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By building things in a micro-approach strategy, Sasol leverages Teradata’s data and analytics in the cloud to get value faster and innovate other areas of the business.

 

 

To learn more, click HERE.

 

#AnalyzeAnything #CustomerSuccess 

 

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Learn how Lufthansa Group, together with Teradata, uses data & analytics to achieve operational excellence to measure three critical KPI’s - Customer Satisfaction, Maximizing Revenue and Minimizing Costs.

 

 

To learn more, click HERE.

 

 #CustomerSuccess #AnalyzeAnything

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Imagine ACME Corp. rolls out a new product and has been receiving hundreds of thousands of reviews about it. It is extremely important to ACME that any issues with the product are addressed as soon as possible so that they don’t lose the trust of their customers. What do we do? 

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The Insights

Fraud costs organizations, including government agencies, billions of dollars each year. With fraud becoming increasingly sophisticated and taking many forms, companies need new ways to identify and mitigate the threat.

 

'Deep Sea Monster' looks like a mystical creature, but in reality it is a visual representation of potentially fraudulent government procurement networks in Russia. Today, government procurement data in Russia is open and can be easily accessed by anyone. The bidding process for procuring goods and services is designed to be open and transparent. As part of the process, each step and procurement action taken by the government and contractors leaves a digital footprint.

 

'Deep Sea Monster' offers a detailed view of business relationships between contractors and government agencies. Each node represents either a contractor or a government body. The size of the node is based on the total amount of federal money received by the contractor. Each line represents a government contract.

 

Initially, identifying an umbrella-like relationships between 'elite' group of contractors and government organizations was difficult, which is why only companies potentially affiliated with fraud were chosen for the analysis. The hypothesis that contractors were trying to hide their identities by creating dummy entities and were actually involved in suspicious activities proved to be true. A short list, prioritized by revenue, of these companies and their dummy aliases could then be created for investigations.

 

The Analytics

Teradata Aster was used to perform the analytics on open government financial data. We worked with detailed contract data from 2015 from one of the central regions of the Russian Federation. A string of company data such as their full name and address was leveraged to identify potentially affiliated companies using a Jaccard distance algorithm. The Jaro-Winkler distance was used for the same purpose for separate words, including first names, last names and other single word entities. Sigma chart in Teradata AppCenter was used to visualize the results.

 

The Benefits

The Accounts Chamber of the Russian Federation estimated that nearly $2 billion (in U.S. dollars) in contracts in 2015 were connected with fraud. This shows that in spite of open tender procedures being a requirement in the several hundred thousand public organizations in Russia, fraud is still a major problem.

 

The Teradata team won the People's Choice Award on Russian Open Government Financial Data Contest BudgetApps 2016 by demonstrating a number of analytic tools and techniques for extracting value from government data. The C-level management of the Ministry of Finance of the Russian Federation emphasized that these tools should be used for government portals.

 

The analytics can be used by the public as an instrument of social control, by procurement process regulators to fight fraud, by internal control departments of government organizations to prevent corruption, and by government audit institutions to estimate and act on unresolved problems. The outcome of the analytics leads to a smarter government that serves its people by using public resources in the most efficient way. 

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Teradata and Vanson Bourne surveyed 700 global organizations with an average revenue of nearly $10 billion about their ambitions, fears and investments in cloud analytics.

 

To learn more, click HERE.

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Teradata added time series data and functions, in addition to already existing temporal capability, to take you farther. How far? The 4th dimension.

 

To learn more, click HERE.

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The Insights

What could movie or TV watching teach us? Turns out, a lot! Entertainment companies have placed exclusive focus, especially in an era of intense competitive pressure, on delivering unique customer experiences that are highly personalized to the idiosyncratic and shifting tastes of the viewing public. Gone are the days of a two sizes fits all entertainment packages where the onus is on the customer to cherry pick content from a static set of options. This leads to a widely felt dissonance in that viewing preferences are vastly unmatched by the actual entertainment offerings. When we add to this the diversity of how entertainment is delivered — through set top boxes, streaming platforms, digital applications, theaters — the issue becomes even more complex and urgent to address.

