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3 Cool AI Projects

July 18, 2017 /

AI is all around us, quietly working in the background or interacting with us via a number of different devices. Various industries are using AI for specific reasons such as ensuring that flights arrive on time or irrigating fields better and more economically.

Over time, our interactions with AI are becoming more sophisticated. In fact, in the not-too-distant future we’ll have personal digital assistants that know more about us than we know about ourselves.

For now, there are countless AI projects popping up in commercial, industrial and academic settings. Following are a few examples of projects with an extra cool factor.

Get Credit. Now.

Who among us hasn’t sat in a car dealership, waiting for the finance person to run a credit check and provide us with financing options? We’ve also stood in lines at stores, filling out credit applications, much to the dismay of those standing behind us in line. Experian DataLabs is working to change all that.

Experian created Experian DataLabs to experiment with the help of clients and partners. Located in San Diego, London, and Sao Paulo, Experian DataLabs employ scientists and software engineers, 70% of whom are Ph.Ds. Most of these professionals have backgrounds in machine learning.

“We’re going into the mobile market where we’re pulling together data, mobile, and some analytics work,” said Eric Haller, EVP of Experian’s Global DataLabs. “It’s cutting-edge machine learning which will allow for instant credit on your phone instead of applying for credit at the cash register.”

That goes for getting credit at car dealerships, too. Simply text a code to the car manufacturer and get the credit you need using your smartphone. Experian DataLabs is also combining the idea with Google Home, so you can shop for a car, and when you find one you like, you can ask Google Home for instant credit.

There’s no commercial product available yet, but a pilot will begin this summer.

AI About AI

Vicarious is attempting achieve human-level intelligence in vision, language, and motor control. It is taking advantage of neuroscience to reduce the amount of input machine learning requires to achieve a desired result. At the moment, Vicarious is focusing on mainstream deep learning and computer vision.

It’s concept is compelling to many investors. So far, the company has received $70 million from corporations, venture capitalists and affluent private investors including Ashton Kutcher, Jeff Bezos, and Elon Musk.

On its website, Vicarious wisely points out the downsides of model optimization ad infinitum that results in only incremental improvements. So, instead of trying to beat a state-of-the-art algorithm, Vicarious is to trying to identify and characterize the source of errors.

Draft Better Basketball Players

The Toronto Raptors is working with IBM Watson to identify what skills the team needs and which prospective players can best fill the gap. It is also pre-screening each potential recruits’ personality traits and character.

During the recruiting process, Watson helps select the best players and it also suggests ideal trade scenarios. While prospecting, scouts enter data into a platform to record their observations. The information is later used by Watson to evaluate players.

And, a Lesson in All of This

Vicarious is using unsupervised machine learning. The Toronto Raptors are using supervised learning, but perhaps not exclusively. If you don’t know the difference between the two yet, it’s important to know. Unsupervised learning looks for patterns. Supervised learning is presented with classifications such as these are the characteristics of “good” traits and these are the characteristics of “bad” traits.

Supervised and unsupervised learning are not mutually exclusive since unsupervised learning needs to start somewhere. However, supervised learning is more comfortable to humans with egos and biases because we are used to giving machines a set of rules (programming). It takes a strong ego, curiosity or both to accept that some of the most intriguing findings can come from unsupervised learning because it is not constrained by human biases. For example, we may define the world in terms of red, yellow and blue. Unsupervised learning could point out crimson, vermillion, banana, canary, cobalt, lapis and more.

Sports Teams Embrace Analytics and the IoT

June 29, 2017 /

In my last sports-related post, I explained how the National Hockey League (NHL) is using IoT devicesto provide the league with deeper insights about the players and the game while immersing fans in new stats and spectator experiences. The NHL is not alone. In fact, IoT devices are finding their way into all kinds of sports for the benefit of leagues, players, and fans.

For example, the National Football League has been placing sensors in player’s shoulder pads to track their location, speed, and distance traveled. Last year, it experimented with sensors in footballs to track their motion, including acceleration, distance, and velocity. That data is being sold to broadcast partners.

Meanwhile, young football players who hope to play the game professionally are tracking themselves hoping to become more attractive recruiting targets.

NBA Teams Score with Insights

The Golden Gate Warriors and Miami Heat are getting some interesting datafrom wearables and other sensors that track the movement of players and the basketball used in a game. Now it’s possible to analyze how players shoot, how high they jump, and the speed at which the ball travels, among other things. One thing that trips me up about it is how some of that data is visualized by the coach.

