Tag Archives: Intelligence

New eBook! State of Mainframe Operational Intelligence for 2018

We’ve recently completed our annual mainframe operational intelligence surveys of IT and data analytics professionals to identify trends, challenges and opportunities faced by enterprises investing in mainframe and Big Data technologies going into 2018. Respondents represented a wide range of IT disciplines and spanned vertical industries including financial services, insurance, health care, IT and government agencies.

Our latest eBook, State of Mainframe Operational Intelligence for 2018, takes a look at the current state of the mainframe within organizations and what professionals are looking to do moving forward.

blog banner SotMF Operational Intelligence New eBook! State of Mainframe Operational Intelligence for 2018

While organizations are looking for ways to optimize mainframe resources, reduce costs and re-invest the savings in newer technologies and use cases, they still rely on these systems for their most critical applications.

Download the eBook now and see the top objectives respondents identify for their mainframe environments.

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Syncsort + Trillium Software Blog

Transform customer engagement with location intelligence (VB Live)

 Transform customer engagement with location intelligence (VB Live)

Location intelligence can help brands deliver dynamic user experiences, better understand their customers and prospects, and boost consumer engagement and delight. Join this VB Live event to earn how to effectively incorporate location intelligence into your digital strategies and transform your customer relationships.

Register here for free. 

Location intelligence has evolved. It’s not just longitude and latitude anymore — it’s context, says David Bairstow, VP of product at Skyhook. It’s the intersections of billions and billions of mobile devices processed against known locations — from airports, sports stadiums, and college campuses to coffee shops and Burger King — and it means a revolution at the intersection between customer intelligence and mobile advertising targeting.

Brands know more about their consumers than anybody else. They know when consumers are on their properties, whether it’s at their physical location or in their online store — but that represents just a fraction of that person’s day.

“Location data helps brands understand their customers when they’re not spending time with them,” Bairstow says. “If Burger King is the brand, they know a particular customer likes Whoppers or Quarter Pounders with cheese, but does that customer also spend a lot of time at Chik-fil-A?”

The technology has become sophisticated enough to deliver the kind of precision required to detect that not only is a customer nearby, but that they’ve actually pulled into a gas station, and they’ve stopped — a perfect scenario in which to deliver a targeted, engaging message. And these kind of marketing and advertising scenarios have always been the promise of location intelligence. But that sophistication also means that marketers can leverage this new facet of customer data by building it into other channels as well.

“Just because it’s coming from mobile location data, doesn’t mean that the only kind of delivery channel is a realtime location-based trigger for a message,” he explains. “If you can learn a lot more about your customers by understanding who they are, what their preferences are, where they go, where they spend their time, then you can build it into a broader, smarter marketing campaign.”

He offers Skyhook’s recent study for a high-end clothing brand, identifying mobile devices as a proxy for people who had visited their stores and analyzing the captive audience data, exploring the common behaviors and traits among all those whoh have visited this store.

When they compared the behaviors of the group that visited the target stores to the broader panel of 50 million devices, they found that these shoppers over-indexed dramatically, Bairstow says. They were like 10 times more likely to visit high-end yoga studios, high-end gyms, and the top at-leisure brands.

“It was a real epiphany for the store,” he says. “With those insights they are now experimenting with working with some of the brands their consumers associate with, as well as building real-time messaging based on when a customer is having another experience which has an association with their own brand.”

Cutting the creep factor

But how do you manage the risk of driving consumers away — or having them refuse to give up their data, and consent? You go back to the basics: delivering value, which needs to start at the beginning of customer engagement with a request for permission to use their location data.

Permission rates vary dramatically depending on the type of app and the trust that people have in a given brand. If the SPG Starwood Rewards app asked to use a customer’s location, that customer understands intuitively how that data might be used when they’re traveling. If it’s a social app or similar, where they can’t immediately see why you need their location, they’re going to say no more often than yes, Bairstow explains. They need to see an explicit value exchange — for instance, ‘I want your location so I can give you offers when you’re near one of my stores’ which provides an explicit benefit to the customer.

“I think there’s a spectrum in terms of what brands should, or need, to give to customers to make them feel comfortable with their use of location data, because ultimately it’s a value exchange,” he says. “It’s, ‘I’m going to give you a better experience because of it,’ or the big one, ‘I’m going to give you money,’ if it’s coupons and promotions.”

To learn more about the customer intelligence breakthroughs that companies like Deloitte and Skyhook are developing, and how to garner location-based insights that supercharge your CRM system and help you build a more powerful marketing plan, don’t miss this VB Live event!

Don’t miss out!

Register here for free.

During this webinar you’ll learn how to:

  • Boost engagement with real-time, location-based consumer engagement and experiences
  • Gain insight into the behavioral patterns of customers and prospects
  • Understand the future of location data for your business


  • David Bairstow, VP Product, Skyhook
  • Prince Nasr Harfouche, Principal, Deloitte Consulting LLP
  • Stewart Rogers, Analyst at Large, VentureBeat (Moderator)

Sponsored by Skyhook

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Big Data – VentureBeat

Expert Interview (Part 1): Gregory Piatetsky-Shapiro on Exciting and Worrisome Advances in Artificial Intelligence

In Part 1 of this two-part interview, Gregory Piatetsky-Shapiro of KDnuggets (@KDnuggets) discusses the how today’s advances in deep learning are cause for excitement and concern.

Tracking Big Data’s Evolution in Data Science, AI & Machine Learning

Machines have always fascinated Piatetsky-Shapiro – ever since he was a kid reading stories about robots by Isaac Asimov and other sci-fi authors.

