox, or Opera. What’s more is the standalone 1Password program for Windows or Mac that doesn’t require those browsers.


The most expensive, yet extensive manager goes to Dashlane. Dashlane offers a free service as well as two different paid plans: Premium ($60 annually) and Premium Plus ($120 annually). Both paid plans allow an unlimited number of passwords and data storage with breach monitoring. The free version is restricted to a single device and a maximum of 50 saved credentials.

Dashlane Premium includes a wide variety of applications, such as syncing passwords, access across all of your devices, a backup of your account, unlimited password sharing, and two-factor authentication. A big stand out with this manager is its unlimited VPN service and dark-web identity monitoring.

The most extensive option is Dashlane Premium plus. With this upgrade, you can expect features such as credit monitoring, identity-restoration assistance, and identity-theft insurance. However, there are even more features available with Dashlane Business.

Dashlane runs on Windows, Mac, Android, iOS, and Linux.

The Verdict

The choice of a password managers is no simple task. The answer ultimately comes down to your personal lifestyle —how much you’re willing to spend and which features are important to you.

If you’re willing to pay for extra features, we suggest Dashlane. With its unlimited VPN service and dark-web monitoring, you can be sure that no important information will be leaked to identity thieves in search for their next victim. Dashlane’s Premium plus extends its reach in security and customer service with its continuous credit monitoring and identity-theft insurance.

For those who want a the most out of a free service, LastPass should be your go-to. With services often cut from other free password managers, LastPass does an excellent job of bringing a smooth, yet elegant interface to many different operating systems.

Where 1Password lacks in flashy designs, it makes up for in simplicity, and Apple fans will for sure enjoy its user-friendly interface. Travel Mode is where it exceeds expectations, letting you secure your data from prying eyes when traveling. However, if you tend to use other applications or prefer to have more options, we suggest going with LastPass or

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Ai Vs. Machine Learning Vs. Deep Learning

Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality.

Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future.

Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market. Among vendors selling big data analytics and data science tools, two types of artificial intelligence have become particularly popular: machine learning and deep learning.

While many solutions carry the “AI,” “machine learning,” and/or “deep learning” labels, confusion about what these terms really mean persists in the market place. The diagram below provides a visual representation of the relationships among these different technologies:

As the graphic makes clear, machine learning is a subset of artificial intelligence. In other words, all machine learning is AI, but not all AI is machine learning.

Similarly, deep learning is a subset of machine learning. And again, all deep learning is machine learning, but not all machine learning is deep learning.

Also see: Top Machine Learning Companies

AI, machine learning and deep learning are each interrelated, with deep learning nested within ML, which in turn is part of the larger discipline of AI.

Computers excel at mathematics and logical reasoning, but they struggle to master other tasks that humans can perform quite naturally.

For example, human babies learn to recognize and name objects when they are only a few months old, but until recently, machines have found it very difficult to identify items in pictures. While any toddler can easily tell a cat from a dog from a goat, computers find that task much more difficult. In fact, captcha services sometimes use exactly that type of question to make sure that a particular user is a human and not a bot.

In the 1950s, scientists began discussing ways to give machines the ability to “think” like humans. The phrase “artificial intelligence” entered the lexicon in 1956, when John McCarthy organized a conference on the topic. Those who attended called for more study of “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Critics rightly point out that there is a big difference between an AI system that can tell the difference between cats and dogs and a computer that is truly intelligent in the same way as a human being. Most researchers believe that we are years or even decades away from creating an artificial general intelligence (also called strong AI) that seems to be conscious in the same way that humans beings are — if it will ever be possible to create such a system at all.

If artificial general intelligence does one day become a reality, it seems certain that machine learning will play a major role in the system’s capabilities.

Machine learning is the particular branch of AI concerned with teaching computers to “improve themselves,” as the attendees at that first artificial intelligence conference put it. Another 1950s computer scientist named Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.”

In traditional computer programming, a developer tells a computer exactly what to do. Given a set of inputs, the system will return a set of outputs — just as its human programmers told it to.