Picture this: The player clips a device to his shorts or wears the device on his wrist so his coach can understand the trajectory of the ball and get statistics about a player’s movements on a cell phone. The new insights help coaches and their teams understand the dynamics of the sport better, but I wonder how practical Basketball by Smartphone App is, given the speed at which the game is played.

Sensors placed somewhere on the players and in the basketball also provide information about players’ movements on the court over time. The visualization looks a like a plate of spaghetti, but within that are patterns that reveal players’ habits, such as the area of the court the player tends to favor.

Beyond Moneyball

Former Oakland A’s general manager Billy Beane is considered the father of sports analytics because in 1981 he was the first to change the makeup of a team and how a team played the sport based on what the numbers said. This is commonly known as “Moneyball” (thanks to the book and movie) or “Billyball.”

One interesting insight was base time. The more time a player spends on-base, the more likely that player will walk to first base rather than strike out.

However, Beane’s early experimentation also demonstrated that numbers aren’t everything. He was fired the next year (in 1982) for overworking pitchers. Stated another way, the stellar turnaround year was not followed by a similarly strong year.

These days, sensors are enabling Major League Baseball (MLB) statistics 2.0. For example, sensors in baseball bats provide insights about the speed and motion of a swing and the point of impact when a ball hits the bat. In the dugouts, coaches and players can get access to all kinds of insights via an iPad during the game. The insights enable them to fine-tune the way they play against the opposing team. It’s also possible to track the movements of a specific player.

Can Virtual Companies Scale?

June 27, 2017 /

Modern technology has enabled more working professionals to telecommute, whether they’re working for traditional companies or progressive companies. Their employers may maintain dedicated office space for each employee nevertheless. Alternatively, there may be shared workspaces that are not assigned to any one employee.

Over the past couple of decades, technology and societal changes have enabled the rise of virtual companies. Those that succeed have some kind of “secret sauce,” which differs from organization to organization.

“If you’re a virtual company, you have to work differently than if you were in an office,” said Bjorn Freeman-Benson, CTO at product design platform provider InVision. “We have to deliberately coordinate our work. And because we’re deliberate, we scale more easily. We’ve got 250 – 300 employees now.”

Worldwide talent pool

Virtual companies tend to be distributed by default because the talent is spread out over several geographic locations. As they grow, one of two things happens:  They either move into office space because they’re unable to operate efficiently, or they stay virtual by placing more emphasis on talent than where that talent resides. New York-based InVision has employees in Montana, Argentina, and other locations, for example.

Similarly, virtual law firm Culhane Meadowshas 55 partners in different locations, most of whom are senior partners with eight or more years’ experience.

“I think we are the only alternative model that’s truly a partnership,” said Culhane Meadows Managing Partner Kelly Culhane. “You really have to use technology purposefully to maintain the standards of traditional law firms.”

Culhane Meadows started out with two advantages: Fortune 100 clients and four founding partners with different areas of expertise. Those four founders are responsible for operations, finance, marketing, and technology, respectively, which provides a solid foundation from which to grow.

If a partner needs a temporary office, she rents it from temporary office space provider Regus. If she needs paralegals or secretaries, they are hired on a contract basis through a temporary staffing partner.

The lower overhead enables Culhane Meadows to provide big law firm service without the big law firm price tag.

Results trump time

Managing a virtual workforce can be challenging, especially if it’s done in a traditional context, not only meaning 8:00 am to 5:00 pm, but scenarios where an employee might work 40 hours one week and many more or fewer the following week.

“We don’t care about butt time, we care about results,” said Freeman-Benson.

His company uses Slack for collaboration. Within Slack, the company has set up different channels so that salespeople can go to a virtual “deal desk” before extending an offer. Similarly, if a customer wants to know what InVision’s security policies are, a salesperson can tap into the security channel.

Some structure is good

Virtual companies often have less formal reporting structures, but not always. For example, Disney-focused MickeyTravels has a two-founder husband-and-wife team and 115 contract travel agents. Some of those travel agents have additional responsibilities, such as managing a group of contract agents or training agents.

Apparently, the business model is working well because MickeyTravels is one of the most successful Disney travel agencies in the world, being among only 12 Disney Platinum agencies.

