Tag Archive | "great"

What It Takes to Launch the Next Great Blog, Podcast, or Video Channel

This week, we had some resources for any new, ambitious content-based project you want to get off the ground. (Or…

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Without Data, There is No Great AI, Says Informatica Exec

“Without data, there’s no great AI, says Amit Walia, President, Products & Marketing at Informatica. “Now that AI is really becoming pervasive and at scale, you really need to give it relevant good contextual data. We see that happening a lot in the world of enterprise. Finally, enterprise is arriving at the point where they want to use AI for B2B use cases, not just consumer use cases that we are used to. AI is a part of everything that we do in data.”

Amit Walia, President, Products & Marketing at Informatica, discusses how data is the lifeblood of the enterprise in an interview on theCUBE at Informatica World 2019:

Without Data There is No Great AI

The language that AI needs or speaks is data. Without data, there’s no great AI. This is something that we’ve known all this while, but now that AI is really becoming pervasive and at scale, you really need to give it relevant good contextual data. We see that happening a lot in the world of enterprise. Finally, enterprise is arriving at the point where they want to use AI for B2B use cases, not just consumer use cases that we are used to. AI is a part of everything that we do in data.

It has really helped to improve productivity and automate mundane tasks. There’s a massive skills gap and I think you look around the economy is fully saturated with jobs. There is still so much work to be done with more data and different data. AI is helping make some of those mundane activities become a lot easier and autonomous.

Data is Becoming a Platform of Its Own

Our data scientists have gone from heroes to superheroes. Think about it. What we are seeing in this world is that data is becoming a platform of its own. It is getting decoupled from the databases, from the applications, and from the infrastructure. To truly be able to leverage AI and build applications on top you cannot let it be siloed and be held hostage to its individual infrastructure components. We’ve seen that fundamental change happening where data as a platform is coming along.

In that context, the catalog becomes a very pivotal start because you want to get a fuller view of everything. You’re not going to be able to move all of your data to one place. It’s impossible. But understanding that metadata is where enterprises are going and then from there you can have a customer experience journey with MDM. You can also have an analytics journey in the cloud with an AWS or an Azure. You can have complete governance and security and privacy journey while understanding anomalous activity.

Metadata Is the New OS

Data is everywhere. It’s like the blood flowing through your body. You’re not going to get all the data in one place to do any kind of analytics. You’re going to let it be there. We say that metadata is the new OS. Bring the metadata, which is data about the data in one place, and from there let AI run on it. What we think about AI is this; LinkedIn is a beautiful place where they leverage the machine learning algorithm to create a social graph about you and me. If I’m connected to John I know now that I can be connected with you. The same thing can happen to the data layer.

When I’m doing analytics and I’m basically searching for some report, through that same machine learning algorithm at the catalog level now we can tell you that this is another table or another report or another user and so on. We can give you help back ratings within that environment for you to do what I call analytics on your fingertips at enterprise scale. That’s an extremely powerful use case of taking analytics, which is the most commonly done activity in an enterprise and make it accurate at enterprise scale.

Without Data, There is No Great AI, Says Informatica’s Amit Walia

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Machine Learning Should Be Used to Deliver Great Brand Experiences, Says PagerDuty CEO

PagerDuty began trading on the New York Stock Exchange for the first time this morning and is now trading at more than 60% above their IPO price of $ 24. That gives the company a market capitalization of more than $ 2.7 billion. PagerDuty offers a SAAS platform that monitors IT performance. The company had sales of $ 118 million for its last fiscal year, up close to 50% over the previous year.

The company uses machine learning to inform companies in real-time about technical issues. “Our belief is that machine learning and data should be used in the service of making people better, helping people do their jobs more effectively, and delivering those great brand experiences every time,” says PagerDuty CEO Jennifer Tejada. “PagerDuty is really about making sure that our users understand that this could be a good thing, being woken up in the middle of the night if it’s for the right problem. It’s a way that can help you deliver a much better experience for your customers.”

Jennifer Tejada, CEO of PagerDuty, discusses their IPO and how machine learning should be used to deliver great brand experiences in an interview on CNBC:

It’s Gotten Harder for Human’s to Manage the Entire IT Ecosystem

If you think about the world today, it’s an always-on world. We as consumers expect every experience to be perfect. Every time you wake up in the morning, you order your coffee online, you check Slack to communicate with your team, and maybe you take a Lyft into work. Sitting behind all of that is a lot of complexity, many digital and infrastructure based platforms, that don’t always work together the way you’d expect them to. As that complexity has proliferated over the years and because developers can deploy what they like and can use the tools that they want it’s gotten harder for human beings to really manage the entire ecosystem even as your demands increase.

