This document discusses data science and related topics. It summarizes that data science involves deriving knowledge from large, structured and unstructured data using techniques like data mining, machine learning, and big data analytics. It provides examples of industries that use these approaches for applications such as fraud detection, sales predictions, and recommendations. The document also outlines Deteo's data science service offerings and expertise in areas like recommendation systems, machine learning, and analyzing structured and unstructured data using tools like Hadoop, R, and Python.
You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season.
Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season.
In this webinar, you will learn about:
current challenges in identifying fraud
what to look for in a big data solution addressing fraud
how big data analytics can identify credit card fraud
best practices
Data has become a key focus for corporate leaders today. Chartered Global Management Accountant (CGMA) designation holders are well placed to help translate data into commercial insights and value.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season.
Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season.
In this webinar, you will learn about:
current challenges in identifying fraud
what to look for in a big data solution addressing fraud
how big data analytics can identify credit card fraud
best practices
Data has become a key focus for corporate leaders today. Chartered Global Management Accountant (CGMA) designation holders are well placed to help translate data into commercial insights and value.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
What is the value of big data? How does a user get that value?
Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers.
In this session, you will learn:
How analysts get value out of big data
How to visualize data at every step of analysis
How analysts can do big data analytics without IT, in one product
Data is being generated at a feverish pace and forward thinking companies are integrating big data and analytics as part of their core strategy from day one. However, it is often hard to sift through the hype around big data and many companies start with only a small subset of data. Can smaller companies benefit from big data efforts? We will discuss several use cases and examples of how startups are using data to optimize their operations, connect with their users, and expand their market.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
From Business Intelligence to Big Data - hack/reduce Dec 2014Adam Ferrari
Talk given on Dec. 3, 2014 at MIT, sponsored by Hack/Reduce. This talk looks at the history of Business Intelligence from first generation OLAP tools through modern Data Discovery and visualization tools. And looking forward, what can we learn from that evolution as numerous new tools and architectures for analytics emerge in the Big Data era.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Succeeding with Analytics: Mastering People, Process, and Technologyibi
Wayne Eckerson and Dr. Rado Kotorov take a journey through the behind-the-scenes characteristics of a great analytics program in this Information Builders Innovation Session presentation.
Real-time Data is Changing the Face of the Insurance IndustryDataWorks Summit
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
Claudia Imhoff of the Boulder BI Brain Trust gives the lowdown on integrating real-time data to leverage modern BI practices for your business in this Information Builders Innovation Session presentation.
Data Driven Innovation: New Business Models, Products and ServicesAnja Hoffmann
How do you spot opportunities for innovation?
How do you build a business model designed around your customer?
The 'new normal' in a world of constant change requires new leadership skills and tools for mastering business transformation.
What is the value of big data? How does a user get that value?
Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers.
In this session, you will learn:
How analysts get value out of big data
How to visualize data at every step of analysis
How analysts can do big data analytics without IT, in one product
Data is being generated at a feverish pace and forward thinking companies are integrating big data and analytics as part of their core strategy from day one. However, it is often hard to sift through the hype around big data and many companies start with only a small subset of data. Can smaller companies benefit from big data efforts? We will discuss several use cases and examples of how startups are using data to optimize their operations, connect with their users, and expand their market.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
From Business Intelligence to Big Data - hack/reduce Dec 2014Adam Ferrari
Talk given on Dec. 3, 2014 at MIT, sponsored by Hack/Reduce. This talk looks at the history of Business Intelligence from first generation OLAP tools through modern Data Discovery and visualization tools. And looking forward, what can we learn from that evolution as numerous new tools and architectures for analytics emerge in the Big Data era.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Succeeding with Analytics: Mastering People, Process, and Technologyibi
Wayne Eckerson and Dr. Rado Kotorov take a journey through the behind-the-scenes characteristics of a great analytics program in this Information Builders Innovation Session presentation.
Real-time Data is Changing the Face of the Insurance IndustryDataWorks Summit
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
Claudia Imhoff of the Boulder BI Brain Trust gives the lowdown on integrating real-time data to leverage modern BI practices for your business in this Information Builders Innovation Session presentation.
Data Driven Innovation: New Business Models, Products and ServicesAnja Hoffmann
How do you spot opportunities for innovation?
How do you build a business model designed around your customer?
The 'new normal' in a world of constant change requires new leadership skills and tools for mastering business transformation.
Building a distributed data-platform - A perspective on current trends in co...Charles Care
Data, dev-ops, and cloud services: Building a distributed data-platform
A lecture given to Computer Science Students at the University of Warwick, February 2012.