“Our agents are well-versed on what we sell so nobody can ask them a question they don’t know the answer to,” said Greg Antonelle, co-founder of MickeyTravels. “The beauty of technology now is you have FaceTime, Skype, GoToMeeting, webinars, and all that stuff.”

NHL and Fans Score with Predictive Analytics

June 23, 2017 /

If you’re a hockey fan, you’ve probably noticed that the statistics are more comprehensive than they once were. That’s not happening by accident.

The National Hockey League (NHL) uses predictive analytics to learn more about fans, improve its direct marketing efforts, track players’ performance on the ice, and improve fan engagement.

Making an IoT Play

During the 2015 All-Star game, sensors were embedded inside pucks and players’ jersey collars which provided insight into where the puck and players were, how fast they were moving, puck trajectory, players’ time on ice and more.

The information was used during replays to better explain how a particular outcome came about. Fans were able to visualize the paths players and pucks had taken, giving them more insight into players’ performance. Experimentation continued at the World Cup of Hockey 2016, which was substantially the same thing — tracking pucks and players.

The key to winning a hockey match is puck possession. If Team A possesses the puck longer than Team B, Team A will score more points over time.

The information derived from the devices, particularly the jerseys, can be used for training purposes and to minimize injuries.

A Data Scientist Predicted Winners and Losers

A couple of years ago, the NHL worked with a data scientist who reviewed historical data including player statistics and team statistics over several seasons. When he crunched the data, he found that there are certain statistics and factors that, over time, can help predict team performance on the ice, especially in the playoffs.

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Thirty-seven different factors were weighted in certain ways and applied to the 16 teams that started the playoffs in April 2015. The goal was to predict how the playoff teams would do when playing against each other. And, as the rounds progressed, how the teams would perform in new matchups.

The results were very interesting. The data scientist was able to predict at the start of the season that the Chicago Blackhawks would win the Stanley Cup. He also was able to predict which team would win each playoff game, most of the time.

“What’s interesting about that is our sport is a pretty unpredictable sport,” said Chris Foster, director of Digital Business Development at National Hockey League, in an interview. “The action is so fast, goals happen rather infrequently and a lot of it has to do with a puck bouncing here or a save there. It’ very fast action that is sometimes hard to predict, but it just shows that data, when properly analyzed, and really smart models are put around it, that predictive analytics can tell you a lot about how a team is going to perform.”

3 Data Governance Challenges Today’s Companies Face

June 21, 2017 /

Some organizations have mastered data governance, but they are in the minority. As data volumes continue to grow, most businesses are finding it hard to keep up.

“You’re going to do this one way or another,” said Shannon Fuller, director of data governance at  Carolinas Healthcare System. “You can do it in a controlled, methodical manner or you can do it when your hair’s on fire.”

Poor data governance can result in lawsuits, regulatory fines, security breaches and other data-related risks that can be expensive and damaging to a company’s reputation. “We don’t have regulation about data lineage and reporting and all that, but it’s going to come,” said Fuller. “Do you want to prepare for that now or do you want to be like Bank of America and spend billions of dollars complying with the law?  Most healthcare organizations don’t have that kind of cash lying around.”

Another problem is legal discovery. Without proper data governance, companies end up handing over information that is not relevant to the case.  Some of that information may be sensitive.

There are valid reasons why companies are struggling with data governance.  Following are three of them.

1. It’s considered a technology problem. Effective data governance requires the use of good tools; however, the use of good tools does not guarantee effective data governance.  Some companies find this out the hard way when they invest in technology but fail to make the necessary adjustments to their culture and business processes.

“The common wisdom is you need an executive sponsor and the support of the C-suite and roll that down. It helps, but there are things you can do from a data governance perspective without having that buy-in,” said Fuller in a recent interview. “It has to be tied to your business processes. In healthcare, that’s one of the biggest stumbling blocks.”

2. Old approaches are applied to new requirements. Data governance policies and procedures require updating as more data flows into and out of organizations. Nevertheless, some companies are trying to apply concepts and constructs developed decades ago to modern requirements, which doesn’t work well.

“I hear people say, ‘This is what I get out of my relational database so why can’t I just use it for everything?’ You’re forcing this rigid structure because it makes people feel warm and fuzzy,” said Jim Scott, director of converged data platform provider MapR.  “It’s dangerous when people have the myopic perspective of governing data the same old way they always have.”

Yet, some of those very organizations are now planning to add streaming data from IoT devices to the mix.