He discovered his love for programming while studying computer science at Technion, where he spent a few weeks in the summer of his first year programming a computer (in APL) to play battleships. “I was soundly defeated by my own program,” he says. “That gave me an appreciation for the abilities of technology. I became more interested in creating programs than playing them.”

Piatetsky-Shapiro’s passion for understanding data and helping others stay up to date on developments in databases led him to launch the first Knowledge Discovery in Databases workshop in 1989, which later grew into full-fledged KDD conferences.

In 1993, after the third KDD workshop, he started KDnuggets News, an e-newsletter focused on data mining and knowledge discovery.  The first issue went to 50 researchers who attended the workshop. Today, the KDnuggets brand has more than 200,000 subscribers across email, Twitter, Facebook and LinkedIn. With over 500,000 visitors in October 2017, KDnuggets.com has become a go-to resource for data science and analytics news, software, jobs, courses, education and more.

blog banner 2018 Big Data Trends eBook Expert Interview (Part 1): Gregory Piatetsky Shapiro on Exciting and Worrisome Advances in Artificial Intelligence

Piatetsky-Shapiro is one of the leading voices in Big Data – a field he says is somewhat amorphous, encapsulating infrastructure and database management, and closely connected to data science, machine learning and artificial intelligence.

(Note: what is now called “data science” was earlier called “data mining” or “knowledge discovery” but it refers to the same field dedicated to analyzing and understanding data and extracting useful knowledge from it.) 

Exciting AI Advances with Deep Learning

“What is really most exciting now is deep learning,” he says.

While the concept of multi-level (deep learning) neural networks has been around since the 1960s, there wasn’t enough data, computer power or clever algorithms to use them effectively. But in the past few years, this approach– rebranded as “deep learning”– received sufficient data and processing powers and has been achieving amazing feats almost every week.

Examples of Deep Learning Breakthroughs

There are many examples of deep learning being deployed today.

Consumers who speak to their smartphone assistants like Siri or Cortana, or to Amazon Alexa or Google Home, are getting good results thanks to deep learning.

Google’s recent advances in machine translation are another big advance, thanks to deep learning.

It used to be that computers would do machine-based translation by using hand-crafted rules derived by thousands of linguistic experts. However, powered by large amounts of text and advanced Deep Learning network, in 2016 Google switched to Google Neural Machine Translation, which eliminates all manual rules and translates entire sentences at a time. This has significantly improved the quality of translations.

Finally, Piatetsky-Shapiro mentioned AlphaGo, a computer program developed by Google DeepMind to play the ancient Chinese game of Go. In 2016, AlphaGo, trained partly on thousands of human championship games, defeated world champion Lee Sedol 4:1.

In 2017, an improved version called AlphaGo Zero, combined Deep Learning and Reinforcement Learning methods and learned to play from scratch, entirely by self-play. After three days and a few million games, the new version reached the level of program that defeated Go world champion in 2016. After 40 days, AlphaGo Zero achieved superhuman level and defeated the previous version 100:0.

Today, it’s considered the strongest Go player in history.

“It’s very exciting and it’s also very scary,” Piatetsky-Shapiro says. As AlphaGo Zero improved its game play, it began choosing very different moves than human experts on a more frequent basis.

Be sure to continue to Part 2 of this interview, where Piatetsky-Shapiro discusses self-driving Artificial Intelligence and how businesses can approach it.

For a more Big Data insights, check out our report, 2018 Big Data Trends: Liberate, Integrate & Trust, to see what every business needs to know in the upcoming year, including 5 key trends to watch for in the next 12 months!

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Syncsort + Trillium Software Blog

Expert Interview (Part 2): Piatetsky-Shapiro on Self-Driving Artificial Intelligence and How Business Should Approach It

In the first half of our two-part conversation with data scientist and KDnggets founder Gregory Piatetsky-Shapiro, he provided examples of advances in deep learning continuing to push the field of AI full steam ahead. In today’s Part 2, Piatetsky-Shapiro notes some artificial intelligence concerns as it continues to advance and how business should approach incorporating AI.

Artificial Intelligence: Some Causes for Concern

The period of time when humans and computers collaborate to solve problems might not last very long. It’s not a matter of if, but when computers will be able to do jobs better than us. The question we should be asking now is What will humans do?

blog banner 2018 Big Data Trends eBook Expert Interview (Part 2): Piatetsky Shapiro on Self Driving Artificial Intelligence and How Business Should Approach It

In the short term, Piatetsky-Shapiro says he’s concerned about the use of technology to automate repetitive tasks previously done by humans. Even devices with limited intelligence will be able to complete jobs that are structured and require a lot of repetition. For example, toll booths on Mass Pike were removed and the job of collectors replaced by EZ-pass radio technology and taking photos of license plates and recognizing the numbers – a limited form of computer vision.

In regards to the developments in the field of Artificial General Intelligence (AGI) – machine learning that is able to perform the same intellectual tasks that humans can – Piatetsky-Shapiro tends to side with entrepreneur Elon Musk and physicist Stephen Hawking. It could put humanity at risk.

“I think we are not likely to have AGI in the next 10 years, but people, in general, have very poor track record of predicting long-term events.”

Even if the probability of AGI is small, its impact could be huge. A program like AlphaGo Zero demonstrates that computers can achieve super human ability in a relatively narrow field and that once they do it, they are probably using a different logic than we do, Piatetsky-Shapiro says.

“What if AGI values are not aligned with what we want to do? That’s a serious problem.”

While the AI Now Institute was founded this year at NYU to address the problem of incorporating values training in artificial intelligence, Piatetsky-Shapiro says he doesn’t think we should pretend there would be any guarantees about the way programs behave. Just like a parent can’t guarantee their children won’t rebel against the values they’re raised with, we shouldn’t assume machines would always follow the rules we put in place.