You want it perfect, you want it right now and you want it the way you’d like it to be. PagerDuty is the platform that brings the right problem to the right person at the right time. We use machine learning, sitting on ten years of data, data on humans behavior and data on all these signals there that are happening through the system, and it really helps the developers that sit behind these great experiences to deliver the right experience all the time.

Machine Learning Should Be Used to Deliver Great Brand Experiences

Going public is the right time for us right now because there’s an opportunity for us to deliver the power of our platform to users all over the world. We are a small company and we weren’t as well-known as we could be and this is a great opportunity to extend our brand and help developers and employees across teams and IT security and customer support to deliver better experiences for their end customers all the time.

At PagerDuty we take customer trust and user trust very seriously. We publish our data policy and we will not use data in a way other than what we describe online. We care deeply about the relationship between our users in our platform. Our belief is that machine learning and data should be used in the service of making people better, helping people do their jobs more effectively, and delivering those great brand experiences every time. PagerDuty is really about making sure that our users understand that this could be a good thing, being woken up in the middle of the night if it’s for the right problem. It’s a way that can help you deliver a much better experience for your customers.

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Why All Great Marketing Contains the Power of the Placebo Effect

Back in the 1950s, a bedridden man faced certain death from cancer of the lymph nodes. Tumors the size of…

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It’s Black Friday Week on Copyblogger: Get Great Deals on Premium Marketing Education

Somehow, we’ve nearly reached the end of the year. If you’re in the U.S., this week is all about gratitude,…

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The Great Facebook Video Swindle

Back in the day, only digital publishers and marketers knew to never trust Facebook. You know, like that one time…

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Building Links with Great Content – Natural Syndication Networks

Posted by KristinTynski

The debate is over and the results are clear: the best way to improve domain authority is to generate large numbers of earned links from high-authority publishers.

Getting these links is not possible via:

  • Link exchanges
  • Buying links
  • Private Blog Networks, or PBNs
  • Comment links
  • Paid native content or sponsored posts
  • Any other method you may have encountered

There is no shortcut. The only way to earn these links is by creating content that is so interesting, relevant, and newsworthy to a publisher’s audience that the publisher will want to write about that content themselves.

Success, then, is predicated on doing three things extremely well:

  1. Developing newsworthy content (typically meaning that content is data-driven)
  2. Understanding who to pitch for the best opportunity at success and natural syndication
  3. Writing and sending pitches effectively

We’ve covered point 1 and point 3 on other Moz posts. Today, we are going to do a deep dive into point 2 and investigate methods for understanding and choosing the best possible places to pitch your content. Specifically, we will reveal the hidden news syndication networks that can mean the difference between generating less than a handful or thousands of links from your data-driven content.

Understanding News Syndication Networks

Not all news publishers are the same. Some publishers behave as hubs, or influencers, generating the stories and content that is then “picked up” and written about by other publishers covering the same or similar beats.

Some of the top hubs should be obvious to anyone: CNN, The New York Times, BBC, or Reuters, for instance. Their size, brand authority, and ability to break news make them go-to sources for the origination of news and some of the most common places journalists and writers from other publications go to for story ideas. If your content gets picked up by any of these sites, it’s almost certain that you will enjoy widespread syndication of your story to nearly everywhere that could be interested without any intervention on your part.

Unfortunately, outside of the biggest players, it’s often unclear which other sites also enjoy “Hub Status,” acting as a source for much of the news writing that happens around any specific topic or beat.

At Fractl, our experience pitching top publishers has given us a deep intuition of which domains are likely to be our best bet for the syndication potential of content we create on behalf of our clients, but we wanted to go a step further and put data to the question. Which publishers really act as the biggest hubs of content distribution?

To get a better handle on this question, we took a look at the link networks of the top 400 most trafficked American publishers online. We then utilized Gephi, a powerful network visualization tool to make sense of this massive web of links. Below is a visualization of that network.

An interactive version is available here.

