2014.3.6 Kim, Yeon-Hee

Big Data AnalyticsData InDecision Out

Volume

• 전통적인 데이터에 비하여 단위가 다른 크기의 데이터(Terabytes,

Petabytes, Exabytes)

• 일반 Database를 이용하여 저장, 관리 및 분석하는 것이 현실적으로

불가능하거나 엄청난 비용이 필요한 데이터

Variety

• Structured, semi-structured, unstructured

• 다양한 데이터 원천 - complex event processing, application logs,

machine sensors, social media data

Velocity

• 새로 유입되는 데이터 스트림의 속도

• Streaming Data 또는 Complex Event Data들의 실시간 처리 및 분석

Volume

Variety

Velocity

빅데이터란 무엇인가?Gartner에서 이야기 하는 빅데이터의 3V

3

발표자프레젠테이션 노트With the rapid evolution of technology over the past several decades, data – the information gathered through technology – has evolved as well. The best way to understand Big Data is by the challenges it produces. Typically Big Data is thought of as a volume problem primarily because of its name. The terabytes, petabytes, and even exabytes of data that companies are producing lead to several challenges in storing, managing, and analyzing an information database. However, variety and velocity also prove to be a part of the Big Data problem. Variety is an issue because of the varying formats and sources that data is being gathered from and consolidated into. Velocity is the final piece of the Big Data problem as the volume and variety of data directly contribute to the problem of velocity. It proves difficult to analyze the data with speed and to distribute and garner insight from such high volume and differing varieties of data.

빅데이터 소스 및 그 비즈니스 가치에 대한 네가지 유형

전통적인 소스의크기가 커짐

기업, 정부기관, 금융사, 비즈니스와 소비자 연구, 설문조사, 투표

비즈니스 퍼포먼스 측면 – 운영 효율, 매출 관리, 전략 기획

SOURCE

VALUE

인터랙션들의디지털 데이터

Online click-stream, Application logs, Call/service records, ID scans, Security cameras

새로운 매출 영역, 소비자 프로모션, Risk management, Fraud detection

SOURCE

VALUE

Web 2.0 현상

소셜 미디어 포스트들에서 생성된 컨텐츠들, 트위터, 블로그, 사진, 비디오, 평가

고객 접점, 고객 서비스, 브랜드 관리, 폭넓은마케팅

SOURCE

VALUE

물건들의인터넷

기계들이 생성하는 센서 데이터 및 서로 연결된기기들 사이의 통신 정보

운영 효율 제고, 비용 최적화, 발생할 수 있는리스크를 미리 파악하여 회피

SOURCE

VALUE

빅데이터 활용 유형

발표자프레젠테이션 노트There are four categories which have fueled the explosion of Big Data prevalence. These are: traditional sources becoming bigger, digital exhaust from interactions, web 2.0, and the internet of connected devices. Each source of Big Data offers unique values to analyze consumers and drive revenue. The spheres of traditional data sources becoming bigger and digital exhaust from interactions have led to an unprecedented increase in structured data with both great opportunities and challenges for businesses.

Source: The Emerging Big Returns on Big Data: Tata Consulting Services

빅데이터를 활용한 더 뛰어난 의사 결정

발표자프레젠테이션 노트According to a 2013 study by TCS, globally, a majority of companies having Big Data initiatives in place have claimed an improvement in decision-making processes in their businesses. Benefits are being recognized uniformly around the world, is your company utilizing this opportunity to embrace Big Data solutions? If not, you may quickly be left behind and not acting upon the many benefits that you can be reaping through Big Data solutions.