“If it is really intelligent, it will have its own values.”

blog gps quote 1 Expert Interview (Part 2): Piatetsky Shapiro on Self Driving Artificial Intelligence and How Business Should Approach It

How Businesses Should Approach Artificial Intelligence 

There are no best practices yet for companies wanting to incorporate AI and machine-learning into their business strategies today because the technology has only been viable for a few years. With that in mind, brands need to be aware of both the capabilities of these tools, but also the limitations.

He shared three guidelines to follow or be aware of when using artificial intelligence:

  1. In order to use these tools effectively, companies need large sets of data – at least 100,000 examples. The more recent the data and the more frequent the data, the more effective the predictions will be.
  2. Make sure there are people in the organization who understand the technology and know what will lead to development.
  3. Have realistic expectations. There’s a lot of randomness when it comes to predicting human behavior. If you build a model that gives you perfect predictions, chances are you probably have false predictors.

To better manage and leverage all the data they’re collecting, Piatetsky-Shapiro recommends enabling more interactive access.

“I think the approach of dumping everything together in one Data Lake and hoping you’ll discover something is probably not very useful,” he says.

Instead, have specific goals you want to answer and look at the data with the goals in mind. Look at what gives the best return on investment and what gives value. Many of the Big Data projects that create big data lakes are not able to show ROI.

“Start with business value and proceed from there,” he says.

Finally, invest in good quality data visualization. Humans are still the best at interpreting data, so the visuals should clearly present patterns that allow business stakeholders to make better decisions.

For a more Big Data insights, check out our report, 2018 Big Data Trends: Liberate, Integrate & Trust, to see what every business needs to know in the upcoming year, including 5 key trends to watch for in the next 12 months!

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Syncsort + Trillium Software Blog

Embrace Artificial Intelligence in Marketing

2017 AO RethinkMktgPodcast featured Paul Roetzer AI Embrace Artificial Intelligence in Marketing

But then around 2011 I developed a fascination with artificial intelligence. That to me became an, oh my gosh, what if that technology was applied to marketing? And I didn’t know at the time what AI was, really, or if this was even possible. But it started this journey for me of discovery. And it led to late last year, November of ‘16, we created the Marketing Artificial Intelligence Institute with the mission to identify the current and future potential of AI and provide that education to marketers so they could look for ways to transform their own marketing, but also their careers.

What do you think is the first thing marketers should know about AI?

Michelle: That’s awesome. … It’s just a huge topic. What do you think is the first thing marketers should know about AI?

Paul: I think the first step for a lot of people is just to understand the basic terminology and what it actually is. Because you hear AI, machine learning, deep learning, natural language processing, natural language generation, image recognition. There are all these terms. And for me even 12 months ago it was just this mashup of words.

So I always start by explaining that AI is the umbrella term. Machines on their own, they don’t know anything. They don’t know a table from a chair. They don’t know how to learn and get better at a task. They’re trained to do this using data and different types of processes to do the training. And so AI is that. It’s this big picture idea of enabling machines to get smart.

And then underneath that are categories like machine learning, which is the most common one you hear. So the key is to not be overwhelmed by the terminology or even the idea of it. Despite the dystopian views that are out there and the things you see in Hollywood, at the end of the day AI is really there now and for the foreseeable future to enhance what you do as a marketer. And the sooner you embrace that and seek ways to have it help what you’re doing and make things more efficient and more personalized, you’re actually going to get ahead of everyone else.

What are things that people might not even think of as Artificial Intelligence that really is?

Michelle: In many regards there’s a lot of artificial intelligence that’s actually being used today and we might not even know about it. What are things that people might not even think of as AI that really is?

Paul: Yeah, it’s an important point. Because my general guidance to people is: Your life is already machine assisted, and your marketing will be, too. And you just won’t know it. And so, as you were saying, you probably as just a general consumer or a person living on this earth will interact with AI dozens if not hundreds of times every day.

If you watch Netflix, Netflix has massive AI investments. Google is an AI-first company. On your Gmail app on your phone when you to go reply to something, if you look at the bottom you’ll see recommended responses, usually like two to five words. Those are called smart replies. There’s AI all over that. If you’re lucky enough to drive a Tesla, Tesla autopilot is enabled by AI using deep learning. So yeah, it’s literally everywhere.

And I think for marketers, they’re going to start seeing that, like the platforms like in Act-On, where you’re using them anyway, they’re just going to start getting smarter and they’re going to start introducing little features into them that make your life easier. And you may never actually go looking for an AI tool to do send-time optimization. It’s just all of a sudden going do it. And you’re going think it’s like magic. In reality it’s AI.

Can you explain what are the five P’s of Artificial Intelligence?

Michelle: Exactly. … So, a lot us in marketing, we all know about the five P’s. Can you explain what the five P’s of AI are and maybe share some examples for each of them?

Paul: We really struggled to understand how to categorize the different technologies that were out there. Over time we started to see patterns developing where we could start to more logically categorize these so they could make sense to everybody. And so we ended up settling on planning, production, personalization, promotion, and performance.

Now each of those categories, some of them are very immature, so the technologies aren’t very far along yet. But I’ll walk through some examples of each so they make a little more sense.

At the planning level, if you look at something like search engine optimization, keyword selection, topic clustering ‒ that tends to be a very human-driven process. That’s something that a machine in the near future should be doing for most marketers.

Production we look at as the curation and creation of content. So specifically in 2015 we started looking at can we use AI’s natural language generation, being the kind of AI we were looking at, to write blog posts, because we do a lot of blog post writing for clients. So over time we realized that you have to create the templates and train them the different branching logic. But once you do that, you can tell a data-driven story at scale hundreds or thousands of times instantaneously.

