Disaster relief efforts show promise of analytics and seemingly unrelated data sources

As monsoon season begins, many Nepal earthquake victims have shelter over their heads thanks in part to an unlikely intersection of two SAS global development projects.

The first project is with the International Organization for Migration (IOM). IOM is the first responder to any crisis that displaces people. IOM provides temporary shelter and helps coordinate the efforts of other relief agencies that provide food, clean water, medical care and security.

IOM is currently assisting thousands of victims in the earthquake-ravaged areas of Nepal. SAS is helping IOM analyze shelter data to help better allocate resources, based on the work we did with them following Typhoon Haiyan in the Philippines.

Using SAS Visual Analytics, IOM can see where the high-risk shelters are, based on factors such as:

  • A dangerous mix of overcrowding, unsafe drinking water and solid waste disposal problems.
  • High numbers of families still living in makeshift shelters.
  • Rapid growth of certain vulnerable populations in a short amount of time.
  • Higher concentrations of diarrhea, fever and skin disease among older people.

As new data comes in, new insights are revealed. As you would expect, Kathmandu is the focus of the bulk of relief efforts. However, after visualizing data on young children, it was revealed that a nearby district had more small children, ages 1-5, and in particular, five times the number of infant girls as Kathmandu. This smaller district had a larger need for diapers, formula, children’s medicine and other supplies for nursing moms. These were quick, but important, insights to guide relief efforts.

Concentration of females under age 1 at IOM evacuation centers (click to enlarge)

Concentration of females under age 1at IOM evacuation centers (click to enlarge)

How can global trade data inform disaster relief?

There’s another side to the Nepal data story, though. In April, SAS announced the launch of SAS Visual Analytics for UN Comtrade, which made 27 years of international trade data available using data visualization software. How is this helping with the Nepal earthquake response?

IOM is building temporary shelters for displaced people in Nepal and needed to understand where/how to quickly procure sheet metal roofing (CGI) before monsoon season.  People are sleeping out in the open due to the fear that more aftershocks will bring buildings down on them, so protection during monsoon season is a big concern.

Using UN Comtrade, we were able to show IOM a graphic of the top exporters of CGI. Some of the findings include:

  • Neighboring India is the world’s largest producer of CGI roofing sheets that are wider than 24 inches, but India rarely sells it to Nepal.
  • Nepal is actually the world’s 7th largest producer so historically there’s good capacity for CGI fabrication in Nepal. Consequently, some of the supply can be sourced locally.
  • There are other potential sellers in the region like China (2nd), Thailand (8th) and Vietnam (9th).

    Top exporters of CGI roofing sheets, via SAS Visual Analytics for UN Comtrade (click to enlarge)

    Top exporters of CGI roofing sheets, via SAS Visual Analytics for UN Comtrade (click to enlarge)

Brian Kelly, who is leading the Nepal response for IOM, shared his thoughts. “Shelter is so important to helping the 63,000 displaced families create a level of stability and protection, especially with monsoon season upon us. With the UN Comtrade information, we were able to secure materials more quickly and, literally, put roofs over peoples’ heads.”

A new era of data-for-good

These projects just scratch the surface of what’s possible when new data, and those that know how to use it, are applied to humanitarian needs. Organizations such as DataKind and INFORMS, through its new Pro Bono Analytics program, are rallying data scientists to lend their time and expertise to helping people around the world. And there are many more data sets out there that could help with relief and other humanitarian efforts.

It’s an exciting time to be in the world of big data and analytics. We’re just beginning to understand how technology can tackle society’s “grand challenges.” Please share your ideas on what unlikely data sources might help with disaster relief. And, how can we bring the world’s analytics talent to bear on these challenges?


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Bringing Hadoop into the mainstream

Jim Goodnight, Mike Olson, Herb Cunitz and Jim Davis discuss Hadoop.

Jim Goodnight, Mike Olson, Herb Cunitz and Jim Davis.

