Seizing the Power

of Predictive Analytics in Government

INDUSTRY PERSPECTIVE

2 | Industry Perspective

Seizing the Power of Predictive Analytics in Government | 3

Most of us have access to more data than we could ever use in a life-time – think about all the statistics, random datasets and social media posts that are at our fingertips. There are literally billions of giga-bytes of data that today, anybody can access with the click of a button.

Making sense of all that information is hard enough for any one individ-ual, let alone an entire organization. And specifically for government agencies, this explosion in data growth is fueling greater demands for solutions that can help them analyze data and ultimately make better decisions. But achieving that goal is no small task.

Consider that unstructured data, which includes Microsoft Word and PowerPoint files, video and audio imagery, social media and more, is expected to take up 79 percent of storage capacity by 2017. Structured data (such as a relational database or spreadsheets), is projected to consume more than 20 percent of organizations’ storage capacity.

Unsurprisingly, these growth pat-terns are posing new challenges for agencies. Some agencies are strug-gling to analyze the influx of new and varying datasets, and others lack the necessary capabilities to do so. Even groups with the proper tools are overwhelmed because IT de-partments can’t keep pace with the current demands for data analysis.

Complicating matters further, the scale and growth of big data challenges often leads agencies to seek custom-built solutions, but not everyone is capable of pursuing this option.

“Many solutions are large and customized, and most organizations are overwhelmed by the rich diver-sity of approaches in technologies and solutions in this space,” said Clint Green, Director of Advanced Analytic Strategy and Development at ViON. “It can be difficult to find an easy place to start and to find success in big data and analytics.”

Organizations know they should be using big data analytics, but many don’t know how to go about this process. That’s why GovLoop partnered with ViON to produce this report and help agencies establish building blocks for a solid big data analytics program to demystify the benefits and challenges, particularly when it comes to using predictive analytics. The real benefit for agen-cies is the power to prepare and plan for future needs and events, based on historical data.

But before we get into the meat of this report, let’s briefly review some common big data buzzwords. You’ll often hear big data defined in terms of the three Vs: volume, variety and velocity. These are all real challeng-es that agencies face with their data, so we’ve elaborated on each of the Vs and included some helpful tips to keep in mind.

Volume: Contrary to popular belief, the size of a database or the number of data points is not the only important measurement of the volume. Instead, it is also critical to measure how much of the collected data is being used in the analytics.

Variety: Big data is often diverse, not only in the types of data that make up the collection but also in the numerous sources of data. It is just as important to track the amount of structured and unstructured data that’s stored as the locations of origin for each piece of data.

Velocity: For big data analytics proj-ects, there are three types of speed to consider: the pace at which data is processed, how quickly analytics are being conducted and how fast the end user receives the analysis.

An important fourth category to keep in mind is complexity. For ex-ample, an agency with 100 terabytes of data may not appear to have a big data problem. But if that data is scattered across 50,000 separate databases that need to be com-bined, the real challenge becomes navigating the complexity of the different data types.

But establishing your need for a big data solution is only part of the pro-cess. In the following section, we’ll guide you through the next steps to build your data analytics capabilities.

Executive Summary

4 | Industry Perspective

The data analytics landscape is rapidly evolving, thanks to more powerful and affordable tools for data gathering and processing. These tools are helping agencies make sense of their data and use that information to save lives, im-prove efficiencies, reduce costs and effectively fulfill their missions.

To better understand the use cases for data analytics, let’s briefly review the four distinct types: descriptive, di-agnostic, predictive and prescriptive analytics. As you can see in this chart, analytics helps agencies determine what happened; why it happened; what will happen; and how they can make it happen. Notice that the level of difficulty and value increases as you move from descriptive up to prescriptive analytics.

Most agencies are not at the pre-dictive level yet, but they are eyeing how to evolve from simply knowing what happened in the past to de-termining what may happen in the future. For that reason, we’ll spend

most of this section discussing the truths and myths about predictive analytics and what those realities mean for agencies.

“Some people’s expectation of predictive analytics is that it can somehow help them make a defin-itive statement about the future,” Green said. “With predictive an-alytics, you’re actually measuring the present against the past and understanding the likelihood of that event or outcome conforming to what has been seen before. Peo-ple who expect magical outcomes and the ability to predict the future are sometimes let down by the fact that a predictive analysis delivers the likelihood of something hap-pening as expected or not, based on previous observations.”