3. The value of data is not understood. Some businesses are throwing every piece of data into a data lake, hoping that that it will have value someday.  Other companies are deciding what to keep and throw away based on current requirements.  When those requirements change, they may regret some of those decisions.

“One of the challenges I hear often is how do you assign value to different datasets because that might impact how you think about your governance policies,” said Sanjay Sarathy, CMO at  Talena.  “How do I leverage data coming out of IoT streams verses the marketing folks who leverage media data?  Thinking through the value of these different datasets will enable you to define how you govern them, cleanse them, and protect them.”

Assigning value can be a difficult challenge, however. Some organizations don’t know where to start. Others struggle to assign an accurate value when the value is both qualitative and quantitative. Even if organizations are able to get the value right and get data governance right, what’s “right” may change when a merger or acquisition happens.

In short, data governance isn’t a static thing, it’s an evolving mindset that requires cultural and technological support along the way to succeed.

Don’t Let Outliers Sabotage Your Cybersecurity Analytics

June 10, 2017 /

Cybersecurity analytics solutions are becoming more intelligent and nuanced to understand anomalous behavior that’s outside the norm and potentially dangerous. Identifying outliers is important, but not every outlier is a threat, nor is every threat an outlier.

“Companies have made hundreds of millions of dollars building tools that look for behavior that’s outside a rule or a set of parameters,” said Jason Straight, SVP of Cyber Risk Solutions and chief privacy officer at legal outsourcing services provider United Lex. “For machines that works pretty well, for people it doesn’t.”

Tracking behavior at the machine level can be as simple as monitoring the number of packets sent to and from a particular machine.

Humans behave differently in different contexts. For example, many of us usually work at particular office Monday through Friday during “normal” work hours. However, if we’re traveling internationally, we’re probably accessing the same corporate network, albeit at a different time from a different IP address that’s located somewhere else in the world.

A rule-based system could be programmed to disallow network access under those conditions, but traveling professionals wouldn’t get much work done. The trick is to balance the needs of users and the business against potential threats.

“Instead of setting a bunch or rules that say if someone logs in from an IP address that they’ve never used before, at a time they’ve never logged in before, and they’re accessing part of the network they’ve never used before, that’s a complicated rule that would require constant updating and it would be impossible to manage on a person-by-person basis,” said Straight.

User Behavior Analytics Can Help

Enterprise security budgets have been heavily focused on keeping outside threats at bay, but more enterprises are realizing that to protect their assets, they need to assume that their network has been hacked and that there’s an active intruder at work.

Similarly, when the average person thinks about a cybersecurity breach, hackers come to mind. However, insiders are a bigger problem. In addition to being responsible for more security breaches than hackers, insiders fail their companies accidentally and willfully.

“If I see a server doing something funny, I can shut it down, take it offline, or reroute the traffic, which doesn’t disrupt an organization much or at all,” said Straight. “If I do that to people, that could be really disruptive.”

User behavior analytics are an effective mechanism for insider threats because they’re able to model a user’s behavior. For example, when an employee is getting ready to leave a job, that person usually visits certain websites and updates her resume, which isn’t the best use of company assets, but it doesn’t justify security intervention. However, when that employee starts downloading files to USB drives, uploading files to file-sharing services, and printing volumes of information, intervention is may necessary.

Monitoring a single user doesn’t always tell the entire story, however, which is why user behavior analytics enable users to see what an individual is doing within the context of a group. For example, if someone in marketing accessed a part of the network she’s never visited before, that’s strange. Whether it actually requires action or not may depend on whether others in her department have accessed that same part of the network and if so, when.

While such capabilities sound attractive, many organizations are failing to get value they expected from user behavior analytics, despite spending seven figures, because they don’t know how to handle the alerts and intelligence, Straight said.

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User behavior analytics can also help determine whether someone’s login credentials have been stolen. Unlike traditional rule-based systems, user, machine learning, and AI are used to model an authorized user’s behavior and that behavior is associated with that person’s login credentials. If someone else tries to use the same User ID and password, her behavior indicates the account has been compromised.

“That’s when you start to see an account that’s never really used more than a departmental server suddenly scanning the entire network, trying to get into different places and being denied access,” said Straight.

Think First

Before investing in a new security tool, it’s essential to understand the problem you’re trying to solve, which is true of any technology. Different security tools serve different purposes.

