Sections

Cybersecurity: Is it Time To Change Our Mindset?

Mark Testoni

For years, the standard approach to cybersecurity has been to build bigger and bigger walls to “keep the bad guys out.” But as the threat of cybercrime has evolved over time, this approach alone is not enough. Here, we look at the growing cybersecurity challenge and key imperatives facing CIOs.

As the Internet has pervaded all aspects of business and personal life, so has the list of cyber threats that could impact your enterprise. It’s not just rival companies looking to steal ideas. Currently an attack on your network could come from a wide range of sources. Your company could find itself under siege from organized crime, terrorist groups, and even foreign governments.

State and commercial interests are merging, with the networks of private companies now seen as key targets when countries are in conflict. For this reason, many corporations are adopting the same cybersecurity strategies as our national security organizations.

The enemy within

A data breach event could potentially cost millions of dollars, leaving your corporate reputation in ruins. With so much at stake, how do you protect your organization and its intellectual property from all attacks?

This is the challenge. Technological developments have moved so fast in recent years that few networks could ever claim to be 100% impenetrable. And as fast as IT security experts establish barriers to their systems, technologically advanced hackers find ways around them.

Rapid detection, agile response

So, how can commercial companies respond to the evolving cyber threat?

What we need is an entirely new mindset when it comes to cybersecurity. We should assume that hackers can and will access our networks. To complement the evolution of perimeter defenses, we need to shift our focus to detecting and acting on attacks as quickly as possible.

If this approach is to be successful, speed is essential. It is not enough to look in the rear-view mirror to understand what happened yesterday. We need a “front windshield view” to analyze, understand, and respond to threats as they occur.

Revolutionary new approach

With traditional computing approaches, companies simply cannot react fast enough to respond effectively to cyber attacks as they take place. These companies are often only able to determine that a cyber attack has already occurred and attempt to limit the damage to their operations and customers. The prevalence of this can be seen in the number of companies issuing reports about data breaches and offering credit monitoring to their compromised customers. Companies need a way to detect attacks as they are happening, and before the attacker has an opportunity to cause damage.

Sophisticated in-memory computing solutions are enabling this revolution in the way we approach cybersecurity. In an environment where there will never be one, single cyber-product answer, we need to bring the best of all worlds together in an integrated, high-performance manner. For example, with our strategic partners SS8, ThreatConnect, and Babel Street, we are leveraging SAP HANA as a high-performance hub to integrate real-time cyber-situational awareness and threat context. This enables the enterprise to understand the threat, find it, and act on it in real time.

This high-performance computing platform can achieve speeds many thousands of times faster than traditional data architectures. This enables the processing of huge data sets in seconds rather than days and allows analysis at true cyber speed. Companies using this capability can detect and stop cyber attacks while they are underway and before their data can be compromised.

Setting priorities

From the outset, we need to understand that breaches are possible and not all targets can be protected equally. Instead we must identify the high-value targets that are most likely to be attacked and prioritize the areas where a security breach would be most damaging.

For example, finance operations and critical infrastructure are key for most organizations. In addition, personal information is a high-value commodity that cyber criminals are increasingly targeting.

Managing security risk

The Internet has given us the greatest opportunity for economic expansion since the Industrial Revolution. And when you consider the fact that e-commerce accounts for trillions of dollars each year, losses due to security breaches seem minimal.

However, cyber crime is evolving and the threat is growing.

There is no absolute solution or quick fix. The imperative for CIOs is to deploy their available resources effectively to close the aperture of risk as much as possible, and re-evaluate their strategy on an ongoing basis. They need solutions with speed to detect and stop attacks while they are underway. And they must use the latest in-memory technology innovations to stay one step ahead of the cyber criminals.

Threats to your organization can come in many forms, including Supply Chain Fraud: Theft That’s Hidden in Plain Sight.

Comments

Mark Testoni

About Mark Testoni

Mark Testoni is president and chief executive of SAP NS2. He is one of the nation’s leading experts in the application of information technology to solve problems in government and industry, especially in the U.S. national security space. With more than 15 years of IT industry experience, 20 years in the U.S. Air Force, and 30 years of public sector management experience, Testoni is a sought-after business strategist and thought leader, with a proven record of rebuilding under-performing organizations and converting visionary ideas into reality. record of rebuilding under-performing organizations and converting visionary ideas into reality.

Tags:

CIO , cybersecurity

2017 Top 5 Data And Analytics Predictions

Pat Saporito

Here are my Top 5 predictions for the new year.

Prediction #1: Strategy rules

Companies continue to refine and operationalize their digital strategy in support of the overall business strategy. They continue to refine their analytics and data strategies to reflect their digital strategy evolution and move beyond digital pilots to truly digital business models.

Prediction #2: Customer engagement trumps customer experience

Organizations continue to leverage mobile, digital, and real-time data to truly engage customers and business partners. They use gamification and other technologies for true engagement. They partner with digital upstarts and create or extend innovation labs for ideation.

