אנליטיקה עסקית בארגונים ממוקדי לקוח

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "אנליטיקה עסקית בארגונים ממוקדי לקוח"

Transcription

1 Big Data vs. big Data? אנליטיקה עסקית בארגונים ממוקדי לקוח אילן ששון ד"ר 30/3/2015

2 מטרות מהזה?Big Data Analytics מה זהScience?Data DDD וחשיבותובארגוניםמוטילקוחושירות מגווןמקורותנתונים מושגיםבסיסייםביצירת Data Products המלצותוגישותלהבניית יכולותאנליטיות מדענינתונים ולמהזה עשוילעניין...אתכם? מושגייסודבכרייתנתונים ניהולפרויקטיםמוטיאנליטיקהעסקית (CRIPS-DM) Data Privacy ולמהזהחשוב דוגמאותקוד Data/Text mining R

3 Trend of Google Searches of Big Data and Data science over time showing the popularity of the terms Data Science the connective tissue between big data processing technologiesand data-driven decisionmaking (DDD) (Provost & Fawcett, 2013)

4 Terminology Data-Driven Decision-Making (DDD) refers to the practice of basing decisions on the analysis of data, rather than purely on intuition. (Provost & Fawcett, 2013) Data Science is a set of fundamental principles that support the extraction of information and knowledge form data. It involves principles, processes, and techniquesfor understanding phenomena via the (automated) analysis of data. Big Data Technologies are used to process and handle big data, and include preprocessing prior to implementing data mining techniques. The new approach to Business Analytics

5 Why do we really care? DDDaffects firm performance the more data-driven a firm is the more productiveis with a 4%-6% increase and highly correlated with higher ROI, ROE, asset utilizationand market value. (Brynjolfsson et al. Strength in numbers: How does datadriven decision making affect firm performance, 2013 MIT). BD Technologies utilization correlates with significant additional productivity growth affects firm performance 3% increase in productivity than the average firm. (TambeP. Big data know-how and business value, 2012 NYU). CompetitiveAdvantage What can I now do that I couldn t do before, or do better than I could do before?

6 3 Principles of the new era of computing Datawill be the basis of competitive intelligence for any organization companies, government entities, cites and individuals Data in this new era notlimited resource Changing how we make decision -Decisions will be based not on intuition or past experience, but on predictive analytics. Changing how we create value - Organizations - private and public - will become social enterprises. Changing how we deliver value -Success will depend upon the ability to create products and services for individuals -not market segments.

7 Big Data Every Where! Lots of data is being collected and warehoused Transactional data Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Social Network Multi media content Scientific data Networks sensors Mobile phones User generated content Internet of Things Data is becoming the new currency - vital natural resource Datafication -taking all aspectsof life and turning them into data (The rise of big data, Foreign Affairs)

8 What to do with these data? Aggregation and Statistics: Data warehouse OLAP Indexing, Searching, and Querying: Keyword based search Pattern matching (RDF/XML) Knowledge discovery: Data mining Text mining Graph mining Statistical modeling Big Data Big Assumptions Collecting and using a lot of data rather than small samples ( N= All ) Accepting messiness in your data Giving up on knowing the causes

9 Big Data Use Cases Big Data can play a significant economic role to Private commerce Public sector National economies

10 big Data The enterprise perspective Enterprise data is big but it is not Google-big OLTP ETL OLAP IT-Oriented Classic BI Boundary Dash-bored OLTP / Dark data/ Log / Social/ web ETL Business-Oriented Big Data Warehouse Augmented DWH + Extreme-Scale- Analytics

11 הפתרון הקיים DWH- מה הם סוגי המוצרים הבנקאיים הנמכרים ביותר? מה היא התפלגות הכנסות על פי מוצרים בנקאיים? מה היא התפלגות ההוצאות על פי יחידות מטה? רווחיות על פי מוצרים על פני מימד הזמן ומימד הסניפים? באילו סוגי מוצרים קיימת מגמת עונתיות?

12 מרחב הבעיה? מי הם הלקוחות הפוטנציאליים ביותר להלוואה מעל 300,000 ש"ח? איך ניתן לקצר את תהליך הטיפול במתן אשראי ללקוח חדש? מה הם המאפיינים של לקוח נוטש? מה הם המאפיינים של לקוח רווחי? אילו מוצרים חדשים מומלץ להציע ללקוחות קיימים? כיצד ניתן לייעל תהליכים בארגון?

13 מרחב הפתרון from Business Intelligence to Business Analytics from DHW/OLAP to Large Scale Data/Text Mining Verification Based Analysis ~ ~ ~ ~ Discovery Based Analysis ~ ~ ~ ~ ~ תהליך אנליטי מבוסס גילוי כלים/אלגורתימים הפועלים על מרחב הנתונים חושפים תבניות חבויות תהליכיהקבצה, ניבויואסוציאציה Unsupervised Learning Machine תהליכים המחייבים בסיס נתונים היסטורי גדול תהליך אנליטי מבוסס אימות משתמש מניח היפותיזה כלשהיא מופעלותטכניקותאישוש/סתירה תהליכיםמבוססימשתמש היכולת להניחהנחותנכונות,בחירת הכלים,ופרשנותהתוצאות תהליכים משלימים

14 Big Data Architecture & Pipeline פנימי/חיצוני מקורנתונים Streams Real Time Analytics Network/Sensor Internet of Things Video/Audio Entity Analytics Information Ingestion Unified Information Access (UIA) Master Data Data Integration Stream Processing Exploration, Analytics Discovery Predictive Operative Descriptive Prescriptive Landing Area Zone & Archive Raw Data Structured Data Unstructured Data Text Analytics Data Mining Machine Learning Complex Event Processing Intelligence Analysis Decision Management BI & Predictive Analytics Reporting & Discovery Business Processes

