The growing number of high-tech crimes - cyber-based terrorism, espionage, computer intrusions, and major cyber fraud - poses a real threat to every individual and organization. To meet the security challenge, businesses need to augment and enhance cyber security and intelligence analysis platforms with big data technologies to process and analyze new data types (e.g. social media, emails, sensors, Telco) and sources of under-leveraged data. Analyzing data in-motion and at rest can help find new associations or uncover patterns and facts to significantly improve intelligence, security and law enforcement insight.
Enhanced intelligence and surveillance insight. Analyzing data in-motion and at rest can help find new associations or uncover patterns and facts. This type of real or near real-time insight can be invaluable and even life-saving.
Real-time cyber attack prediction & mitigation. So much of our lives are spent online, and the growing number of high-tech crimes, including cyber-based terrorism, espionage, computer intrusions, and major cyber fraud, pose a real threat to potentially everyone. By analyzing network traffic, organizations can discover new threats early and react in real time.
Crime prediction & protection. The ability to analyze internet (e.g. email, VOIP), smart devices (e.g. location, call detail records) and social media data can help law enforcement organizations better detect criminal threats and gather criminal evidence. Instead of waiting for a crime to be committed, they can prevent them from happening in the first place and proactively apprehend criminals.
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perations Analysis focuses on analyzing machine data, which can include anything from IT machines to sensors, meters and GPS devices. It?s growing at exponential rates and comes in large volumes and a variety of formats, including in-motion, or streaming data. Leveraging machine data requires complex analysis and correlation across different types of data sets. By using big data for operations analysis, organizations can gain real-time visibility into operations, customer experience, transactions and behavior.
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Data Warehouse Modernization (formerly known as Data Warehouse Augmentation) is about building on an existing data warehouse infrastructure, leveraging big data technologies to ?augment? its capabilities. There are three key types of Data Warehouse Modernizations:.
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With the onset of the digital revolution, the touch points between an organization and its customers have increased many times over; organizations now require specialized solutions to effectively manage these connections. An enhanced 360-degree view of the customer is a holistic approach that takes into account all available and meaningful information about the customer to drive better engagement, more revenue and long term loyalty. It combines data exploration, data governance, data access, data integration and analytics in a solution that harnesses the volume, velocity and variety.
For Telecommunications service providers, customer loyalty is key to revenue. With deregulation, its all about knowing who your customers and prospects are, attracting new ones while keeping the customers you have and making sure your network is optimized to perform best for your subscribers at the least cost.
Customer analytics is at the heart of understanding just who your customers are, what is their behavior and preferences and how you can find new revenue in both existing and new customers. To do this requires the integration and analysis of data both inside and outside the business. In addition to subscriber data, data on network usage, device usage, social media and even call center detail records are critical in getting the most complete view of customer behavior and usage. With the requisite data and the correct analytics, you can get an accurate view of which customers to target, what their preferences are and how best to reach them.
Infrastructure costs are a major driver of telecommunications profit and loss and network capacity planning is a key component. Too much infrastructure in a given situation wastes precious dollars while insufficient infrastructure risks customer dissatisfaction and churn.
To visualize both highly congested areas and areas with excess network capacity, the company uses Datameer to integrate and analyze market demographic data with network data and ultimately to generate a network traffic heat map. This enables them to make an informed decision about areas where demand was very near to capacity, in which case LTE rollouts should be prioritized and marketing efforts should be temporarily scaled back areas. They also identify geographic areas where excess capacity exists and where they can potentially ramp up marketing activities.
To improve forecast accuracy, the company uses Datameer to monitor and track actual versus forecasted network traffic and to continually fine-tune the forecasting model. They also built a what-if model to analyze how different revenue growth and increase in specific access technology usage will impact capex investments. They also use Datameer to analyze new over-the-top (OTT) services, understand the network impact, and build the best business strategy to accommodate these new services.
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Setting up a pro-active call center for each provider with actionable insights to offer a superior customer service experience while reducing fixed and variable costs in delivering service, reducing churn and identifying new product offerings to improve customer loyalty and brand perception.
Benefits will include:
With the new insight generated through big data analytics, the telecommunications company will gain a complete view of its customers and which mobile towers are being used by power users. As a result they will save over hundred million dollars in network optimization, by upgrading the towers that are mostly used by power users.
Telecommunication companies can increase revenue by using big data to understand customer buying journey, better understand behavior of customers who are at risk of churn and can save millions of dollars each year is capital company expenditures for infrastructure.
Campaigns at most service providers are based on customer profile data with demographics information (age, income, gender, profession, etc.) combined with fairly simple rules such as sending the subscriber an offer to renew their contract prior to expiration at a favorable price. Service providers have a wealth of data about subscriber usage and location, however, do not often have tools to analyze it.
