The Evolution of Media: Visualizing a Data-Driven Future
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The Evolution of Media: Visualizing a Data-Driven Future

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The Evolution of Media: Visualizing a Data-Driven Future

In today’s highly-connected and instantaneous world, we have access to a massive amount of information at our fingertips.

Historically, however, this hasn’t always been the case.

Time travel back just 20 years ago to 2002, and you’d notice the vast majority of people were still waiting on the daily paper or the evening news to help fill the information void.

In fact, for most of 2002, Google was trailing in search engine market share behind Yahoo! and MSN. Meanwhile, early social media incarnations (MySpace, Friendster, etc.) were just starting to come online, and all of Facebook, YouTube, Twitter, and the iPhone did not yet exist.

The Waves of Media So Far

Every so often, the dominant form of communication is upended by new technological developments and changing societal preferences.

These transitions seem to be happening faster over time, aligning with the accelerated progress of technology.

  • Proto-Media (50,000+ years)
    Humans could only spread their message through human activity. Speech, oral tradition, and manually written text were most common mediums to pass on a message.
  • Analog and Early Digital Media (1430-2004)
    The invention of the printing press, and later the radio, television, and computer unlock powerful forms of one-way and cheap communication to the masses.
  • Connected Media (2004-current)
    The birth of Web 2.0 and social media enables participation and content creation for everyone. One tweet, blog post, or TikTok video by anyone can go viral, reaching the whole world.

Each new wave of media comes with its own pros and cons.

For example, Connected Media was a huge step forward in that it enabled everyone to be a part of the conversation. On the other hand, algorithms and the sheer amount of content to sift through has created a lot of downsides as well. To name just a few problems with media today: filter bubbles, sensationalism, clickbait, and so on.

Before we dive into what we think is the next wave of media, let’s first break down the common attributes and problems with prior waves.

Wave Zero: Proto-Media

Before the first wave of media, amplifying a message took devotion and a lifetime.

Add in the fact that even by the year 1500, only 4% of global citizens lived in cities, and you can see how hard it would be to communicate effectively with the masses during this era.

Or, to paint a more vivid picture of what proto-media was like: information could only travel as fast as the speed of a horse.

Wave 1: Analog and Early Digital Media

In this first wave, new technological advancements enabled widescale communication for the first time in history.

Newspapers, books, magazines, radios, televisions, movies, and early websites all fit within this framework, enabling the owners of these assets to broadcast their message at scale.

With large amounts of infrastructure required to print books or broadcast television news programs, it took capital or connections to gain access. For this reason, large corporations and governments were usually the gatekeepers, and ordinary citizens had limited influence.

AttributeDescription
📡 Information FlowOne-way
💰 Barriers to EntryVery high
📰 DistributionControlled by mass media companies and government
🏆 IncentiveTo cast a wide net, and to not alienate viewers or advertisers

Importantly, these mediums only allowed one-way communication—meaning that they could broadcast a message, but the general public was restricted in how they could respond (i.e. a letter to the editor, or a phone call to a radio station).

Wave 2: Connected Media

Innovations like Web 2.0 and social media changed the game.

Starting in the mid-2000s, barriers to entry began to drop, and it eventually became free and easy for anyone to broadcast their opinion online. As the internet exploded with content, sorting through it became the number one problem to solve.

For better or worse, algorithms began to feed people what they loved, so they could consume even more. The ripple effect of this was that everyone competing for eyeballs suddenly found themselves optimizing content to try and “win” the algorithm game to get virality.

AttributeDescription
📡 Information FlowTwo-way
💰 Barriers to EntryVery low
📰 DistributionControlled by technology companies and algorithms
🏆 IncentiveTo cast a narrow net, to engage and mobilize a specific audience

Viral content is often engaging and interesting, but it comes with tradeoffs. Content can be made artificially engaging by sensationalizing, using clickbait, or playing loose with the facts. It can be ultra-targeted to resonate emotionally within one particular filter bubble. It can be designed to enrage a certain group, and mobilize them towards action—even if it is extreme.

Despite the many benefits of Connected Media, we are seeing more polarization than ever before in society. Groups of people can’t relate to each other or discuss issues, because they can’t even agree on basic facts.

Perhaps most frustrating of all? Many people don’t know they are deep within their own bubble in which they are only fed information they agree with. They are unaware that other legitimate points of view exist. Everything is black and white, and grey thinking is rarer and rarer.