15 Data-Analytic Thinking One of the most critical aspects of data science is the support of data-analytic thinking throughout the organization Data-oriented business environment Basic understanding of basic principles In order to assess and envision opportunities accurately (data-analytics projects) Professional advantage in being able to interact competently (dataanalytics team) Business units must interact with data science team (domain knowledge) Data science project require close interaction with business people responsible for decision making

16 Conveying the message. Data miningis movingfrom the research arena into the pragmatic world of business There is continuouseffort of refining algorithms and coming up with new ones Now with new developments in algorithms and architecture smallscale development teams can build large-scale projects Practicaldata mining weighs the trade-offs between the most advanced and accurate model with the costs and complexity in realworld business environment New analytics tools and platforms make data mining much more easier and powerful for people at all levels of expertise Hadoop-based computing ecosystem is evolving rapidly, making project with very large-scale datasets much more affordable

17 The Ladder Approach Build a foundation Learn to think analytically(data mining models, visualization, statistics etc.) Develop a strategy and road map based on business needs (pick a theme) C-level management engagement (presentation) Adopt a step-by-step process (problem definition results: CRISP-DM) Pick and learn a tool (R, Python etc.) Practice on small datasets Build a portfolio Deliverable POCs and pilot projects (3-5) Quick-wins Practice on small datasets Write-up findings (storytelling) Deliver solutions Adopt technology infrastructure (HDFS, MapReduce, NoSQL Spark SQL. etc.) Ongoing revisions of models (data products) Continue to apply advanced analytics Business Scope & Deliverables

18 Rethinking the Business & IT Model Data Management & Business Analytics are Core Business Competencies o o o The Business Owns the Data Recognize Analytics as a Business Driven and Owned Process Technology is an Enabler Shift to Business Configurable and Controlled o o Acknowledge the Differencebetween Software Development and Business Analytics Redefinethe IT Support Model to Enable The Business to Acquire, Assess, Analyze, Test, and Deploy Analytical Outcomes Change the IT funding & Financial Model o o Current Infrastructure Model is Geared towards Legacy & Transactional Platform Recognize Analytics as a Business Driven and Owned Process Technology is an Enabler מקור השקף: מצגת MetLife כנסביגדאטהIBM אוקטובר 2013

19 Big Data Adoption התוויתתוכניתעבודה בחינתתרחיש (אחדאויותרלמימוש) קורסMining Big Data, Data Science & Data בן 10 מפגשיםקורס Data- Business בן 8 מפגשיםלאנשי Analytic Thinking הקמתקבוצת «360» R&D Team Infrastructure & Operations Business Unit Analysts Business IT Support Team

20 The Data Journey מהעושיםכיוםבארגון :.1 OLAP דוחותמימדימוצרשיווקתמחור.2 מודליםשלכרייתנתונים?... Internal Data מידע תפעולי קיים במחסן הנתונים New Internal Data (Dark Data) מידע קיים שלא מוגדר במחסן הנתונים, מידעמובנה, מיילים, מידע טקסטואלי (סוכנים, שמאים..) New External Data הערכה: 80% מהמידע בארגון אינו מובנה ואינו ממודל ולפיכך אינו זמין לניתוח ואנליזה בכלים הקיימים והמסורתיים מידעממקורותחיצוניים: אינטרנט, מתחרים,רשתותחברתיות, מידע סלולארימבוססמיקום, טלמטיקה סנסוריםועוד Data Management before Business Analytics בשלב ראשון לא נרתיח את האוקיינוס... Big Data doesn t have to be big it can be managed and built incrementally. Big Data may or may not include social media (eventually it will). Big Data may or may not include external data (eventually it will). Sometimes information is good enough.

21 Data Products Motivation: turning data assets products and services A data product is an algorithm, software, application, presentation or reproducible report based on data analytics A data product is the production output from a statistical analysis, data mining, text miming, AI etc. Initially online companies: search algorithms (Google) similar offerings (Amazon) recommendations for people you may know (Facebook) A data product is a product that facilitates an end goal through the use of data. DJ Patil Developing and launching data products, particularly if you are an offline business it won t be second nature... Data-as-a-Service (DaaS) - a cloud strategy used to facilitate the accessibility of businesscritical data in a well-timed, protected and affordable manner B2B "renting" data service

22 The Model Assembly Line Do you own the data? Business model Do you have the data? Data quality? Type of analysis Do you havethe data? Do you ownthe data? (legal issues, consider anonymized personal data) Is it high-quality and useful data? Do you have a business model? (bundling, selling, free) What types of analysisare you offering? (descriptive analytics vs. predictive analytics) Do you have differentiationor competitive advantage? (proprietary vs. commodity data) Competi tive adv.

