Filed under Industry Analysis.

First published on Medium.com.

In business, vision isn’t some mythical ability to see the future. It’s about being able to recognize a pattern and apply it to something new, before others see it coming.

In this post, we’ll introduce you to one such pattern, the gestation of new media within old media. We’ll then review some examples of how the pattern has repeated itself over the past 30-years, from one Big Thing to the Next.

We’ll then apply the pattern in the here-and-now, to see how it points to The Next Big Things. Read more »

Filed under Industry Analysis.

According to Murthy Nukala, “any marketer who doesn’t have a graph within the next twelve months will be at a permanent competitive disadvantage.” That’s a pretty ominous warning, particularly when most marketers have no idea what a graph is, let alone how to use it.

coloured-lines

Graphs are a useful way to represent relationships between things. And the World Wide Web is called a Web for a reason: it is a huge network composed of pieces of content and links among them. So it isn’t surprising that graphs are being used everywhere in IT these days to represent particular kinds of relationships among types of content living on the Web.

Since every kind of relationship among things on the Web seems to have its own graph, it can get a little confusing keeping them all straight. That also makes it hard to determine which of those many graphs could be important to you and your business.

You might find it all befuddling enough to develop graphophobia and just ignore them all. That would be a shame, because leveraging these mysterious graphs could truly revolutionize your business.
Here I’ll connect some of the dots and help you understand what graphs are and why they are important. Read more »

Filed under Industry Analysis.

Joyce Hostyn argues that Better Human Understanding, Not Big Data, Is the Future of Business. Some excerpts (with my emphasis):

Despite the best of intentions, we’re not data driven, we’re hypothesis driven. Our stories (our mental models) are merely hypotheses of how the world works. But we see them as reality and they influence what data we collect, how we collect it and the meaning we glean from it…

In a quest to become data driven, are marketers trapping themselves with outdated mental models of data and analytics? “Big data is being wasted on marketing. The true power of analytics is in revealing cultural dynamics.”

Many echo these concerns about data-driven marketing, and the need to be skeptical and hypothesis-driven. (1) (2) (3)

Hostyn concludes her thoughtful article with a number of questions, including:

  • Can we leverage big data to zoom out and understand patterns and trends, then zoom back in for a dive deep into the hearts and minds of individuals?
  • Are we willing to develop hypotheses with the potential to disrupt our old mental models? Create experiments to test those hypotheses. Prototype to think. Collect feedback. Iterate.

At Primal, we’ve invested years exploring this mode of hypothesis-setting as a lens into big data. It involves a collaboration between humans and machines across the full spectrum of analytical and synthetical thinking.

What follows is a summary of that exploration and what we’ve learned to this point.

Read more »

Filed under Industry Analysis.

Call me sentimental, but perhaps some of you, too, are starting to miss the human side of sentiment analysis.
measurement

Machine-based statistical analysis is not the entirety of sentiment analysis – or at least it shouldn’t be. There is still an important role for humans to play in that process. In addition there are opportunities for other tools to assist humans in the process, working in complementary ways with statistical-based sentiment analysis tools. Read more »

Filed under Use Cases.

usermodel

Developers have long created software that customers use directly. But now, we’re creating solutions that incorporate internal representations of end-users and adapt to individual needs.

In this post, we’re going to introduce you to the most important component in your solution stack, the user model, explain why it’s so important, and show you how to incorporate user modeling into your solution.

Read more »

Filed under Industry Analysis.

Bots have a bad reputation. Whether they’re sinister botnets, carrying out coordinated attacks, or annoying spambots, polluting our digital universe with crappy auto-generated content, bots are a much maligned bunch.

You might ask, if bots are bad and a huge portion of Twitter activity is already generated by bots, Why is Primal creating another one?

We believe that intelligent, well-intentioned bots can add a lot of value.

If we build a bot that truly understands you as an individual and can do useful work on your behalf, it might be a welcome addition to the social scene.

In this post, we’ll share some observations and insights into the question, Can a Twitter bot do good?
Read more »

Filed under Developers.

We’re using Spray for some products here at Primal. Spray is a really neat library, but it’s missing the ability to gather runtime metrics, and while you can instrument your app any way you’d like, we want to do things the Spray way.

Along with our upcoming Agent framework, which implements a pretty cool inter-actor protocol, we have other services that will be hosted in Spray. The guys that figure out whether or not people like our features, and the other guys that figure out how well they’re working, want to know what’s happening, and the guys that code it don’t want that stuff interfering too much with the way that they write their code.

In this post we’ll have a look at how we’re integrating the Coda Hale Metrics library with Spray. Eventually we’ll be making a pull request to the Spray project to integrate this thing for real.

