Voices in AI – Episode 62: A Conversation with Atif Kureishy


About this Episode

Episode 62 of Voices in AI features host Byron Reese and Atif Kureishy discussing AI, deep learning, and the practical examples and implications in the business market and beyond. Atif Kureishy is the Global VP of Emerging Practices at Think Big, a Teradata company. He also has a B.S. in physics and math from the University of Maryland as well as an MS in distributive computing from Johns Hopkins University.
Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI, brought to you by GigaOm, I’m Byron Reese. Today my guest is Atif Kureishy. He is the Global VP of Emerging Practices, which is AI and deep learning at Think Big, a Teradata company. He holds a BS in Physics and Math from the University of Maryland, Baltimore County, and an MS in distributive computing from the Johns Hopkins University. Welcome to the show Atif.
Atif Kureishy: Welcome, thank you, appreciate it.
So I always like to start off by just asking you to define artificial intelligence.
Yeah, definitely an important definition, one that unfortunately is overused and stretched in many different ways. Here at Think Big we actually have a very specific definition within the enterprise. But before I give that, for me in particular, when I think of intelligence, that conjures up the ability to understand, the ability to reason, the ability to learn, and we usually equate that to biological systems, or living entities, and now with the rise of probably more appropriate machine intelligence, we’re applying the term ‘artificial’ to it, and the rationale is probably because machines aren’t living and they’re not biological systems.
So with that, the way we’ve defined AI in particular is: leveraging machine and deep learning to drive towards a specific business outcome. And it’s about giving leverage for human workers, to enable higher degrees of assistance and higher degrees of automation. And when we define AI in that way, we actually give it three characteristics. Those three characteristics are: the ability to sense and learn, and so that’s being able to understand massive amounts to data and demonstrate continuous learning, and detecting patterns and signals within the noise, if you will. And the second is being able to reason and infer, and that is driving intuition and inference with increasing accuracy again to maximize a business outcome or a business decision. And then ultimately it’s about deciding and acting, so actioning or automating a decision based on everything that’s understood, to drive towards more informed activities that are based on corporate intelligence. So that’s kind of how we view AI in particular.
Well I applaud you for having given it so much thought, and there’s a lot there to unpack. You talked about intelligence being about understanding and reasoning and learning, and that was even in your three areas. Do you believe machines can reason?
You know, over time, we’re going to start to apply algorithms and specific models to the concept of reasoning, and so the ability to understand, the ability to learn, are things that we’re going to express in mathematical terms no doubt. Does it give it human lifelike characteristics? That’s still something to be determined.
Well I don’t mean to be difficult with the definition because, as you point out, most people aren’t particularly rigorous when it comes to it. But if it’s to drive an outcome, take a cat food dish that refills itself when it’s low, it can sense, it can reason that it should put more food in, and then it can act and release a mechanism that refills the food dish, is that AI, in your understanding, and if not why isn’t that AI?
Yeah, I mean I think in some sense it checks a lot of the boxes, but the reality is, being able to adapt and understand what’s occurring, for instance if that cat is coming out during certain times of the day ensuring that meals are prepared in the right way and that they don’t sit out and become stale or become spoiled in any way, and that is signs of a more intelligent type of capability that is learning the behaviors and anticipating how best to respond given a specific outcome it’s driving towards.
Got you. So now, to take that definition, your company is Think Big. What do you think big about? What is Think Big and what do you do?
So looking back in history a little bit, Think Big was actually an acquisition that Teradata had done several years ago, in the big data space, and particularly around open source and consulting. And over time, Teradata had made several acquisitions and now we’ve unified all of those various acquisitions into a unified group, called Think Big Analytics. And so what we’re particularly focused on is how do we drive business outcomes using advanced analytics and data science. And we do that through a blend of approaches and techniques and technology frankly.
Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com
Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

How PayPal uses deep learning and detective work to fight fraud

Hui Wang has seen the nature of online fraud change a lot in the 11 years she’s been at PayPal. In fact, a continuous evolution of methods is kind of the nature of cybercrime. As the good guys catch onto one approach, the bad guys try to avoid detection by using another.

Today, said Wang, PayPal’s senior director of global risk sciences, “The fraudsters we’re interacting with are… very unique and very innovative. …Our fraud problem is a lot more complex than anyone can think of.”