23 The Model Assembly Line: A case study of DaaS Cellular companies Do you own the data? Business model Do you have the data? Data quality? Type of analysis Competi tive adv. מידע מיקומי based) (Location מרכזי מידע מיקומי מפתחי אפליקציות חברתיות ערים חלוקהגיאוגרפית - מרכזהעיר, איזוריקניות, איזוריבילוי, מרכזיעסקים תדירותעדכוןהנתונים - יומי/שבועי/חודשי נגישותלנתונים - Online/Batch סיווגלקוח עסקי, פרטי סוג תקשורת - SMS Voice, based) (Location עורקי תחבורה ראשיים Pricing Models עיריות ומוסדות תכנון ממשלתיים Volume based model Quantity-based pricing (amount) Pay-per-call (PPCall) Data type based model based on the type or attribute of data Subscription based model an unlimited amount of data חלוקהגיאוגרפית - עיר,פרבר סוגכביש - מהירביןעירוני, עירוני, אוטוסטרדה תדירותעדכוןהנתונים - יומי/שבועי/חודשי נגישותלנתונים- Online/Batch

24 Implementations Approaches The Full Service Approach:Relying on a 3rd party to develop and maintain the model The Full Control Approach: In house model development and deployment The Consultant Approach: Hybrid methodology

25 Implementations Approaches The Full Service Approach:Relying on a 3rd party to develop and maintain the model The Full Control Approach: In house model development and deployment The Consultant Approach: Hybrid methodology o Pros: o the ideal solution for companies who are resource constrained o the ideal solution for companies lacking technical and analytics staff o the model development can rely on expertise provided by the vendor o the quickest path to implementation o Cons: o reliance on the vendor to provide a solution without any independent review o not being able to make changes to the model directly o Internal staff is not trained to ensure attainment of desired results

26 Implementations Approaches The Full Service Approach: Relying on a 3rd party to develop and maintain the model The Full Control Approach:In house model development and deployment The Consultant Approach: Hybrid methodology o Pros: o the ideal solution for companies with analyticsand IT resources o Helps to protect IP in case of a novel idea or product o This approach offers the most flexibility in making revisions or customizations to the model o Cons: o The firm can t take advantage of any data or expertise accumulated by vendors and consultants o If a fundamental modeling error has been made, it may never be discovered o historically the slowest path to deployment, with successful implementations measured in years(?)

27 Implementations Approaches The Full Service Approach: Relying on a 3rd party to develop and maintain the model The Full Control Approach: In house model development and deployment The Consultant Approach: Hybrid methodology o Pros: Build your own core competencies coupled o the ideal solution for companies lacking depth in their analytics department, but who have available resources in systems and IT o There is a built-in independent review phase in this approach. o Companies are able to make changes directly to the model as needed with high-end data science consultancy o Cons: o If companies lack internal technical or analytical resources, they may be at the mercy of the vendor in the future should a model update or revision be needed. o Some companies attempt to update vendor models, but lack the in-depth knowledge of modeling techniques used. As a result, they may inadvertently make fundamental modeling errors o Continuous management attention

28 Roles in Data Science Data Scientist Applied statistician X computer scientist Computer science Math Statistics Machine learning Domain expertise Communication and presentation skills Data visualization No one person can be the perfect data scientists A team.? Data Scientist (noun): better at statistics than any software engineer and better at software engineering than any other statistician Josh Wills shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data (McKinsey, 2011)

29 Data Scientist Skills required to exploit big data Skills to work with business stakeholders to understand the business issue and context Analytical and decision modeling skills for discovering relationships within data and proposing patterns Data management skills are required to build the relevant dataset used for the analysis. Broad combination of soft and technical skills Sample of Program Offerings DB -Databases BI Business Intelligence, Data Warehousing ST Advanced-Level Statistics BA Business Analytics, Web Analytics DM Data Mining, Machine Learning, Text Mining, Natural-Language Processing BD Big Data Technologies, Visualization KM Knowledge Management, Social-Web Analysis קוסמולוגים של היקום הדיגיטלי

30 Building Models Introduction A model captures the knowledge exhibited by the data and encodes it in some language no model can perfectlyrepresent the real world Automatic or semi-automatic extraction of Interesting Non-trivial Implicit Previously unknown Potentially useful Forecasting what may happen in the future Classifying items into groups by recognizing patterns Clustering items into groups based on their attributes Associating what events are likely to occur together Sequencingwhat events are likely to lead to later events

31 Building Models Introduction Models fall into the categories of data mining: descriptive and predictive Predictive Tasks Use some variables to predictunknown or future values of other variables Descriptive Tasks Find human-interpretable patterns that describe the data Supervised learning Unsupervised learning Meta learning (ensemble learners) 31

32 Types of Data Mining Tasks Many business problems have as an important component one of these DM tasks: Affinity grouping (a.k.a. associations, market-basket analysis ) What items are commonly purchased together? Similarity Matching What other companies are like our best small business customers? Description/Profiling What does normal behavior look like? Clustering Do my customers form natural groups? Unsupervised Predictive Modeling (including causal modeling & link prediction) Will customer X churn next month/default on her loan? How much would prospect X spend? Who might be good friends on our social networking site? Supervised 32

33 Data Mining vs. Deployment

34 Merging Traditional & Big Data approaches

35 Merging Traditional & Agile approaches Time to market slow process Disconcert between the business people (consumers) and IT people (producers) The overall cost is high Breaking down the walls Discovery process and not a traditional SW development project Business owns the data

36 Codification of The Process Extracting useful knowledge from data to solve business problems can be treated systematicallyby following a processwith reasonably well-defined stages CRISP-DM- The Cross Industry Process for Data Mining - ( (CRISP-DM; Shearer, 2000) Structured process with critical points: Human Intuition High-powered analytical tools A well-understood processthat places a structure on a problem which still involves art science + craft + creativity + common sense 36

37 CRISP-DM The point of actuallyusing your results This process diagram makes explicit the fact that iteration is the rule rather than the exception not a linear process Preparatory activity what data? where is the data? accuracy and reliability of the data Both mathematical and logical The most substantial components (65%) timeconsuming and laborintensive 37