Read more »

Filed under Industry Analysis.

Awhile back, we started exploring how individual interest graphs, powered by Primal’s data service, can be used to improve the performance of recommendation engines.

recommender-fail

Our surprising conclusion: Marketers and technology buyers are sold on the promise of personalized recommendations. Unfortunately, they don’t know how to tell recommenders apart, even when they’re built on radically different technical approaches. And the results are mixed, at best. End users are left frustrated, wondering when this promise of personalized recommendations will actually be delivered.

In this post, we’re going to show you how to ask the right questions of your technology provider, to make an informed choice based on business considerations, not technical jargon. We’ll highlight some of the common risks and pitfalls, and conclude with a statement of what to expect from your technology provider.  Read more »

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Filed under Use Cases.

Small conversational data such as tweets or text messages are a goldmine of individual interests. Millions of people everyday tweet about their favourite food, send a Kik or Facebook message about a recent TV episode, or take a photo on Instagram while attending a sporting event or concert.

However if you’re trying to recommend content or promote offers within a conversation, the ability to determine a user’s interests from these small data inputs is a huge challenge.

John Koestier in Venture Beat writes:

…tweets are difficult to register commercial intent…

If I tweet about my wife’s illness, are you going to target me with a random medicine? Or if you tweet about a great dinner you’re just about to eat, will you really be receptive to ads about a Greek restaurant just down the road?

In this post, we’ll show you an elegant solution for content recommendations, applied in messaging and social media platforms.
Read more »

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Filed under Use Cases.

Back in the early days of the Web, the Yahoo! Directory filled a huge gap in the ecosystem.

This portal helped us make sense of the Web. It organized content based on topics, connected those topics into a hierarchy to make sense of it all, and standardized the presentation of the content so that it was convenient and accessible.

But where can you find a portal that makes content from the Web accessible in your application? If you want to incorporate content from the Web into your app, let alone personalize it to the individual interests of your users, you’re undoubtedly confronting some daunting problems:

  • the content isn’t described in a way your application can understand;
  • the content isn’t organized in a way that matches your app; and
  • the content isn’t presented in a way that makes it easy to incorporate into your user experience.

In the sections that follow, we’re going to introduce you to a simple solution. Primal is like a personalized Web portal for your app. It’s a data service that makes it easy to incorporate Web content from sources you trust, tailored to the needs of each individual user of your application.

Read more »

Filed under Industry Analysis.

We know what personalization means and the compromises it imposes on our individual privacy.

Or do we?

This is perhaps the most insidious myth among the technorati: In order for people to benefit from advanced and personalized technologies, they need to compromise their individual privacy.

This idea is remarkably pervasive and damaging, driving both consumers and businesses away from the opportunities of personalization and next-generation information services.

In this post, I’m going to introduce you to the myth and the underlying villain, Big Data. I’m also going to argue that innovation is a much better path forward than evil, or doing nothing at all.
Read more »

Filed under Developers.

We’re building some cool new stuff at Primal. Awesome ideas and talented people are a must have in order to build successful and exciting products, but we also need our tools to step up to the challenge. This is why we’ve started adopting Scala and Akka. In this post, I’m going to describe how we’re using Scala’s implicits in order to implement a very important part of our internal messaging fabric, without having to over-burden the business logic. Read more »

enterprise-cloud-compupting

Filed under Use Cases.

In this post, we’ll show you how to add Primal’s data service to your content curation solutions, as a fully automated, machine-editor.

We’ll also highlight the practical applications and benefits of using a smart machine as a complement to your manual and crowdsourcing strategies. Specifically:

1. How to filter out irrelevant content from your content supply.
2. How to provide personalized collections of content.

For our demo, we’ll use a content aggregator called Alltop, and show you how to recreate these examples and build a similar solution yourself.

Read more »

Filed under Industry Analysis.

I was meeting with two guys, one a technologist, one a business advisor.

The discussion was focused on Primal’s technology: semantic user models, knowledge representation, yada, yada…

The business advisor, having listened patiently for some time, finally interjects, “Tell me what this means to Trixie!”

“Trixie?”

“Yes, the everyday person. Tell me a story of why Trixie would care about any of this?”
Read more »

Filed under Developers.

Primal does a lot of heavy lifting in knowledge representation and content filtering. If you ask it to grab you some relevant content around your interests, it will do precisely that.

But what if you don’t want to have to ask? Search engines are fantastic, but they still require that you go to them and then try to figure out how to formulate your query in a way that gets you decent results.

Primal already has the ability to understand what you want, and we’re now working on some technology that will let Primal deliver you the content that you truly care about before you know you want it.

Read on to learn more about Primal’s new software agent and content streaming framework.
Read more »