Personalization is where we’ve seen most of the money going. Things that right now a human has to set rules for, the machine can absolutely do that better than a human if it has enough data to do it. So you’re going to see a lot of personalization over the next 12-to-24 months. That’s where most of the use cases for marketers will emerge.

Then you get into promotion. That one is also ripe to be disrupted. Not a ton of great tools in that space yet, but more developing. An example of that would be Albert, which does digital media buying. You just give it the budget and the creative, and it runs all the infinite variations, and makes all the changes itself based on performance data.

And then the last one would be performance. And that we mainly look at as taking analytics data, and finding insights out of it, and then figuring out what to do next. That space is also extremely immature.

Those five Ps then enable us to look at all these different AI-powered tools. And we’re tracking over 500 of them.

How to get get started with AI and Machine Learning?

Michelle: Is there an area that you would recommend that someone should get started with AI? Is it personalization? Or how do you typically recommend people think about it?

Paul: There are two general recommendations I have for getting started. The first is to pick a single-use case. And so by that I mean take a look at your existing marketing structure, your average monthly spend, and where your time goes, and look and see if any of those are really data-driven and really time-intensive, that once you understand what AI’s capable of you could say, well, that would be logical that an AI tool might exist to do that, and go do a search for a tool for that.

The other is to go talk to your core martech stack. So if you have a marketing automation platform, email marketing platform, whatever it may be, go talk to them and say: What are you guys working on? Are there any more intelligently automated features that you’re either beta testing or that are coming up that we could experiment with to start better comprehending what’s possible? And I would actually maybe even reverse those and start there.

Do you think there’s a world for both marketers and AI?

Michelle: Do you think there’s a world for both marketers and AI?

Paul: I think in the near term, which I would look at that three-to-five-year range, more than anything AI is going to enhance the knowledge and capabilities of marketers. And the ones who take the initiative to understand it, embrace it, and apply it, they will have a competitive advantage over their peers. It basically gives you superpowers in certain areas. That’s the reality of what most people will experience.

Will it replace jobs? Yes. It’ll transform the industry within the next decade. Which ones? I don’t know. Any great technological advance in the history of society has done that. It takes jobs, but it also gives jobs that you can’t guess would exist. And I think that’s what marketing will see. I think the industry will continue to grow, lots of opportunities will continue to exist for marketers to evolve. But they’re going have to embrace the opportunity evolve. If marketers just sit back and pretend like AI isn’t going to have this impact, then those are the people that would be in trouble.

Michelle: How can we learn more about you, PR2020, and the Marketing AI Institute?

Paul: Our website is just pr2020.com. That’s the agency. And then we do have the separate site which is marketingaiinstitute.com. As of today it’s just a content hub. We try and publish two to three times a week. We do a lot of interviews. And we’re really just trying to connect marketers to the resources right now and see where that site goes.

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Act-On Blog

Shiplap and Artificial Intelligence: Why AI is Important for Everyone, Not Just Those in Silicon Valley

shiplap Shiplap and Artificial Intelligence: Why AI is Important for Everyone, Not Just Those in Silicon Valley

I want to discuss the urgent need to advance AI for all American business, not just Silicon Valley insiders. But first I have a confession to make. Like millions of others, I’m a fan of the HGTV show “Fixer Upper.” Don’t judge! It’s a nice way to unwind after work. Now entering its fifth and final season, fans of the show know that most episodes follow a simple format: Agreeable hosts Chip and Joanna Gaines take potential homebuyers to three ugly houses around Waco, Texas, and explain how the wrecks could be renovated into modern dream homes within the parameters of the buyer’s budget. Tours of dilapidated farmhouses and garish bachelor pads ensue. A fixer-upper is selected, redesign details are conveyed, crummy cabinetry and inconvenient walls are demolished, setbacks and obstacles are dispatched, and the episode closes with a tour of the now-stunning homestead transformed into a showpiece by JoJo’s creative vision and Chip’s physical labor. Catharsis achieved.

While this home improvement pageant may seem far removed from the technology space, I propose that the show’s formula mirrors our digital evolution. And the present burgeoning of AI capabilities and solutions form a key component in that journey from awful to awesome.

For example, the ugly homes selected for renovation on “Fixer Upper,” with their Congoleum floors and Formica countertops, were once paragons of modern convenience. Times change, as do lifestyles, and what worked architecturally in the Reagan era seems absurdly awkward now. Put in technology terms, there is very little similarity between the way you currently use your smartphone and the way your parents used their wall-mounted landline phone back in the analog day. And just try to imagine using all your now-essential mobile apps on the once vaunted BlackBerry 7230.

Conversely, old houses sometimes contain desirable and time-tested features worth saving or repurposing: apron sinks, oak floors, shiplap. The same is true in technology. Witness the recent surge in artificial intelligence development: Anyone of a certain age will recall that the kinds of artificial neural networks currently powering Siri and Alexa were also hot way back in the 1980s. They’re important again today because we’ve lately been unburdened by the kind of resource constraints that prevented AI fruition in the 20th century.

Back then, the AI dream was stymied by expensive and restricted access to compute capacity coupled with a paucity of data. That blight was largely solved by the rise of the cloud, which allowed vast numbers of researchers and innovators almost unimaginable storage for data and power for computation from pretty much anywhere.

For example, Libratus, the poker AI that recently beat some of the world’s best Texas Hold ’Em players, was built with more than 15 million core hours of computation. While that project was powered by the Pittsburgh Supercomputing Center, something like AWS gives anyone the ability to cheaply spin up the equivalent of 100 high-power machines for about 150 bucks. Or consider the pace and volume of trades on Wall Street today: such commonplace and widespread algorithmic trading would have been unimaginable even with state-of-the-art technology just a few short years ago.

