Big Data emerged with Internet companies as special projects managed by elite practitioners to solve unique problems. This approach will not be sustainable for enterprises. This presentation describes how big Data projects must become part of the fabric of your enterprise if they are to succeed.

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Making Big Data a First-class
Citizen in the Enterprise
Tony Baer
tony.baer@ovum.com
IT014-002860
January 24, 2014

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Contents
 The premise
 What we mean
 Scope – focus on Hadoop, the most popular emerging Big Data
platform
 Addressing the enterprise
 IT organization
 Data center infrastructure, processes, policies, and practices
 Creating value; competitive benefits to the business
 The endgame

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The premise: Voting Big Data off the island!
Big Data must become a first-class citizen in the enterprise and
cannot exist on its own island.

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Hadoop user base and use cases are changing
 Users are changing from Internet companies to mainstream
enterprises.
 Use cases are changing from Internet search, ad optimization
to customer churn analysis, sales and promotions, and
operations.

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What does it mean for Big Data to become a first-class
citizen?
 IT organization
 No more SWAT teams!
 Must map to existing people and skills
 Data center
 Must map to existing infrastructure, subject to same constraints
 Enterprise
 Must address real business problems, not abstract data science research

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Contents
 The premise
 Addressing the enterprise
 IT organization
 Data center infrastructure, processes, policies, and practices
 Creating value; competitive benefits to the business
 The endgame

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Mapping Big Data skills to the IT organization
 Enrich, don’t replace, your existing app developers, DBAs, system
administrators
 Huge existing SQL skills base – you’re not going to replace them
 Large Java developer base, lots of scripting language diversity
 Popularity of JavaScript/JSON
 Skills:
 Technology – the easy part
 Domain and data science – not so easy
Don’t forget the people part!

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Extending the IT organization for Big Data
 SQL and NoSQL/Hadoop platforms are converging
 SQL access to Hadoop
 Hadoop platform SQL support
 BI tool Hadoop support
 MapReduce approaches to Advanced SQL platforms
 MongoDB, CouchDB, Riak
 Empowering web JavaScript developers with familiar JSON
 Data science?
 The apps are coming…
SQL on Hadoop and Big Data apps are works in progress…

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Addressing the data center
 Like most Internet technologies, Hadoop conceived in zone of trust
 Small, elite band of practitioners
 Big concern? Getting access to available cluster resources elsewhere
inside the firewall
 Enterprise?
 Security
 Data stewardship
 Coping with finite resource
 Availability and reliability

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Data center: Big Data must be secure like any
database management system or data warehouse
 AAA enforced for access, authentication, authorization
 Must become more granular by user, data
 Must become more unified
 Integrate with LDAP/Active Directory
 Data privacy mandates
 This is a policy, not a technology, issue
 “Don’t be creepy” – don’t blindside your customers based on
knowledge they didn't know you have
 Regulation plays driving role for some sectors

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Data center: Big Data platforms must behave like any
database management system or data warehouse
 Data stewardship/lifecycle
 Data quality, protection, lifecycle management, retention
 Resource management
 Capacity utilization critical
 Availability/reliability
 Performance management essential for large clusters
Major change from early Internet adopters

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Address the business
Good business cases count!
 Do:
 Focus on existing problems (the problems are often more obvious
than you think…)
 Identify key points of pain, like any new IT solution
 Don’t
 Concoct “interesting” data science problems for the heck of it
 Get carried away with data (with lots of data, there are lots of chances
for detecting irrelevant trends)
 Give up after a few tries …. iterate!
Don’t get caught up in a data science project

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Hadoop benefits: Solving familiar problems in new
ways
Customer
holistic view
Predictive churn
analysis
upsell/cross-sell,
next-best-offer,
cross-channel ID
resolution
Risk mitigation
Fraud detection,
counter-party risk
management,
credit scoring
Operational
efficiency
Machine data for
managing smart
grids, smart urban
infrastructure,
supply chain
logistics
Not arbitrary data science

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Contents
 The premise
 What we mean
 Scope – focus on Hadoop, the most popular emerging Big Data
platform
 Addressing the enterprise
 IT organization
 Data center infrastructure, processes, policies, and practices
 Creating value; competitive benefits to the business
 The endgame

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The endgame: What becoming a first-class citizen
really means
 Big Data – and emerging platforms like Hadoop – originated as
specialized IT systems requiring specially skilled practitioners.
 This model is not sustainable as Big Data crosses over to the
enterprise.
 Big Data must get off its island.
 Big Data must be accessible to the IT organization, fit into the data
center, and address real business problems.

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Big Data: Embrace and extend
 IT organization
 Embrace existing SQL, Java, and other programming language skills
 Extend skills to understand handling of larger volumes and varieties of data and
new analytic techniques to supplement SQL
 Data center
 Embrace existing policies and practices for data stewardship, resource
management, security, performance management
 Extend policies and practices to accommodate platform with different workload
characteristics, and support of active archiving
 Business
 Embrace existing competitive problems; don’t look for new problems because the
data and platform are different
 Extend approaches to problem solving by incorporating new data types and new
forms of analyses to deepen understanding and insights