In deep learning, though, Wang and her team might have found a way to help level the playing field between PayPal and criminals who want exploit the online payment platform.

Deep learning is a somewhat new approach to machine learning and artificial intelligence that has caught fire over the past few years thanks to companies such as [company]Google[/company], [company]Facebook[/company], [company]Microsoft[/company] and Baidu, and a handful of prominent researchers (some of whom now work for those companies). The field draws a lot of comparisons to the workings of the human brain because deep learning systems use artificial neural network algorithms, although “inspired by the brain” might be a more accurate description than “modeled after the brain.”

How DeepFace sees Calista Flockhart. Source: Facebook

A visual diagram of a deep neural network for facial recognition. Source: Facebook

Essentially, the stacks of neural networks that comprise deep learning models are very good at recognizing patterns and features of the data they’re trained on, which has led to some huge advances in computer vision, speech recognition, text analysis, machine listening and even video-game playing in the past few years. You can learn more about the field at our Structure Data conference later this month, which includes deep learning and artificial intelligence experts from Facebook, Microsoft, Yahoo, Enlitic and other companies.

It turns out deep learning models are also good at identifying the complex patterns and characteristics of cybercrime and online fraud. Machine-learning-based pattern recognition has long been a major part of fraud detection practices, but Wang said PayPal has seen a “major leap forward” in its abilities since it began investigating precursor (what she calls “non-linear”) techniques to deep learning several years ago. PayPal has been working with deep learning itself for the past two or three years, she said.

Some of these efforts are already running in production as part of the company’s anti-fraud systems, often in conjunction with human experts in what Wang describes as a “detective-like methodology.” The deep learning algorithms are able to analyze potentially tens of thousands of latent features (time signals, actors and geographic location are some easy examples) that might make up a particular type of fraud, and are even able to detect “sub modus operandi,” or different variants of the same scheme, she said.

Some of PayPal's fraud-management options for developers.

Some of PayPal’s fraud-management options for developers.

The patterns are much more complex than “If someone does X, then the result is Y,” so it takes artificial intelligence to analyze them at a level much deeper than humans can. “Actually,” Wang said, “that’s the beauty of deep learning.”

Once the models detect possible fraud, human “detectives” can get to work assessing what’s real, what’s not and what to do next.

PayPal uses a champions-and-challengers approach to deciding which fraud-detection models to rely on most heavily, and deep learning is very close to becoming the champion. “We’ve seen roughly a 10 percent delta on top of today’s champion,” Wang said, which is very significant.

And as the fraudulent behavior on PayPal’s platform continues to grow more complex, she’s hopeful deep learning will give her team the ability to adapt to these new patterns faster than before. It’s possible, for example, that PayPal might some day be able to deploy models that take live data from its system and become smarter, by retraining themselves, in real time.

“We’re doing that to a certain degree,” Wang said, “but I think there’s still more to be done.”

Will Samsung’s mobile wallet plans work? We’ll know in 7 months

Samsung has entered the mobile payments fray with its acquisition of LoopPay, giving it the technology to turn its smartphones into wireless credit cards that can purchase goods and service with a wave of the wrist. LoopPay is clearly Samsung’s answer to Apple Pay, but there’s still one missing piece from its payments puzzle.

With LoopPay’s technology the consumer electronics giant now has all of the technical tools to take on Apple Pay, but Samsung still needs to form direct partnerships with the card-issuing banks. If it doesn’t, then the upcoming transition to new chipped smart cards will be awfully rough on its contactless payments technology.

Today LoopPay’s technology relies on what is essentially a spoofing of the credit card. It records the credit card number off of your plastic’s magnetic stripe, and when its fob or smartphone sleeve is waved over a payment terminal, it transmits that number through a magnetic field, emulating the physical card swipe. The technology works at nearly all point-of-sale terminals today, and I can vouch for its effectiveness. I’ve used a Loop fob to buy coffee at Starbucks and tools at my local hardware store with no difficulty. As Samsung incorporates this technology into its phones, it will work the same way.

The problem is that this kind of static magnetic transaction is going to be phased out of the U.S. retail industry starting in October (it already has been in many other regions of the world). The U.S. is adopting EMV (the name comes from the initials of backers Europay, MasterCard and Visa), which will replace magnetic cards with smart chip cards that store encrypted data that LoopPay won’t be able to emulate — at least not without the cooperation of the banks.