An exponential leap in resource availability, coupled with open source and APIs, also enabled the mobile revolution, which now lets us interact on the go with our little hand-held extensions of more powerful systems on the cloud. Add to all that the emergence of IoT and you start to see that pretty much every “thing” you can think of can now become a point of computation.

Which is all well and good, but has resulted in new challenges to comfortable human habitation—the unbridled proliferation of applications.

Time was, the average working person would deal with a few primary software packages and become expert in those applications. Nowadays, nearly everything you do requires use of a distinct application, while the concept of mastering any one of them grows more elusive by the minute. The way we schedule, organize, travel, pay our bills, create products, purchase or provide goods and services, communicate personally or professionally, consume news or acquire new skills—everything, everywhere is prefaced by some interface to technology that requires mental investment on your part to maneuver. Everything is an app and the cognitive load is crushing us as a people.

In our technology and in our homes, what we need today is different from what we built yesterday. Our old architecture no longer suits our way of life.

Hence, the hype around AI. The investment and development and deployment of new solutions utilizing machine learning and advanced analytics and autonomous everything is driven by the need to simplify and simultaneously expand our technologically dense existence, to reduce that cognitive load, to alleviate the friction between us and our machines. In the context of “Fixer Upper,” we are at the stage where we are standing in a smelly, cramped, derelict kitchen while JoJo maps out the possible vista of bright stainless steel, subway tile, and stupendous stone-topped islands opening on a vast and welcoming living space. I can almost touch the shiplap accent wall…

The point is that we are just starting to visualize what can be done to reimagine our way of life with the expanded resources we have at our disposal, and this is an important phase in making our technology meet our new needs. We can now use natural language processing engines and machine vision and edge intelligence to ease the burden and unlock new potential. This visualization is necessary to spur us toward obtaining that “dream kitchen,” so I won’t criticize all the AI hype. It’s a necessary motivator. We have yet to deal with demolition (legacy systems). We’re still going to face foundation issues (security). We’ll still have to solve our plumbing problems (integration). But the vision of that open, frictionless existence makes all the impending labor worthwhile no matter where you live.

There will undoubtedly be things that don’t work well, which we won’t discover until after we’ve moved into our technological fixer upper and started living in it. But if it’s anything like the vision, it will be a lot better than the ramshackle wreck we’re starting with.

I also believe the Discovery Channel’s “Fast N’ Loud” served up great lessons about enterprise integration, but that’s another story.

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The TIBCO Blog

[Infographic] What’s Big in Business Intelligence for 2018?

It’s that time of year again. You know, the time when people and organizations start to put together their big predictions for the year to come. With the explosive growth of BI and analytics in 2017, we had to get in on the action.

Last year we had five big predictions for 2017, which included businesses reaching new levels of data complexity, BI tools liberating users, machines getting smarter, information democracy rising, and BI separating the winners from the losers.

In 2018, it’s not about a complete change in direction. Instead, it’s about building on what has happened in the past year to take BI and analytics to the new heights. How will organizations make sense of all of their complex data? Augmented Analytics. What happens when data is truly democratized? Collaborative/cooperative BI. What exactly will separate the winners from the losers? If you’re asking us, it’s embedded analytics.

So, without further ado, here are our top trends that will emerge in 2018.

Trends2018 Infographics [Infographic] What’s Big in Business Intelligence for 2018?Embed this infographic on your site:

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Blog – Sisense

House Intelligence Committee advances deeply flawed NSA surveillance bill

 House Intelligence Committee advances deeply flawed NSA surveillance bill

A bill to extend one of the NSA’s most powerful surveillance tools, and further peel back American civil liberties, was approved today by the House Permanent Select Committee on Intelligence in a strict party line vote (12-8), with Republican members voting in the majority.

The committee and the public had less than 48 hours to read and discuss the bill. Democratic committee members openly criticized the short timeframe, amongst other problems.

“This bill was shared with my office less than 24 hours ago, and here we are marking up legislation that has incredibly profound constitutional implications for all Americans,” said Rep. Jackie Speier (D-CA). She continued: “We could be sitting here, thoughtfully debating the precarious balance between security and civil liberties and the best path forward, but instead, the majority has decided to do otherwise.”

The bill is the FISA Amendments Reauthorization Act of 2017, and it was introduced on the evening of November 30 by House Intelligence Committee Chairman Devin Nunes (R-CA). It is the latest legislative attempt to reauthorize Section 702, one of the NSA’s most powerful surveillance authorities that allows for the targeting and collection of communications of non-U.S. persons not living in the United States. The NSA also uses Section 702 to justify the “incidental” collection of American communications that are predictably swept up during foreign intelligence surveillance, too.

The bill has many problems that you can read about here, from potentially restarting one of the NSA’s most invasive forms of surveillance to treating constitutional rights as optional.

But instead of probing these privacy defects in the bill, much of the Friday morning hearing was dominated by heated partisan debate around a single topic that one Democratic committee member described as “political dynamite.”

That issue is “unmasking,” the process by which the identities of Americans whose communications are collected through the broader FISA law are revealed at request by government officials. The Nunes bill includes several oversight provisions for this process.

According to the committee’s Ranking Member Adam Schiff (D-CA), the issue has nothing to do with Section 702, and it has no rightful place in the Nunes bill.

Close to one hour into the disagreements, Rep.Denny Heck (D-WA) bemoaned the lost opportunity to have a conversation on the balance between national security and civil liberties.