Machine learning is different because no one tells the machine exactly what to do. Instead, they feed the machine data and allow it to learn on its own.

In general, machine learning takes three different forms: 

Reinforcement learning is one of the oldest types of machine learning, and it is very useful in teaching a computer how to play a game.

For example, Arthur Samuel created one of the first programs that used reinforcement learning. It played checkers against human opponents and learned from its successes and mistakes. Over time, the software became much better at playing checkers.

Reinforcement learning is also useful for applications like autonomous vehicles, where the system can receive feedback about whether it has performed well or poorly and use that data to improve over time.

Supervised learning is particularly useful in classification applications such as teaching a system to tell the difference between pictures of dogs and pictures of cats.

In this case, you would feed the application a whole lot of images that had been previously tagged as either dogs or cats. From that training data, the computer would draw its own conclusions about what distinguishes the two types of animals, and it would be able to apply what it learned to new pictures.

By contrast, unsupervised learning does not rely on human beings to label training data for the system. Instead, the computer uses clustering algorithms or other mathematical techniques to find similarities among groups of data.

Unsupervised machine learning is particularly useful for the type of big data analytics that interests many enterprise leaders. For example, you could use unsupervised learning to spot similarities among groups of customers and better target your marketing or tailor your pricing.

Some recommendation engines rely on unsupervised learning to tell people who like one movie or book what other movies or books they might enjoy. Unsupervised learning can also help identify characteristics that might indicate a person’s credit worthiness or likelihood of filing an insurance claim.

Various AI applications, such as computer vision, natural language processing, facial recognition, text-to-speech, speech-to-text, knowledge engines, emotion recognition, and other types of systems, often make use of machine learning capabilities. Some combine two or more of the main types of machine learning, and in some cases, are said to be “semi-supervised” because they incorporate some of the techniques of supervised learning and some of the techniques of unsupervised learning. And some machine learning techniques — such as deep learning — can be supervised, unsupervised, or both.

The phrase “deep learning” first came into use in the 1980s, making it a much newer idea than either machine learning or artificial intelligence.

Deep learning describes a particular type of architecture that both supervised and unsupervised machine learning systems sometimes use. Specifically, it is a layered architecture where one layer takes an input and generates an output. It then passes that output on to the next layer in the architecture, which uses it to create another output. That output can then become the input for the next layer in the system, and so on. The architecture is said to be “deep” because it has many layers.

To create these layered systems, many researchers have designed computing systems modeled after the human brain. In broad terms, they call these deep learning systems artificial neural networks (ANNs). ANNs come in several different varieties, including deep neural networks, convolutional neural networks, recurrent neural networks and others. These neural networks use nodes that are similar to the neurons in a human brain.

However, those GPUs also excel at the type of calculations necessary for deep learning. As GPU performance has improved and costs have decreased, people have been able to create high-performance systems that can complete deep learning tasks in much less time and for much less cost than would have been the case in the past.

Today, anyone can easily access deep learning capabilities through cloud services like Amazon Web Services, Microsoft Azure, Google Cloud and IBM Cloud.

If you are interested in learning more about AI vs machine learning vs deep learning, Datamation has several resources that can help, including the following:

Wifi Vs. Ethernet Vs. 4G: Which Should You Use?

Since the days of dial-up connections, we’ve come a long way when it comes to getting a computer or laptop connected to the Internet. The most well-known method is WiFi which allows people to connect to the Internet via a router. But what about the other methods available for you to use? What are some other ways you can get a computer or laptop online, and when is it ideal to use them?


The more familiar method of getting a computer online, WiFi works by talking to a nearby router with access to the Internet. With devices coming with WiFi adapters built into them, WiFi connections are a solid choice for when you want to connect to the Internet.


WiFi is great for connecting to networks, whether they be at your home, your workplace, or in a public place such as an airport. With its ability to connect to the Internet wirelessly, it can be very useful for getting a device online where wires would be a huge hassle to use. Given how so many devices come pre-made with a WiFi adapter within them, you may be able to use WiFi functionality out of the box. Otherwise, if you need a PCI or USB adapter to get it online, they can be very inexpensive and last you years.

