Good news! Your analytical model is ready for production. Bad news. You needed it three months ago. Sound familiar? Often, models take so long to implement because companies produce and deploy them in two separate environments – business and IT – that rely on very different processes. This paper describes how taking a factory approach to building models will enable you to meet the needs of both business analysts and IT. Not sure what a “factory approach” is? Don’t worry. The paper describes that too.

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Breaking Down the Barriers Between
Model Development and Deployment
How a Factory Approach Speeds Time to Market

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The Problem
It takes too long to efficiently deploy models across
your business and get a return on your investment.
Let’s Change That™

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A model’s value lies in how quickly it’s deployed
In every case, models only drive action when they’re efficiently integrated
into production systems.
Predictive models play a role in different industries:
Retail banking Health care
Government Retail

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Why does it take so long to get a model
through production?

5. A tale of two environments
Sandbox versus production
Model development
and deployment
often happens in
two different
environments.
The problem: IT has
to convert and adapt
models as they
move from sandbox
to promotion.

6. The answer: automation
The answer: Move to a single architecture that removes the need to
convert models – and automates some routine tasks.

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7 benefits of a factory approach

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1. Dedicated analytics data store
At its heart, an analytics factory is a dedicated analytics production
data store that also handles the complete model life cycle.
Everything is handled within a single production environment
that feeds into the data warehouse.

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2. Value for Hadoop:
analytical base tables
Describing a company’s interactions can grow the
number of columns quickly, sometimes into the
tens of thousands. But Hadoop can easily handle
those wide ATBs, which makes it a great fit
for the analytics factory.

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3. Reuse of feature development
code in production
An analytics factory reuses the feature code in its native form,
without the need for a manual conversion process.
This is a migration rather than a conversion, so that when you
deploy your model, the logic you need to build the ABT is
already there in production.

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4. Simplified integration
of new logic
An analytics factory is a continually evolving
environment. As you create new models,
your analytics factory automatically incorporates
the data sources and logic of those models into
the production process.

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5. Scoring and decision interfaces
As you deploy predictive models to the analytics factory, you’ll want to make
sure those models deliver their output of scores, decisions or data streams to
other systems. An analytics factory sets up the following interfaces:
Publish-subscribe
On-demand/near-real time
Real-time
Streaming

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6. Containers to further speed
deployment
Containers allow applications and models to look
uniform as they cross between non-production
and production environments, or even between
on-site and cloud. This makes it possible for many
teams of modelers to share the same infrastructure.

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7. Production hardening
An analytics factory validates a model’s scoring logic using
a systematic template and process to record each test the
scoring engine goes through. This ensures that a model’s logic is
sound before the model goes into production.

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Why you should implement an analytics factory

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Significant cost savings
An analytics factory drives real cost savings by dramatically shortening
the model deployment process and reducing intensive manual IT costs.

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Top-line and bottom-line value
from analytics
At analytics factory powers timely and relevant
customer interactions and decisions, increasing sales
and return on marketing.
It also reduces fraud and credit losses, speeds up
product time to market and shortens supply chains.

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Increased innovation
Containers’ current analytical process makes it hard to innovate.
In contrast, an analytics factory embraces innovation by tying
data scientists and analysts more closely to deployment.

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Cultural change
An analytics factory not only fosters collaboration
between business and IT, it breaks down the barriers
between model development and production.
As a result, it nurtures a culture of agile
development and continuous deployment.

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