38 CRISP-DM Business Understanding A creative problem formulation -what is the problem? Think carefully about the use scenario and the actual business need What exactly do we want to do? How exactly would we do it? What parts of this use scenario constitute possible data mining models? Data Understanding It is important to understand the strengths and limitations of the data. Historical data often are collected for purposes unrelatedto the current business problem. Estimating the costsand benefits of each data source Data having varying degrees of reliability Cost of acquiring the data Data manipulation Data quality 38

39 CRISP-DM Data Preparation Pre-processing tasks Data conversions Data transformations (e.g., normalization, scaling etc.) Missing values, Outliers Redundant or non-informative features (i.e., feature selection, between-predictors correlations) Dimensionality reduction techniques (e.g., PCA, SVD) Modeling The primary place where data mining techniques are applied to the data It is important to have some understanding of the fundamental ideas of data mining, including the sorts of techniques algorithms and tuning parameters. Evaluation The evaluation stage is to assess the data mining results rigorously and to gain confidence that they are valid and reliable before moving on. Measuring models performance and generalization 39

40 Basic Principles - Privacy Collection limitation -Data should be obtained lawfully and fairly, while some very sensitive data should not be held at all. Data quality - Data should be relevant to the stated purposes, accurate, complete, and up-to-date; proper precautions should be taken to ensure this accuracy. Purpose specification -The purposes for which data will be used should be identified, and the data should be destroyed if it no longer serves the given purpose. Use limitation -Use of data for purposes other than specified is forbidden. Source: the OECD (Organization for Economic Co-operation and Development (OECD), 1980).

41 41 Data Science Course אפליקציות ושימושים של Big Data הצגת מגוון מודלים לכריית נתונים Predictive and Descriptive Analyticsו- Exploratory Data Analysis הכוללים בין היתר: Cluster Analysis Association Analysis Decision Trees & Random Forest Support Vector Machine Neural Networks Anomaly Detection Graph mining,social Network Analysis והצגת מושגי יסוד כדוגמת: Degree & Degree Distribution Centrality, Betweeness, Closeness Centralization ועוד שיטות לכריית נתונים טקסטואליים מבוססות NLP לצורך Text Categorization Information Extraction הצגת מושגי יסוד Information Retrieval ושיטות של ייצוג נתונים טקסטואליים מבוססי Bag-Of-Words שימוש בסביבת R לצורך תחקור סטטיסטי, כרייה והצגה של נתונים גישות ויזואליזציה וגרפיקה לאפליקציות מבוססות ניתוח נתונים טקסטואלי ) graph co-occurrences network, neighborhood ועוד) טכנולוגיות מתקדמות לניהול נתונים וארכיטקטורות אחסון ועיבוד הצגת מודל CRISP-DM לניהול פרויקטי אנליטיקה עסקית

42 Why R? R is a free and open source language and environment for statistical computing and graphics. R is already the most popular amongst the leading software for statistical analysis. Key features: It s a mature & widely used NYT Excellent graphics capabilities Highly extensible, with over 4300 user-contributed packages It s easy to use and has excellent online help and associated documentation -Manuals, tutorials, etc. provided by users of R

43 ביג דאטההוא ייצוג של תהליך בעל מגמות אבולוציוניות: מורכבות גיוון והתמחות תודה על ההקשבה

Database Marketing, Business Intelligence and Knowledge Discovery

Database Marketing, Business Intelligence and Knowledge Discovery Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski

More information

BIG DATA & DATA SCIENCE

BIG DATA & DATA SCIENCE BIG DATA & DATA SCIENCE ACADEMY PROGRAMS IN-COMPANY TRAINING PORTFOLIO 2 TRAINING PORTFOLIO 2016 Synergic Academy Solutions BIG DATA FOR LEADING BUSINESS Big data promises a significant shift in the way

More information

Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD

Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

More information

Understanding Your Customer Journey by Extending Adobe Analytics with Big Data

Understanding Your Customer Journey by Extending Adobe Analytics with Big Data SOLUTION BRIEF Understanding Your Customer Journey by Extending Adobe Analytics with Big Data Business Challenge Today s digital marketing teams are overwhelmed by the volume and variety of customer interaction

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

Big Data and Analytics: Challenges and Opportunities

Big Data and Analytics: Challenges and Opportunities Big Data and Analytics: Challenges and Opportunities Dr. Amin Beheshti Lecturer and Senior Research Associate University of New South Wales, Australia (Service Oriented Computing Group, CSE) Talk: Sharif

More information

BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics

BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics BIG DATA & ANALYTICS Transforming the business and driving revenue through big data and analytics Collection, storage and extraction of business value from data generated from a variety of sources are

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

More information

How to Enhance Traditional BI Architecture to Leverage Big Data

How to Enhance Traditional BI Architecture to Leverage Big Data B I G D ATA How to Enhance Traditional BI Architecture to Leverage Big Data Contents Executive Summary... 1 Traditional BI - DataStack 2.0 Architecture... 2 Benefits of Traditional BI - DataStack 2.0...

More information

Big Data 101: Harvest Real Value & Avoid Hollow Hype

Big Data 101: Harvest Real Value & Avoid Hollow Hype Big Data 101: Harvest Real Value & Avoid Hollow Hype 2 Executive Summary Odds are you are hearing the growing hype around the potential for big data to revolutionize our ability to assimilate and act on

More information

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be

More information

This Symposium brought to you by www.ttcus.com

This Symposium brought to you by www.ttcus.com This Symposium brought to you by www.ttcus.com Linkedin/Group: Technology Training Corporation @Techtrain Technology Training Corporation www.ttcus.com Big Data Analytics as a Service (BDAaaS) Big Data

More information

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved.