Wave 3: Data Media

Between 2015 and 2025, the amount of data captured, created, and replicated globally will increase by 1,600%.

For the first time ever, a significant quantity of data is becoming “open source” and available to anyone. There have been massive advancements in how to store and verify data, and even the ownership of information can now be tracked on the blockchain. Both media and the population are becoming more data literate, and they are also becoming aware of the societal drawbacks stemming from Connected Media.

As this new wave emerges, it’s worth examining some of its attributes and connecting concepts in more detail:

  • Transparency:
    Data literate users will begin to demand that data is transparent and originating from trustworthy, factual sources. Or if a source is not rock solid, users will demand that limitations of methodology or possible biases are openly revealed and discussed.
  • Verifiability and Trust:
    How do we know data shown is legitimate and bonafide? Platforms and media will increasingly want to prove to users that data has been verified, going all the way back to the original source.
  • Decentralization and Web3:
    Anyone can tap into large amounts of public data available today, which means that reporting, analysis, ideas, and insights can come from an increasingly growing set of actors. Web3 and decentralized ledgers will allow us to provide trust, attribution, accountability, and even ownership of content when necessary. This can remove the middleman, which is often large tech companies, and can allow users to monetize their content more directly.
  • Data Storytelling
    Growing data literacy, and the explosion of data storytelling is a key approach to making sense of vast amounts of data, by combining data visualization, narrative, and powerful insights.
  • Data Creator Economy:
    Democratized data and the rise of storytelling are intersecting to create a potential new ecosystem for data storytellers. This is increasingly what we are focused on at Visual Capitalist, and we encourage you to support our Kickstarter project on this (just 6 days left, as of publishing time)
  • Open-Ended Ecosystem:
    Just like open source has revolutionized the software industry, we will begin to see more and more data available broadly. Incentives may shift in some cases from keeping data proprietary, to getting it out in the open so that others can use, remix, and publish it, and attributing it back to the original source.
  • Data > Opinion:
    Data Media will have a bias towards facts over opinion. It’s less about punditry, bias, spin, and telling others what they should think, and more about allowing an increasingly data literate population to have access to the facts themselves, and to develop their own nuanced opinion on them.
  • Global Data Standards:
    As data continues to proliferate, it will be important to codify and unify it when possible. This will lead to global standards that will make communicating it even easier.

Early Pioneers of Data Media

The Data Media ecosystem is just beginning to emerge, but here are some early pioneers we like:

  • Our World in Data:
    Led by economist Max Roser, OWiD is doing an excellent job amalgamating global economic data in one place, and making it easy for others to remix and communicate those insights effectively.
  • USAFacts:
    Founded by Steve Ballmer of Microsoft fame to be a non-partisan source of U.S. government data.
  • FRED:
    This tool by the Federal Reserve Bank of St. Louis is just one example of many tools that have cropped up over the years to democratize data that were previously proprietary or hard to access. Other similar tools have been created by the IMF, World Bank, and so on.
  • FiveThirtyEight:
    FiveThirtyEight uses statistical analysis, data journalism, and predictions to cover politics, sports, and other topics in a unique way.
  • FlowingData:
    At FlowingData, data viz expert Nathan Yau explores a wide variety of data and visualization themes.
  • Data Journalists:
    There are incredible data journalists at publications like The Economist, The Washington Post, The New York Times, and Reuters that are tapping into the early beginnings of what is possible. Many of these publications also made their COVID-19 work freely available during the pandemic, which is certainly commendable.

Growth in data journalism and the emergence of these pioneers helps give you a sense of the beginnings of Data Media, but we believe they are only scratching the surface of what is possible.

What Data Media is Not

In a sense, it’s easier to define what Data Media isn’t.

Data Media is not partisan pundits arguing over each other on a newscast, and it’s not fake news, misinformation, or clickbait that is engineered to drive easy clicks. Data media is not an echo chamber that only reinforces existing biases. Because data is also less subjective, it’s less likely to be censored in the way we see today.

Data is not perfect, but it can help change the conversations we are having as a society to be more constructive and inclusive. We hope you agree!

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Every Mission to Mars in One Visualization

This graphic shows a timeline of every mission to Mars since 1960, highlighting which ones have been successful and which ones haven’t.

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Timeline: A Historical Look at Every Mission to Mars

Within our Solar System, Mars is one of the most similar planets to Earth—both have rocky landscapes, solid outer crusts, and cores made of molten rock.