While WiFi is great, it’s not without some gripes. For use in public areas, you have to get within a decent range of the router. While you can technically connect from anywhere within its radius, you’ll need to be quite close to achieve download speeds that won’t have you tapping your fingers on the table. Sometimes there are small obstacles you’ll need to pass before you gain access to a public WiFi such as getting its password, signing up with an account, and even buying a data plan for the time that you use. This makes it more of a hassle to get online than, say, a mobile connection.

In the home WiFi isn’t perfect either. Interference with devices such as microwaves and fridges can cause weak or unstable signals. A neighbour’s routers can interfere with your own if both of the wireless channels are too close, so you’ll need to know about WiFi channels and how to change it on your router if you don’t want your router to decide for you. Even then, sometimes routers and WiFi adapters can give spotty, unstable, or even no WiFi signal without much explanation as to why which can be annoying.


While Ethernet may seem a little outdated compared to its wireless brothers, it still has a place within the modern age. What can Ethernet do better than WiFi and mobile connections?


By far the best aspect of using an Ethernet connection is its ability to draw the maximum amount of data your router and/or connection can handle. When going wireless using either WiFi or mobile, you naturally lose some of the connection through signal loss as it travels through the air and goes through obstacles such as walls and furniture. Cables naturally avoid this, so you’ll be seeing connections as fast and as stable as your Internet will allow. This is particularly good if you play very quick online games, as they require the lowest pings possible to play well.

It also means you can dodge WiFi interference, as your connection will be across the wire and not wireless. This means it doesn’t matter how many wireless connections the household and neighbours are using – your own connection won’t be fighting for a spot amongst them.

Of course, not being wireless, its strongest weakness is the fact that you need to run a wire between yourself and the router. In large houses or up/down stairs this can prove problematic if not impossible! As such, Ethernet is only really useful if you’re close to the router or if you’re able to get the wire to your router without inconveniencing others. If it really proves too much of a struggle to get the wire across, WiFi might be your best choice!

Mobile Internet

While mobile devices naturally use mobile Internet the most, you can use 3G and 4G connections on a computer or laptop. You can do this in one of two ways: either attach a USB dongle (usually sold by mobile network providers) that receives mobile Internet, or pick up a mobile WiFi router which acts like a normal router except it connects itself to mobile Internet like a phone would. So, what can mobile connections bring?


The key aspect for using mobile Internet is the ability to use it anywhere with coverage. If you find yourself in a spot where you can’t connect to a public WiFi router, but you can get a phone signal fine, you can get onto the Internet using a 3G or 4G connection. This makes mobile data an incredibly handy choice for someone always on the move. Who wants the convenience of the availability of mobile networks while also using the power of a laptop or computer over something like a phone or a tablet.

Despite how widespread mobile coverage can be and how 4G speeds are getting very good, it’s not the de-facto choice for computers just yet. Mobile connections can be quite expensive, and oftentimes more pricey than a home Internet connection. It also comes with some quite strict data usage, so performing “regular” computer-based actions like downloading large software and streaming HD video may get you into trouble. On top of all that, you may find it’s not as quick or stable as a WiFi connection can be. As such, it’s a great option if you find yourself unable to connect using Ethernet or WiFi; otherwise, you’re probably best off with those two!


So, which is best for you? Ideally, if you use the Internet mostly at home and your computer is very close to the router, connecting it up with an Ethernet cable can give you the best quality. If you can’t get a cable to reach, or you like to take a laptop out and about with you in urban areas, a WiFi connection will do the job nicely. However, if you’re an avid explorer and find yourself in places without routers around you, you can still get use out of your laptop by using mobile Internet.

Do you use one of the above methods extensively? Did you used to use one method then swapped to a different one and stuck with it? Let us know below.

Simon Batt

Simon Batt is a Computer Science graduate with a passion for cybersecurity.

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Usb 3.0 Vs 3.1 Vs 3.2 – What’s The Difference?

USBs have been a real game changer for the world of technology, and they’ve been constantly evolving since the introduction of USB 1.0 in 1996.