LoopPay's most recent iPhone 6 sleeve with detachable "card" module

LoopPay’s most recent iPhone 6 sleeve with detachable “card” module

LoopPay founder Will Graylin and Samsung’s head of mobile payments Injong Rhee assured me in an interview on Thursday that both LoopPay and Samsung have been in discussions with multiple banks and those partnerships are forthcoming. They also said that LoopPay’s technology is already optimized to handle EMV payments as soon as those first bank deals are signed.

I have no reason to doubt Graylin and Rhee, since even before the acquisition LoopPay already had the backing of at least one credit card powerhouse — Visa was an investor — and Samsung itself wields enormous clout. If it commits to making a Loop-powered wallet a key feature in its smartphones, then banks will want to come to the table, just as they came to the table with Apple Pay.

But Graylin and Rhee wouldn’t offer any details on the specific banks they’re talking to or any timeline for when those deals would be in place. That’s worrisome because the clock is ticking. If those deals don’t come down by October then Samsung may find itself with a mobile wallet that increasingly doesn’t work.

What happens in October

This year, banks will start replacing your plastic with chipped cards, and by the end year MasterCard expects that half of all U.S. credit cards will support chip-and-PIN transactions. Meanwhile, U.S. retailers are replacing their point-of-sale terminals with new card readers that accept EMV transactions.

The transition to EMV in the U.S. was originally expected to be slow – and it will take years before that last small merchant upgrades its hardware – but recent big security breaches like the one affecting Target have lit a fire under the major retailers, explained Osama Bedier, a long-time veteran of the mobile payments space. Bedier founded and is now CEO of payments terminal maker Poynt. Previously, he ran [company]Google[/company] Wallet from its launch until 2013, did product development at PayPal and is an advisor to and investor in LoopPay competitor Coin.

By the October deadline, the top 100 biggest retailers in the U.S. will accept EMV payment, accounting for 40 percent of all in-store retail transactions, Bedier said. Why the hurry? If they don’t, they’ll be liable for any fraudulent transactions made on chipped card at their stores.

Every point-of-sale terminal maker is developing an EMV reader, including Square

Every point-of-sale terminal maker is developing an EMV reader, including Square

That’s a huge shift in the U.S. retail landscape, but LoopPay and other digital credit card makers like Coin, Plastc and Swyp like to point out that even new chipped cards will continue to sport magnetic stripes so they will be able to load them into their universal cards. Conversely, even new payments terminals will still have magnetic stripe readers, so every merchant will technically be able to accept a transaction with their devices.

The infrastructure will remain in place for retailers to continue accepting their digital cards, so everything is hunky-dory, right? Here’s the problem: just because a merchant can technically accept a mag stripe transaction doesn’t mean they will.

EMV transactions are more secure because they use cryptograms instead of the numbers printed on your card face. When digital card holders start sending that insecure static data over payment networks instead of using the encrypted chip on their physical cards, the banks will notice, and a certain point they’re going to start rejecting purchases.

“It all depends on how long the grace period is,” Bedier said. “It could be three months. It could be six months. But the card issuers will start declining transactions.”

Samsung’s opportunity

The key for any of these universal card makers is to demonstrate there’s enough utility and demand for their technology that the banks will gladly climb on board, Bedier pointed out. And here’s where Samsung has a big advantage.

On its own, LoopPay was a small company selling a niche product. But with the might of Samsung behind it, it has enormous advantages over its digital wallet competitors, who are mainly startups trying to crowdfund their products. If Samsung were to make a big commitment to embedding LoopPay’s tech in all of its forthcoming Galaxy smartphones and its wearables, or if Samsung created a detachable phone module that you could hand to a waiter or sales clerk, then the banks would likely eat it up. The banks want to offer their millions of Android customers an alternative to [company]Apple[/company] Pay.

Could LoopPay's technology make it into the Galaxy Gear?

Could LoopPay’s technology make it into the Galaxy Gear?