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved. Mike Maxey Senior Director Product Marketing Greenplum A Division of EMC 1 Greenplum Becomes the Foundation of EMC s Big Data Analytics (July 2010) E M C A C Q U I R E S G R E E N P L U M For three years,

More information

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume

More information

Well packaged sets of preinstalled, integrated, and optimized software on select hardware in the form of engineered systems and appliances

Well packaged sets of preinstalled, integrated, and optimized software on select hardware in the form of engineered systems and appliances INSIGHT Oracle's All- Out Assault on the Big Data Market: Offering Hadoop, R, Cubes, and Scalable IMDB in Familiar Packages Carl W. Olofson IDC OPINION Global Headquarters: 5 Speen Street Framingham, MA

More information

Data Catalogs for Hadoop Achieving Shared Knowledge and Re-usable Data Prep. Neil Raden Hired Brains Research, LLC

Data Catalogs for Hadoop Achieving Shared Knowledge and Re-usable Data Prep. Neil Raden Hired Brains Research, LLC Data Catalogs for Hadoop Achieving Shared Knowledge and Re-usable Data Prep Neil Raden Hired Brains Research, LLC Traditionally, the job of gathering and integrating data for analytics fell on data warehouses.

More information

Buyer s Guide to Big Data Integration

Buyer s Guide to Big Data Integration SEPTEMBER 2013 Buyer s Guide to Big Data Integration Sponsored by Contents Introduction 1 Challenges of Big Data Integration: New and Old 1 What You Need for Big Data Integration 3 Preferred Technology

More information

Reference Architecture, Requirements, Gaps, Roles

Reference Architecture, Requirements, Gaps, Roles Reference Architecture, Requirements, Gaps, Roles The contents of this document are an excerpt from the brainstorming document M0014. The purpose is to show how a detailed Big Data Reference Architecture

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

More information

Predictive Analytics Enters the Mainstream

Predictive Analytics Enters the Mainstream Ventana Research: Predictive Analytics Enters the Mainstream Predictive Analytics Enters the Mainstream Taking Advantage of Trends to Gain Competitive Advantage White Paper Sponsored by 1 Ventana Research

More information

COMP9321 Web Application Engineering

COMP9321 Web Application Engineering COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411

More information

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat ESS event: Big Data in Official Statistics Antonino Virgillito, Istat v erbi v is 1 About me Head of Unit Web and BI Technologies, IT Directorate of Istat Project manager and technical coordinator of Web

More information

Sunnie Chung. Cleveland State University

Sunnie Chung. Cleveland State University Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:

More information

Integrating a Big Data Platform into Government:

Integrating a Big Data Platform into Government: Integrating a Big Data Platform into Government: Drive Better Decisions for Policy and Program Outcomes John Haddad, Senior Director Product Marketing, Informatica Digital Government Institute s Government

More information

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya Chapter 6 Basics of Data Integration Fundamentals of Business Analytics Learning Objectives and Learning Outcomes Learning Objectives 1. Concepts of data integration 2. Needs and advantages of using data

More information

Advanced Big Data Analytics with R and Hadoop

Advanced Big Data Analytics with R and Hadoop REVOLUTION ANALYTICS WHITE PAPER Advanced Big Data Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional

More information

Big Data Explained. An introduction to Big Data Science.

Big Data Explained. An introduction to Big Data Science. Big Data Explained An introduction to Big Data Science. 1 Presentation Agenda What is Big Data Why learn Big Data Who is it for How to start learning Big Data When to learn it Objective and Benefits of

More information

The University of Jordan

The University of Jordan The University of Jordan Master in Web Intelligence Non Thesis Department of Business Information Technology King Abdullah II School for Information Technology The University of Jordan 1 STUDY PLAN MASTER'S

More information

The Scientific Data Mining Process

The Scientific Data Mining Process Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

More information

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH 205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology

More information

Lambda Architecture. Near Real-Time Big Data Analytics Using Hadoop. January 2015. Email: bdg@qburst.com Website: www.qburst.com

Lambda Architecture. Near Real-Time Big Data Analytics Using Hadoop. January 2015. Email: bdg@qburst.com Website: www.qburst.com Lambda Architecture Near Real-Time Big Data Analytics Using Hadoop January 2015 Contents Overview... 3 Lambda Architecture: A Quick Introduction... 4 Batch Layer... 4 Serving Layer... 4 Speed Layer...

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist 2015 Analyst and Advisor Summit Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist Agenda Key Facts Offerings and Capabilities Case Studies When to Engage

More information

PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION

PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION A m a r t y a B h a t t a c h a r j y & S u n e e l G r o v e r P r i n c i p a l S o l u t i o n A r c h i t e

More information

Navigating Big Data business analytics

Navigating Big Data business analytics mwd a d v i s o r s Navigating Big Data business analytics Helena Schwenk A special report prepared for Actuate May 2013 This report is the third in a series and focuses principally on explaining what

More information

ANALYTICS STRATEGY: creating a roadmap for success

ANALYTICS STRATEGY: creating a roadmap for success ANALYTICS STRATEGY: creating a roadmap for success Companies in the capital and commodity markets are looking at analytics for opportunities to improve revenue and cost savings. Yet, many firms are struggling

More information

TDWI Best Practice BI & DW Predictive Analytics & Data Mining

TDWI Best Practice BI & DW Predictive Analytics & Data Mining TDWI Best Practice BI & DW Predictive Analytics & Data Mining Course Length : 9am to 5pm, 2 consecutive days 2012 Dates : Sydney: July 30 & 31 Melbourne: August 2 & 3 Canberra: August 6 & 7 Venue & Cost