Before explaining further, let’s detail how the visualization works:

  • Each colored circle is called a node. A node represents one publisher/website
  • Node size is related to Domain Authority. The larger the node, the more domain authority it has.
  • The lines between the nodes are called edges, and represent the links between each publisher.
  • The strength of the edges/links corresponds to the total number of links from one publisher to another. The more links from one publisher to another, the stronger the edge, and the more “pull” exerted between those two nodes toward each other.
  • You can think of the visualization almost like an epic game of tug of war, where nodes with similar link networks end up clustering near each other.
  • The colors of the nodes are determined by a “Modularity” algorithm that looks at the overall similarity of link networks, comparing all nodes to each other. Nodes with the same color exhibit the most similarity. The modularity algorithm implemented in Gephi looks for the nodes that are more densely connected together than to the rest of the network

Once visualized, important takeaways that can be realized include the following:

  1. The most “central” nodes, or the ones appearing near the center of the graph, are the ones that enjoy links from the widest variety of sites. Naturally, the big boys like Reuters, CNN and the NYTimes are located at the center, with large volumes of links incoming from all over.
  2. Tight clusters are publishers that link to each other very often, which creates a strong attractive force and keeps them close together. Publishers like these are often either owned by the same parent company or have built-in automatic link syndication relationships. A good example is the Gawker Network (at the 10PM position). The closeness of nodes in this network is the result of heavy interlinking and story syndication, along with the effects of site-wide links shared between them. A similar cluster appears at the 7PM position with the major NBC-owned publishers (NBC.com, MSNBC.com, Today.com, etc.). Nearby, we also see large NBC-owned regional publishers, indicating heavy story syndication also to these regional owned properties.
  3. Non-obvious similarities between the publishers can also be gleaned. For instance, notice how FoxNews.com and TMZ.com are very closely grouped, sharing very similar link profiles and also linking to each other extensively. Another interesting cluster to note is the Buzzfeed/Vice cluster. Notice their centrality lies somewhere between serious news and lifestyle, with linkages extending out into both.
  4. Sites that cover similar themes/beats are often located close to each other in the visualization. We can see top-tier lifestyle publishers clustered around the 1PM position. News publishers clustered near other news publishers with similar political leanings. Notice the closeness of Politico, Salon, The Atlantic, and The Washington Post. Similarly, notice the proximity of Breitbart, The Daily Caller, and BizPacReview. These relationships hint at hidden biases and relationships in how these publishers pick up each other’s stories.

A More Global Perspective

Last year, a fascinating project by Kalev Leetaru at Forbes looked at the dynamics Google News publishers in the US and around the world. The project leveraged GDelt’s massive news article dataset, and visualized the network with Gephi, similarly to the above network discussed in the previous paragraph.

This visualization differs in that the link network was built looking only at in-context links, whereas the visualization featured in the previous paragraph looked at all links. This is perhaps an even more accurate view of news syndication networks because it better parses out site-wide links, navigation links, and other non-context links that impact the graph. Additionally, this graph was generated using more than 121 million articles from nearly every country in the world, containing almost three-quarters of a billion individual links. It represents one of the most accurate pictures of the dynamics of the global news landscape ever assembled.

Edge weights were determined by the total number of links from each node to each other node. The more links, the stronger the edge. Node sizes were calculated using Pagerank in this case instead of Domain Authority, though they are similar metrics.

Using this visualization, Mr. Leetaru was able to infer some incredibly interesting and potentially powerful relationships that have implications for anyone who pitches mainstream publishers. Some of the most important include:

  1. In the center of the graph, we see a very large cluster. This cluster can be thought of as essentially the “Global Media Core,” as Mr. Leetaru puts it. Green nodes represent American outlets. This, as with the previous example, shows the frequency with which these primary news outlets interlink and cover each other’s stories, as well as how much less frequently they cite sources from smaller publications or local and regional outlets.
  2. Interestingly, CNN seems to play a unique role in the dissemination to local and regional news. Note the many links from CNN to the blue cluster on the far right. Mr. Leetaru speculates this could be the result of other major outlets like the NYTimes and the Washington Post using paywalls. This point is important for anyone who pitches content. Paywalls should be something taken into consideration, as they could potentially significantly reduce syndication elsewhere.
  3. The NPR cluster is another fascinating one, suggesting that there is heavy interlinking between NPR-related stories and also between NPR and the Washington Post and NYTimes. Getting a pickup on NPR’s main site could result in syndication to many of its affiliates. NYTimes or Washington Post pickups could also have a similar effect due to this interlinking.
  4. For those looking for international syndication, there are some other interesting standouts. Sites like NYYibada.com cover news in the US. They are involved with Chinese language publications, but also have versions in other languages, including English. Sites like this might not seem to be good pitch targets, but could likely be pitched successfully given their coverage of many of the same stories as US-based English language publications.
  5. The blue and pink clusters at the bottom of the graph are outlets from the Russian and Ukrainian press, respectively. You will notice that while the vast majority of their linking is self-contained, there seem to be three bridges to international press, specifically via the BBC, Reuters, and AP. This suggests getting pickups at these outlets could result in much broader international syndication, at least in Eastern Europe and Russia.
  6. Additionally, the overall lack of deep interlinking between publications of different languages suggests that it is quite difficult to get English stories picked up internationally.
  7. Sites like ZDnet.com have foreign language counterparts, and often translate their stories for their international properties. Sites like these offer unique opportunities for link syndication into mostly isolated islands of foreign publications that would be difficult to reach otherwise.