“I’m voting no because I believe that this bill sets up a false choice between whether or not we can be secure, or whether or not we can protect our rights to privacy, especially under the Fourth Amendment,” Rep. Heck said. “Benjamin Franklin famously quipped, and I’m amazed that he has not yet been quoted today, that those who would trade privacy for security deserve neither.”

Rep. Heck continued: “I find that the weight of this bill trades off privacy for security, and I believe that is a false choice because I believe we can have both.”

We agree. The Nunes bill threatens the privacy of American communications and potentially opens up U.S. persons to an invasive type of NSA surveillance that the agency voluntarily ended this year.

The Nunes bill goes backward. Surveillance reform must move forward.

This story originally appeared on the EFF’s blog.

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Big Data – VentureBeat

Power BI expands access to intelligence for external guest users

Power BI was first introduced with a simple commitment: Empower people and organizations with access to critical intelligence. The recent general availability of Power BI Premium in June broadened the service’s reach, allowing organizations to distribute BI content broadly without requiring recipients to be licensed individually.

Today I am happy to announce that Power BI users can seamlessly distribute Power BI apps and dashboards to guest users outside of their organization – recipients are able to securely sign into the service using their own organization’s security credentials or personal email address, while the content owner is able to maintain control over the internal data.

This new feature is the result of Power BI integration with Azure Active Directory (AD) business-to-business (B2B) collaboration.

For example, consider a scenario where an automotive manufacturer working with many diverse suppliers wants to streamline its supply chain logistics – all the components, materials and services necessary to run its manufacturing operations. The organization plans to use Power BI to monitor key supply chain performance metrics by building a BI portal its employees and partners can access.

Previously the automaker would have needed to create duplicate identities for users belonging to partner organizations, requiring those users to remember multiple sets of credentials, and creating challenges for governance enforcement and identity management. Alternatively, the automaker could have invested the time and cost of building an app with Power BI Embedded that employs custom authentication.

In this instance Power BI’s integration with Azure AD B2B enables seamless, secure access for guest users from partner organizations – the automaker can create a Power BI app in the service, invite guest users, and distribute the BI content to them to access by authenticating via their organization’s Azure AD credentials.

External users can be licensed to receive BI content in two ways – either the content is allocated to Power BI Premium capacity, or the external user is assigned a Power BI Pro license. And in the instance of the external user being assigned a Power BI Pro license, this can be done by either the external user’s administrator or, as a new capability, by the sharing organization’s administrator.

Power BI integration with Azure AD B2B provides the peace of mind organizations can employ trusted Azure AD authorization policies to protect their data, including conditional access policies and risk-based authentication. Admins are also able to set policies for external B2B invites, such as the ability to turn off or restrict the ability for users to send invitations.

Next steps:

  • AAD B2B with Power BI is available starting today.  Read the documentation and try inviting an external user now!
  • For more information and a step-by-step guide to distributing BI content with Power BI and Azure AD B2B read this whitepaper. Also visit Azure documentation for more information on Azure AD B2B.

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Business Intelligence Emboldens Digital Transformation

In May 2017, a computational social scientist from The Psychometrics Centre at the University of Cambridge stood before an audience at the Linux Foundation’s Apache Big Data conference and revealed how close we’ve come to the ultimate goal of marketing: an easily scalable, highly accurate way to predict customer preferences using minimal data.

When she was still a PhD candidate, Sandra Matz created a Facebook ad campaign targeting people based on nothing more than how extroverted their Facebook Likes indicated they were. People with Likes associated with extroverts saw ads for a party game played in a group. People with more introverted Likes saw ads for a quiet game meant to be played solo.

The campaign required only simple algorithms and no advanced analytics. Yet over seven days of testing, the targeted ads generated up to 15 times higher click-through and conversion rates—and significantly more purchases and revenue for the game company.

SAP Q317 DigitalDoubles Feature3 Image2 Business Intelligence Emboldens Digital Transformation“We developed this approach to show that you can achieve highly accurate behavioral and psychological targeting with a minimal amount of data and relatively simple machine learning tools,” says Matz, who is now an assistant professor of management at Columbia University’s business school.

As effective as this experiment was, Matz suggests that it’s still rudimentary compared to what could be done with more and richer data from more sources. And it’s downright primitive given the possibilities of applying more sophisticated Big Data analytics.

These possibilities have created a watershed moment for marketing and its role in the business.

Spiraling Down the Marketing Funnel

Tension has always simmered over marketing’s contribution to business success. The business knows it can’t sell products or services if it doesn’t make customers aware of them, but the impact of marketing strategy on sales and revenue is hard to quantify and reliably replicate—which, in the age of the data-driven enterprise, often leaves some business leaders not just undervaluing marketing but actively mistrusting it. No wonder human resources consultancy Russell Reynolds reports that the 2016 turnover rate among CMOs was the highest it has seen since it began tracking the statistic in 2012.

Most companies still determine customers’ readiness to buy by using a primitive model known as the marketing funnel, which sorts customers into increasingly smaller groups as they progress from first becoming aware of a company to buying, using, and finally advocating for the company’s products. Different versions have different definitions and numbers of stages, and some approaches see the model as a circle, but they all have one thing in common: their ability to sort customers into various stages is limited by the amount of knowledge the company has about each customer.

As a result, the marketing funnel ends up leaking. Some customers back away because they feel harassed by campaigns that don’t apply to their needs, while some of those who are interested fall through the cracks from a lack of attention. Many data-hungry business leaders think of the marketing funnel as no more than a variation of “throw something against the wall and see if it sticks,” and with the proliferation of digital channels and diffusion of customer attention, they have less patience than ever with that approach.

The silver lining is that a more precise, quantifiable way to build customer relationships is emerging. Done properly, it promises to defuse the tension between marketing and the rest of the business, too.