Furthermore, Samsung would have much larger potential retail appeal than Apple could ever hope to achieve any time in the near future. Graylin explained that LoopPay can route secure EMV data through the mag stripe reader, effectively turning a static transaction into a dynamic one. That means LoopPay could process EMV transactions at any terminal, as it wouldn’t be restricted to working with chip-and-PIN readers or systems with near-field communications (NFC) radios, which is the big limitation of Apple Pay.

With the banks’ cooperation, Samsung could also go beyond the EMV standard to offer tokens – temporary credentials good for only one or a limited number of payments – just the way Apple Pay does. Since LoopPay would be connected to the cloud through the Samsung mothership, it could constantly update its encrypted credit card data from the banks.

“I think we’re going to offer a very unique experience,” Graylin said. “I think people will soon see that.”

So over the next seven months we shouldn’t just be looking out for announcements on how Samsung will incorporate LoopPay’s technology into its products. We should also be watching for the specific banking deals Samsung signs. If it gets enough of them quickly, Samsung could find itself with a mobile wallet that could rival Apple Pay. If it doesn’t, Samsung’s fledgling mobile payments plans could wind up buried in the same heap as Google Wallet and Softcard.

Visa wants to track your smartphone to prevent credit card fraud

In two weeks I’m flying to Barcelona to attend Mobile World Congress, and as I have in every previous year I’ve gone to this conference, I’m going to engage in an annual ritual. Before I leave, I’m going to call up my banks and tell them my itinerary. Otherwise I risk my cards getting declined when I make my first purchase off the plane.

It’s a pain but I’m happy to go through the hassle because it adds an extra layer of protection against fraud. And nothing looks more suspicious than a card being used in Spain eight hours after it was used to buy a latte in Chicago. [company]Visa[/company] on Thursday announced a new service that might mean I’ll no longer need to make that annual call.

Visa is offering location tracking to its banking partners, which will verify your location on your smartphone whenever your card is swiped. If the locations don’t match up, it doesn’t mean the transaction will be automatically declined – sometimes your phone’s battery goes dead or you leave it in the hotel room — but Visa uses it as an additional input for its fraud detection algorithm. And if it does get a location match, then there’s far smaller chance the bank will decline a legitimate transaction even if otherwise looks suspicious. Visa estimates it can reduce such mistakenly declined transactions by 30 percent with the new tech.

So how does Visa get in your phone? It’s working with card-issuing banks to embed a location-tracking module into their regular banking apps. Whenever the card is swiped, Visa partner Finsphere, a geospatial analytics company, pings the app for a coordinates and then reports its findings to Visa in real time (Visa says the process takes less than a millisecond). The feature will be available to banks in April, so hopefully we’ll see it soon.

Finsphere and Location Labs launched an independent service called PinPoint in 2010 that performed many of the same calculations, alerting users to possible fraudulent transactions. The difference here is Finsphere’s work with Visa directly affects whether a transaction is approved or declined.

You have to opt into the service, so your banks won’t track you — at least not wirelessly — without permission. This raises the question of whether you want yet another company or app following your movements, but in this case it makes sense to me. My bank already tracks my activities – it knows where I use my card and what crap I buy – so location tracking isn’t as big a deal for me as long as it will keep my card activated and help stop identity theft.

My only concern is if Visa tracks more than it needs to. I asked Visa and Finsphere for some details on their data policies, and their answers alleviated many of my worries. Finsphere CEO Mike Buhrmann told me in an email that Finsphere and Visa only collect the location data necessary to make fraud determinations (usually just the Zipcode you’re in), and they discard the data after it’s served its purpose. Visa added that location tracking is only active when you’re outside of your home area — it only kicks in when it detects a purchase in a different city — and that it doesn’t use any of the info it gathers for marketing purposes.

“No running tab is kept, but we do know your last region of location so transactions can more effortlessly be approved or actual fraud detected in case you lost your credit card,” Buhrmann said.

This post was updated at 11:45 AM with Visa and Finsphere’s answers to my data privacy questions.



Lawyer who sold fake shares in Facebook IPO gets 46 months in prison

In the months before Facebook became a public company in May 2012, shares in the social media network were the most sought-after investment opportunity in the company. Some people were so desperate to get them, they wired millions of dollars to a New Jersey lawyer who claimed to have the inside track on large blocks of shares.

The investment failed to pan out, however, since the lawyer, 61-year-old Fred Todd, was a Ponzi schemer who did not have access to any of the shares. Now, Todd will get to explore a new social network of his own — in federal prison, where he was sentenced to spend 46 months.