Talking about the changes, each generation of USB 3 brought noticeable improvements, which were generally well received by users. The naming convention on the other hand, was a disaster.

Both the official and marketing names were revised multiple times. For instance, USB 3.0 was renamed as USB 3.1 Gen 1, and renamed again as USB 3.2 Gen 1×1. While the latter is the currently used name, some people still prefer the older names, which adds to the confusion.

If you’re (understandably) confused as well, the table below should be helpful.

Original NamesRevised NamesCurrent NamesOld Marketing NameCurrent Marketing NameUSB 3.0USB 3.1 Gen 1USB 3.2 Gen 1×1SuperSpeedSuperSpeed USB 5 Gbit/sUSB 3.1USB 3.1 Gen 2USB 3.2 Gen 2×1SuperSpeed +SuperSpeed USB 10 Gbit/sUSB 3.2USB 3.2 Gen 2×2SuperSpeed USB 20 Gbit/sThe naming of various iterations of USB 3.X

For ease of reading, we’ve organized the sections below in chronological order with the current generation-based naming convention, i.e., USB 3.2 Gen 1×1, 2×1, and 2×2.

USB 3.2 Gen 1×1, originally introduced as USB 3.0 in November 2008, had several significant improvements over USB 2.0, its predecessor. 

The revised USB 2.0 standard supported all types of connectors from Type-A to Mini-AB. USB 3.0 dropped support for the mini connectors and instead supported new versions of Type-A, Type-B, Type-C, Micro-A, Micro-B, and Micro-AB connectors.

The USB 3.0 Type-A connectors were backward compatible with the USB 2.0 version. However, this is not the case for the rest of the connectors, as the other versions don’t physically match. You’ll need an adapter to use these USB 3.0 connectors with a USB 2.0 port.  

With these connectors, USB 3.0 specified 150 mA or 0.6 W current for one unit load devices, and up to 900mA or 4.5 W for max six load devices at 5 V. Additionally, USB 3.0 ports could bump the available current up to 1.5 A or 7.5 W if implementing the USB Battery Charging Specification.

And most importantly, USB 3.0, which was marketed as SuperSpeed, introduced max transfer speeds of up to 5 Gbit/s, a massive 10x increase from USB 2.0’s Hi-Speed USB.

USB-IF introduced the USB 3.1 standard in 2013, and this is where the rebranding mess began. USB 3.0 was renamed to USB 3.1 Gen 1, while USB 3.1 was marketed as USB 3.1 Gen 2, or SuperSpeed +. 

USB 3.1 Gen 1 superseded USB 3.0, meaning it had the same specs, with 5 Gbit/s max bandwidth over a single lane using 8b/10b encoding.

USB 3.1 Gen 2 improved on these specs with a new max data transfer rate up to 10 Gbit/s with 128b/132b encoding. Of course, this was only a theoretical max. But the real-world max speeds were still very impressive at over 7Gbit/s.

While the connector types didn’t change from USB 3.0 to 3.1, one significant difference was the use of USB Power Delivery (PD) standard. The revised USB PD Rev 2.0 standard was released as part of the USB 3.1 suite, which updated USB PD to support various USB-C features such as Alternate Mode.

In terms of power, USB PD introduced Power Rules which defined normative voltage levels at 5 V, 9 V, 15 V, and 20 V. Fixed power profiles were also dropped, meaning power supplies could support maximum source output power ranging anywhere from 0.5 W to 100 W.

USB-IF released USB 3.2 in August 2023, and this is where the naming convention really became an issue. USB 3.0, which was rebranded as USB 3.1 Gen 1, was absorbed by USB 3.2 and once again rebranded as USB 3.2 Gen 1×1.

This can get confusing, but as one of our readers put it, thinking of anything referring to Gen 1 as 3.0 could be an easy way to remember.

Similarly, USB 3.1 was renamed as USB 3.2 Gen 2×1, while USB 3.2 was branded as USB 3.2 Gen 2×2. The old marketing names, SuperSpeed and SuperSpeed + were also changed to SuperSpeed 5 Gbit/s and SuperSpeed 10 Gbit/s. Following this trend, the marketing name for USB 3.2 was SuperSpeed 20 Gbit/s.