 

Each circle represents the viewer size and prominence of a specific program (or movies) or closely related set of programs (e.g., a series) over time. These circles are grouped into logical clusters (the spoke-like outgrowth that comprise of several closely linked circles in a sac). Some examples of programs that have resulted in the large circle include "Hunger Games" and "Avengers". Some of these circles are much older TV serials but still remain popular draws among certain segments of the viewing public. Furthermore, each circle shows a relationship with other circles sometimes in the same genre (sac) and sometimes by spanning entertainment genres. The thickness of the lines, connecting the circles indicates the number of times these programs have been seen together. Clusters can thus consist of a wide number of content pieces. Over time, these logical groupings of entertainment clusters and their relationships can be used to create highly personalized packages based on initial viewing preferences that are expressed by the subscriber to services (e.g., Cable TV). 

 

The Analytics

This visualization was created using Teradata Aster Analytics. We used detailed program viewership data (e.g., subscriber ID, programs watched, duration, repeat viewerships, program categories, geography, subscription duration, program release date) that were loaded into Aster Analytics for analysis. Several datasets were included to align video content consumption with actual customer households resulting in a massive dataset of more than 600 million rows of video content transactions on a catalogue of several thousand titles. Aster's native data prep and graph algorithms (e.g., modularity, degree) were used to determine logical program groups and the relationships within and between the groups. A visualization of the entertainment clusters was then rendered using native visualization capabilities on Teradata's AppCenter framework.

 

The Benefits

There are at least three benefits that are realized through this advanced analytics implementation:

 

First, entertainment companies can gain new and timely insights into the relevance of their broad content portfolios vis-á-vis their customers that can be segmented across many demographic and socio-economic groups. What this engenders is an ability to provide entertainment tiers that fit the demand profile and price elasticities of customers.

 

Second, customers are now more satisfied and can get creatively packaged content that not only fit their current preferences but also expose them to other content types that they could grow to appreciate.

 

Third, content providers can now expect more strategic support (e.g., royalty payments, production support) from their buyers given that a clear data driven view into entertainment content clusters has been established. They can now confidently work on generating viewing material that they know will be surefire hits with the viewing public. 

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The Insights

 

What would you see in a giant corona, or a large circle of light, with colorful leaves of varying lengths sprouting around it? This corona depicts a network of calls made in a geographic area prior to a terrorist incident.

 

Frantic calls are usually made after an incident occurs by people affected by it. However, calls made before the incident are also informative and can contain clues about the perpetrators. These are the patterns of calls that law enforcement and intelligence agencies want to identify, isolate, and use to locate the perpetrators as well as to prevent future incidents from occurring.

 

Each leaf shows a distinct network of calls that were made. Each node within a network, or leaf, is a call that was made to others within that network. Certain calls spanned networks, and some nodes appeared to have made calls of great frequency before the incident and then fell completely silent in its aftermath.

 

This graph of connections can also be used to identify unknown individuals who were called from known individuals' numbers. These known numbers and their connections can be isolated within geographic regions to determine the likely pool of suspects, which can then be used for further investigations.

 

The Analytics

 

This visualization was created using Teradata Aster Analytics. We used detailed cell phone call data (hundreds of gigabytes or terabytes, typically) that was loaded into Aster Analytics for analysis. Aster's native graph algorithms (e.g., Closeness) were used to determine the structure of these calling networks and the strength of the associations within these networks. A visualization of the underlying node-edge table that depicts the network linkages was then rendered using native visualization functions.

 

The Benefits

 

At least three benefits are realized through this advanced analytics implementation:

 

First, using calling connections to isolate unusual calling patterns ensures that when such patterns are repeated, there is a higher likelihood of thwarting harmful incidents before they occur.

 

Second, identifying a nefarious circle of connections ensures that the likely perpetrators are quickly identified, helping to bring the culpable parties to justice.

 

Third, call records of the suspected perpetrators can be identified and parsed prior to an incident to determine the nature of specific security threats or code words that are likely to be used in terrorist operations.

 

 

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Architectural and Licensing Requirements for A Modern Analytics Platform - Neil Raden, Hired Brains Research

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Video game companies are fully utilizing technology to understand the contextual factors that drive sustained brand engagement.

 

To learn more, click HERE.

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