Source: The Emerging Big Returns on Big Data: Tata Consulting Services

Key Challenges of Big Data Across Regions of the World

Q23: Mean Rating of 16 Challenges in Getting Business Value from Big Data (Scale of 1-5)

Rank Challenge Score

1 Getting business units to share information across organizational silos 3.37

2 Being able to handle the large volume, velocity and variety of Big Data 3.35

3 Determining what data (both structured and unstructured, and internal and external) to use for different business decisions 3.34

4 Building high levels of trust between the data scientists who present insights on Big Data and the functional managers 3.26

5 Finding and hiring data scientists who can manage large amounts of structured and unstructured data and create insights 3.23

6 Getting top management in the company to approve investments in Big Data and its related investments (e.g., training, etc.) 3.22

7 Putting our analysis of Big Data in a presentable form for making decisions (e.g., visualization/visual models) 3.21

8 Finding the optimal way to organize Big Data activities in our company 3.2

9 Understanding where in the company we should focus our Big Data investments 3.18

10 Determining what to do with the insights that are created from Big Data 3.18

기술은 이러한 과제들을 해결할 수 있는 다양한 방안을 제공할 수 있음

하지만 여전히 존재하는 이슈들

발표자프레젠테이션 노트There are several issues that exist due to the nature of Big Data implementations. The graph above shows the top ten obstacles that exist in companies implementing Big Data solutions across the globe. A majority of issues tend to be organizational in nature, and technology can definitely help provide enablers to overcome these obstacles.

회피Evading

구상Envisioning

평가Evaluating

실행Execution

확장Expanding

0

10

20

30

40

50

기업비율

(%)

40% 이상의 기업들이 여전히 빅데이터를 회피하고 있음

The “5Es”: 빅데이터 여정의 다섯 단계

Source: Based on survey data from Gartner and IBM

발표자프레젠테이션 노트Companies go through what we call the 5E stages of the Big Data journey: evading (not considering), envisioning (1-3 years out), evaluating (planning in the next 12 months), executing (actively working on projects), and expanding (have deployed a few projects successfully, and growing). Based on surveys done by Gartner and IBM, an astounding 40%+ of the market is still in the evading stage of their Big Data journey. This means that there are many companies who are losing valuable opportunities to gain business insight from their Big Data yet are unable to execute most likely because they have not explored the means to do so.

회피Evading

구상Envisioning

평가Evaluating

실행Execution

확장Expanding

빅데이터 기술에대한 갭 좁히기

관련 Use Case 이해

데이터 시각화를통하여 데이터의가치를 평가

각 빅데이터 성숙 단계별로 요구되는 것들각 단계별로 던질 수 있는 “무엇을”, “왜”, “어떻게”, 등의 질문들이 다양한 분석 니즈로 연결

데이터에서 나온인사이트를

시각적으로 표현

필요한분석 능력을 구상

어떠한 데이터소스가 결합될 수

있는가

적용될 분석의 넓이

분석할 정형, 비정형, 반정형, 데이터의

범위

멀티 소스 데이터에대한 공통 모델생성

고급 분석 모델 적용

비정형 및 반정형데이터를 어떻게정형화 할 것인가

이해관계자들에게시각적 분석 기능을

보급

빅데이터 분석의가용 범위를 확장

실시간 분석 니즈에대한 업데이트 주기

고려

다른 빅데이트 Use Case로 확장

발표자프레젠테이션 노트Each stage of the Big Data journey provides different challenges in order to evolve towards maturity. Within the evading stage, the needs are those involving closing gaps in Big Data technical skills, understanding relevant use cases, and finding the ability to visualize data to access value. Within the envisioning stage, one must figure out how to visually express insights in data, envision the analytic capabilities needed, and understand which data sources can be combined. In the evaluating stage, the needs become related to finding out the breadth of analytics that need to be applied, the scope of multi-structured data needed to analyze, and how to create a common model of multi-source data. In the executing stage, figuring out how to apply advanced analytic models, structuring data that in unstructured and semi-structured, and disseminating visual analytics to key stakeholders are the main priorities. Finally, when a company reaches the expanding stage, broadening availability of big data analytics, considering frequency of updates and real-time analysis needs, and expanding to other big data use cases are necessary.