On Wednesday, the U.S. Attorney for New Jersey Paul Fischman announced the prison term, and a requirement for Todd to pay $6.53 million in restitution to his victims. A press release set out some of the details of the scheme:

In February 2012, Todd and his conspirators offered a pair of investors (referred to in the information as the “Facebook victims”) the opportunity to purchase large blocks of Facebook shares prior to the company’s initial public offering, or IPO, in May 2012. The offer was particularly attractive because large blocks of the shares were extremely difficult to get and were expected to increase in value at the time of the IPO. Weinstein and his conspirators did not actually have access to the shares.

Based on misrepresentations by the conspirators, the Facebook victims wired millions of dollars between February and March of 2012 to an account Weinstein and a conspirator controlled. Weinstein and another conspirator provided investors with false documents showing companies owned by various conspirators held assets, which would secure the Facebook victims’ investment.

In the years since the IPO, which turned out to be a short-term debacle for [company]Facebook[/company], the company’s shares have been on an upswing, trading today around $76.

UK authorities drop HP Autonomy fraud probe

The U.K.’s Serious Fraud Office has dropped its investigation into Hewlett-Packard’s allegations of fraud by the management of British big data firm Autonomy, which HP bought in 2011.

In a statement Monday, the SFO said there was “insufficient evidence for a realistic prospect of conviction,” and handed jurisdiction over the case to its American counterparts. There was little detail in the SFO’s statement, as it does not want to undermine the investigations that are still being carried out by the U.S. Securities and Exchange Commission (SEC) and the Federal Bureau of Investigation (FBI).

HP alleged that Autonomy misrepresented its financials ahead of HP’s disastrous $11 billion acquisition, which led to a $5 billion write-down and contributed to the defenestration of HP CEO Leo Apotheker. Autonomy’s former management, led by erstwhile CEO Mike Lynch, strenuously deny the allegations (and set up a website to put across their version of events.)

“As the SFO made clear, the U.S. authorities are continuing their investigation and we continue to cooperate with that investigation,” HP said in a statement. “HP remains committed to holding the architects of the Autonomy fraud accountable.”

HP’s shareholders also sued the company for not performing due diligence on the acquisition and, while those two parties agreed to settle in mid-2014, the courts have repeatedly blocked this settlement for giving HP too much protection from further suits.

This article was updated on January 20th to include HP’s statement.

Managing government-sized IT headaches

Wasteful government spending in IT, like government spending in most areas, has resisted significant reform across generations of both people and technology. And so taxpayers should probably not hold their breath for real improvements in costs, strategy or ethics. But it is interesting and perhaps instructive to look at the dynamics of the sporadic efforts to change, particularly as they involve grappling with new cloud, mobile, social and analytics technologies.

Continuing failures

The Justice Department this week joined a whistleblower lawsuit against CA Technologies that alleges at least $100 million in IT contract overcharges. This is just the latest in a pattern of mismanagement and mis-expenditures that has challenged the U.S. government for decades, and several efforts have been launched to address it. The White House Office of Management and Budget this month enhanced its guidelines for its IT investment portfolio reviews, while the General Accounting Office reported that 30% of the federal IT investments on the OMB’s IT dashboard are classified as needing attention or of significant concern. In a report this week, federal agencies are struggling to meet a 2012 OMB directive to make all email management and records electronic by 2016.

New governance

The House and Senate last month passed the Digital Accountability and Transparency Act (DATA Act) on unanimous votes in the House and Senate. The bill is intended to enforce standardize coding for grant and contract spending as a means to enable more transparency and efficiency in government expenditures.

As in the private sector, governments must find the balance between centralizing for efficiency and management—and allowing departmental control to select and manage the best specialized systems for their needs. The House this year passed the Federal Information Acquisition Reform Act (FITARA), which if enacted would strengthen the role of the CIO with more control over personnel and budget, and direct reporting to department heads or commissioners. Although such direct reporting is one positive step, Simon Szykman, CIO at the Department of Commerce explains a dynamic that any enterprise CIO will recognize: “The answer to how you matter really comes down to senior leadership. I think the CIO matters as much or as little (as) the deputy secretary and secretary and CFO think the CIO matters.”