“Do you want to understand problems you haven’t identified or are you trying prevent data leakage?” said Avivah Litan, vice president and distinguished analyst at Gartner. “You have to be real clear, and then you also need to spend some time training the models and supervising them.”

Why Collaboration is Critical in Technology Acquisition

June 9, 2017 /

Technology teams and lines of business are often seduced by cool new technologies, products, and services. The business is drawn to promises of better insights, higher productivity, improved economics, and ease of use. IT is drawn to increasingly powerful technologies that enable the team to more effectively implement and manage an increasingly complex ecosystem.

However, the art of the possible often overshadows what’s practical or what the business is trying to achieve.

Solutions architects can help better align technology acquisition with business goals, albeit not single-handedly. They need to collaborate with the business, IT, and vendors to orchestrate it all.

“A solutions architect has a foot in enterprise architecture, a foot in business program management, and a foot in vendor product management,” said Dirk Garner, principal consultant at Garner Software.
“We help determine what the business needs are and align the right technologies and products. Solutions architecture is really at the center of those things.”

Architecting the right solutions

Sound technology acquisition starts with a business problem or goal. Then, it’s a matter of selecting “the right” technologies and products that will most effectively solve the business problem or help the business achieve its goal.

“So often we do it backwards, we say we have this technology so let’s do this,” said Garner. “Once you understand the business environment, it’s assessing the current state of technology and then taking a look at what you actually need to pursue opportunities and survive in the business environment.”

Given the fast pace of business today, there’s an inclination to just acquire technology now. However, there are often trade-offs between short-term pain relief and a longer term benefit to the business. A sounder approach is to compare current capabilities with the capabilities required and then define a roadmap for getting there.

“The number one challenge is that people are myopic,” said Garner. “Vendors focus on how great their product is [rather than] what the customer needs. The business always comes to the table with unrealistic expectations – how little money they want to spend and how fast they want things delivered.”

Since IT can’t meet those expectations, lines of business purchase their own technology, not realizing that they’ll probably need IT’s help to implement it.

“You hear a lot about collaboration today but when you talk to these people, they’re still siloed,” said Curt Cornum, VP and chief solution architect at global technology provider Insight Enterprises. “Even within the IT department, when you get into those types of conversations they’re not talking to each other as much as they should.”

The persistent silos are keeping businesses from meeting their goals and staying competitive. Meanwhile, their agile counterparts are pulling ahead because their business and IT functions are working in unison. Collaboration is critical.

Rapid Tech Change Challenges IT Leaders

June 1, 2017 /

Faster technology innovation and competitive pressures are taking their toll on IT. Gone are the days when IT procured and managed all of an organization’s technology. The reason: IT can’t deliver fast enough on what individual operating units need.

To help keep their companies stay competitive, IT departments are evolving from centralized organizations to hub-and-spoke organizations that serve individual operating units and the enterprise simultaneously. But even then, keeping up with the latest technologies is challenging.

“Things are progressing at such an exponential rate, that it’s tough to keep up and you’re a little more uneasy about the decisions you make,” said Steve Devine, director of IT at international law firm O’Melveny. “Solutions are being developed so quickly and hitting the so market quickly, that it’s much harder to differentiate between the solutions that are coming out.”

Part of the problem is the technology landscape itself. Everything runs on software today, including businesses and hardware. Much of that software is developed in an Agile fashion so it can be delivered faster, in weeks or months verses years. The result is often a minimally viable product that is continually enhanced over time versus a traditional product that includes more features out of the gate, albeit at a much slower pace.

The cloud has also helped accelerate the pace of software innovation and the economics of software innovation because software developers no longer have to build and maintain their own infrastructure. They can buy whatever they need on demand which speeds software testing and DevOps, further accelerating software delivery.

The on-demand nature of the cloud and shift to minimally viable products lowers the barrier to market entry, which means the number of vendors in virtually every product area has exploded, and so have the number of products hitting the market.

Keeping up with all of that challenges even large IT departments.

Security is front and center

IT departments have always had some security element, but with the growing number and types of threats, they are necessarily expanding their capabilities. That means changes such as adopting more types of security products and services, and having talent on hand that understands all the details.

“With so many outsiders trying to hack into systems, even if you understand security systems, the technology is always changing,” said Jermaine Dykes, senior IT project manager Wi-Fi Strategy & Operations at telecommunications infrastructure company Mobilitie.