모든데이터에대한접근성과가버넌스확보

Self-service 데이터탐색에서전사대시보드까지

고급분석및예측분석의활용

반정형및비정형데이터분석

실시간업데이트되는데이터에대한분석

1

2

3

4

5

빅데이터 분석을 위한 다섯가지 주요 기능요소MicroStrategy Analytics Platform은 모든 비즈니스 사용자들이 이러한 기능을 활용할 수 있도록 함

발표자프레젠테이션 노트There are five important capabilities that Big Data analytics provides:Ability to access all Enterprise data, combine it together and apply full governance on the disparate sourcesAbility to have Self-service data exploration and take it all the way to production level dashboardsAccessibility of advanced and predictive analytics by business users using simple and understandable toolsAnalysis of semi-structured and unstructured dataReal-time analysis from live updating data.

The top three capabilities are where most mainline opportunities exist for companies to see quicker and substantive returns off their Big Data solutions, and the MicroStrategy Analytics Platform has market-leading capabilities in all 3 owing to 20+ years of R&D investment.

User / Departmental Data

Data Warehouse Appliances

MapReduce & NOSQL Databases

Relational Databases

MultidimensionalDatabases

ColumnarDatabases

SaaS-Based App Data

HANA

BigInsights

Parallel Data Warehouse

Elastic Map Reduce

Analysis Services

Redshift

Brin

g Al

l Rel

evan

t Dat

a to

D

ecis

ion

Mak

ers

빅데이터 생태계에 있는 모든 데이터 소스를 마치 하나의 Database인 것 처럼 활용

1. 모든 데이터 활용

Distribution

발표자프레젠테이션 노트MicroStrategy enables every organization to tie into all of their data sources whether we’re talking about user or departmental data, multidimensional databases, relational databases, data warehouse appliances, columnar databases and even at the highest end of the scale, Hadoop systems or MapReduce databases. We’ve build optimized connectors for all these sources. This graph provides a visual glance at all our data partners that we support integration with.

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Hadoop 환경에서의 쿼리 실행 시간

Seco

nds

Ex

ecut

ion

Tim

es

M

inut

es

GBs Data Volume PBs

In-m

emor

y

RD

BMS

Hadoop

In-memoryHOT

RDBMSWARM

HadoopCOLD

11

발표자프레젠테이션 노트There are three main data layers to discuss when talking about data retrieval and query execution. In-memory are usually smaller amounts of data stored directly on the system on in a cube to access information quickly. RDBMS stand for relational database management systems which are more traditional ways to store structured type of data. Hadoop environments are the ones that have high levels of data volume and can have varying levels of structure and data type. The current state of technology to access data interactively from Hadoop is such that it makes sense for scenarios of low level data access, and for queries that are completely unanticipated. It can be more strategic to create an architecture to layer data in all 3 layers to balance out query performance against data storage and processing.

User / Departmental Data

Data Warehouse Appliances

MapReduceDatabases

RelationalDatabases

MultidimensionalDatabases

ColumnarDatabases

SaaS-BasedApp Data

MicroStrategyMultisource Engine

2 & 3

Join data on-the-fly.

No need to move it to a staging database first.

빅데이터 생태계에 있는 모든 데이터 소스를 마치 하나의 Database인 것 처럼 활용

다양한 소스의 데이터 결합

1

2

1

23

12

발표자프레젠테이션 노트MicroStrategy’s architecture also enables multi-source, federated data access across multiple systems so that business users and developers can effectively access an organizations’ entire big data ecosystem as if it were a single database. Here you can see a dashboard where we’re bringing relevant data to the user of this dashboard from multiple data sources, including one visualization where we’re joining data dynamically from multiple systems.

Stunning Visualizations

Instant Query Results

Effortless Dashboards

No IT Needed

모든 사용자들에게 빠르고 쉽게 인사이트를 제공

2. Self-Service를 통한 분석 능력 배포

13

발표자프레젠테이션 노트MicroStrategy Visual Insight is a pioneering new technology in agile analytics. It combines stunning visualizations with on-screen filtering and at the back end, there’s a very high speed in-memory database that powers everything for speed of thought interactivity between user and data. There are really two common use cases for this technology. The first is just pure visual data discovery, the idea of using visualizations rapidly shifting between different types of interactive visualizations to find information that you’re looking for, to isolate that information, to find trends, to look for root causes, to find the nuggets of insight into your data. The second major use case is dashboard creation. That’s the idea of bringing together multiple visualizations in an interactive dashboard that can then be published and shared with a variety of other people.