Remember when the morning talk show hosts started talking about Twitter? That was weird at first. But now, even your small, home-town news stations have a Twitter handle, and so does your boss, most likely.

“Big data” took a similar route into the mainstream vernacular. At first, we heard pundits saying that only the banks had big data. Or only big government needed to worry about big data. But then, before we knew it, 60 Minutes and The Atlantic were running regular features discussing big data.

I’m not sure if Hadoop will ever hit that level of mainstream attention, but it has become an everyday topic with the leaders I talk to at conferences and customer events.  And the Hadoop naysayers are getting harder to find.

Why is that? Four reasons:

  1. Organizations are seeing Hadoop as more than just a dumping ground for their data. They’re approaching it with strategic business problems and learning how to treat it as an analytics platform.
  2. The early adopters who took the risks with the platform are seeing real results, and now everyone else is realizing it’s time to catch up.
  3. Today’s data volumes make it impossible to ignore Hadoop. We talked about this when discussing the Internet of Things, which is an undeniably huge growing source of big data.
  4. Hadoop is becoming enterprise hardened and easier to implement and maintain. Vendors like Cloudera and Hortonworks are developing ecosystems around Hadoop that improve its stability and offer layers of governance and security that make it a viable option for even the most conservative companies.

Recently at the SAS Global Forum Executive Conference I discussed some of these topics on a panel with SAS CEO Jim Goodnight, Cloudera co-founder Mike Olson and Hortonworks president Herb Cunitz.

Jim was the first to say he's seeing more customers use Hadoop for analytics, and the other panelists agreed, mentioning Hadoop use cases from MasterCard and the Financial Industry Regulatory Authority. Herb and Mike both talked about how the technology that started out in many IT shops is now catching the attention of business leaders too.

Watch the video below to hear us discuss the growing use of Hadoop in the cloud, and learn one thing that Jim says is stupid to do with Hadoop (hint: it involves a straw). Fair warning, if you stop watching too early, you won’t hear why boards of directors are suddenly paying attention to Hadoop now too.

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US Senate takes up fight against patent trolls

med500004 (1)With the recent introduction of the Protecting American Talent and Entrepreneurship (PATENT Act), the US Senate set aside partisan politics to take on a problem that plagues all industries, but especially high-tech.

In front of Congress, in the media and in a previous blog post, I have decried the current patent litigation landscape in the US. Simply put, patent trolls produce nothing, employ practically no one, and yet they threaten US innovation and economic growth.

The number of patent lawsuits is at historic levels, and promising to increase again in 2015. According to United for Patent Reform, an organization of like-minded companies and trade association of which SAS is a member, “The number of patent lawsuits filed in the first quarter of 2015 was up 30% over the number filed in the fourth quarter of 2014. The percentage of those suits filed by patent trolls was also higher in this quarter than in the last (62% vs. 57%).”

Having been the target of such wasteful and frivolous suits, SAS is on the front lines of the battle both in the courts, and in the legislatures seeking a solution to this legalized extortion. I applaud the senators who have taken up this fight.

The legislation, S. 1137, is sponsored by Senate Judiciary Committee Chairman Chuck Grassley (R-IA), Ranking Member Patrick Leahy (D-VT), Senate Majority Whip John Cornyn (R-TX), Sen. Chuck Schumer (D-NY), Sen. Orrin Hatch (R-UT), Sen. Amy Klobuchar (D-MN) and Sen. Mike Lee (R-UT).

The introduction of this legislation demonstrates their leadership in the complex and critical area of patent reform. The bill attempts to protect defendants and consumers from frivolous and damaging lawsuits by clarifying the litigation process, increasing transparency and adding more risk for the plaintiffs, while recognizing concerns raised by other patent stakeholders. The proposed changes will make great strides in protecting American job creators from patent trolls while reaffirming America’s commitment to innovation, entrepreneurship and consumer welfare.