More information

Data Mining for Everyone

Data Mining for Everyone Page 1 Data Mining for Everyone Christoph Sieb Senior Software Engineer, Data Mining Development Dr. Andreas Zekl Manager, Data Mining Development Page 2 Executive Summary Contents 2 Data mining in the

More information

Hadoop s Advantages for! Machine! Learning and. Predictive! Analytics. Webinar will begin shortly. Presented by Hortonworks & Zementis

Hadoop s Advantages for! Machine! Learning and. Predictive! Analytics. Webinar will begin shortly. Presented by Hortonworks & Zementis Webinar will begin shortly Hadoop s Advantages for Machine Learning and Predictive Analytics Presented by Hortonworks & Zementis September 10, 2014 Copyright 2014 Zementis, Inc. All rights reserved. 2

More information

Lluis Belanche + Alfredo Vellido. Intelligent Data Analysis and Data Mining

Lluis Belanche + Alfredo Vellido. Intelligent Data Analysis and Data Mining Lluis Belanche + Alfredo Vellido Intelligent Data Analysis and Data Mining a.k.a. Data Mining II Office 319, Omega, BCN EET, office 107, TR 2, Terrassa avellido@lsi.upc.edu skype, gtalk: avellido Tels.:

More information

The Future of Data Management

The Future of Data Management The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah (@awadallah) Cofounder and CTO Cloudera Snapshot Founded 2008, by former employees of Employees Today ~ 800 World Class

More information

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ End to End Solution to Accelerate Data Warehouse Optimization Franco Flore Alliance Sales Director - APJ Big Data Is Driving Key Business Initiatives Increase profitability, innovation, customer satisfaction,

More information

Journée Thématique Big Data 13/03/2015

Journée Thématique Big Data 13/03/2015 Journée Thématique Big Data 13/03/2015 1 Agenda About Flaminem What Do We Want To Predict? What Is The Machine Learning Theory Behind It? How Does It Work In Practice? What Is Happening When Data Gets

More information

Healthcare Measurement Analysis Using Data mining Techniques

Healthcare Measurement Analysis Using Data mining Techniques www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 03 Issue 07 July, 2014 Page No. 7058-7064 Healthcare Measurement Analysis Using Data mining Techniques 1 Dr.A.Shaik

More information

DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support

DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support Rok Rupnik, Matjaž Kukar, Marko Bajec, Marjan Krisper University of Ljubljana, Faculty of Computer and Information

More information

birt Analytics data sheet Reduce the time from analysis to action

birt Analytics data sheet Reduce the time from analysis to action Reduce the time from analysis to action BIRT Analytics is the newest addition to ActuateOne. This new analytics product is fast and agile, and adds to the already rich Actuate BIRT product lineup the simpleto-use

More information

Big Data. Introducción. Santiago González <sgonzalez@fi.upm.es>

Big Data. Introducción. Santiago González <sgonzalez@fi.upm.es> Big Data Introducción Santiago González Contenidos Por que BIG DATA? Características de Big Data Tecnologías y Herramientas Big Data Paradigmas fundamentales Big Data Data Mining

More information

How the oil and gas industry can gain value from Big Data?

How the oil and gas industry can gain value from Big Data? How the oil and gas industry can gain value from Big Data? Arild Kristensen Nordic Sales Manager, Big Data Analytics arild.kristensen@no.ibm.com, tlf. +4790532591 April 25, 2013 2013 IBM Corporation Dilbert

More information

PRIME DIMENSIONS. Revealing insights. Shaping the future.

PRIME DIMENSIONS. Revealing insights. Shaping the future. PRIME DIMENSIONS Revealing insights. Shaping the future. Service Offering Prime Dimensions offers expertise in the processes, tools, and techniques associated with: Data Management Business Intelligence

More information

Adobe Insight, powered by Omniture

Adobe Insight, powered by Omniture Adobe Insight, powered by Omniture Accelerating government intelligence to the speed of thought 1 Challenges that analysts face 2 Analysis tools and functionality 3 Adobe Insight 4 Summary Never before

More information

Big Data and Your Data Warehouse Philip Russom

Big Data and Your Data Warehouse Philip Russom Big Data and Your Data Warehouse Philip Russom TDWI Research Director for Data Management April 5, 2012 Sponsor Speakers Philip Russom Research Director, Data Management, TDWI Peter Jeffcock Director,

More information

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica

More information

Data Warehouse Architecture Overview

Data Warehouse Architecture Overview Data Warehousing 01 Data Warehouse Architecture Overview DW 2014/2015 Notice! Author " João Moura Pires (jmp@di.fct.unl.pt)! This material can be freely used for personal or academic purposes without any

More information

<Insert Picture Here> Oracle Retail Data Model Overview

<Insert Picture Here> Oracle Retail Data Model Overview Oracle Retail Data Model Overview The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into

More information

Data Virtualization and ETL. Denodo Technologies Architecture Brief

Data Virtualization and ETL. Denodo Technologies Architecture Brief Data Virtualization and ETL Denodo Technologies Architecture Brief Contents Data Virtualization and ETL... 3 Summary... 3 Data Virtualization... 7 What is Data Virtualization good for?... 8 Applications

More information

A Knowledge Management Framework Using Business Intelligence Solutions

A Knowledge Management Framework Using Business Intelligence Solutions www.ijcsi.org 102 A Knowledge Management Framework Using Business Intelligence Solutions Marwa Gadu 1 and Prof. Dr. Nashaat El-Khameesy 2 1 Computer and Information Systems Department, Sadat Academy For