검증된 Self-Service 컨텐츠를 전사 대시보드로 전환

Insight 생성! Self-Service 대시보드를 이용하여 빠르게 컨텐츠를프로토타입

정형 대시보드 전환을 위하여 Visual Insight 항목을 Export

정형화를 위한 추가 기능 구성:

• Custom branding

• Multimedia content

• Transactions

• Decision workflow

• Prescriptive analytics

• Real-time data

수천명의 전사 사용자들에게 배포. Insight 자산화!

FAST, LOW-IMPEDANCE PUBLISHING

Self-Service Dashboard를 Production Dashboard로 가공하여 게시

1

2

3

4

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발표자프레젠테이션 노트Self-service dashboards allow for quick production and analysis. These can easily be turned into high-scale production level dashboards with other powerful features listed in the slide.

Production Business IntelligenceStandalone

Visual Data Discovery

Dashboards Reports and Statements

OLAP AnalysisAdvanced Analysis

MicroStrategy에서는 비즈니스 민첩성과 가버넌스가 동시에 존재

Self-Service 분석과 전사 BI의 긴밀한 통합

Only MicroStrategy Delivers Agility and Governance• Common metadata for trusted single version of the truth

• Flexibility for business to quickly work with new data

Move Seamlessly between Styles

15

발표자프레젠테이션 노트MicroStrategy offers tight integration of all of its services. Even though Visual Insight can be used as a standalone service, it can be easily integrated into the enterprise portion of the MicroStrategy platform. This offers great flexibility and transferability of data as well as opportunities to analyze the data from several different angles.

수천명의 사용자들에게 검증된 대시보드를 배포

Proven Server Scalability

Built-in clustering, failover, and

comprehensive administrative tools for

performance optimization

In-Memory PerformanceTested sub-second response times on web and mobile,

even at highest user volume and concurrency

Advanced Monitoring

ToolsAdmin tools to

monitor, report and alert on system

utilization

Automatic Content

PersonalizationUsers only see the data they’re entitled to see,

and only access functionality they are

authorized to use

PUBLISH

Team DepartmentEnterprise

Value Chain

10s 100s

1,000s10,000+

Visual Insight dashboard

발표자프레젠테이션 노트MicroStrategy offers unlimited scalability based on the organization’s need. Whether it’s for a single team or department or the entire organization, MicroStrategy allows users to access the data and share it with others.

Industry’s most powerful SQL Engine and 300+ native analytical functions

분석 성숙도 향상을 통해 경쟁 기업들을 앞서갈 수 있음

3. 다양한 고급 분석 기능들에대한 단일 접점

Projections

Relationship Analysis

Benchmarking

Trend Analysis

Data Summarization

Anal

ytic

al M

atur

ity

What is likely to happen based on past history?

What factors influence activity or behavior?

How are we doing versus comparables?

What direction are we headed in?

What is happening in the aggregate?

Optimization What do we want to happen?

World’s most popular

advanced analytics tool.

Free, open source.

More

Specialty Tools

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발표자프레젠테이션 노트Coupled to its ability to analyze large scale data, MicroStrategy also incorporates a strong, robust set of analytical functions ranging from data summarization to trend analysis, benchmarking, relationship analysis and projections. You can think of this as an analytical maturity model that we completely support within our platform. We expose these rich analytical functions to busness users in very easy to use ways so that they access and use all this analytical functionality, and quickly and eaily climb the maturity model if they want to embrace more sophisticated analytical approaches. Within MicroStrategy, we have over 300 analytical functions which we believe support about 90% of all analytical use cases. In the rare case where a particular organization sees a use cases where they need to go beyond the functions that are supported directly in MicroStrategy; we also offer a very tight integration with R, which is the world’s most popular advanced analytical package. R offers a library of over 3000 rarely used and exotic functions – everything you would ever need and more. What we can do is we can imbed an R model directly into MicroStrategy and enable business users to tap into that model through a very easy to use visual interface.