Not just the feds

Lest anyone believe these failures are endemic to the federal government alone, however, there are states that have struggled with their Affordable Care Act exchange websites even more than the feds have struggled. Oregon’s Governor John Kitzhaber this week announced his intention to sue Oracle over their failed health exchange site, which the state last month decided to move to the federal site. Maryland is rebuilding its healthcare exchange with the software developed by Connecticut. Massachusetts has also decided to scrap its site and start again, perhaps leaning on healthcare.gov for a time; and the state has started to look at its broader pattern of IT contract failures.

Efforts at improvement are global. The Open Government Partnership was last month joined by France as the 64th country seeking best practices and shared standards as part of an international consortium.

More than IT failures

More than massive IT failures, the cost of poor IT choices and execution extends to larger operational and human costs. Earlier this year, the Centers for Medicare and Medicaid Services (CMS) was skewered in an Office of Inspector General (OIG) finding of $75 billion to $250 billion in fraud and waste. Former IBM CEO Sam Palmisano made news in 2010 for reporting an offer to cut $900 billion in fraud in this system, for what was said to be “free”, that the Obama Administration in turn was said to have turned down. In a recent, outrageous example with human cost, the Veterans Health Administration was found this week to have registered only one veteran in an eye injury database for which it received $6.9 million in funding from 2010 to 2014 (while the Department of Defense was able to register 23,663 patients in its database in the same timeframe).

A commitment to the cloud and other new technologies

Still, governments forge on with new IT investments. Cloud, mobile, analytics and social technology are all part of new efforts at innovation.

Salesforce.com this week announced that it has received the authority to operate (ATO) its new PaaS and SaaS Government Cloud offerings under the Federal Risk and Authorization Management Program (FedRAMP). Not surprisingly, Vivek Kundra was Salesforce’s front man on the announcement. This ATO status will ease Salesforce’s adoption in numerous government areas.

Kundra had a decade of local, state and federal tech management experience culminating in a two-and-a-half-year stint as President Obama’s first selection as Federal CIO, where he implemented a “Cloud First” policy to encourage cloud use as part of his broader attempts at reform. (Yes, the path from government procurer and regulator to industries procured and regulated remains well trod.) The big prize to date in cloud, however, has been AWS’s 2013 winning of a $600 million multiyear cloud contract for the CIA that IBM disputed for months afterward.

Beyond its all-too-familiar data gathering on American citizens, the U.S. government this week was reported to have rolled out an analytics-based energy savings pilot.  Massachusetts has funded a joint university-industry Open Cloud Project to support big data innovation. The U.S. Marine Corps is testing the use of mobile data access with commercial providers and equipment in the field. And the police have been stepping up their use of social media in tracking crime.

Much government innovation in IT is currently focused on improved, citizen-oriented, front-end access to government services. Wyatt Kash’s recent article in Information Week, however, quotes one official’s longer-term vision. Dr. YoungSun Lee, the head of South Korea’s National Information Society Agency, expects by 2020 to achieve “ambient intelligence…when all humans and things are connected together:

He foresees open data leading to a shift in the ways government will function: from an era of e-government, where information is delivered to citizens, to one where predictive analysis will foster a “creative government,” in which “government provides customized services for each individual.”


In short, government IT tends to have more corruption, waste, and general mismanagement than enterprise IT. Still, they have many enterprise problems writ large. And we can see the following government challenges that also afflict the enterprise:

  • A tendency to focus more on managing and minimizing IT spending and costs than on the usually larger, attendant, operational and opportunity costs in the suboptimal application of technology.
  • A tradeoff between the well-publicized issues (not reiterated here) of privacy, legality, and the sheer volume of developing government data sources and the emerging new capabilities in well-applied data mining and analysis.
  • A teetering balance between governance that brings needed standardization and integration among technologies used across departments and the flexibility required to optimize technology’s use in special, departmental situations.
  • An immediate emphasis on new front-end, customer-facing applications and capabilities, while increasingly predictive analytics loom as an impending, transformative force.

Like the government, enterprise CIOs would probably be well advised to welcome the transparency and standards that make simultaneously well-managed and more autonomous department-level management feasible. But the management of people is as important as the management of systems, and the larger operational picture is always key to optimizing both spending and new opportunities.