Analysis :Big data and analytics platform enables service providers to analyze subscriber usage and digital behavior (channel interaction, social media) for opt-in subscribers and combine it with subscriber profile to identify and deliver targeted offers in real-time.
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Telecom operators have historically focused on managing the network with little visibility to the impact it has on the customer's experience. The operator was forced to work with snapshots of network data in fragmented views or at a summary level in order to plan network capacity or provide information to customer care and marketing about customer transactions.
Analysis : With the network analytics solution from Big-Data, the service provider gets the measurements and metrics necessary to successfully manage their entire network end to end, optimize network spend and proactively address service issues and identify monetization opportunities. Benefits include:Identifying and resolving network bottlenecks in minutes Proactively managing customer experience and churn Managing and planning for capacity requirements to maintain and improve the quality of service Optimizing network investment to maximize impact for most lucrative customer segments
The proliferation of smartphones presents new opportunities and challenges: consumers want the best deals for all purchases based on their real-time location while requiring the services provider to honor their privacy preferences and provide only relevant offers when requested/opted-in.
Analysis : Big Data enables service providers to analyze real-time location data over time for opt-in subscribers to understand subscribe lifestyle. Combining lifestyle and mobile profiles with subscriber usage and digital behavior allows service provider to create targeted offers for opt-in subscribers. This drives much higher response rates for marketing offers, resulting in higher revenue.
The project is to compile the customer location details analysis of authentic mobile customer based on which further l-commerce activities can be pursued.The profiling of customers can be done in terms of location, customer behavior.
Analysis : The initial phase is focused on location based analysis where this would enable us to give patterns or most possible locations for commerce from location data.
Call Detail Records: Records consisting of caller?s data like time, incoming, outgoing calls further the SMS,GPRS data needs to analyzed(based on L-commerce approach (a data file consisting of all these parameters is reqd.)TRx configurations of sites and site locations. These TRx additions actually done after the higher cell usages are identified. So these data are relevant to us.
Call detail records pattern analysis needs to analyzed day to day. R-tool scenarios needs to be automated for this purpose. TRx additions are basically done on monthly basis , hence monthly analysis is perfect , exact location data are obtained from this reports.
In most organisations the contact centre channel data is analyzed typically from a SLA(Service Level Agreement perspective). For example TAT (Turnaround time), Average wait time etc. But the actual transcript of the conversation can yield powerful insights regarding telecom infrastructure usage. Surge in Contact centre keyword frequency as a lead indicator to infrastructure bottlenecks.
Telecom providers are competing with each other to get greater ARPU (Average revenue per user) from data services as opposed to voice services. In this competitive environment a telecom provider launched a new but extremely viral gaming application on Mobile devices. A few days after its launch it started observing a burst of calls to the call centres and on text mining the transcript Data Scientists found a sharp spike in the keywords alluding to performance. The specific intelligence regarding keyword burst and specific time of day at which this was encountered was shared with the infrastructure planning group which then put a plan in place to throttle the bandwidth dynamically based on usage.
This is a security use case where if an investigation team wants to find out if there were multiple phones with the same person. When a call is made typically the following data points are captured - subscriber , date, time, and duration. Depending on the type of call, additional data can be gathered, including switch data, cell tower IDs, device identification (serial) numbers, as well as International Mobile Subscriber Identity (IMSI) and International Mobile Equipment Identity (IMEI) codes. The unique ID of the cell tower a handset was connected to when a connection was made is one of the most important components for collocation analysis
By examining terabytes of CDR/Tower records from the switch one can triangulate on a few collocation events. A co-location event can be defined as the same cellphone tower being used to route calls during a specific point in time. This is almost like looking for a needle in a haystack and traditional solutions would have trouble handling the massive volume of tower and switch data. But with a combination of massive Hadoop clusters and columnar database architecture, these queries can be executed at lightning speed to surface a significant few events of interest from the massive ocean of log data across devices
Typically in most telecom infrastructures IDS ( Intrusion detection systems ) sit at the periphery of the network monitoring malicious activity and recording the same as log entries or alarm events into a log file. Firewalls and application logs also store a plethora of important events which if triangulated thru a central log repository to provide a comprehensive picture of any patterns which are dormant in the attack
One key components to enabling a Central Log File repository with events streaming from multiple devices which are ingested and collated centrally. Once this central log file is set up to store the torrent of event data, it can be channelized into intelligence to optimize network infrastructure and aid security of the telecom assets. Flutura Decision Sciences is convinced that setting up of a Network Intelligence team consisting of Security experts and Data Scientists who work in a collaborative fashion can yield dramatic game changing insights to catapult an organisation to the next level