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Streaming AnalyticsInteractive Search Text Analytics

Quickly investigate:

• Website logs• Application usage• Surveys and free form

text fields

• Event and error monitoring logs

80% 이상의 비즈니스 데이터는 비정형 데이터이며, 이 비율은 꾸준히 늘어날 것으로 예측됨

4. 비정형 및 반정형 데이터의 활용

Find keyword and event occurrences in any data

Apply semantic and syntactic models to text data

Assess rapidly changing data streams

Extract relevant information to:

• Optimize search engine marketing

• Understand sentiment on topics

• Get a 360 degree view of customers

• Detect fraud

Analyze an array of data from:

• Sensors and devices• Images, audio, and video• Email and document

management systems

• Other operational and transactional data

발표자프레젠테이션 노트80% of data in businesses is expected to be unstructured or semi-structured data. Organizations are interested in extracting useful nuggets of information from this under-utilized data asset. Interactive searches, text analytics, and streaming analytics are some of the prominent use cases for analysis of unstructured and semi-structured data.

DAT

A PR

OC

ESSI

NG

, AN

ALYT

ICS

& D

ELIV

ERY

Dashboards Reports and StatementsSelf-Service Analytics OLAP Analysis

MicroStrategy Analytics Platform

Three Ways to Query Multi-structured Data

1. Direct connection to source• Parse structure with lightweight

“Schema-on-read” functions

• Import data or Create a modeled environment

Third Party Software

2. Using Web Services• Requires data to be exposed as a

Web Service

• Data will need to be structured prior to access

3. Offline “Process and Store”• Using specialty analytics (text,

streaming, image processing) and stored as structured

• Direct connection to processed data

Semi-Structured Data Unstructured Data

DAT

A ST

OR

AGE

Web Logs Social media posts

Surveys Server Logs Geo-spatial

E-mail Image Audio Video

Sensor + Machine Data Documents

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발표자프레젠테이션 노트There are several ways to query multi-structured data. MicroStrategy provides three ways to access and analyze multi-structured data for analytic capabilities. Direct connection to the source of the data whether in databases, Hadoop or NoSQL, and applying lightweight parsing on the text as it is being retrieved from the source.Web Services connector to any Web Service that has been setup to expose unstructured or semi-structured data in a somewhat structured format.Of course, connection to unstructured data processed offline and stored as structured content.

Option 2 and 3 will require the setup of a third-party tool or custom code for specialty analysis on unstructured data.

스트리밍 데이터 및 Complex Event Processing 데이터에 대한 실시간 모니터링

5. 스트리밍 데이터의 실시간 분석

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발표자프레젠테이션 노트There’s opportunities for Real-Time data updates to your dashboards that are created. This streamlined process will lead to instant updates and opportunities to more quickly respond to business insights.

* BI SURVEY 12, BARC, 2012.

According to BARC’s BI Survey 12, MicroStrategy’s median customer data volume is 10x higher than the average of the next ten vendors.*

Median Customer Data Volumes

GBs

MicroStrategy는 빅데이터 환경에서 가장 지배적인 솔루션

MicroStrategy는 빅데이터 분석의 리더

0 100 200 300 400 500 600

Competitor Average

MicroStrategy

“We have this thing that’s running. It’s one of the most amazing things I’ve seen.It’s running against the entire Facebook user base, 1.1 billion users.”