More information

15.564 Information Technology I. Business Intelligence

15.564 Information Technology I. Business Intelligence 15.564 Information Technology I Business Intelligence Outline Operational vs. Decision Support Systems What is Data Mining? Overview of Data Mining Techniques Overview of Data Mining Process Data Warehouses

More information

Capitalize on Big Data for Competitive Advantage with Bedrock TM, an integrated Management Platform for Hadoop Data Lakes

Capitalize on Big Data for Competitive Advantage with Bedrock TM, an integrated Management Platform for Hadoop Data Lakes Capitalize on Big Data for Competitive Advantage with Bedrock TM, an integrated Management Platform for Hadoop Data Lakes Highly competitive enterprises are increasingly finding ways to maximize and accelerate

More information

SURVEY REPORT DATA SCIENCE SOCIETY 2014

SURVEY REPORT DATA SCIENCE SOCIETY 2014 SURVEY REPORT DATA SCIENCE SOCIETY 2014 TABLE OF CONTENTS Contents About the Initiative 1 Report Summary 2 Participants Info 3 Participants Expertise 6 Suggested Discussion Topics 7 Selected Responses

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information

Certificate Program in Applied Big Data Analytics in Dubai. A Collaborative Program offered by INSOFE and Synergy-BI

Certificate Program in Applied Big Data Analytics in Dubai. A Collaborative Program offered by INSOFE and Synergy-BI Certificate Program in Applied Big Data Analytics in Dubai A Collaborative Program offered by INSOFE and Synergy-BI Program Overview Today s manager needs to be extremely data savvy. They need to work

More information

Turning Big Data into Big Insights

Turning Big Data into Big Insights mwd a d v i s o r s Turning Big Data into Big Insights Helena Schwenk A special report prepared for Actuate May 2013 This report is the fourth in a series and focuses principally on explaining what s needed

More information

Why big data? Lessons from a Decade+ Experiment in Big Data

Why big data? Lessons from a Decade+ Experiment in Big Data Why big data? Lessons from a Decade+ Experiment in Big Data David Belanger PhD Senior Research Fellow Stevens Institute of Technology dbelange@stevens.edu 1 What Does Big Look Like? 7 Image Source Page:

More information

Extending the Enterprise Data Warehouse with Hadoop Robert Lancaster. Nov 7, 2012

Extending the Enterprise Data Warehouse with Hadoop Robert Lancaster. Nov 7, 2012 Extending the Enterprise Data Warehouse with Hadoop Robert Lancaster Nov 7, 2012 Who I Am Robert Lancaster Solutions Architect, Hotel Supply Team rlancaster@orbitz.com @rob1lancaster Organizer of Chicago

More information

BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES

BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES Relational vs. Non-Relational Architecture Relational Non-Relational Rational Predictable Traditional Agile Flexible Modern 2 Agenda Big Data

More information

ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam

ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam ECLT 5810 E-Commerce Data Mining Techniques - Introduction Prof. Wai Lam Data Opportunities Business infrastructure have improved the ability to collect data Virtually every aspect of business is now open

More information

BIG Data Analytics Move to Competitive Advantage

BIG Data Analytics Move to Competitive Advantage BIG Data Analytics Move to Competitive Advantage where is technology heading today Standardization Open Source Automation Scalability Cloud Computing Mobility Smartphones/ tablets Internet of Things Wireless

More information

A Visualization is Worth a Thousand Tables: How IBM Business Analytics Lets Users See Big Data

A Visualization is Worth a Thousand Tables: How IBM Business Analytics Lets Users See Big Data White Paper A Visualization is Worth a Thousand Tables: How IBM Business Analytics Lets Users See Big Data Contents Executive Summary....2 Introduction....3 Too much data, not enough information....3 Only

More information

A Review of Data Mining Techniques

A Review of Data Mining Techniques Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

More information

SPATIAL DATA CLASSIFICATION AND DATA MINING

SPATIAL DATA CLASSIFICATION AND DATA MINING , pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal

More information

ICT Perspectives on Big Data: Well Sorted Materials

ICT Perspectives on Big Data: Well Sorted Materials ICT Perspectives on Big Data: Well Sorted Materials 3 March 2015 Contents Introduction 1 Dendrogram 2 Tree Map 3 Heat Map 4 Raw Group Data 5 For an online, interactive version of the visualisations in

More information

Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010

Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010 Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010 Ernst van Waning Senior Sales Engineer May 28, 2010 Agenda SPSS, an IBM Company SPSS Statistics User-driven product

More information

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce Analytics in the Cloud Peter Sirota, GM Elastic MapReduce Data-Driven Decision Making Data is the new raw material for any business on par with capital, people, and labor. What is Big Data? Terabytes of

More information

Oracle Big Data Discovery Unlock Potential in Big Data Reservoir

Oracle Big Data Discovery Unlock Potential in Big Data Reservoir Oracle Big Data Discovery Unlock Potential in Big Data Reservoir Gokula Mishra Premjith Balakrishnan Business Analytics Product Group September 29, 2014 Copyright 2014, Oracle and/or its affiliates. All

More information

Big Data. Fast Forward. Putting data to productive use

Big Data. Fast Forward. Putting data to productive use Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize

More information

IBM SPSS Modeler Professional

IBM SPSS Modeler Professional IBM SPSS Modeler Professional Make better decisions through predictive intelligence Highlights Create more effective strategies by evaluating trends and likely outcomes. Easily access, prepare and model

More information

Big Data, Start Small! Dr. Frank Säuberlich, Director Advanced Analytics (Teradata International) 26 th May 2015