"Where's the data?" The role of metadata in enabling the transformation to a ...Roland Bullivant
Silwood Technology's presentation at the Enterprise Data World 2016 Conference in San Diego. Discusses the importance of understanding the metadata which underpins all enterprise systems in the process of transformation to a data driven business. It explores why this metadata is critical, how it is usually discovered and the specific problems of accessing and understanding it in large, complex and customised packages from SAP, Oracle and Salesforce. It also outlines how Silwood's metadata discovery tool helped Boeing and Hydro Tasmania accelerate delivery of information led projects.
Open Data Science Conference Agile DataDataKitchen
To rephrase an old saying: ‘It takes a village to raise an Analyst.’ Data Analysts and Scientists are working in teams delivering insight and analysis on an ongoing basis. So how do you get the team to support experimentation and insight delivery without ending up in an IT Engineer vs Analyst vs Data Governance war? We present 5 shocking steps to get these teams of people working together with practical, doable steps that can help you achieve data agility.
Leader européen de la distribution du train, Voyages-Sncf.com a depuis plus d’1 an pris le virage du Big data, notamment pour développer la connaissance client et permettre ainsi une amélioration de son expérience utilisateur.
Découvrez par quelques illustrations comment cela révolutionne le marketing digital de Voyages-Sncf.com et comment cette nouvelle démarche a été implémentée au sein de l’entreprise.
Par Marie Laure Cassé
La vidéo de la conférence est à retrouver sur : http://www.xebicon.fr/programme.html
The Nitty Gritty of Advanced Analytics Using Apache Spark in PythonMiklos Christine
Apache Spark is the next big data processing tool for Data Scientist. As seen on the recent StackOverflow analysis, it's the hottest big data technology on their site! In this talk, I'll use the PySpark interface to leverage the speed and performance of Apache Spark. I'll focus on the end to end workflow for getting data into a distributed platform, and leverage Spark to process the data for advanced analytics. I'll discuss the popular Spark APIs used for data preparation, SQL analysis, and ML algorithms. I'll explain the performance differences between Scala and Python, and how Spark has bridged the gap in performance. I'll focus on PySpark as the interface to the platform, and walk through a demo to showcase the APIs.
Talk Overview:
Spark's Architecture. What's out now and what's in Spark 2.0Spark APIs: Most common APIs used by Spark Common misconceptions and proper techniques for using Spark.
Demo:
Walk through ETL of the Reddit dataset. SparkSQL Analytics + Visualizations of the Dataset using MatplotLibSentiment Analysis on Reddit Comments
Using Data Mining Technique, Loginworks is offering the web data mining solutions. One of the leading Data mining companies delivering data mining services.
https://www.loginworks.com/data-mining/
Entry Points – How to Get Rolling with Big Data AnalyticsInside Analysis
The Briefing Room with Robin Bloor and IBM
Live Webcast Sept. 24, 2013
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=7501927&rKey=664935ceb7de1aec
Where to begin? That question remains prominent for many organizations who are trying to leverage the value of big data analytics. Most sources of big data are quite different than traditional enterprise data systems. This requires new skill sets, both for the granular integration work, as well as the strategic business perspective required to design useful solutions.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the pain points associated with modern data volumes and types. He will be briefed by Rick Clements of IBM, who will tout IBM's big data platform, specifically InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. He will also present specific use cases that demonstrate how IT and the line of business can springboard over existing challenges, gain insight and improve operational performance.
Visit InsideAnalysis.com for more information
Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights.
Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement.
A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
Customer value analysis of big data productsVikas Sardana
Business value analysis through Customer Value Model for software technology choices with a case study from Mobile Advertising industry for Big Data use case.
Data Sciences & Analytics Discover the unknown power of the knownYASH Technologies
Our data science’s and analytics’ competency accelerates the data-driven decision making process and empowers you with capabilities that will guide you in deriving deeper insights. We can transform your business into a more nimble and connected organisation through our extensive portfolio
Data Sciences & Analytics Discover the unknown power of the knownYASH Technologies
Our data science’s and analytics’ competency accelerates the data-driven decision making process and empowers you
with capabilities that will guide you in deriving deeper insights. We can transform your business into a more nimble and
connected organisation through our extensive portfolio
Similar to Deteo. Data science, Big Data expertise (20)
2. Data Science
Data science is the process of deriving valuable knowledge from "Big Data" consisting
of structured, unstructured or semi-structured data that large enterprises produce.
3. Big Data
Big data is a set of techniques and technologies which operates wits data sizes
beyond the ability of commonly used software tools to capture and manage within a
tolerable elapsed time.
4. Data Mining
Data mining is a process that analyzes a large amount of data to find new
and hidden information that improves business efficiency. Various industries
have been adopted data mining to their mission-critical business processes
to gain competitive advantages and help business to grow.