Prediction #3: Data governance gets its due

Data governance finally gets center stage as companies recognize the true value of data quality and understandability for self-service and in increasing customer and partner extended self-service. More complete end-to-end data governance programs are put in place; however, companies right-size governance to focus on priority areas and avoid governance bloat.

Prediction #4: Agility is king, long live the cloud

Companies begin to seriously embrace the cloud for agility for new applications and to extend access to customers and partners. They develop hybrid architectures and find ways to make data accessible both in on-premises and cloud environments.

Prediction #5: AI/machine learning takes hold

Companies move to leverage more automated decisioning and assisted decisioning using artificial intelligence (AI) and machine learning. They develop professional development and incentives to address anticipated cultural resistance to AI. They increase use of business systems analysts to develop business rules to make AI and machine learning a reality.

What do you think? Anything else on your list? Connect with me here, or on Twitter at @PatSaporito

Last year’s biggest trends shine a light on the coming year’s big trends. See More Than Noise: 5 Digital Stories From 2016 That Are Bigger Than You Think.

Comments

Pat Saporito

About Pat Saporito

Pat Saporito is the Senior Director, Global Center of Excellence for Business Intelligence, at SAP. She provides thought leadership to help current and prospective customers leverage best practices in business intelligence, their data assets and SAP Business Objects solutions to improve business performance.

Digital Boardroom Improves Public Transport Insights On Repairs, Delays, And More

Iver van de Zand

The public transport industry is a heavy innovator. Trains and railways are equipped with IoT sensors to measure performance, delays, and incidents. Bus, subway, and train repair, and maintenance costs are intensely watched due to budget pressures. In the meantime, passengers are more critical and demand immediate action whenever downtimes or delays occur.

In various interactions with public transport industry customers, I’ve noticed they are generally well informed regarding delays, have good insights on maintenance and repair, maintain remote visibility on infrastructure incidents, and are equipped with rolling budgeting models. The thing is that these insights tend to be isolated. None of the customers I spoke to has integrated and consolidated insights on them all, so they are like “blind men in the wood” when I ask which unexpected repair incidents affect which train delays.

Challenges

The major areas of interest for public transport companies are:

  • Incidents management: including both infrastructure incidents and incidents in the actual train, bus, subway, etc.
  • Repair costs and maintenance: focusing on supplier management, unplanned downtime, and cost reduction
  • Delays management: tied to narrowing the gaps between planned and actual arrival/departure times, rankings, and geospatial insights

As I said above, today’s key challenge for the industry is not measuring those individual subject areas, rather it is in consolidating and interrelating them. The key is the ability to understand how an issue in one subject area affects an issue in another one, and to what extent.

More challenges

Are we done with challenges? No, we are not. Today’s competitive public transport market requires more than just consolidating insights. It also requires agility, meaning analysts must be able to consolidate insights in real time, to continuously compare their actuals against budgets and forecasts, and even to be able to adjust and simulate them while analyzing. We call this the closed-loop portfolio of monitoring, budgeting, and forecasting. And last, they need instant insights from massive data volumes; remember, they all use IoT devices – and we know what they generate.

So in summary, insights must have:

  • Real-time reporting
  • Predictive-forecast capability
  • The ability to handle massive data
  • Closed-loop capabilities of combining monitoring, planning, and predicting metrics on the fly while analyzing
  • Simulation functionality

Digital boardroom insights

I used the concept of the digital boardroom on top of in-memory platforms to analyze over 30 million records of public transport delays, incidents, and maintenance data. The digital boardroom uses three touch screens to provide insights in real time on the transactional level. This article describes how the digital boardroom works. What impresses me most is its ability to combine versions of actual, planned, forecasted, and predicted data; in other words, I can analyze, adjust, predict, simulate, and re-analyze the end result. Now we’re talking!

It all starts with creating an agenda that lists the various insights required. The agenda items refer to models and stories with real-time insights. You can skip between agenda items with a single click, and every single insight can be further explored with new attributes, filters, or simulations.

Watch the video below for a full overview of using the digital boardroom to address the challenges I’ve described. Please be aware that I anonymized the data though the algorithms, structures, and “cadence” of the data from a real-world example.

Learn more about the digital boardroom for public transport.

Comments

Iver van de Zand

About Iver van de Zand

Iver van de Zand is a Business Analytics Leader at SAP responsible for Business Analytics with a special attention towards Business Intelligence Suite, Lumira and Predictive Analytics.

How AI Can End Bias

Yvonne Baur, Brenda Reid, Steve Hunt, and Fawn Fitter

We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.

Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.

In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.

Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.

That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.

Bias: Bad for Business

When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.

Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.

As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.

Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.

AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.

That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.

At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.

Look for Where Bias Already Exists

In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.

There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.

Code Is Only Human

The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.