Now that there is meaningful legislation pending in both the Senate and the House of Representatives, I encourage Congress to act quickly in moving the legislation forward to bring common sense back to patent litigation.

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What's your defense against cyberattacks?

461990699Sophisticated cyberattacks are on the rise. And cybersecurity professionals are in demand. There’s a real shortage of talent in both the public and private sector, with a recent Booz Allen report recommending an increase in skills to protect government networks. Likewise, new IDC research sponsored by SAS recommends integrating analytics into the core of your cyber detection efforts.

How is your business tackling this problem from both the human and technology sides? Do you have people and processes in place to protect your assets and your reputation? What's your strategy to detect intrusions in real time?

The way I see it, cybersecurity requires the ability to store tremendous amounts of data, apply advanced analytics to determine when threats are happening in real time, and then immediately take action to take those activities offline.

Let’s look more closely at the problem – and the solution.

Today’s cyber criminals can gain access to your entire network from any single computer or entry point on the network. They can even come in through a contractor who has connected temporarily to your network or through a remotely managed system on the network.

Then, once they’re inside your network, the hackers quietly and methodically work their way through your systems – often for months – before their presence is even detected.

This isn’t a farfetched scenario taking place at some dodgy company in the bad part of town. It’s happening today on the networks of your favorite brands in almost every industry.

The solution is no longer a matter of identifying weak entry points, reinforcing security at the perimeter and stopping the cyber criminals before they get in. You have to assume they're already there.

But how can you find them hiding away in your network? And how can you stop them? You have to analyze the network traffic, compare it to normal traffic patterns and investigate any anomalies. That sounds simple enough, right? Just look for anything out of the ordinary.

The problem is that even an average sized company today sees 100,000 network transactions PER SECOND. Most companies aren't set up to monitor that much traffic, let alone store and analyze it all in real time.

But now you can. With low cost storage options like Hadoop, storing that much data is within reach. And with event stream processing, analyzing all of your network activity on the fly – not after the fact –  is possible too. Finally, with in-memory and visual analytics capabilities, you can see the unusual network patterns and react immediately. Of course, the system also sends out alerts and connects with your existing perimeter defense systems to notify security experts immediately.

This might sound like science fiction, but it’s happening now. In fact, it was unveiled this week at the RSA conference in San Francisco and will be available in the fall.

You can learn more about the evolving nature of cyber threats in this interview with Security Intelligence expert Stu Bradley.

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2Q 2015 Intelligence Quarterly: Digital marketing in the modern era

Intelligence Quarterly 2Q 2015How well do you truly know your customers? Maybe you can identify them on multiple channels, and you know how to cross-sell products in various situations. But do you know your customers better than your competitors do? And do you know them well enough to keep their data safe?

Truly knowing your customers is about more than identity. It’s also about preferences, needs and the contextual knowledge of time and place. In the digital world, customers expect to be known as individuals with distinct preferences, not just as one member of a segment. Understanding digital habits and demographic data is just the beginning.

If you think digitization starts and ends with amassing data, think again. The world is shifting from data being king to knowledge being king. Knowing your customer is powered 10 percent by your internal data and 90 percent by your ability to model the behavior of your customer.

It should therefore come as no surprise that knowing your customer starts with analytics. An analytics factory can help you industrialize the process of customer knowledge, starting with a decision hub that can be used to open up consistent dialogue with customers across all your touch points.

We’ve packed this issue of Intelligence Quarterly with examples of companies approaching it the right way – the customer-centric way. For instance:

  • Trade Me, New Zealand’s largest online marketplace, leads a paradigm shift in how we think about advertising. And data is required.
  • Telenor, a telecommunications leader in Norway, changes its sales model by analyzing mobile data.
  • FANCL, a large Japanese retailer, uses analytics to change the way it communicates with customer.
  • Dustin Group, one of the leading Nordic resellers of IT business solutions, doubles conversion rates with analytics.