Big Data, Start Small! Dr. Frank Säuberlich, Director Advanced Analytics (Teradata International) 26 th May 2015 Big Data, Start Small! Dr. Frank Säuberlich, Director Advanced Analytics (Teradata International) 26 th May 2015 Agenda Introduction Big Data And The Emergence Of The Logical Data Warehouse Architecture

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:

More information

Some Research Challenges for Big Data Analytics of Intelligent Security

Some Research Challenges for Big Data Analytics of Intelligent Security Some Research Challenges for Big Data Analytics of Intelligent Security Yuh-Jong Hu hu at cs.nccu.edu.tw Emerging Network Technology (ENT) Lab. Department of Computer Science National Chengchi University,

More information

Big Data Executive Survey

Big Data Executive Survey Big Data Executive Full Questionnaire Big Date Executive Full Questionnaire Appendix B Questionnaire Welcome The survey has been designed to provide a benchmark for enterprises seeking to understand the

More information

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge

More information

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing

More information

Chukwa, Hadoop subproject, 37, 131 Cloud enabled big data, 4 Codd s 12 rules, 1 Column-oriented databases, 18, 52 Compression pattern, 83 84

Chukwa, Hadoop subproject, 37, 131 Cloud enabled big data, 4 Codd s 12 rules, 1 Column-oriented databases, 18, 52 Compression pattern, 83 84 Index A Amazon Web Services (AWS), 50, 58 Analytics engine, 21 22 Apache Kafka, 38, 131 Apache S4, 38, 131 Apache Sqoop, 37, 131 Appliance pattern, 104 105 Application architecture, big data analytics

More information

BIG DATA: FROM HYPE TO REALITY. Leandro Ruiz Presales Partner for C&LA Teradata

BIG DATA: FROM HYPE TO REALITY. Leandro Ruiz Presales Partner for C&LA Teradata BIG DATA: FROM HYPE TO REALITY Leandro Ruiz Presales Partner for C&LA Teradata Evolution in The Use of Information Action s ACTIVATING MAKE it happen! Insights OPERATIONALIZING WHAT IS happening now? PREDICTING

More information

Data Isn't Everything

Data Isn't Everything June 17, 2015 Innovate Forward Data Isn't Everything The Challenges of Big Data, Advanced Analytics, and Advance Computation Devices for Transportation Agencies. Using Data to Support Mission, Administration,

More information

An Introduction to Advanced Analytics and Data Mining

An Introduction to Advanced Analytics and Data Mining An Introduction to Advanced Analytics and Data Mining Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda What are Advanced Analytics and Data Mining? The toolkit

More information

CS590D: Data Mining Chris Clifton

CS590D: Data Mining Chris Clifton CS590D: Data Mining Chris Clifton March 10, 2004 Data Mining Process Reminder: Midterm tonight, 19:00-20:30, CS G066. Open book/notes. Thanks to Laura Squier, SPSS for some of the material used How to

More information

UNIFY YOUR (BIG) DATA

UNIFY YOUR (BIG) DATA UNIFY YOUR (BIG) DATA ANALYTIC STRATEGY GIVE ANY USER ANY ANALYTIC ON ANY DATA Scott Gnau President, Teradata Labs scott.gnau@teradata.com t Unify Your (Big) Data Analytic Strategy Technology excitement:

More information

HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS.

HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. ALTILIA turns Big Data into Smart Data and enables businesses to

More information

Advanced In-Database Analytics

Advanced In-Database Analytics Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??

More information

[callout: no organization can afford to deny itself the power of business intelligence ]

[callout: no organization can afford to deny itself the power of business intelligence ] Publication: Telephony Author: Douglas Hackney Headline: Applied Business Intelligence [callout: no organization can afford to deny itself the power of business intelligence ] [begin copy] 1 Business Intelligence

More information

Big Data Can Drive the Business and IT to Evolve and Adapt

Big Data Can Drive the Business and IT to Evolve and Adapt Big Data Can Drive the Business and IT to Evolve and Adapt Ralph Kimball Associates 2013 Ralph Kimball Brussels 2013 Big Data Itself is Being Monetized Executives see the short path from data insights

More information

SAP Solution Brief SAP HANA. Transform Your Future with Better Business Insight Using Predictive Analytics

SAP Solution Brief SAP HANA. Transform Your Future with Better Business Insight Using Predictive Analytics SAP Brief SAP HANA Objectives Transform Your Future with Better Business Insight Using Predictive Analytics Dealing with the new reality Dealing with the new reality Organizations like yours can identify

More information

Hadoop in the Hybrid Cloud

Hadoop in the Hybrid Cloud Presented by Hortonworks and Microsoft Introduction An increasing number of enterprises are either currently using or are planning to use cloud deployment models to expand their IT infrastructure. Big

More information

DATA SCIENCE CURRICULUM WEEK 1 ONLINE PRE-WORK INSTALLING PACKAGES COMMAND LINE CODE EDITOR PYTHON STATISTICS PROJECT O5 PROJECT O3 PROJECT O2

DATA SCIENCE CURRICULUM WEEK 1 ONLINE PRE-WORK INSTALLING PACKAGES COMMAND LINE CODE EDITOR PYTHON STATISTICS PROJECT O5 PROJECT O3 PROJECT O2 DATA SCIENCE CURRICULUM Before class even begins, students start an at-home pre-work phase. When they convene in class, students spend the first eight weeks doing iterative, project-centered skill acquisition.

More information

Data Mining Analytics for Business Intelligence and Decision Support

Data Mining Analytics for Business Intelligence and Decision Support Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing

More information