LIGHTWEIGHT BUSINESS INTELLIGENCE COREY EHMKE APRIL 2013

WHO AM I? A developer with a long memory & a longer history An active open source contributor A senior engineer at Trunk Club A lifelong learner

Service-oriented startup Software optimizes and streamlines business processes Technology is a key differentiator Engineering provides leverage for scaling the business TRUNK CLUB

OUR ENGINEERING GOALS Startups make critical decisions on a daily basis Better data leads to better decision-making Our mission is ∴ to provide this data in a timely and useful form

SOME CRITICAL DATA POINTS Marketing campaign performance Member on-boarding funnel Trunk lifecycle Stylist interactions Product performance

BUSINESS INTELLIGENCE: THREE APPROACHES

WHAT IS BUSINESS INTELLIGENCE? Collection & organization of mission-critical knowledge Historical view of business operations Tools to support decision making

THE NAÏVE APPROACH

THE NAÏVE APPROACH Reporting out of the transactional database Raw SQL embedded in your code for “performance” Granting direct db access to stakeholders

THE NAÏVE APPROACH: SHORTCOMINGS Fighting the schema with complex joins Poor performance Impact on production resources

DON’T TRUST THIS GUY WITH YOUR DATABASE.

THE ENTERPRISE APPROACH

THE ENTERPRISE APPROACH Distinct BI database Nightly ETL (extract, transform, load) Schema designed for reporting Separate hardware and software stack Combination of static and dynamic reports

THE ENTERPRISE APPROACH: SHORTCOMINGS 24 hour delay in information Expensive to configure and maintain Requires highly specialized resources Hard to change your mind or adapt Enterprise-y

VERY, VERY ENTERPRISE-Y

DON’T TRUST THESE GUYS WITH YOUR DATABASE, EITHER.

TRADITIONAL BI: THE WRONG HAMMER

TRADITIONAL BI: THE WRONG HAMMER Waterfall approach Painful data migrations Brittle ETL processes No automated testing Logic embedded in the data store

IN SHORT... TRADITIONAL BI DOESN’T FEEL AGILE.

A LIGHTWEIGHT APPROACH TO BUSINESS INTELLIGENCE

THE FOUR NOBLE GOALS OF THE LIGHTWEIGHT BI APPROACH Provide real-time data to support decisions Leverage familiar technology & infrastructure Use existing development staff Support an iterative, agile approach

TRUST YOUR DEVELOPERS.

GETTING STARTED Collaborate! Determine KPIs that actually matter Figure out what sort of questions the business is asking on a daily basis

TURN INFERENCES INTO FACTS Find a way to tell a story with your data Design your schema based on facts De-normalize like a boss

PRESENT ANSWERS Provide a central, single source of truth Present the data wherever it’s needed Dashboard design is harder than you think Plan for iterations & ongoing collaboration

PLEASE GODS NO.

DON’T MAKE THESE.

BRINGING RUBY TO THE PARTY Makes agile, test-driven development easy Quickly deploy new apps Plenty of visualization libraries Powerful SQL & NoSQL ORMs Great data munging capabilities

LEVERAGING MONGODB Flexible and dynamic schemas Support for native datatypes Powerful querying and aggregation Fast and performant Easy to scale up

GO AHEAD... TRY THIS AT HOME LIGHTWEIGHT BI TECHNIQUES

PARALLEL DB DEPLOYMENT Modern frameworks support multiple orms Use SQL for transactions Use NoSQL for reporting Business logic in your apps, not in your database

STATISTICAL MODELS Collections of facts, not attributes Data spanning deep and wide object graphs De-normalized and optimized for reporting

STATISTICAL MODELS module  MemberDataProfiles    class  Performance        include  Mongoid::Document        include  Mongoid::Timestamps        field  :member_id,                                  :type  =>  Integer        field  :annual_trunk_frequency,        :type  =>  Integer,    :default  =>  0        field  :average_trunk_value,              :type  =>  Float,        :default  =>  0.0        field  :is_customer,                              :type  =>  Boolean,    :default  =>  false        field  :keep_rate,                                  :type  =>  Float,        :default  =>  0.0        field  :last_transaction_date,          :type  =>  Date        field  :member_creation_date,            :type  =>  Date        field  :total_value,                              :type  =>  Float,        :default  =>  0.0        field  :total_number_of_trunks,        :type  =>  Integer,    :default  =>  0    end end