If you can’t add your name to this list of companies, it’s time to ask why. What are you doing to please your customers? And how are you gaining knowledge to do that? The knowledge that you derive from customer data allows you to treat customers differently, to anticipate market changes and to meet global expectations.

‘Know your customer’ regulations

As you build your customer programs, also consider the regulatory importance of customer data. Especially in the banking industry, “know your customer” (KYC) initiatives are becoming crucial to verifying and protecting the identity of each customer.

The same data you use to improve digital marketing programs can also be used to prevent identity theft, financial fraud, money laundering and terrorist financing. When considered from this perspective, knowing your customer moves beyond marketing to become a broader corporate initiative. Customer knowledge moves beyond helping the customer find the best product. It can also help all customers and employees feel safe that their data is secure and their investments are protected.

To accomplish KYC, consider whether your customer view is consistent across the house, allowing you to take preventative measures in the front office. Is your data secure, and how much of your IT budget goes into moving and storing transactional data for operational purposes?

Without analytics and data management, your data can be a liability as much as an asset. It is what you do with the data that can determine the risk-weighted performance of the information flowing into your organization. If your company isn’t embracing technology transformation, you won’t be able to address the increased demands from clients or deal with the challenges and disruptions of the digital marketplace.

Following the advice in this issue – and future issues – of Intelligence Quarterly will help you gain knowledge of your customers to simultaneously meet customer needs, ensure that customers are who they say they are, and fully protect the use of customer data. It all starts with analytics.

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Is Hadoop a storage platform or an analytics architecture?

482172147Hadoop is everywhere. It’s changing the way we store and analyze data – and it’s changing the IT landscape.

One of the analysts we work with in the big data space says he’s feeling like it’s 1995 all over again. Why? Because Hadoop is so cheap that people are starting to replicate data again. It reminds him of the early days of the data warehousing craze. Remember that? When replicating data for different purposes was the norm?

Then we hit the 2010 time frame and we were talking about having too much data to store. And definitely too much to copy and collect in multiple systems. Now, with Hadoop entering the mainstream, you can just spin up a cluster, grab the data, make a copy and store it for later.

If you don’t think Hadoop is important to your company or your industry, think again. This is incredibly important to you. There is an opportunity to store tons and tons of data in Hadoop at a fraction of the cost compared to what you’re paying with relational database systems.

Loading data into Hadoop

What did you do in the past if you wanted to capture a bunch of data? You had to call IT, ask to allocate a few terabytes of storage, take these data sources and load them up. Next, you had to return to IT to request access to the data. What’s wrong with that? It’s expensive and time consuming, and it increases license fees for storage and data use.

The alternative is a Hadoop data loading system that makes it easy for data scientists to gain access to data and prep it without an IT request for data management support. Data scientists can play an important role here in reducing the workload for IT and gaining self-service access to Hadoop.

Developing an analytic architecture

What other opportunities does Hadoop create? And how can you make Hadoop successful in your environment?

You need to think of Hadoop as more than a simple storage container. Instead, look at Hadoop as a modern analytic architecture where you can:

  • Load and store data without limiting it to a tabular format.
  • Use visual analytics to explore the data in the location where new data is continually ingested and available for analysis.
  • Persist high-performance analytics right inside your Hadoop clusters.
  • Conduct analytic procedures inside the clusters using in-memory capabilities.

With these options, you can use Hadoop more strategically – and without learning a new programming language. You don’t just store data there and pull it out when it’s time to analyze. Instead, you can send processing requests down to the Hadoop cluster and use in-memory capabilities to analyze the data that is stored there.

Find out more about cleansing, processing and preparing data in Hadoop.

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Event stream processing: a life raft in the data deluge

Niagra Falls, Ontario, CanadaDo you ever have stress dreams? You know, where you’re taking an exam for which you haven’t studied, or you’re forced to wait tables in a sea of angry restaurant customers?