As evident from the brand name, USB 3.2 operates with dual-lane differential SuperSpeed pairs and uses 128b/132b encoding to offer max speeds of up to 20 Gbit/s. 

Another very noticeable change with USB 3.2 was that it deprecated all connector types aside from the USB Type-C connector. USB-C specifies a symmetrical connector with 12 A pins on top and 12 B pins at the bottom. Because of the rotational symmetry, you needn’t worry about the correct orientation as with other connector types.

The exclusive use of USB-C meant that the implementation of features such as Alternate Mode was also much more prevalent with USB 3.2. For instance, with the DisplayPort alt mode, you could transfer both USB and Video data simultaneously.

To recap, here are the main differences as detailed in the sections above:

USB 3.0USB 3.1USB 3.2Form FactorUSB 3.0 specified the use of Type-A, Type-B, Type-C, Micro-A, Micro-B, and Micro-AB chúng tôi connector types remained consistent in USB 3.1USB 3.2 specified the exclusive use of Type-C, and dropped support for all other connector types.Power DeliveryThe standard USB 3.0 specification supports up to 4.5 W of chúng tôi USB PD, USB 3.1 can support up to 100 W of chúng tôi 3.2 also supports up to 100 W of power with USB PD.Max BandwidthUSB 3.0 supported a theoretical max bandwidth of 5 Gbit/s.USB 3.1 supported a theoretical max bandwidth of 10 Gbit/s.USB 3.2 supported a theoretical max bandwidth of 20 Gbit/s.

Aside from these major technical differences, there are a few more things worth talking about, starting with the pricing. Each iteration of USB 3 saw the use of improved technology, which subsequently meant increased product prices on the customer’s end.

The exact price difference between the generations differs according to the product, but you can always count on the newer generation products with better specs to cost higher than the older ones.

Second, is the matter of appearance. USB 3.0 originally used blue colored ports, whereas USB 3.1 adapted teal colored ports instead. Some manufacturer’s also use purple or violet for USB 3.1 ports.

While in previous iterations of USB, red color was adapted on those ports/connectors which was limited to charging, USB 3.2 Gen 2×2 adapted this color wholly.

Finally, let’s talk about actual implementation. With how immensely popular the USB standard is, implementing a new version worldwide is a herculean task that takes years. USB 2.0 had 8 years to establish itself as a standard.

On the other hand, USB 3.0 only had around 4 years between its introduction and the release of USB 3.1. It’s the same story with USB 3.1 and USB 3.2. In fact, USB 3.2 was the likely the least popular, as by the time it’s implementation actually began in 2023, USB4 was already introduced.

USB4, which is based on the Thunderbolt 3 protocol, is currently the latest and fastest USB standard. Aside from doubling the data signaling rates compared to it’s predecessor, USB4 brought forth numerous improvements, which deserve an article of their own.

As modern devices are already adopting USB4, the implementation of USB 3.2 has been quite limited. Recently, the EU proposed mandatory USB-C, which further impacts the usage of USB 3.0 and USB 3.1 as well.

While this doesn’t bode well for USB 3, it’s likely a good thing for USB in general, and users as well. Even though USB4 products are slightly more expensive, the numerous improvements make it well worth it. And the prospect of a universal connector type is always welcome as well.

Google Keep Vs. Onenote Vs. Evernote: We Name The Note

Google shook things up last week when it dusted off its old Notebook service and relaunched it as Keep. Google’s new software muscles in on the space currently dominated by Microsoft OneNote and Evernote, two note-taking apps that save your text, Web links, photos, audio recordings, and more, helping to keep your life organized.

Whether you’re about to start using a note-taking app or are considering defecting from your current choice, you must first think about the features you need most. One app may excel at OCR support and another might be best for anywhere access, while a third may be ideal for content sharing within a team. To help you make an informed decision, here’s a closer look at how Google Keep, Microsoft OneNote, and Evernote stack up in a variety of categories.


