GOVERNMENT AND DEFENSE

The government and defense segment covers all the government organizations and law enforcement agencies. This sector has to deal with various issues such as tax collection, public interest, safety, and education. With the growing importance of mobile, social media, analytics, cloud, and web over the past few years, the government sector has started witnessing an increasing pressure from other industry verticals to adopt newer technologies. In addition, government sectors are investing extensively in smart technologies for developing smart cities. Due to this, huge amount of data is being generated from various data sources such as sensors, cameras, and mobile devices.

Predictive analytics can help the government sector to lower the total cost of ownership and achieve efficiency. The industry has employed advanced analytical solutions with an aim to get actionable insights out of the massive chunk of data from various sources. Various countries are utilizing the benefits of analytics to optimize the governmental workflow. For instance, the US government has improved its defense and homeland security by using the capabilities of predictive analytics. For homeland security, the predictive and diagnostic analytics have become important, especially for military operations, counter terrorism, and disaster management. Furthermore, predictive analytics has found its applications in tax departments, state transportation, health departments, and public security. For instance, tax department deals with large volumes of transactional data. With predictive analytics, tax department can have a deep investigation into data to predict potential tax evasions. , Additionally, government and defense industry is gearing up for advanced analytical applications, which is expected to give this industry real-time insight about the demography of the population and predictions for national threats. The data can be managed and analyzed efficiently by government and defense bodies to prepare effective strategies. Infusing predictive analytics in these industries is expected to fix priorities and give a broader insight of the developments to authorities at local level such as municipalities.

COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY

The vendors of predictive analytics software in government and defense are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 20 vendors evaluated in the data quality tools market include Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, IBM Corporation, Information Builders, Inc., Knime Ag, Microsoft Corporation, Microstrategy Incorporated, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, SAP SE, SAS Institute Inc, Sisense, Tableau Software Inc and Teradata Corporation

Use Cases of Predictive Analytics in Government and Defense

  • Home Land Security: One of common element of predictive analytics, is the use of geospatial data, which identifies the areas of likely crime based on upcoming criminal data or scheduled events. The use of geospatial data permits resources to be better assigned to those geographies that have the highest likelihood of crime; hence, geospatial analysis provides law enforcement agencies to take a rapid response plan to mitigate a threat as soon as it is detected.  Another use case of predictive analytics is to detect various crime types, such as stalking, thefts, and assaults. The use of predictive analytics in these types of scenarios uses directly for a homeland security situation where threats can be perceived as terrorist based in nature. For homeland security initiatives, data gathering is of the utmost importance, and predictive analytics helps to identify patterns and make proper response to viable threats and better safeguard the nation against terrorist-related threats.

  • Law Enforcement: The law enforcement officers and the local police are facing a quite lot of challenges these days due to increase in crime rate day by day. Hundreds of police agencies are trying their level best to technology to capture and put a bar on this issues taking place around the world. Hence, there is an increase in demand for predictive analytics technology in thwarting criminal activity. The predictive analytics software helps to target investigations precisely, forecast criminal activities, properly deploy and allocate resources available, while ensuring the safety of officers and public.

  • Public Health Safety: Predictive data analysis capabilities has now reached to public health ministry to support quality clinical improvement. Predictive analytics is used to predict the prevalence of the diabetic condition accurately and help design effective public health policies. The launch of Virtual Diabetes Register (VDR) (data analytical tool) combines and filters various sources of health information to more accurately predict the number of people those are diagnosed with the condition, as well as dictating persons who are likely to develop it in the future.

 

Case Studies of Predictive Analytics in Government and Defense

MapR

Converged Data Platform: Government agencies are actively deploying the MapR Converged Data Platform to analyze fast-growing data in a more cost-effective method for understanding criminal behavior, identifying crime/incident patterns, and uncovering location-based threats.

Case Study: MapR helps Financial Crimes Enforcement Network (FinCEN), a bureau of the US Treasury Department for crime prediction and prevention.

MapR has provided converged data platform capabilities such as machine learning and anomaly detection for pattern identification in a bid to reduce the crime rate. MapR analytics solution analyzes bank transactions on large-scale bases to reduce domestic and international money laundering, terrorist financing, and other financial crimes by supporting local agencies such as police departments. MapR provides bank transaction intelligence in real-time basis to understand criminal behavior, identify crime/incident patterns, and uncover location-based threats.