For many of us, the stress nightmare of the modern era involves trying to make sense of a never-ending stream of data. Being asked to make decisions based on a constant flow of ever-changing variables – that’s not only stressful, it’s the downside of the information barrage that is the Internet of Things. Getting it right is tricky – and worse than just being unprepared. It’s like trying to navigate a boat at the edge of Niagara Falls.

That’s an apt analogy because more than 3,000 tons of water crash over those falls every second. The volume of streaming data is even bigger. With the Internet of Things pushing out data from more and more devices, streaming data is transmitted continuously at rates of millions of events per second. Storing this data and then analyzing it later is just not practical in this new world.

Big data, big challenge

When it comes to crunching all that data, and doing it fast, the challenge is immense. The brilliant people in SAS R&D saw what was coming and knew what was needed: software that processes and analyzes data in milliseconds or even microseconds – before it is stored. We call it SAS® Event Stream Processing. To date, this solution has helped many of our customers remain competitive and maximize the value of their goods and services.

One example I like to share is a consumer bank’s fight against fraud. The bank is using SAS Event Stream Processing to detect, monitor and address suspicious transactions and fraudulent behavior in 8 million online banking accounts. With 1 million payments per day, the bank sees an average of 35 transactions per second, with a peak of 230 transactions per second. Thanks to real-time alerting, these suspicious transactions are put into an investigation queue and addressed within 30 minutes. As I’ve noted before, fighting fraud is something that has to happen in the moment. Catch it after the fact, and you’re way too late.

And in some cases, midstream processing is actually saving the world. None of us want to see another offshore drilling accident, right? That’s why a leading energy company is using SAS Event Stream Processing to continuously monitor the performance of its electrical pumps in offshore oil platforms. More than 2 million sensors on hundreds of platforms yield about 3 trillion rows of data per minute. Crunching all that data, the SAS solution was able to predict – and thus prevent – a pump failure, protecting the ocean and saving the customer millions of dollars in the process. I’m proud of that.

Cleaning while streaming

So how do we actually do it? Half the battle is cleaning the data as it’s streaming. Sensors can give false readings, and data can be inconsistently formatted. Normalizing data while it’s flowing is a key part of success because that’s when you’re able to detect patterns and define priorities. SAS Event Stream Processing determines what data is relevant, if and when it needs immediate action, where it should be surfaced for situational monitoring, and where it should be stored for more in-depth analysis. And it all happens in the blink of an eye.

Pretty cool stuff.

The reason I’m so pumped about event stream processing is that the stream is only going to get bigger. Analyst firm Gartner Inc. predicts that the Internet of Things will grow to 26 billion units by 2020 – an almost 30-fold increase from 2009. Not sure what that really means? Consider it this way: We’re already drinking from a fire hose. Soon we’ll be trying to sip from Niagara Falls.

I, for one, am glad we’ve got help.

More on event stream processing

For more information about how SAS can turn streaming data into actionable insight, check out the following resources:

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5 questions to prepare you for the Internet of Things

42-45884927In my last post, I talked about the Internet of Things, and how the data streaming from cars, planes and industrial machines can be used to improve business decisions.

With the number of streaming data sources growing by 20 percent annually, I’m not the first person to notice this trend. As executives in every industry start to see the potential in these data streams, they’re all asking the same types of questions.

Recently, I’ve talked to banking CEOs, telecom CIOs, retail CMOs and heads of government agencies, and the conversations are very similar. Primarily, they’re asking, "How can I use all this data to discover new opportunities?" More specifically, the questions – and basic answers – sound like this:

Q. With all the data out there, how can I store it efficiently?
A. Hadoop.

Q. What if I need the data right away? How can I get it quicker?
A. Streaming data.

Q. Now that I have access to all this data, where do I start?
A. Data visualization.

Q. How can I use this data to discover new possibilities?
A. Advanced analytics.

Q. How can I get my analysis done quicker to get a jump on the competition?
A. In-memory, distributed processing.

Let’s look at each of those answers a bit more closely:

Hadoop. I cannot over emphasize the importance of understanding what you’re capable of doing with Hadoop. The best way to work with Hadoop is to create an analytical platform, so you can do more than just store your data there. You need to be able to access data in Hadoop, run analytics inside the Hadoop environment, inside the cluster, and even run in-memory calculations inside the Hadoop cluster.