Keep on Android

Google Keep is available online and via an Android app. Both iterations are free. OneNote and Evernote have Web and app elements that you can use for free, plus paid premium editions.

OneNote is available as part of a Microsoft Office 365 subscription, starting at $100 per year per home user. It also comes bundled within Office desktop suites starting at $140. As a stand-alone product, OneNote 2013 costs $70. You can use OneNote for free as a Web app through Microsoft SkyDrive, and on Windows Phone, Android, or iOS. The Office version provides additional features, such as the ability to clip screenshots or “print” documents directly to OneNote.

Evernote is free for up to 60MB per month of data. The data cap of the Premium upgrade ($5 per month or $45 per year) jumps to 1GB of bandwidth each month. You also get faster performance, better security, and deeper search capabilities. Evernote for Business gives IT admins oversight and control, with additional collaboration options, for $120 per user per year.

Winner: All three note-taking platforms have free options, so we can’t ding any of the contenders for being overpriced. That said, Evernote’s paid versions offer greater functionality through apps and add-ons.

Platforms and ecosystems

Evernote notes on Windows 8

We’ve heard no official confirmation, but it’s reasonable to expect Google to develop iOS and Windows Phone versions of the Keep app eventually. For now, Keep is best for Google-centric users of Google Drive online storage and productivity tools.

Evernote matches Google Keep and OneNote with Web access, and also provides native apps for Android, iOS, Windows Phone, and BlackBerry, in addition to dedicated client software for Windows and Mac OS X. Evernote has developed an extensive community and provides a variety of its own apps as well as third-party apps on its Trunk website.


Content in OneNote, shown in a browser, resides on SkyDrive.

Within a browser, Google Keep lets you view notes as either a list or a grid resembling tidy sticky notes. You can assign colors to notes, but you can’t order or group notes.

OneNote and Evernote, on the other hand, each use a notebook-and-notes metaphor. You can create a notebook for a dedicated topic—such as Website Project, Summer Vacation, or Income Taxes—and then create multiple related notes within it.

OneNote and Evernote also let you tag notes with keywords. You can create separate notebooks in OneNote. Each notebook can contain multiple sections, and each section can have multiple pages and color coding. The desktop version of OneNote also allows you to create a Section Group, the equivalent of embedding a notebook in a notebook. Evernote has a feature similar to the OneNote Section Group, which permits you to group notebooks into Stacks. Evernote Business users can stack professional content within a Business Library.

Winner: Evernote provides more controls for organizing your information.

Rich Media

Evernote on an iPhone

Note-taking apps are most powerful when you use them for more than text. Google Keep on the Web lets you add only an image from your PC, while its Android app allows you to capture a picture or an audio clip. Keep transcribes the audio to text, embedding both inside your note.

Features vary according to the hardware and software you have on hand. For example, you can annotate OneNote notes using a digital stylus in either the OneNote desktop version or the OneNote MX app for Windows 8, but only if you have a touchscreen device and a digital stylus to work with.

Winner: Evernote’s ecosystem of apps expands its rich-media possibilities.

Text editing

Michelle Mastin OneNote lets you annotate documents and images, as does Evernote.

Although a note-taking tool isn’t meant to replace your word processor, it should make your text look better than a jumble of misaligned characters. After all, you’re trying to get organized.

OneNote on iOS is similar but lacks numbered lists, although the OneNote app for Windows Phone enables numbered lists and text formatting. The Web and Office versions of OneNote both provide more-comprehensive text editing, while the OneNote MX app for Windows 8 uses an innovative radial menu for formatting.

With only color coding for organization, Keep in a browser resembles a Pinterest-style jumble of content.

If you insert a checkbox on Evernote’s Android or iOS app, Evernote automatically adds a checkbox on each line when you tap Enter. On the Web version, in contrast, you have to add the checkbox manually at the beginning of each line; if you try to do it en masse, prepare for heartbreak.

Winner: It makes sense that OneNote, brought to you by the makers of Word software, provides the richest text formatting.

Business features

Evernote’s administrator options are user-friendly.