Business Outcome:

  • Provided actionable insights to reduce the crime rate by identifying the criminal behavior, identify crime/incident patterns, and uncover location-based threats

 

IBM Corporation

Case Study: New York Police Department (NYPD) used IBM’s Predictive analytics solution for proactively identifying crime hotspots.

The New York Police Department (NYPD) deployed IBM’s predictive analytics solution to capture and connect the dots of crime-related information. With the help of this solution, the NYPD department was able to recognize crime patterns through computational analysis and obtained insights that helped commanding officers proactively identify hotspots of criminal activity. It also helped in the effective deployment of manpower and resources to target the threats.

 

Problem

Conventional Crime Analysis

Predictive Analytics

Using historical crime data

Mapping of crimes (identification of hotspots)

Advanced hot spot identification models, risk terrain analysis

Determine when areas will be at most risk of crime

Graphing/mapping frequency of crimes in a given area by time/date (or specific events)

Spatiotemporal analysis methods

Identifying geographic features that increase the risk of crimes

Finding locations with the highest crime instances and drawing inferences

Risk-terrain analysis

 

Business Outcome:

  • Dropped the murder and robbery rate of New York by 03% and 04% respectively.

 

SAS

Case Study: The New Zealand's Ministry of Health used SAS analytics solution to predict the potential number of diabetes cases in near future.

The Ministry of Health and the experts from the New Zealand Society for the Study of Diabetes (NZSSD) collaborated to establish a Virtual Diabetes Register (VDR). The register collects and combines health data from varied sources and filters it for a more accurate prediction of the number of people who are likely to catch diabetes in the near future.

Business Outcome:

  • Cost-effective as compared to the traditional survey-based method
  • Increases the speed of data collection to take precautionary measures

 

LexisNexis

Case Study: The Florida Department of Law Enforcement (FDLE) used an advanced analytics solution by LexisNexis to strengthen the public safety and domestic security.

LexisNexis has provided its advanced analytics solutions to the Florida Department of Law Enforcement (FDLE) to enhance public safety and domestic security. This solution helps FDLE conduct independent investigations and multi-jurisdictional as well as special criminal investigations in coordination with local, state, and federal authorities to focus into four key investigative areas: major drugs, violent crime, public integrity, and fraud/economic crimes.

Business Outcome:

  • Solve domestic cases faster
  • Save more lives and protect the public from critical situations

 

MapR

Case Study: MapR is assisting local US government agencies traffic (operational) optimization

Local US government agencies are actively deploying the MapR Converged Data Platform to help in aggregating real-time traffic data gathered from road sensors, GPS devices and video cameras, which subsequently enables traffic managers in identifying potential problems in a public bus network

Business Outcome:

  • Prevention of potential traffic problems in dense urban areas by adjusting public transportation routes in real time
  • Increased operational efficiency of public bus network

 

Splunk

Case Study: Splunk helped OOCL to address operational risks

Splunk helped OOCL to optimize operations with predictive tools to identify business risks backed by full visibility into daily operations and end-to-end business service levels. The analytics tools were also user to monitor client application performance and identify any bottlenecks.

Business Outcome:

  • 100 percent client-to-server visibility to enable seamless
  • Seamless information flow for reliable logistics operations
  • Identify problems in near real time

 