STREAMING ETL Event-triggered, continuous data extraction Calculations are defined in code rather than SQL or ETL scripts Allow resource-intensive data munging to happen in the background Provide near-real-time data

STREAMING ETL #  rabbit_notifier.rb class  RabbitNotifier        def  self.notify_of_action(model,  action,  extras  =  {})            notify(                :model_actions,                  model,                headers(model,  extras.merge('action'  =>  action.to_s))            )          end ... end

STREAMING ETL #  listeners.rb ListenerConfig.map  do    route  :product_shipped,      :to  =>  UpdatesProduct    route  :product_returned,    :to  =>  UpdatesProduct end

STREAMING ETL #  updates_product.rb def  self.with(json)    product  =  Product.init_with_params(extracted_params(json))    product.update_stats! end def  self.extracted_params(json)    JSON.parse(json,  :symbolize_names  =>  true) end

STREAMING ETL #  product.rb class  Product    include  Mongoid::Document    include  Mongoid::Timestamps    include  Products::Calculations    field  :name    field  :price,                          :type  =>  Float,      :default  =>  0.0    field  :cost,                            :type  =>  Float,      :default  =>  0.0    field  :profit_margin,          :type  =>  Float,      :default  =>  0.0    field  :keep_rate,                  :type  =>  Float,      :default  =>  0.0    field  :quantity_shipped,    :type  =>  Integer,  :default  =>  0    field  :quantity_returned,  :type  =>  Integer,  :default  =>  0    field  :profitability,          :type  =>  Float,      :default  =>  0.0    field  :trunkability,            :type  =>  Float,      :default  =>  0.0    field  :positive_feedback,  :type  =>  Array,      :default  =>  []    field  :negative_feedback,  :type  =>  Array,      :default  =>  [] ...

STREAMING ETL #  product.rb  cont’d        ...    def  self.init_with_params(params={})        product  =  Product.where(:name  =>  params[:name]).first            product  ||=  Product.new(params)    end    def  update_stats!        calculate_stats        save    end end

APIS EVERYWHERE Provide easy access to your data Allow reuse of data in novel ways Quickly build dashboards and data explorers Use the Faceted gem to make building APIs easy

LIGHTWEIGHT BUSINESS INTELLIGENCE: EXAMPLES

LEVIATHAN

OUR TECHNOLOGY ECOSYSTEM

LEVIATHAN Records events from all applications Subscribes to all message queues Collects and displays real-time data Browse, search, & drill-down interface Longitudinal analysis with dynamic cohorts

LEVIATHAN: EVENT MODEL class  Event    include  Mongoid::Document    include  Mongoid::Timestamps    field  :label    field  :application    field  :details,  :type  =>  Hash,  :default  =>  {}    def  self.record!(label,  application,  params={})        Event.create(            :label  =>  label,            :application  =>  application,            :details  =>  params        )    end    def  self.search_details(criteria={})        where("details.#{criteria.keys.first}"  =>  /#{criteria.values.first}/i)    end end

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LEADERBOARD

LEADERBOARD Real-time sales performance Prominently placed in sales team area Visibility = competition

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PRODUCT INFORMANT

PRODUCT INFORMANT Inventory performance metrics Extracts facts from a dozen RDBMS tables Delivers on decision support

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SUMMING UP Real-time data is valuable and possible Step outside of purely relational thinking Use the technologies you’re most familiar with

SUMMING UP Wrap your data in easy-to-use APIs Build micro-apps to deliver stakeholder-specific dashboards Be agile, not enterprisey

KEEP IN TOUCH! [email protected] @bantik on twitter bantik.github.com trunkclub.com/corey