Because of its similarities to Earth and proximity, humanity has been fascinated by Mars for centuries. In fact, it’s one of the most explored objects in our Solar System.

But just how many missions to Mars have we embarked on, and which of these journeys have been successful? This graphic by Jonathan Letourneau shows a timeline of every mission to Mars since 1960 using NASA’s historical data.

A Timeline of Mars Explorations

According to a historical log from NASA, there have been 48 missions to Mars over the last 60 years. Here’s a breakdown of each mission, and whether or not they were successful:

#LaunchNameCountryResult
11960Korabl 4USSR (flyby)Failure
21960Korabl 5USSR (flyby)Failure
31962Korabl 11USSR (flyby)Failure
41962Mars 1USSR (flyby)Failure
51962Korabl 13USSR (flyby)Failure
61964Mariner 3US (flyby)Failure
71964Mariner 4US (flyby)Success
81964Zond 2USSR (flyby)Failure
91969Mars 1969AUSSRFailure
101969Mars 1969BUSSRFailure
111969Mariner 6US (flyby)Success
121969Mariner 7US (flyby)Success
131971Mariner 8USFailure
141971Kosmos 419USSRFailure
151971Mars 2 Orbiter/LanderUSSRFailure
161971Mars 3 Orbiter/LanderUSSRSuccess/Failure
171971Mariner 9USSuccess
181973Mars 4USSRFailure
191973Mars 5USSRSuccess
201973Mars 6 Orbiter/LanderUSSRSuccess/Failure
211973Mars 7 LanderUSSRFailure
221975Viking 1 Orbiter/LanderUSSuccess
231975Viking 2 Orbiter/LanderUSSuccess
241988Phobos 1 OrbiterUSSRFailure
251988Phobos 2 Orbiter/LanderUSSRFailure
261992Mars ObserverUSFailure
271996Mars Global SurveyorUSSuccess
281996Mars 96RussiaFailure
291996Mars PathfinderUSSuccess
301998NozomiJapanFailure
311998Mars Climate OrbiterUSFailure
321999Mars Polar LanderUSFailure
331999Deep Space 2 Probes (2)USFailure
342001Mars OdysseyUSSuccess
352003Mars Express Orbiter/Beagle 2 LanderESASuccess/Failure
362003Mars Exploration Rover - SpiritUSSuccess
372003Mars Exploration Rover - OpportunityUSSuccess
382005Mars Reconnaissance OrbiterUSSuccess
392007Phoenix Mars LanderUSSuccess
402011Mars Science LaboratoryUSSuccess
412011Phobos-Grunt/Yinghuo-1Russia/ChinaFailure
422013Mars Atmosphere and Volatile EvolutionUSSuccess
432013Mars Orbiter Mission (MOM)IndiaSuccess
442016ExoMars Orbiter/Schiaparelli EDL Demo LanderESA/RussiaSuccess/Failure
452018Mars InSight LanderUSSuccess
462020Hope OrbiterUAESuccess
472020Tianwen-1 Orbiter/Zhurong RoverChinaSuccess
482020Mars 2020 Perseverance RoverUSSuccess

The first mission to Mars was attempted by the Soviets in 1960, with the launch of Korabl 4, also known as Mars 1960A.

As the table above shows, the voyage was unsuccessful. The spacecraft made it 120 km into the air, but its third-stage pumps didn’t generate enough momentum for it to stay in Earth’s orbit.

For the next few years, several more unsuccessful Mars missions were attempted by the USSR and then NASA. Then, in 1964, history was made when NASA launched the Mariner 4 and completed the first-ever successful trip to Mars.

The Mariner 4 didn’t actually land on the planet, but the spacecraft flew by Mars and was able to capture photos, which gave us an up-close glimpse at the planet’s rocky surface.

Then on July 20, 1976, NASA made history again when its spacecraft called Viking 1 touched down on Mars’ surface, making it the first space agency to complete a successful Mars landing. Viking 1 captured panoramic images of the planet’s terrain, and also enabled scientists to monitor the planet’s weather.

Vacation to Mars, Anyone?

To date, all Mars landings have been done without crews, but NASA is planning to send humans to Mars by the late 2030s.

And it’s not just government agencies that are planning missions to Mars—a number of private companies are getting involved, too. Elon Musk’s aerospace company SpaceX has a long-term plan to build an entire city on Mars.

Two other aerospace startups, Impulse and Relativity, also announced an unmanned joint mission to Mars in July 2022, with hopes it could be ready as soon as 2024.