Data management. Data management for analytics is not the same thing as data management for an enterprise data warehouse. Analytical data management adds value along the way by completing summarizations and adding metadata to variables before putting them into memory.

Visualization. Visual analytics provides capabilities beyond general business reporting, by giving you a way to explore and understand all your data. Visual statistics takes it even further by making it easy to explore, discover and predict by implementing statistical algorithms without having to write any code.

Advanced analytics. This one is a no brainer. To see real value in your data, you need to move beyond basic analytics to optimization, forecasting, text analytics, event stream processing and more. With a wide range of advanced analytics at your disposal, you’ll reveal optimal and lucrative opportunities, expose risks, deepen customer understanding, and deliver predictive insights.

In-memory, distributed processing. Now that Hadoop is easily available, storing your data is no longer an issue. The real issue is whether you can process it quickly enough. In-memory processing can help you keep up with increasing data demands. You can not only do things quicker but you can do new things -- and change the way you’ve always done things in the past, so true innovation can happen.

Your competitors might be looking at one or two of these areas, but if you can develop a strategy that excels in all five, you’ll operate more efficiently, make faster, smarter decisions, and get big value out of data from the Internet of Things.

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The Internet of Things is not just for consumers

Close up of clothes dryer buttons

Will your dryer be connected to the Internet?

One of the buzzwords we continue to hear a lot about is the Internet of Things. Most of us have heard the term by now, and we’ve read about the consumer benefits of smart thermostats or smart cars that transmit and receive data, and make adjustments automatically. But what are the real benefits here? Is this really just a story about changing the way I heat my home?

Actually, there’s a tremendous amount of data being generated from the Internet of Things, and smart businesses are just starting to realize the potential. At the Consumer Electronics Show in Las Vegas last month, the CEO of Samsung said that 90 percent of what they produce will be connected to the Internet by 2017. It’s not just TVs; it’s everything: home theaters, washers, dryers. Everything that they do. And another couple of years after that, 100 percent of what they produce will be connected to the Internet.

It must be serious business. Because Samsung’s competitors are already being accused of sabotaging their washers in stores. Wow. It is getting tough out there with these devices.

It’s funny, but at the same time, it’s for real. If you don’t think the Internet of Things is for real, think about what Apple has done with Apple Pay, Apple Healthkit and Apple Homekit. People have wondered how Apple will continue to succeed with phone sales alone.  It’s not about the phone. It’s about the ecosystem, and changing the way people do business. Analytics will be a large part of that ecosystem.

The Internet of Things is not just about futuristic and superfluous features on consumer devices. This is something we should all be taking advantage of, and it goes well beyond the realm of the consumer.

You could look at every industry and pick ten examples, but I’ll name just a few.

  • In the oil and gas industry, a modern drilling platform generates 8 terabytes of data a day. They’re obviously monitoring this data in some control room, but what about applying analytics to that data stream? Now you can understand what’s about to happen and predict failures and save a whole lot of money.
  • In the airline industry, the Boeing 787 generates 40 terabytes of data an hour. Every single component is connected to the internal backbone or the network within that airplane. Why is that important? Predictive maintenance is just one example. Airlines can identify potential failures before they occur and improve air safety for everyone.
  • In the automotive industry, connected cars are generating a gigabyte of data a second. What are we going to do with that? There are all sorts of possibilities. Everything from insurance companies understanding driver habits to the part suppliers understanding how the components are performing can be improved. Plus, the manufacturer can use streaming data to work with the consumer to keep everything working optimally from a maintenance perspective.

These data streams are going to change every industry. And “stream” is the operative word. This is not data that you put into a data warehouse to store for future analyses. This is data that you analyze as it flows into the network so you can feed automated decisions back to the devices and to the business.

