C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

BIG DATA GOVERNANCE

GÖRKEM ŞEVİK

BİLGİ YÖNETİMİ ÇÖZÜM LİDERİ

Big data governance

is part of a broader data governance program

that formulates,

monitors

and enforces policies

relating to big data.

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

BİG DATA GOVERNANCE CONSIDERATİONS

Big transaction data

Human generated data

Web & social media

Internet of Things

Big Data Reference Architecture

provides a common framework that describes all the components needed for the required functionality.

Enterprise data architects

can use a big data reference architecture to plot big data road maps and discover gaps in technology implementations.

Data scientists and business users

can take advantage of this approach to make sense out of a complex landscape.

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A comprehensive

Big Data Reference

Architecture

Consists of

16 regions

in 3 main sections

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SECTION 1 INGESTION

REGION 1: BIG DATA SOURCES

Access big data sources

Virtually blend data from multiple sources without physically moving

REGION 2: HADOOP DISTRIBUTIONS

Access and process on the Hadoop distributions

Push processing down to the Hadoop for improved performance

REGION 3: DATA STREAMS

Examine, filter and analyze large volumes of real-time data

REGION 4: DATABASES

Read and write more than 60 relational and big data sources

Manage the business glossary for attributes in these data sources

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SECTION 2 CLEANSING, INTEGRATION AND GOVERNANCE

REGION 5: BIG DATA

INTEGRATION

Self-service big data preparation to

enhance analyst productivity and

improve the quality of data

Bridge the skills gap, giving all users

access to their Hadoop data

regardless of technical ability

Business analysts and data scientists

to summarize, aggregate, merge,

transpose, or join data in Hadoop

REGION 6: TEXT ANALYTICS

Derive value from the unstructured data by answering business questions related to call logs,

claims adjuster notes, social media, etc.

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SECTION 2 CLEANSING, INTEGRATION AND GOVERNANCE

REGION 7 and 8:

BIG DATA DISCOVERY AND

BIG DATA QUALITY

Business users and data scientists

to profile data natively, in cluster and in

parallel,

standardize, parse, match or de-duplicate

data in Hadoop without writing code

Gender analysis, pattern analysis, etc.

Run in different runtime

environments, incl. in-stream, in-

memory, in-Hadoop

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SECTION 2 CLEANSING, INTEGRATION AND GOVERNANCE

REGION 9 and 10: METADATA AND INFORMATION POLICY MANAGEMENT

Common metadata repository that spans data management, analytics and third-party data

sources and tools, to store, manage and deliver metadata

Workflow driven business glossary to relate and track the associated business rules, reference

data, Technical owners, data stewards and other roles

Visualization of relationships and impact analysis to see how changes to one data element affect

information in other systems

Document acceptable use standards

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SECTION 2 CLEANSING, INTEGRATION AND GOVERNANCE

REGION 11: MASTER DATA MANAGEMENT

Organically built on top of data quality

Support multiple data domain

Data access from Apache Hive, Cloudera Impala

Include data glossary and

lineage

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SECTION 3 ANALYTİCS, SECURİTY AND LİFE CYCLE

REGION 12: DATA WAREHOUSES AND DATA MARTS

Run model scoring inside databases for improved performance and governance

REGION 13: BIG DATA ANALYTICS AND REPORTING

Advanced analytical capabilities for big data analytics, visualization and reporting

Take governance of analytical models into consideration

Streamline the process of creating, managing, administering and monitoring analytical models

Integrate with workflow engine

REGION 14: BIG DATA SECURITY AND PRIVACY

Data stewards classify data from an information security perspective, such as «Public»,

«Internal», «Confidential»

Dynamic data masking and encryption

Right users have access to the right data

Secure Data

Access

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SECTION 3 ANALYTİCS, SECURİTY AND LİFE CYCLE

REGION 15: BIG DATA LIFECYCLE MANAGEMENT

Understand and categorize the usefulness of the data and how it relates to other

data sets

Consider data source onboarding, how and when to archive data, what stage the

data is in its life cycle, and who owns the decision rights around that data

REGION 16: CLOUD

Manage cloud-based data

Support cloud-based executions with web-based services

architecture

Deploy to Amazon EC2, Rackspace or other platform-as-a-service

providers

SAS offers a robust platform and comprehensive approach to big data governance, data managementand analytics that can accommodate the data evolution.

C op yr i g h t © 2016, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

THANK YOU

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