We can all agree that it’s hard to say with certainty what will happen a year from now or even a month from now. But agencies can use the data they have to better predict the probability of an event or action oc-

curring. That’s the power of predic-tive analytics. It enables agencies to make decisions earlier on with less data, based on their past knowl-edge. For example, law enforcement agencies are starting to use predic-tive analytics to determine where crime is likely to occur. Rather than waiting to evaluate crime stats over a given month or year, they can quickly analyze data on a daily basis to make decisions on where to send officers during different shifts.

The possibilities for using predic-tive analytics really are endless, but agencies must ensure they have the proper foundation in place.

Laying the Foundation for Predictive AnalyticsThe journey to predictive analytics

adoption requires exper-tise and measured expec-tations internally of how it will be used in the agency.

“For an agency to hope to come right in at the predictive level would be a mistake,” Green said. “This is where the magic is most often sold to organi-zations, and this is where the disappointment is most often going to be felt. Some organizations attempt to come in at the predictive level without the needed infrastructure and experience.”

Before an agency can understand what may happen in the future, it needs historical context about what has happened and why. These insights come from descriptive and di-agnostic analytics, which are building

Predictive Analytics: Measuring the Real Value

DESCRIPTIVEANALYTICS

What happened?

DIAGNOSTICANALYTICS

Why did it happen?

PREDICTIVEANALYTICS

What will happen?

PRESCRIPTIVEANALYTICS

How can we make it happen?

D I F F I C U L T Y

I N F O R M A T I O N

O P T I M I Z A T I O N

VALUE

Using Google Analytics to track website traffic, such as page views and number of visitors

Mining data to determine what caused a spike in Web traffic over the past month

Using algorithms and data to predict where burglaries will most likely occur in a city

Rerouting travel, based on traffic predictions and pedestrians’ actions (think self-driving cars)

Source: Gartner & GovLoop

Seizing the Power of Predictive Analytics in Government | 5

Moving from Development to DeploymentImplementing a new technology or feature can be both exciting and scary. There’s excitement about the new capabilities that tools like data analytics can offer, but there are also concerns about the unknown, espe-cially when it comes to security and integration with existing IT systems.

To ease those fears, agencies often use research and development labs, or sandboxes, to work out the kinks. The pitfall for some is that they remain in that stage indefinitely and never take the leap to actually roll out the capability into production for the larger user base.

“Everybody wants to go toward this idea of innovative technologies, but some will spend years and years in a research and development stance and never take the lessons they learned and the technologies that they’ve gained and put them into an operational environment,” Green said. “The other big problem is whenever you’re in that kind of test environment, you never reach the scale that you’re going to need to be in the production environment that serves your business needs. So while functions may work in a lab environment, they won’t necessarily scale to a production environment.”

Although the sandbox environment serves a valuable purpose, it was never meant to be a final destina-tion. With that in mind, how can agencies effectively transition from the sandbox to an operational envi-ronment?

“If you’re risk-averse, you’ll never want to get out of that sandbox until you’re perfect,” Green said.

But don’t let the need for perfec-tion sabotage the good work and insights you’ve gleaned during the testing phase. The key is to identify the risk, determine how it can be mitigated and move closer toward an operational environment.

How to Achieve Full Operations FasterThe question agencies must answer is how much risk can they mitigate in a short period of time in the sand-box environment before moving to an operational environment. One way to achieve full operations faster is by using trusted infrastructure that is ready for use, rather than spending resources to build that environment on your own.

“At ViON, we try to get you looking at your data as soon as possible, so that you can get the value out of it without spending the time and effort trying to stand up a big foundation,” Green said. His ad-vice to agencies: Don’t spend your time trying to be the expert on the hardware and software, when you’re already the expert on the data.

When investing in infrastructure, agencies should ensure that it’s scalable and can accommodate their evolving needs. Doing so will free up analytics developers, do-main experts and data scientists to spend less time on labor-intensive infrastructure tasks and more time cleansing and analyzing the data.

The key is to remember that you’re building a big data analytics environ-ment for the real world and not for research and development. That’s why establishing a strong founda-tion is vital. In addition to having the right infrastructure in place, agencies should also prioritize time to clean and understand their data and look for gaps in the data on a continual basis.

blocks for predictive analytics. Green often refers to predictive analytics as the Holy Grail because agencies say they want it, compa-nies say they can provide it, but there’s often a gap between what’s promised and the reality of what agencies actually get.