“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)

Don’t Do What You’ve Always Done

To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.

SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.

Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.

Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.

Look Beyond the Obvious

AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.

To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.

Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.

That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.

Context Matters—and Context Changes

Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.

Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.

Moving Forward with AI

Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.

In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.

Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.

O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.

As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!

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


About the Authors:

Yvonne Baur is Head of Predictive Analytics for Sap SuccessFactors solutions.

Brenda Reid is Vice President of Product Management for Sap SuccessFactors solutions.

Steve Hunt is Senior Vice President of Human Capital Management Research for Sap SuccessFactors solutions.

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

Comments

Tags:

2017: The Year Businesses Will Learn The True Meaning Of Digital Transformation

Hu Yoshida

Over the last 10 years, the exponential growth and power of technology have brought some fascinating, if not mind-bending, opportunities. Machines talk to one another with computer-connected humans on the other end observing, analyzing, and acting on the explosion of Big Data generated. Doctors use algorithms that mine patient history or genetic information to detect possible diagnoses and treatment. Cars are programmed with data-driven precision to direct drivers to the best-possible route to their destination. And even digital libraries for 3D parts are growing rapidly – possibly to the point where we can soon print whatever we need.

With all of this technology, it is common sense to believe that productivity would also rise over the same span of time. However, according to a recent 2016 productivity report released by the Organisation for Economic Co-operation and Development (OECD), this is, sadly, not the case. In fact, most advanced and emerging countries are experiencing declining growth that is cutting across nearly all sectors and affecting both large and small firms. But more interesting is the agency’s observation that this trend does not exclude areas where digital innovation is expected to improve information sharing, communication, and finance.

See how IT can help organizations shift to real-time operations. Read the EIU report.

Although nearly 5 billion people on our planet have a computer in their pocket or their hands at any moment of the day, our digital ways have not translated into productivity gains for the enterprise. The culprit? Businesses are not changing their processes to allow that technology to reach its full potential.

Technology alone does not bring real digital transformation

Every week, I hear how companies worldwide are so excited about their digital transformation initiatives. Some are developing their own applications or executing a new digital commerce strategy. Others may decide to deploy a new analytics tool. No matter the investment, there is always great hope for success. Yet, they often fall short because the focus is typically on how technology will change the business – not how the enterprise will change to fully embrace the digital innovation’s potential.

Take, for example, a bank’s decision to allow the loan process to be initiated through a mobile app or online store. The bank may receive the information from the consumer faster than ever before, but no real benefit is achieved if it still takes three weeks to approve or decline the loan request. Technology may be changing the customer experience online, but back-office processes are unaffected. The same old ways of work are still happening, and productivity is not improving. For a digital world where everything is supposed to be automatic and immediate, a customer will inevitably turn to a competitor that will approve the loan faster.

True digital transformation requires more than technology. Companies must evolve their processes with a keen focus on outcomes, not just infrastructure. All too often, they are focused on creating this sort of digital facade where it appears to be a digital experience for the customer, but, in reality, the back-office still has not caught up to support that level of digitization.

Deep digital transformation starts with process innovation

In the coming year, most companies will look to transition to real-time analytics that drives predictive decision-making and possibly draw from the Internet of Things. While this technology presents a clear opportunity for greater insight, organizations are no better off unless they transform business processes to act quickly on them.

Traditional data processes require days to move data from one database to another, process it, and generate reports in an easy-to-understand format. In-memory computing accelerates these processes from days and weeks to hours and minutes – paving the way for transformative power by moving decision-making closer to data generation. However, no matter how fast the analysis, no benefit is realized if downstream processes and decisions do not capitalize on the resulting insight. Like the loan process I mentioned earlier, you need to make sure that the back office and front office are aligned in order to produce improved business outcomes. Legacy systems and databases may still hinder the ability to achieve faster results, unless they are aligned with in-memory analytics.

The ability to modernize core systems with technologies like in-memory computing and innovative new applications can prove to be highly transformational. The key is to integrate these new technologies into an overall business architecture to support digital transformation and deliver real business improvements.

Are you ready to transform your business? Learn 4 Ways to Digitally Disrupt Your Business Without Destroying It.

Comments

Hu Yoshida

About Hu Yoshida

Hu Yoshida is responsible for defining the technical direction of Hitachi Data Systems. Currently, he leads the company's effort to help customers address data life cycle requirements and resolve compliance, governance and operational risk issues. He was instrumental in evangelizing the unique Hitachi approach to storage virtualization, which leveraged existing storage services within Hitachi Universal Storage Platform® and extended it to externally-attached, heterogeneous storage systems. Yoshida is well-known within the storage industry, and his blog has ranked among the "top 10 most influential" within the storage industry as evaluated by Network World. In October of 2006, Byte and Switch named him one of Storage Networking’s Heaviest Hitters and in 2013 he was named one of the "Ten Most Impactful Tech Leaders" by Information Week.