O’Melveny’s Devine said his company’s IT department has evolved from a “keeping-the-lights-on” type of shop to a security-focused organization in which members maintain expertise in their specific areas.

“Retaining talent is really key with all the emphasis on security, machine learning and AI,” said Devine. “People in that world are very hard to find and very hard to keep.”

Enabling analytics is critical

As more businesses become insight-driven, IT organizations need to provide a solid, governed foundation for data usage that can be leveraged by different parts of the organization as necessary. That way, departments and lines of business can access the data they need without exposing the enterprise to unnecessary risks.

“Big data is huge. Gone are the days when we used a huge server and IT was considered overhead,” said Mobilities’ Dykes. “Today’s IT leaders need to have a vision about how they can incorporate data analytics to propel their organizations into the 21st century.”

More analytics solutions use machine learning and AI to improve the quality of insights they deliver, but quite often the hype about the solutions outpaces their actual abilities.

“The healthcare industry uses machine learning for diseases and things of that nature, but if you look at other industries, it’s basically nowhere,” said O’Melveny’s Devine. “The early adopters pay a price because you spend a lot of cycles getting something like that implemented and a lot of times it’s just a non-starter once you’ve gone through all that.”

Why Marketing Is So Smart, Yet So Dumb

May 27, 2017 /

Marketing is considered the most analytically advanced function in most companies. Yet, consumers and businesses are still bombarded with irrelevant promotional messages.

It’s true that marketers have had access to “modern” analytics tools longer than most others in an organization. It started with web analytics and then grew to encompass other digital channels and even offline channels.

In the last decade, there has been a push toward “multi-channel” and “omnichannel” analytics. Multi-channel analytics is designed to optimize marketing effectiveness within and across channels. Omnichannel analytics focuses on improving a continuous user experience across channels.

Marketing analytics is difficult, in other words, despite the availability of more and better tools.

“What are the exact ads, the exact conversations, and the exact place that drove someone to make a purchase on my site or in my store?” said Chris Madden, co-founder of digital marketing agency Matchnode. “I think in 0% of the cases does the super smart, data-driven marketer, CMO, or CEO know what drove the sale.”

It’s Complicated

The number of online channels has exploded over the past couple of decades with the rise of Search Engine Marketing (SEM), social media, and mobile, to name a few. Anyone familiar with even one of those channels knows that change is constant, and if you don’t keep up, you’ll slip up eventually.

“We’ve seen Facebook come out twice in the past year claiming that their method for measuring engagement metrics [was] wrong. There will be more growing pains as these platforms stabilize,” said Mitul Jain, vice president at data science platform provider r4 Technologies.

Meanwhile, brick and mortar entities are tracking what’s happening in stores using kiosks, digital point of sale (POS) systems, customers’ smartphones, and security cameras. They also have e-commerce sites. Their big challenge is to understand the relationship of online and offline channels.

We’re Tracking Activities, Not People

Activities are being monitored in every channel whether posts, clicks, downloads, foot traffic, or credit card swipes, but not all of that information is being stitched together into a coherent, accurate picture.

“Analytics does not do a very good job of knowing that the person on my phone is the same person on my tablet and desktop,” said Matchnode’s Madden. “The marketers who are doing well are those that start with the person.

Attribution is Difficult

Most of the time, there isn’t a 1:1 relationship between a message and an outcome (e.g., a sale, download, or donation). Usually, the final outcome is influenced by several factors that may include search-based research, search or social media advertising, product reviews, direct mail and email offers, apps, and websites.

The natural and incorrect thing to do is to attribute the last interaction to the outcome. The shopper visited the site and bought something, so the ecommerce site gets full credit. However, since several other factors likely influenced the decision, what percentage the sale should be attributed to each? That’s the burning question.

Data Quality Could Improve

Marketing tends to use several different systems and platforms, each of which may differ enough to affect data quality. Perhaps fields or tags are implemented differently, or there are five instances of a customer record, all of which are inconsistent.

“Large sites may not have the code in the right places or double instances of code. For example, an .edu site with multiple departments may have different tracking codes on the same site, which can create a lot of confusion,” said Max Thomas, CMO at fintech startup YayPay.

Thomas audits a client’s data points to make sure they’re correct early in the relationship. Quite often he discovers that the client hasn’t set up website analytics correctly or they haven’t set up conversion tracking correctly. If either or both of those things are true, the client is referencing faulty data.