The tools aren’t the issue. Similar to cloud computing solutions and oth-er technologies, data analytics can provide great value to organizations and decision-makers. But adopting

these innovative tools and tech-niques requires a foundation and a plan for moving forward.

“It’s not just about having your data organized and accessible,” Green said. “It’s also about having an orga-nization that understands the values of its decision-making process and can capitalize on better ways of doing things.”

As organizations become more comfortable with understanding what has happened and why, they can start to make decisions more quickly on what will likely happen and ultimately improve response times.

Now that we’ve reviewed some of the myths and truths about predic-tive analytics, let’s tackle another common barrier that agencies face: moving from the research and development phase of analytics to a production environment.

6 | Industry Perspective

The path to embracing analytics won’t be without challenges, which is why Green recommended that agencies take a crawl-walk-fly approach and build on lessons learned. Here are a few tips to keep in mind as you start your analytics journey and progress to more advanced capabilities:

Ensure you have the right resources & big data infrastructure to support evolving analytics needs.

Make time to collect, cleanse & organize your data on a regular basis.

Start with the basics. Before you try to pre-dict what will happen, understand what has happened in the past.

Be patient. Initial re-turns may take time, but analytics will produce exponential returns.

Research and development envi-ronments are a great place to start crawling, work through barriers and challenges and replicate a real world operational environment. There are risks and concerns that come with moving tools from a development phase to full production, whether it be integration and security concerns or a lack of historical precedence to guide the work, but agencies don’t have to let these roadblocks stop them from moving forward.

Where feasible, determine how certain risks can be mitigated and what measures can be adopted to ensure analytics tools and data processes meet federal security

requirements. Having the right hardware and software in place can help to resolve these issues, leaving more time to focus on analytics work and outcomes.

“We provide that platform that allows the advanced analytics to be run in a much timelier fashion,” Green said. “You can drastically reduce the time it takes to reach the fly phase of analytics by using a proven solution from the start to build that base foundation and infrastructure.”

Building the proper foundation is key to the success of any data analytics program. Agencies must ensure they have the infrastructure, tools and talent in place to answer basic questions, such as what happened. From there, they can start to understand why a certain event happened and eventually predict what may happen in the future.

It can be tempting to bypass the foundational steps and pursue the ben-efits that come from high-value, high-difficulty analytics, such as predictive analytics. But the end result for many agencies that take this route is disap-pointment and unmet expectations.

“You can’t get to step two without having done step one, which is to collect and organize the data, and then build your analytical capabilities,” Green said. “They build on each other. People who think that they can only invest in one thing fail to understand that a more robust descriptive analytic sub-system delivers a higher-performing predictive analytic system.”

Mapping Your Analytics Journey: Crawl, Walk...Fly

Conclusion “You can’t get to step two without having done step one, which is to collect and organize the data, and then build your analytical capabilities. They build on each other. People who think that they can only invest in one thing fail to understand that a more robust descriptive analytic subsystem delivers a higher-performing predictive analytic system.”

Clint Green, Director of Advanced Analytic Strategy and Development at ViON

Seizing the Power of Predictive Analytics in Government | 7

About ViON

About GovLoop

Designing and implementing innovative solutions that meet dramatical-ly changing IT requirements is ViON’s mission. Founded in 1980, we’ve grown from a small product reseller into a leading systems integrator delivering customized solutions and best of breed offerings from the world’s premier OEMs to large public and private organizations.

Known for our engineering expertise and exacting standards, ViON ensures that only those with the highest level of training, experience and industry certifications design, install, maintain and support our breadth of solutions.

We focus on data management, so you can focus on your organization’s success. We’re on the leading edge of Big Data and Cyber Analytics, Cloud, Video Surveillance and Storage. ViON’s cloud-based “as a Service” Program Management Office delivers direct access to the technology you need for today and tomorrow.

From the data center to the cloud, let ViON’s passion for innovative solutions secure the competitive advantage required for your enterprise. Visit ViON.com for more information.

Big Data and Cyber Analytics Solutions: vion.com/dataadapt

GovLoop’s mission is to “connect government to improve government.” We aim to inspire public-sector professionals by serving as the knowl-edge network for government. GovLoop connects more than 250,000 members, fostering cross-government collaboration, solving common problems and advancing government careers. GovLoop is headquar-tered in Washington, D.C., with a team of dedicated professionals who share a commitment to connect and improve government.

For more information about this report, please reach out to info@govloop.com.

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