As more players are added to the mix, the pressure is on to be the first company or agency to truly make it to Mars. If (or when) we reach that point, what’s next is anyone’s guess.

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Thematic Investing: 3 Key Trends in Cybersecurity

Cyberattacks are becoming more frequent and sophisticated. Here’s what investors need to know about the future of cybersecurity.

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Global X Cybersecurity ETF

The following content is sponsored by Global X ETFs
Global X Cybersecurity ETF

Thematic Investing: 3 Key Trends in Cybersecurity

In 2020, the global cost of cybercrime was estimated to be around $945 billion, according to McAfee.

It’s likely even higher today, as multiple sources have recorded an increase in the frequency and sophistication of cyberattacks during the pandemic.

In this infographic from Global X ETFs, we highlight three major trends that are shaping the future of the cybersecurity industry that investors need to know.

Trend 1: Increasing Costs

Research from IBM determined that the average data breach cost businesses $4.2 million in 2021, up from $3.6 million in 2017. The following table breaks this figure into four components:

Cost ComponentValue ($)
Cost of lost business$1.6M
Detection and escalation$1.2M
Post breach response$1.1M
Notification$0.3M
Total$4.2M

The greatest cost of a data breach is lost business, which results from system downtimes, reputational losses, and lost customers. Second is detection and escalation, including investigative activities, audit services, and communications to stakeholders.

Post breach response includes costs such as legal expenditures, issuing new accounts or credit cards (in the case of financial institutions), and other monitoring services. Lastly, notification refers to the cost of notifying regulators, stakeholders, and other third parties.

To stay ahead of these rising costs, businesses are placing more emphasis on cybersecurity. For example, Microsoft announced in September 2021 that it would quadruple its cybersecurity investments to $20 billion over the next five years.

Trend 2: Remote Work Opens New Vulnerabilities

According to IBM, companies that rely more on remote work experience greater losses from data breaches. For companies where 81 to 100% of employees were remote, the average cost of a data breach was $5.5 million (2021). This dropped to $3.7 million for companies that had under 10% of employees working from home.

A major reason for this gap is that work-from-home setups are typically less secure. Phishing attacks surged in 2021, taking advantage of the fact that many employees access corporate systems through their personal devices.

Type of AttackNumber of attacks in 2020Number of attacks in 2021Growth (%)
Spam phishing1.5M10.1M+573%
Credential phishing5.5M6.2M+13%

As detected by Trend Micro’s Cloud App Security.

Spam phishing refers to “fake” emails that trick users by impersonating company management. They can include malicious links that download ransomware onto the users device. Credential phishing is similar in concept, though the goal is to steal a person’s account credentials.

A tactic you may have seen before is the Amazon scam, where senders impersonate Amazon and convince users to update their payment methods. This strategy could also be used to gain access to a company’s internal systems.

Trend 3: AI Can Reduce the Cost of a Data Breach

AI-based cybersecurity can detect and respond to cyberattacks without any human intervention. When fully deployed, IBM measured a 20% reduction in the time it takes to identify and contain a breach. It also resulted in cost savings upwards of 60%.

A prominent user of AI-based cybersecurity is Google, which uses machine learning to detect phishing attacks within Gmail.

Machine learning helps Gmail block spam and phishing messages from showing up in your inbox with over 99.9% accuracy. This is huge, given that 50-70% of messages that Gmail receives are spam.
– Andy Wen, Google

As cybercrime escalates, Acumen Research and Consulting believes the market for AI-based security solutions will reach $134 billion by 2030, up from $15 billion in 2021.

Introducing the Global X Cybersecurity ETF

The Global X Cybersecurity ETF (Ticker: BUG) seeks to provide investment results that correspond generally to the price and yield performance, before fees and expenses, of the Indxx Cybersecurity Index. See below for industry and country-level breakdowns, as of June 2022.

Sector (By security type)Weight
Cloud28.0%
Network25.1%
Identity17.7%
Internet15.0%
Endpoint12.8%
CountryWeight
🇺🇸 U.S.71.6%
🇮🇱 Israel13.2%
🇬🇧 UK8.2%
🇯🇵 Japan5.5%
🇰🇷 South Korea0.9%
🇨🇦 Canada0.6%

Totals may not equal 100% due to rounding.

Investors can use this passively managed solution to gain exposure to the rising adoption of cybersecurity technologies.

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