Frequently Asked Questions

  • How will the Predictive Analytics Market perform in near future?
    The predictive analytics market size is expected to grow from USD 4.6 billion in 2017 to USD 12.4 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.17% during the forecast period. Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services.
  • What are the opportunities in the predictive analytics market?
    With the rise in touchpoint and the need for collecting data to understand consumer behavior, every touch by a consumer has become an important data point that can be processed to reveal user behavior. With the exponential rise in individual and organizational data, businesses are now deploying teams of data scientists and analysts to process the collected data. Another factor accelerating adoption is the revenue generating potential of predictive analytics. This is compelling firms to invest in predictive analytics.
  • What is the competitive landscape in the market?
    The predictive analytics ecosystem comprises vendors, such as Alteryx, Inc. (US), AgilOne (US), Angoss Software Corporation (Canada), Domino Data Lab (US), Dataiku (US), Exago, Inc. (US), Fair Isaac Corporation (FICO) (US), GoodData Corporation (US), International Business Machines (IBM) Corporation (US), Information Builders (US), Kognitio Ltd. (UK), KNIME.com AG (Switzerland), MicroStrategy, Inc. (US), Microsoft Corporation (US), NTT DATA Corporation (Japan), Oracle Corporation (US), Predixion Software (US), RapidMiner (US), QlikTech International (US), Sisense, Inc. (US), SAP SE (Germany), SAS Institute, Inc. (US), Tableau Software, Inc. (US), TIBCO Software, Inc. (US), and Teradata Corporation (US). The exponential growth in data volume is due to the expansion of businesses worldwide, which is driving the rise in data volumes and sources. The accumulation of big data in a single location has rapidly developed the evaluation capabilities of data science experts in every organization. Additionally, companies prefer to provide stand-alone solutions rather than combined solutions. This is eventually resulting in a rise in the number of big data analytics startups, which are driving noteworthy innovations.
  • What are the regulations that will impact the market?
    Predictive analytics leads to ad hoc analysis, which assists companies to have all workable solutions for their business specific questions and forecast past, present, and possible future predictive scenarios. In the current competitive business scenarios, companies need more than accurate predictive statements and reports from its predictive analytics. Companies now need more forward-looking, predictive insights that can help them shape impactful business strategy and improve the day-to-day decision-making in real time.
  • How are mergers and acquisitions evolving the market?
    July 2017, SAP collaborated with energy and services company Centrica to help their customers in managing assets and energy consumption on insights available through the IoT. February 2017, Oracle announced the expansion of its IoT portfolio with the introduction of 4 new cloud solutions to assist businesses to fully utilize the advantages of the digital supply chain. By applying advanced predictive analytics to devise signals, IoT applications can help in automating business processes and operations across the supply chain to enhance customer experience. March 2016, the company has extended their strategic partnership to offer combined capabilities of cloud analytics and big data to their users. This will help users to automate and simplify the decisions while attaining greater business insights for smarter business decisions.
  • What are the dynamics of the market?
    Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services. Globalization and economic growth are also playing major roles in driving greater data generation worldwide. Also, the rise in connected and integrated technologies has provided a platform to predictive analytics software vendors for leveraging this development and the unprecedented growth of the internet. Additionally, the eCommerce sector has modified the traditional shopping behavior of customers. Dedicated email campaigns, online/social media advertising, and cognitive analyzing of customers are the key enablers driving sales and increasing customers’ loyalty. With connected devices coming to the forefront, retailers are focusing on real-time analysis of customers’ shopping behavior and market basket analysis for analyzing consumers’ perception, which can be used for building tailor-made offers to increase customer retention. Similarly, with the rise in the global IoT analytics demand in the retail sector, the market is expected to have unprecedented growth opportunities for predictive analytics.

