1. CUT THROUGH THE PREDICTIVE
ANALYTICS NOISE
Real-Time
Scoring Engine
Easy-to-Use
Platform
Communicate
Insights
Multifunctional
Data Science Platform
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs

2. INTRODUCTION
2
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
The increasing pressure to remain competitive and to deliver
revenue growth has forced companies to focus on ways to better
mitigate risk, optimize pricing strategies, conduct 1-1 marketing,
and leverage data-driven decision making across every functional
area. This rising awareness has led to a rapid expansion of
business analytics (BA) tools, mainly predictive and prescriptive,
across various industries and almost every functional use case.
Hence, the proliferation of data science applications for data
processing needs.
Organizations are looking into analytic tools that automate and
ease current decision making processes, increase productivity and
consequently transition current decisioning methods from reactive
to proactive to actionable. There’s just one problem. Even though
organizational needs are crystal clear, choosing the right solution or
in many cases multiple solutions can be quite hazy.
The sheer volume of vendors and tool options for advanced
analytics currently in the market can be daunting to buyers and can
prolong the selection process.
“Everybody is cooking up something different.”

3. So how do buyers cut through the analytics noise in pursuit of the right
Data Science Platform for business analytics needs?
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs 3

4. 4
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
TABLE OF CONTENTS
8 Guidelines for choosing the right Data Science Platform for your business:
1. Can the data science platform tackle all aspects of business analytics? ............................................................................................................... 05
2. Is the Data Science Platform easy-to-use? ............................................................................................................................................................... 06
3. Can the Data Science Platform handle large volumes of data? ............................................................................................................................. 08
4. Does the Data Science Platform support and blend both structured and unstructured data? .......................................................................... 09
5. Does the Data Science Platform address problems that require real-time scoring? ........................................................................................... 10
6. Does the Data Science Platform support optimization functions? ........................................................................................................................ 11
7. Does the Data Science Platform have the capability to manage models? ........................................................................................................... 13
8. Does the Data Science Platform provide you with the ability to effectively communicate your analytic insights company-wide? ................. 14

5. 5
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
To secure a profitable revenue stream, companies must have the means to: analyze
past business outcomes; forecast what might happen in the future via models that
represent patterns and trends; and know the best action to take in order to generate
profit and stay competitive in the booming marketplace. It is no surprise then, that
BA plays a key role in decision making. Analytics helps companies digest historical
trends via descriptive analytics, perceive possible future outcomes with predictive
analytics, and provides a preferred course of action using prescriptive analytics.
To meet the rising demands of your business, the right Data Science Platform
should be multifunctional and scalable. It should provide you with access to all BA
requirements, whether it be descriptive, predictive or prescriptive. Having access to
a complete Data Science Platform that addresses all elements of the Data Mining
Process including Data Understanding, Data Preparation, Modeling, Evaluation,
Deployment and Business Understanding for multiple business applications, will
significantly increase efficiencies in the overall data mining process. Additional
functions such as strategy optimization will help your organization answer questions
like “What should I do?” to help you formulate the next best action. Keep in mind
that choosing a Data Science Platform that offers more functionality up-front is
cost-effective in the long run, as it will inevitably overcome the challenges of
purchasing separate applications to accommodate your growing
company’s numerous analytics needs, thereby avoiding future costs.
1. Can the Data Science Platform tackle all
aspects of business analytics?
$125,000
$200,000
$150,000
$230,000

6. 6
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
An easy-to-use platform will influence the user’s interaction with
the software and results in a pleasant user journey experience thus
contributing to significant time savings. Conversely, difficult to use
platforms can create a frustrating user experience, affecting
productivity, increasing the learning curve, and eventually resulting
in product abandonment – which can be quite costly for the
company!
Factors to consider that make up an easy-to-use Data Science
Platform:
a. Easy-to-Use Workflow
b. Interactive Graphical User Interface (GUI)
c. Programming Capabilities
2. Is the Data Science Platform
easy-to-use?
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7. 7
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
2. Is the Data Science Platform
easy-to-use? (Continued)
Factors to consider that make up an easy-to-use GUI:
A. Easy-to-Use Workflows
Workflows should increase user
efficiencies by enabling the user
to repeat steps and utilize other
workflows as templates for new
projects. For added flexibility,
users should also have the ability
for self-documentation within the
workflow. Finally, users must have
access to automated capabilities
that enable re-running individual steps
in the process or the entire workflow with updated and current
data. These features can drastically improve the overall user
experience.
B. Interactive GUI
Workflows should possess essential components which minimize
the learning curve. These include wizard-driven features guided
by wizard-step and point-and-click technology for quick and easy
navigation and efficient model development. Automated features
such as dragging, dropping, connecting process steps on the visual
canvas and specifying their inputs, outputs, and parameters should
enable users to easily construct workflows in a fraction of the time.
C. Programming Capabilities
Not all users are alike! Having a
GUI that interacts with users of
all skill levels and expertise is key.
A Data Science Platform that
provides its users with access to
multiple coding languages for
various advanced programming
needs and at the same time
enables automated code creation,
with a few easy mouse clicks, will
enable more users to take advantage
of predictive analytic capabilities.

8. 8
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
To manage large volumes of data, companies may want to consider
a Data Science Platform that offers in-database analytic capabilities.
The in-database option provides an efficient and cost-effective way
for organizations to enhance their data exploration capabilities
through an optimized analytical processing environment directly
within the database.
It also has inherent security benefits that CIOs love! By utilizing
in-database drivers, analysts can build models within enterprise
data warehouses such as Netezza, Teradata, Oracle, and Microsoft
APS. There are also Data Science Platforms offering direct
connections to Hadoop.
3. