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
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.
- Provided actionable insights to reduce the crime rate by identifying the criminal behavior, identify crime/incident patterns, and uncover location-based threats
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.
Conventional Crime Analysis
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
- Dropped the murder and robbery rate of New York by 03% and 04% respectively.
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.
- Cost-effective as compared to the traditional survey-based method
- Increases the speed of data collection to take precautionary measures
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.
- Solve domestic cases faster
- Save more lives and protect the public from critical situations
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
- Prevention of potential traffic problems in dense urban areas by adjusting public transportation routes in real time
- Increased operational efficiency of public bus network
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.
- 100 percent client-to-server visibility to enable seamless
- Seamless information flow for reliable logistics operations
- Identify problems in near real time
Predictive Analytics Software in Government and Defense Quadrant
Find the best Predictive Analytics Software solution for your business, using ratings and reviews from buyers, analysts, vendors and industry experts
- Product Quality and Reliability
- Support for Custom Data Connectors
- Custom Scripting Language
- Deployment Type
- Hybrid (Deployment type)
- Target Users
- Database Administrators
- Business Analysts
- Data Scientists
- Non Technical Users
- Application Developers
- Support for Languages
- Support for R
- Delivery Mode
- Separate Platform
- As a Service / Connector Free
- Add-on Funtionalities
- Machine Learning / AI
- Streaming / Real-Time
- Mobile Support / Mobile BI
- Product Features and Functionality
- Integration with Big Data Frameworks / Data Stores
- Apache Spark
- Enterprise Features
- Analytics Workflow
- Shared Data Sources
- Server Side Data Processing
- Cloud Hosted Data
- Licensing - Data Volume
- Costs & Units
- Cost - $ per license
- Hybrid (Please specify)
- Core Features
- Visual Analytics Design / Code Free
- Data Investigation
- Statistical Modelling
- Times Series Exploration
- Root Cause Analysis
- Advanced Condition Prediction
- Predictive Grouping
- No. of Third Party Data Providers
- Natural Language Processing (NLP)
- Event Detection
- Breadth and Depth of Product Offering
- Data Management
- Data Preparation (Data Management)
- Interactive Visualisation
- Real Time Dashboarding
- Static Visualisation
- Report Generation
- Data Blending
- Report Automation
- Data Collections
- Customer Data
- Transaction Data
- Geo Spatial
- Location [Pincodes]
- Marketing Data
- Use Cases
- Business Intelligence
- Data Visualisation
- Customer Response Modelling
- Demand Forecasting
- Data Preparation
- Operations Management
- Fraud Detection & Prevention
- Pricing Elasticity Analysis
- Location Intelligence
- Risk Management
- Customer Data Platform
- Sales and Marketing Management
- Network Management
- Workforce Management
- Supply Chain Management
- Web and Social Media Management
- Financial Management
- Root Cause Analysis (Use case)
- Predictive Maintenance and Asset Management
- Event Detection (Use case)
- Services Offered
- Support and Maintenance
- Custom Predictive Algorithms
- Requirement Definition
- Managed Services
- Report Authoring
“Navigate through Journey of Hypotheses"
“Worth the price"
“Decision making made easier with this data analysis program."
The software’s ability to organize and use variable for tool application is what works best for me. Evaluation of the behavior of dependent and independent variables for linear regression analysis makes it easy to compile reports, further enabling easier decision making. It is also extremely user-friendly, with each icon distinctly visible. If I had to pick an area of improvement, I would say it is the quality of its graphics. They do not seem very professional and perhaps they can be updated to seem so.
I believe that this is an ideal software for organizations lookimg to systemize its data and work using dependent as well as independent variables. It works excellently to present inferential statistics to help organizations grow. However, if you’re looking for exceptional graphics, then this might not be the one for you.