OneNote, on the other hand, supports management features through SharePoint or SkyDrive Pro. The IT admin can manage business data stored there, as well as control user access through Active Directory and Group Policy. You can share notebooks with the whole company, or with designated individuals or teams. Individuals can access their personal OneNote notebooks on SkyDrive, as well as the company notebooks for which they have permission on SharePoint or SkyDrive Pro.

Like OneNote, Evernote Business lets companies manage notes and data related to the business, while allowing individual users to create and maintain personal notes and notebooks outside the grasp of the IT admin.

Winner: Evernote is easier to manage than the SharePoint or SkyDrive Pro back end for OneNote.

Data management

Business editions of Office 365 let you share OneNote content with your team.

Be aware that Google can be fickle, as it has axed more than 70 features or services since it began “spring cleaning” in 2011 (RIP, Google Reader).

With either Evernote Business or OneNote used with SharePoint or SkyDrive Pro, business data belongs to the employer and stays under the control of the IT admin. If a user leaves a company, he or she no longer has access to the company notebooks and data, but retains access to their personal notes.

If an Office 365 subscription lapses, the locally installed OneNote software reverts to read-only mode. However, the data still exists through SkyDrive, and you can still use OneNote by way of its Web or mobile apps.

Winner: A tie. All three services provide roughly the same assurances of data ownership, but no means of exporting or archiving your data outside of proprietary formats.

The champion

Google Keep, Microsoft OneNote, and Evernote each offer distinctive benefits. Unless you’re Google-focused or Microsoft-centric, however, Evernote is the most diverse and capable service.

Google Keep is nice and simple, but its capabilities are extremely limited.

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Business Analytics Vs Business Intelligence

Business Analytics vs Business Intelligence

Business Intelligence is one of the most important aspects of data analysis and is an integral part of modern companies. The definition of business analytics techniques is somewhat ambiguous and is constantly changing according to the changing dynamics of the companies. In a nutshell, business analytics can be defined as a set of applications, practices, skills, and technologies that help companies make strategic and vital decisions, thereby allowing the company to achieve its goals and ambitions.

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After understanding the importance and immense potential of data analytics, many brands and organizations have started investing many resources in them. However, most of this data analytics is limited to dashboards and reports, whereas the field of data analytics is large and has many more possible opportunities. While the popular forms of data analytics are essential, it is necessary to understand that many forms of data analytics can come together to help brands become empowered in their decisions and choices. At the same time, it is essential to remember that companies are becoming more independent and keen on expanding their horizons through technology, so they must identify the value of data and its interaction at all possible stages.

The ability to break down concepts and gain a proper insight into how data functions can help companies build and manage applications independently. At the same time, this insight can help companies gain knowledge about how various units of a company work together on the one hand and the requirements of the IT sector to develop products and services that can enable effective communication and goal achievement hand.

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Business Analytics vs Business Intelligence Infographics

Below infographics on Business Analytics vs Business Intelligence throws light on the significant differences between the two.

Why is understanding data so important for companies?

While it is easy to understand why data is an essential aspect of modern companies and brands, there are also also certain pitfalls related to it. The first and most important is security, while the other two include integrity and accuracy, which are equally, if not more important. Once these three things have been guaranteed, determining effective results through data analysis is the only important thing left. Every company knows that data is used to provide valuable insights. When brands are armed with these insights, they can make decisions that improve their overall functioning and management. However, rarely is data used in a raw state; they have to be processed and presented so that they can apply strategically and comprehensively.

The latest analytical tools make it much easier for companies to gain these insights, but there is always a journey to make this data usable and valuable. Maintaining data accuracy at all stages is extremely important because inaccuracy in data can lead to wrong insights, and,, if implemented,, can affect the entire functioning of the company negatively. That is why the quality of the data sample is much more important than the quantity of data. Many companies,, instead of focusing on the quality, focus on gathering large amounts of data without thinking about whether it is correct or incorrect. Added to this, integrity plays a vital role in data analytics.

What is business intelligence skills?

We can analyze and investigate business performance using multiple methods to restructure it and achieve profitable gains and solutions. Top analytics consulting firms believe that business intelligence skills and analytics techniques will undergo significant changes and greater adoption in the coming years. Many analytics feel that companies will now shift from information technology reports to developing business intelligence tools capable of delivering informed choices about companies’ growth strategy and development. These will lead to four significant changes, faster processing capabilities, mobile applications, social decision-making models, and more spending on solutions providers.

Business analytics techniques have the power to process data at a much more rapid pace.

The amount of data available in a company is almost endless. To make sense of this data, there is a need to handle this vast data systematically and quickly. Today, BI analytics has gone mainstream, and even small and new companies are looking at using this technique to harness the immense potential in the market. Besides, many companies need this technology to forge ahead and explore newer opportunities and challenges. That is why analytic marketers are searching for new methods to create business analytical tools that can quickly process data and can be adopted by companies across different sectors. With tools that can be used across IT teams, these business analytical tools redefine how companies function and carry.

Business analytical tools are at the next stage of development, namely mobile applications.

Mobile smartphones are gaining rapid acceptance across the globe. According to a new report, almost 2 million worldwide will have smartphone access by the end of this year. Business marketers must look for new ways to integrate smartphones into business analytics techniques. Besides, many marketers and company professionals rely on mobile phones to keep them updated on the functioning of their company, especially when they are traveling or away from their offices. Business analytics companies are looking to invest in mobile BI functions, and software designers will soon look at manufacturing products aimed at mobile phones rather than desktop users. With many companies and brands already going mobile, business analytics techniques on smartphones already have a ready audience.

Business analytical tools should enable companies to make decisions on social platforms.

Social media platforms are viral and present in almost all countries worldwide. Today, all companies are on social media platforms, making it essential to have business analytical tools that integrate social network capabilities with decision-making capabilities. While this might be a little tricky and complex, integrating business analytical skills with social networking may no longer be an option but a requirement in the coming years.

Increased spending on business analytics techniques consulting

With so many complex and practical applications available in the market, experts feel that there will undoubtedly be an increase in business analytics techniques consulting, especially in the coming years. Companies are pressuring business analytics firms to provide faster and better tools to help achieve their business analytical goals.

Business Analytics vs Business Intelligence, How are they different?

This is how business analytics techniques can help companies. Now coming to BI. Defined as a technology-driven process for analyzing data and presenting actionable information to help companies, BI encompasses a lot of business intelligence tools, applications, and methodologies. BI is, therefore, an umbrella term and a focused concept. Both business analytics techniques and business intelligence skills are related terms. They are generally strategies and decisions that can help companies across sectors like research and development, customer care, credit, and inventory management. Both of these help companies meet business challenges and use fresh opportunities that arise within the sector.

Business analytics techniques and BI can have far-reaching consequences for the functioning of brands and companies across categories. Some areas they can help impact include critical product analysis, improved customer service, up-selling opportunities, simplified inventory management, and competitive price insights. By allowing companies to understand customers’ and client’s needs in real time, they can help maximize resources effectively and minimize losses.

Business Analytics vs Business Intelligence – Future

But with time, this will become a necessity because social media is a growing platform that no company can ignore, not today and not in the coming years. That is why many corporate companies are now looking at BI programs that can help them not just upgrade their decision-making abilities but also reduce their operational costs and help them use existing opportunities.

Data interpretation and manipulation methods of choice keep changing according to the market’s requirements. That is why companies must be clear about the tools and techniques they use to reach their eventual goal. When companies understand the flexible nature of the economy and their business, it becomes much easier for them to handle these changes through tools that can reach the goal, even in challenging situations.

Conclusion – Business Analytics vs Business Intelligence

In conclusion, Business Analytics vs Business Intelligence both have immense potential, and there are a lot of challenges present in both these sectors, primarily related to the field of technical and social networking. Companies must remember that business analytics techniques are not the same as BI. The requirements of one field are different, and so are the benefits of each of them. A company can only use technology effectively if it invests in it and uses it in a proper and systematic manner. By working with end-users, consultants can help companies use the right tools to use the data to make decisions that will empower the brand and take them to the next level of growth and development.

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