The Internet of Things will bring streaming analytics and event stream processing into the mainstream. As businesses look at the opportunities in the Internet of Things, they will find that the real advantage comes in the “Analytics of Things.”

Where are you seeing the Internet of Things in your industry? What ideas do you have for analyzing streaming data? You can get more ideas by reading this article about the Internet of Things. Or download an in-depth report about the use of sensor data in multiple industries.

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Q1 2015 Intelligence Quarterly: Doing good with data

Q1 2015 Intelligence Quarterly

There is widespread evidence that big data analytics helps achieve short- and long-term development goals around the world. Poverty, disease, hunger, illiteracy and many other global development challenges all benefit when analytics are applied.

In this blog post, I’ll address not only how to use data for good causes, but also how to protect public data and avoid any misuse or unintended negative uses of personal data. Keeping data safe and secure is a valid concern that cannot be ignored, and we need to address both the good and the bad in our plans for using data to improve the human condition.

What is ‘data for development’?

Data for development is a growing initiative that applies big data analytics to improve policies and infrastructures for health, welfare, safety and security all around the world. For example, can we make cyberspace safe through monitoring and governance? Could we reduce the cost of health care while improving the quality of care and saving lives? Can we make the world safer for our children and especially at-risk youth in our communities?

In the Q1 issue of Intelligence Quarterly magazine, we draw our attention to a subset of the data for development movement and look at the ways big data analytics is used to improve safety and security, including:

  • Preventing cyberattacks.
  • Combating financial crime.
  • Thwarting terror threats.

For these programs to work, public and private organizations need access to data and analytics solutions that can offer greater speed, frequency, detail, accuracy and information sharing.

As these tools become more readily available, we also must mitigate the potential misuse of data. This means balancing the individual’s right to privacy with society’s right to security and safety.

‘Data for good’ must prevent ‘data for bad’

Despite the tremendous opportunity for good, there’s significant potential for data abuse and misuse. There is a widespread lack of trust among governments, citizens, international organizations and the private sector when it comes to collecting and analyzing data. These anxieties stem from many sources, including power asymmetries, a perceived decline of privacy, and the potential for misusing data to violate civil, commercial or human rights.

Establishing meaningful and enforceable protections for collecting, storing, processing and sharing data is essential. But how can we work through the complexity and identify key points of focus for progress? Governments, the private sector and the development community can take action by mitigating concerns in three priority areas:

  • Technology. As billions of sensors come online and collect data, and as we analyze and synthesize more of this data, it is essential to build systems with privacy in mind. This starts with an understanding of how data is generated and how it is often most secure at its source. If we can move the analytics to the data instead of moving the data around to be analyzed in multiple, unsecure places, we can help ensure the security and reliability of the data. Essentially, the data doesn’t need to move; the models do.
  • Governance. International norms should be developed to oversee the quality of data, the open sharing of certain types of data and the protection of data to ensure they aren’t altered by political influence. Monitoring and accountability mechanisms should work effectively with broader data ecosystems, through linked interoperability standards, data sharing protocols and requirements for the data at multiple levels of official use and status. Most importantly, these new governance systems will have to work to earn and maintain the trust of individuals and organizations around the globe.
  • Legislation. Clear, robust policy and legal frameworks also must be developed to prevent the misuse of data. One element requiring continual attention is the degree to which data that has been shared is “anonymized.” Somewhat on the opposite end of the spectrum is the right to be counted. Additional legislative actions might include rights for: identity, privacy, data ownership, due process, participation, nondiscrimination, equality and consent principles.

These three areas are emerging as important starting points for large-scale uses of data for good causes. Balancing trade-offs between the public good and potential harm to individuals is hard work, but it can be done. It all starts with communicating the benefits of using data – combined with technology, governance and legislation – to improve safety, security and development efforts around the world.

Read Intelligence Quarterly to learn more.

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