Guy BayesHead of Enterprise BI, Facebook

Everyday Big Data

Cutting-Edge Big Data

200 petabytes powered byHadoop + MicroStrategy in-memory technology

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발표자프레젠테이션 노트MicroStrategy historically has been the leader in large data analytical applications. You can see here, again, looking at BARC’s BI Survey 12 data, that 39% of MicroStrategy customers are running MicroStrategy on top of over a terabyte of data. That’s almost triple the volume of data of the average BI customer. And as you can see, MicroStrategy’s median customer data volumes are also significantly higher – an order of magnitude higher than the average of the next ten vendors in the BI space.

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=Mz5MrLqyaYatpM&tbnid=-BfoJv8L23s1QM:&ved=0CAUQjRw&url=http://ctaar.rutgers.edu/teaching/testscanning/benchmark3000.html&ei=5LdVUq-AMsS82wXWpYDgAw&psig=AFQjCNFGy96vTq0za3r5-Z5xPGKmNhXtNQ&ust=1381435678068377http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=Mz5MrLqyaYatpM&tbnid=-BfoJv8L23s1QM:&ved=0CAUQjRw&url=http://ctaar.rutgers.edu/teaching/testscanning/benchmark3000.html&ei=5LdVUq-AMsS82wXWpYDgAw&psig=AFQjCNFGy96vTq0za3r5-Z5xPGKmNhXtNQ&ust=1381435678068377https://www.youtube.com/watch?v=ioD9oiJoDuEhttps://www.youtube.com/watch?v=ioD9oiJoDuE

Hadoop을 활용한 분석 사례

Streaming Service Analytics• Hadoop Source : Amazon Elastic Map Reduce

• 스트리밍 서비스 사용에 대한 짧은 수명의 분석

• 분석가들이 MR 코딩 없이 Hadoop의 데이터에 접근

• 수주가 소요되는 DW로의 ETL 작업에 대한 지름길

Full View of Online Commerce Customers• Hadoop Source : Apache Hadoop

• DW에 저장된 고객 트랜젝션 데이터를 Hadoop에 저장된클릭 스트림 데이터와 연결

• 온라인 고객들에 대한 전체적인 뷰 확보

More Precise Targeting of Digital Ads• Hadoop Source : Cloudera Impala

• 10억가지 이상의 차원 조합이 나오는 트레픽 데이터

• 광고주들에게 뛰어난 ROI 제공

• 모델을 준비하고 튜닝하는 시간을 혁신적으로 단축

Multi-Channel Digital Distribution Provider

22

발표자프레젠테이션 노트These are some of our customers who have had great success in utilizing MicroStrategy to analyze their hadoop data.

Hadoop 데이터 소스의 MicroStrategy 활용 패턴

Maturity of Data Access

주제영역 별 하둡데이터 인메모리로딩, 시각화 분석

1

하둡에 직접셀프 서비스파라미터 쿼리

2

Multi-dimensionalBusiness Model

모델 기반의 하둡데이터 접근

3

RDBMS

ETL

DW, 인메모리, Hive 간의 멀티소스스키마 모델 쿼리

4

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발표자프레젠테이션 노트There are certain patterns within Hadoop utilization as a data source. The 4 listed are the ways organizations evolve in their utilization of Hadoop access over time.

WHAT IS YOUR NEXT STEP?

Let us help you.

THANK YOU

발표자프레젠테이션 노트The best part of all is that we’ve designed all of this capability, this entire platform of functionality to be extremely fast and extremely flexible so you can start quickly, deploy it anywhere and consume it anywhere.

슬라이드 번호 1슬라이드 번호 2슬라이드 번호 3슬라이드 번호 4슬라이드 번호 5슬라이드 번호 6슬라이드 번호 7슬라이드 번호 8슬라이드 번호 9슬라이드 번호 10슬라이드 번호 11슬라이드 번호 12슬라이드 번호 13슬라이드 번호 14슬라이드 번호 15슬라이드 번호 16슬라이드 번호 17슬라이드 번호 18Three Ways to Query Multi-structured Data슬라이드 번호 20슬라이드 번호 21Hadoop을 활용한 분석 사례슬라이드 번호 23슬라이드 번호 24