Predictive Analytics Software in Government and Defense

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SPSS Modeler reduces the complexities involved in the transformation of data by providing easy-to-use models. SPSS Modeler is extensively used across various languages to analyze data from multiple databases. It majorly helps in analyzing data to predict customer churn rates and data sets. The application can be used across various industry verticles.
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Rapid Miner Studio enables users to create complex predictive models by using a drag and drop visual interface. It has a library of 1500+ machine learning algorithms and functions that can be used to build models specific to any situation. It also offers templates for common cases such as prediction of customer churn, fraud detection, predictive maintenance, etc. It provides proactive recommendations at each step for guidance. Rapidminer helps increase productivity across teams.
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SAP Predictive Analytics software enables users to create, deploy and maintain various predictive models. These on-premise tools can help users anticipate future behavior and outcomes and better guide the decision-making ability to help grow the business. SAP Predictive Analytics Cloud works alongside the BI and planning tools to visualize, plan and predict context. The tool uses in-memory technology and machine learning to uncover relevant predictive insights in real-time.
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ORACLE Analytics Cloud’s Database Platform allows the use of seamless predictive analytics software within the platform, giving it an edge over other vendors. ORACLE Analytics Cloud helps mine various data types, eradicate movement of data, and deliver actionable insights. Application developers deploy this analytics model along with SQL and R functions. ORACLE Analytics Cloud helps predict the behavior of customers, the gap between the demand and supply, and make better marketing strategies.
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Angoss uses data and predictive modeling to present insights that help users make better decisions faster. It uses advanced statistical algorithms for the prediction of outcomes. These outcomes are generated across all stages of model cycles. It helps improving predictive analytics for organizations looking to monetize their data.
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SAS Advanced Analytics provides users with better response time and faster insights provided by its in-memory analytics. SAS Advanced Predictive Analytics software helps organize data in a structured manner, making it easy to understand and present. It enables the user to analyze past, present, and future models using quality-tested algorithms. Automation of large-scale forecasts is also possible without the need for high levels of technical knowledge.
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Use the full potential of information to unleash the capability of the human resources of an organization.
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Information Builders WebFocus RStat is a cost-effective, robust, intuitive, and accurate predictive analytics software. WebFocus can help organizations by extracting meaningful insights from data of any kind. It creates interactive dashboards to consolidate information which increases the chances of actionable insights to be used in the everyday conduct of data-driven businesses.
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FICO Decision Management Suite is an integrated environment for development that is compatible with web as well as mobile applications. It is a platform that handles real-time streaming of data including its visualization, indexing, search, and pre-processing, based on rules that are defined in advance. The company's USP is the ability to provide models based on precise customer requirement to reduce time and cost.
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MicroStrategy’s features and algorithms provide enterprises with advanced predictive analytics capabilities. MicroStrategy is useful for deploying models with governed data. It integrates seamlessly with R and can be connected to any source with the use of APIs. Some of its important features include: Scalable integration with R, incorporation of statistics, ARIMA, etc. and minimal IT support required
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Rapid assembling of predictive data that changes crude information into a business affecting service. This product has advantages for all types of users: analytics leaders, data scientists, IT professionals, and business analysts. It helps analytics leaders in terms of managing productivity, collaboration, coordination, and measuring team growth. Data scientists benefit in terms of automation, modelling, flexibility, and reproducibility. IT professionals gain advantages pertaining to scalability, code & integration, operationalization, and data governance; while business analysts obtain data access, preparation, exploration, and automated ML benefits.
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GoodData is a cloud-based platform with high SLA availability and maintenance. It allows for easy incorporation of already existing data warehouses. It allows the platform to be integrated into web or mobile applications. It is one of the most dominant cloud data warehouse that meets most versatile analytics platform requirements.
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Extensive set of cloud services that enables associations to address business problems, construct, oversee, and convey applications on a massive, worldwide system utilizing various tools and frameworks. Microsoft Azure ML Studio can be used to prepare and manage the data they need for machine learning. It can improve productivity through its powerful capabilities that can integrate the current model cycle with that of the app lifecycle.
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Natural language search and AI-powered bits of knowledge discovery make creating bits of knowledge a characteristic, instinctive, and intuitive experience. Spotfire has strong built-in predictive analytical methods that are smart, yet easy to use. Its intelligent data wrangling helps you clean and modify data, and auto-records it so you can edit it later as well. It is flexible and can scale secured documents as well.
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NTT Data offers effective solutions that supplement the decision-making process in an organization. This is possible across multiple business platforms and across different development and deployment capabilities. With the help of a comprehensive analytics and business insight methodology, NTT Analytics Solutions can change a client organization into an information-driven pioneer.
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Teradata predictive analytics software provides “Flip the Switch” analytics which allows on-the-fly switching of best campaign users from reverse modeling to forward prediction. Teradata Analytics for Enterprise Applications eradicates the complexity of enterprise application integration, delivers real-time access to integrated data from ERP and other enterprise applications, as well as provides transparency and visibility into business and customer insights.
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KNIME is an open source software and it helps create data science applications and services. Being open, intuitive, and able to integrate new developments, this platform makes reusable components accessible to all the users and helps understand data science workflows. The software provides actual data analysis as well as a number of processes and has productivity funtions to help operations.
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Sisense makes the analytics process easy for users right from the preparation of data to the creation of insights. Sisense is an intelligence software known for its agility and easy implementation. It can be used by varied companies. This platform offers a range of business analytics features. It is designed to make complex data preparation and visualizations simple to make better business decisions and intelligent strategies.
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Domino delivers predictive models using ML and AI techniques capturing all the dependencies of experiments. It is perfect for models across cloud databases as well as distributed systems. Powering model-driven organizations to rapidly create and convey models that drive business impact.
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