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The Defining Moment

The Cambridge University experiment is one more step toward the long-held marketing dream of the “segment of one.” This concept of marketing messages that are highly granular, even individually tailored, has been around since the late 1980s. Over the last 15 to 20 years, as customer behavior has become digitalized as never before, marketers have been optimistic that they could capture this data and use it to tailor their messaging with laser-like precision.

Yet what’s achievable in theory has been impossible in practice. We’re still struggling to find the right tools to move beyond the basics of demographic targeting. The rise of the internet, smartphones, and social media has generated more types of information about customer behavior in larger amounts than ever before. But using digitally expressed sentiment about everything from toys to turbines as the basis for accurately disseminating highly individualized marketing messages is still time consuming and cost prohibitive.

However, experiments like Matz’s are bringing us closer to creating highly personalized customer experiences—perhaps not always at the individual level but certainly at a level of granularity that will let us unequivocally determine how to best target and measure marketing programs.

Liking Lady Gaga

Between 2007 and 2012, Psychometrics Centre researchers gathered seven million responses to a simple questionnaire for Facebook users. The carefully designed questions measured respondents’ levels of extroversion, agreeableness, openness, conscientiousness, and neuroticism, a constellation of basic personality traits known as the Big Five.

With the respondents’ permission, the researchers used simple machine learning tools to correlate each person’s responses with the official Facebook Pages that the person had liked, such as Pages for books, movies, bands, hobbies, organizations, and foods. They soon saw that certain personality traits and certain Likes went hand in hand.

For example, most people who liked Lady Gaga’s Page tested as extroverts, which made liking the Lady Gaga Page a relevant data point indicating that someone was probably an extrovert. By 2016, Matz was able to create a lively Facebook ad to be shown only to people who had liked a significant number of official Pages that seemed to be linked to extroversion. A more serene ad was shown only to those whose Likes suggested that they were introverts.

SAP Q317 DigitalDoubles Feature3 Image4 Business Intelligence Emboldens Digital TransformationDespite the large size of the Psychometric Centre’s data set, what’s most remarkable about its work is how few data points within that data set were necessary to build a reliable profile that could model useful predictions. Matz told EnterpriseTech that the algorithm the Centre developed needs, on average, just 65 liked Pages to understand someone’s Big Five personality traits better than their friends do, 120 to understand them better than their family members, and 250 to understand them better than a partner or spouse. This may be the first sign that the era of true behavioral marketing is upon us.

Of course, most marketers want to know far more about customers than how outgoing or reserved they are. Scraping Facebook Likes isn’t enough to give them the holistic customer understanding they crave—not when they have an entire universe of other data to consider. The race is on to identify from the vast spectrum of available customer data not only which specific online behaviors have a predictive element such as extroversion or introversion but also which ones will drive the most potent response to specific product or service messaging.

Complicated? Yes—but we are within reach of the algorithms we need to connect the dots for greater customer insight. By reaching out over new channels with more accurate behavior-based messaging, companies could transform the entire customer journey.

A Customized Journey for Each Customer

Attribution, the ability to know the source of a sales lead, is key to behavioral targeting. The more details a business knows about what its customers have already done, the more accurately it can predict what they will do next.

In the past, developing a customer profile relied on last-touch attribution analysis, that is, evaluating the impact of the last interaction a prospective customer had with a brand before becoming a lead. The problem was that companies could rarely be certain what that last touch was, given how much activity still takes place offline and isn’t captured or quantified.

Companies also couldn’t be certain how, or even if, a last touch—be it downloading a white paper, visiting a store, or getting a word-of-mouth recommendation—accelerated the customer through the marketing funnel. They could only predict revenue by looking at how many people were deemed to be at a specific stage and extrapolating from past data what percentage of them were likely to move ahead.

SAP Q317 DigitalDoubles Feature3 Image5 Business Intelligence Emboldens Digital TransformationToday, we’re capturing so much more information about people’s activities that we have a far more accurate idea of both what the last touch was and how influential it was. Behavioral targeting makes any content a customer interacts with valuable in analyzing the customer’s journey. A company can use hard data about those interactions to see where each individual prospect is in the customer journey and predict how likely each one is to continue moving forward.

The company can then generate a tailored offer or other event to nudge individuals along based on what has been successful with other customers who buy the same things and behave in the same ways. For example, a large grocer may send out two million individualized offers each week based on loyalty card activity. This may not strictly create a segment of one, but it creates many small segments of customers with similar behaviors based on what the grocer knows to be effective.

As Cambridge University’s experiment in creating an algorithm to identify and target introverts and extroverts proves, more precise messaging is more effective. By using more complex machine learning algorithms to further filter and refine successful messages to target smaller groups, companies could boost their conversion rates to as high as 50%—an exponential increase beyond today’s average rates.

By using machine learning to speed up the testing of different campaigns and to continuously compare results, companies could rapidly create a dataset about every potential customer’s responses and then benchmark it against others’ responses. This would let them determine individual prospects’ likely responses based on concrete actions rather than assumptions.

For super-luxury brands with a limited number of customers and the ability to capture a vast amount of information about each one, this could lead to true segment-of-one marketing. For other brands, the challenge is not just to figure out who the customer is and what messages to send but also how to scale that personalization to segments of tens of thousands (or hundreds of thousands) of customers at a time. To do that both effectively and quickly, companies will need to leverage machine learning, the Internet of Things, and other advanced technologies that enable accurate predictive models. Companies can then benchmark their projected hit rates against their actual results and refine their algorithms for even greater agility and responsiveness.