5. Machine Learning
Machine Learning is a process that gives computers the ability to learn without being
explicitly programmed.
Examples: spam filtering, recommendation systems, sales predictions.
6. Business domains
Any kind of data analyses is based on two major components:
technical tools and domain expertise. Deteo has significant practical
experience in the following industries proven by long term
cooperation with appropriate customers from:
• Banking sector
• Insurance
• Human resource management
• IT and Telecom
• Accounting
• Retail
7. Business challenges we can address
New possibility for growth depends on the ability to analyze, predict and make
decision based on existed data related to customers and market:
Retail
• Market basket analysis to provide information on what products or services
combinations were purchased or consumed together. This allows to promote and
optimize products and maximize profit.
• Analyze customer retention and locality based on recent purchases activities.
• Data mining helps detect fraudulent behavior with credit card or online
transactions
• Clustering/Segmentation for targeted marketing
8. Business challenges we can address
Bank and Insurance
• Detect risky behavior of customers
• Claim prediction based on information available from previous events
• Fraud detection
eCommerce
• Collaborative filtering and recommendation systems that make automatic
prediction about the interests of users by collecting preferences and tastes
information from many similar users of such systems.
• Mining social networks could be applied both to target marketing and sentiment
analysis
• Intranet search to provide capabilities to find and answer the questions based on
information available within corporation or organization networks
• Analysis on streaming/online data to prepare information for further processing
10. Approach
In scope of Data Science service offering we are able to complete the following
scope of activities:
• Comprehensive review of customers’ current business, plans and systems
• Recommendations on connecting Data science tools and approaches to
customers’ existing Business and IT infrastructure
• Perform Data Analysis
• Data Visualization and Advanced Reporting
• Support and Maintenance or Solution Hand Over
11. Initiation
•Project initiation
•Team setup
•Define business
needs
Analysis
•Define business goals in
technical metrics
•Analyze current
infrastructure
•Analyze existing data
•Analyze level of data
sensitivity
•Develop required
algorithms
•Validate algorithms on
small portion of data
Data Mining
•Prepare required
infrastructure
•Perform data
masking of sensitive
data
•Run data mining
algorithms
Results
Analysis
•Root-cause
analysis
•Risks assessment
•Recommenda-
tions to fix
Reporting
•Transform mined
data into graphics,
charts and tables
understandable
for stakeholders
•Plan meeting
where prepared
reports are
presented
Hand Over
•Prepare
knowledge
transfer plan
•Prepare technical
and business
documentation
•Provide training
for customers
experts
•Handover
developed
solution to
customer
Iteration cycle: 3-6 weeks
Regular status meetings
13. Case study: Car insurance
Business challenge
We received historical data about car accidents from insurance company for the last 5
years. Data was anonymized, so contained no personal information. Customer asked us
to analyze this data. There was an assumption that insurance risk was not equal for
different groups of cars.
Our solution
Using Microsoft cloud stack of technologies for data analysis we run several
experiments and have defined groups of cars with equal risk probability. Based on this
information Customer was able to adjust his insurance fee card, so for two car groups
insurance fee was decreased for 10% and customer proposition became more valuable
on the market.
14. Business challenge
We received unstructured logs from server farm that represented
servers and services activities. Idea was to analyze it and to find the
most problematic servers and try to analyze the reasons.
Our solution
Using Hadoop Apache technology stack we loaded and processed
about 500 GB of text files. As a result, we identified servers that failed
the most often and defined the most probable preconditions of the
fault.
Next step is to implement online logs processing and analysis in order
to predict server or service fault.
Case study: Logs analysis
15. • Recommendation systems
• Machine learning
• Visualization
• Data Mining
Stream processing
NoSQL databases Hadoop based infrastructure
• Microsoft HD Insight
• Oracle BigData appliance
• IBM InfoSphere BigInsights
Tools
• Hadoop, Spark, Hive, Pig
• Azure
• R, Python, Java
Vendors
• Oracle, Microsoft, IBM
• Apache
• QlikView, Tableau
Stream processing
• IBM InfoSphere Streams
• Oracle Real-Time Decisions
• Apache Storm in MS Azure
Data science
• Recommendation systems
• Machine learning
• Visualization
• Data Mining
• MongoDB
• Cassandra
• Neo4j
When the data becomes a real problem of its size and variety – it’s time for Big Data solutions
16. Trainings and certifications
Deteo’s data science team has passed following trainings and certifications
Coursera
• Machine Learning
• Mining Massive Datasets
• Computing for Data Analysis
• R Programming
Online Stanford University
• Statistical Learning
Other
• Hadoop: Map Reduce and Big Data
• MongoDB for Developers
• MongoDB for DBAs
17. Interested to know more about our abilities?
Please ping us at contact@deteo.info