We’re Biased

Humans are biased creatures. What we perceive is based on beliefs and experience, most of which is subjective. Our subjective view or bias causes us to do many things that skew analytical results such as selecting non-random samples or cherry-picking data.

“The mistake people are making is they don’t let the data talk to them. They’re looking for something in the data that’s not there,” said D. Anthony Miles, CEO and founder of consulting firm Miles Development Industries Corp. “You have to ask what the data is telling you and what it isn’t telling you.”

Marketers tend not to look at analytical results critically, however. They tend to accept analytical at face value unless it’s out of sync with their beliefs. If they were looking at analytical results critically, they’d ask why a particular analytical result occurred or didn’t occur.

“The data can and should tell the story, but we make up our own story and look for data to support it, so we may miss the most important thing because we were looking for something else,” said Matchnode’s Madden.

Pesky PII

Finally, marketing can only be so accurate without a critical mass of Personally Identifiable Information or PII, some of which consumers do not want to give and some of which is illegal. Without the missing data points, it’s difficult to reach consumers with the right message at the right time for the right reason.

The lack of that “last mile” data is the reason why some people think marketing will never be 100% accurate. What do you think? Before you answer, think of a compromising message that might be sent to you, just at the wrong moment.

How Cybersecurity Analytics Are Evolving

May 23, 2017 /

As the war between the black hats and white hats continues to escalate, cybersecurity necessarily evolves. In the past, black hats were rogue individuals. Now they’re hactivists, crime groups, and hackers backed by nation states.

“Hackers have gotten a lot more sophisticated,” said Sanjay Goel, a professor in the School of Business at University of Albany. “It used to be they’d break into networks, do some damage, and get out. Now they have persistent attacks and targeted execution.”

Hackers are automating attacks to constantly search for vulnerabilities in networks. Meanwhile, fraudulent communications are getting so sophisticated, they’re fooling even security-aware individuals. Analytics can help, but nothing is a silver bullet.

Moats Are Outdated

Organizations used to set up perimeter security to keep hackers from breaching their networks. Since that didn’t work, firewalls were supplemented with other mechanisms such as intrusion detection systems that alert security professionals to a breach and honey pots that lure hackers into a place where they can be monitored and prevented from causing damage.

Those tools are still useful, but they have necessarily been supplemented with other methods and tools to counter new and more frequent attacks. Collectively, these systems monitor networks, traffic, user behavior, access rights, and data assets, albeit at a grander scale than before, which has necessitated considerable automation. When a matter needs to be escalated to a human, analytical results are sent in the form of alerts, dashboards, and visualization capabilities.

“We really need to get away from depending on a security analyst that’s supposed to be watching a dashboard and get more into having fully-automated systems that take you right to remediation. You want to put your human resources at the end of the trail,” said Dave Trader, chief security officer at IT services company GalaxE.Solutions.

Predictive analytics analyzes behavior that indicates threats, vulnerabilities, and fraud. Slowly, but surely, cybersecurity budgets, analytics, and mindsets are shifting from prevention to detection and remediation because enterprises need to assume that their networks have been breached.

“All the hackers I know are counting on you not taking that remedial step, so when there’s a vulnerability and it’s a zero-day attack, the aggregator or correlators will catch it and then it will go into a ticket system so its three to four days before the issue is addressed,” said Trader. “In the three to four days, the hackers have everything they need.”

Why Break In When You Can Walk In?

Fraudsters are bypassing traditional hacking by convincing someone to turn over their user ID and password or other sensitive information. Phishing has become commonplace because it’s effective. The emails are better crafted now so they’re more believable and therefore more dangerous. Even more insidious is spear phishing which targets a particular person and appears to be sent from a person or organization the person knows.

Social engineering also targets a specific person, often on a social network or in a real-world environment. Its purpose is to gain the target’s trust, and walk away with the virtual keys to a company’s network or specific data assets. Some wrongdoers are littering parking lots with thumb drives that contain malware.

Behavioral analytics can help identify and mitigate the damage caused by phishing and social engineering by comparing the authorized user’s behavior in the network and an unauthorized user’s behavior in the network.

Bottom Line

Breaches are bound to happen. The question is whether companies are prepared for them, which means keeping security systems up to date and training employees.

Far too many companies think that hacking is something that happens to other organizations so they don’t allocate the budget and resources they need to effectively manage risks. Hackers love that.

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