The Next Steps of Predictive Marketing

Effective behavioral targeting requires companies to identify all the relevant data points, including external data points that indicate which information is valuable. This calls for data scientists who can spot and remove the irrelevant data points that are at the far ends of the curve and distill what remains into meaningful algorithms. It also requires machine learning tools capable of high-volume, high-speed listening, assessing, learning, and making recommendations to improve the algorithm over time.

Once you’ve created a baseline of primary criteria, you can determine the important criteria by which to segment your customer base. To use an oversimplified example, imagine that you own a coffee shop and you want to increase sales of high-margin bakery items. You need to look not at the customers who always get a muffin with their coffee or at those who never do but at those who buy a muffin sometimes, so that you can start to identify the triggers that make them choose to indulge.

To scale this process, look at both user-based and item-based affinities. User-based affinities link customers who have similar interests and shopping patterns. Item-based affinities link customers based on what they buy, individually or in groups of items. Using machine learning to pair and cross-reference these two factors will enable you to create messages that are personalized enough to seem individualized, even though they’re actually targeting small, multi-person segments.

SAP Q317 DigitalDoubles Feature3 Image6 Business Intelligence Emboldens Digital TransformationRetailers of all types collect data about individuals, down to location, date, time, and SKU of the sale. They may experiment with behavioral targeting by making in-the-moment offers based on what they already know about their customers. For example, they may use a mobile app with geofencing to be alerted when a customer using the app is in the store. The alert triggers back-end systems to look up the customer’s purchase history, generate a relevant offer, and deliver that offer to the customer’s smartphone while the customer is still in the store.

The Line Between Marketing and Manipulation

Just the idea of receiving marketing messages influenced by their behavior will disturb some customers. When marketing is designed, as behavioral targeting is, to maximize engagement, the value of the content depends less on whether it’s useful to the audience or even true and more on whether it gets the target audience to engage and reveal another piece of the behavioral puzzle. As a result, companies considering behavioral marketing must consider a question as old as marketing itself: where is the line between advertising and propaganda?

Creating personal profiles of customers based on their actions and personalities will become inexpensive and easy, for better or worse. Better will lead to more relevant and compelling offers based on predictive models of what customers would like to buy next. Worse will create (or at least look like) scalable, granular manipulation.

If companies hope to apply this level of targeted marketing without coming across as intrusive or invasive, they will need to be completely transparent about what they’re doing and how—and with whom they’re sharing the information. Most shoppers say they’re willing to give up data about themselves if it leads to a better shopping experience and more relevant recommendations.

Numerous studies show that customers are comfortable sharing their buying patterns and preferences as long as it doesn’t compromise their personally identifiable information. Nonetheless, they may decide otherwise if they believe that by welcoming you into their lives, they’re throwing open the doors to strangers as well.

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As data mining for behavioral targeting becomes more common, companies will have to offer customers the opportunity to opt in and out at varying levels of detail. They will also need to identify and flag the significant minority of customers who prefer not to be profiled in such depth (or at all). Machine learning will be invaluable in responding to complaints on social media, tracking the relevant details of offers that were ignored or got negative reactions, and otherwise ensuring that companies don’t misuse customer data or misunderstand consumer wants and needs.

“The entire paradigm of targeting and campaign implies a vendor doing something to customers,” says Mark Bonchek, founder and “chief epiphany officer” at Shift Thinking, a Boston-based consulting firm that helps companies pursue digital transformation. “It implies getting people to do what you want them to do rather than helping them do what they want to do,” he says. “Be clear on the mental model behind your behavioral targeting. Is it more like a friend figuring out the right gift for a friend or a salesperson trying to close a deal with a prospect? People don’t want to be targets.”

Instead, Bonchek suggests, think of behavioral targeting as a way to build a reciprocal relationship that lets you enhance the customer experience at multiple touch points, not all of them actual transactions. Utility companies send customers information about their own and their neighbors’ energy use so they can benchmark themselves. The utilities often follow up with suggestions about how to save both power and money. Meanwhile, a credit card issuer could help customers understand their purchasing patterns and discover new stores or service providers.

“Loyalty is an emotion first and behavior second,” Bonchek says. “It’s the difference between pushing customers through a funnel and helping them achieve a shared purpose.”

The Art of Scientific Marketing

In mid-20th century New York City, a small local chain of markets developed a national reputation for customer service. It let favored customers call in orders and pay for them at pickup. Managers kept lists—handwritten lists, no less—of their best customers’ preferred products and called those customers with special offers. People were happy to pay slightly higher prices overall in exchange for exclusive bargains and highly customized service.

Although it leverages new technologies like machine learning and Big Data, behavioral targeting will in many ways bring us full circle to that hands-on era in which companies created relevant offers that made customers feel valued and understood. Matz believes it would be a competitive advantage for companies to let customers interact with their profiles and even correct them to ensure that they only receive offers that meet their needs and preferences.

As more situational data pours in from smartphones and wearables to be analyzed by AI, she adds, behavioral targeting could become something more immersive than mere marketing. “If you know from that data that someone is not just an extrovert with specific preferences but that they’re currently in a good mood, you can start fine-tuning messages for that particular point in time,” she says. “We’ll move beyond static profiles to interactions based on characteristics that fluctuate.”

With enough data to work with, she suggests, behavioral targeting could become less about making offers and more about informing customers about their options at any given moment, in real time. D!

About the Authors

Denise Champion is Vice President of Strategy, Research, and Insights for Global Marketing at SAP.

Jeff Harvey is Global COO, SAP Analytics & Insight at SAP.

Lori Mitchell-Keller is Global General Manager, Consumer Industries at SAP.

Jeff Woods is Global COO, SAP Leonardo | Data and Analytics.

Fawn Fitter is a freelance writer specializing in business and technology.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


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