I would encourage readers to explore this interactive more. Isolating individual publications can give deep insight into what syndication potential might be possible for any story covered. Of course, many factors impact how a story spreads through these networks. As a general rule, the broader the syndication network, the more opportunities that exist.

Link Syndication in Practice

Over our 6 years in business, Fractl has executed more than 1,500 content marketing campaigns, promoted using high-touch, one-to-one outreach to major publications. Below are two views of content syndication we have seen as a result of our content production and promotion work.

Let’s first look just at a single campaign.

Recently, Fractl scored a big win for our client Signs.com with our “Branded in Memory” campaign, which was a fun and visual look at how well people remember brand logos. We had the crowd attempt to recreate well-known brand logos from memory, and completed data analysis to understand more deeply which brands seem to have the best overall recall.

As a result of strategic pitching, the high public appeal, and the overall “coolness” factor of the project, it was picked up widely by many mainstream publications, and enjoyed extensive syndication.

Here is what that syndication looked like in network graph form over time:

If you are interested in seeing and exploring the full graph, you can access the interactive by clicking on the gif above, or clicking here. As with previous examples, node size is related to domain authority.

A few important things to note:

  • The orange cluster of nodes surrounding the central node are links directly to the landing page on Signs.com.
  • Several pickups resulted in nodes (publications) that themselves generated many numbers of links pointing at the story they wrote about the Signs.com project. The blue cluster at the 8PM position is a great example. In this case it was a pickup from BoredPanda.com.
  • Nodes that do not link to Signs.com are secondary syndications. They pass link value through the node that links to Signs.com, and represent an opportunity for link reclamation. Fractl follows up on all of these opportunities in an attempt to turn these secondary syndications into do-follow links pointing directly at our client’s domain.
  • An animated view gives an interesting insight into the pace of link accumulation both to the primary story on Signs.com, but also to the nodes that garnered their own secondary syndications. The GIF represents a full year of pickups. As we found in my previous Moz post examining link acquisition over time, roughly 50% of the links were acquired in the first month, and the other 50% over the next 11 months.

Now, let’s take a look at what syndication networks look like when aggregated across roughly 3 months worth of Fractl client campaigns (not fully comprehensive):

If you are interested in exploring this in more depth, click here or the above image for the interactive. As with previous examples, node size is related to domain authority.

A few important things to note:

  1. The brown cluster near the center labeled “placements” are links pointing back directly to the landing pages on our clients’ sites. Many/most of these links were the result of pitches to writers and editors at those publications, and not as a result of natural syndication.
  2. We can see many major hubs with their own attached orbits of linking nodes. At 9PM, we see entrepreneur.com, at 12PM we see CNBC.com, 10PM we see USAToday, etc.
  3. Publications with large numbers of linking nodes surrounding them are examples of prime pitching targets, given how syndications link back to stories on those publications appear in this aggregate view.

Putting it All Together

New data tools are enabling the ability to more deeply understand how the universe of news publications and the larger “blogosphere” operate dynamically. Network visualization tools in particular can be put to use to yield otherwise impossible insights about the relationships between publications and how content is distributed and syndicated through these networks.

The best part is that creating visualizations with your own data is very straightforward. For instance, the link graphs of Fractl content examples, along with the first overarching view of news networks, was built using backlink exports from SEMrush. Additionally, third party resources such as Gdelt offer tools and datasets that are virtually unexplored, providing opportunity for deep understanding that can convey significant advantages for those looking to optimize their content promotion and syndication process.

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Get Copyblogger to Tell the World Just How Great You Are

Hey, writers! Whether you’re a freelancer, an employee, or you write content to support your business, you might find it…

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Why ‘You’re Not Your Audience’ Isn’t Always Great Advice

If there were such a thing as the Ten Commandments of Marketing, “You are not your audience” would likely make…

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Be a Bad Writer to Be a Great Writer

I wrote about writing practice last week for a specific reason. Summer is quickly arriving here in the Northern Hemisphere, and when the seasons change, I reevaluate my habits and goals. What should I stop doing (aka, What’s not working?) What could I optimize? What would I like to add to my routine? You probably
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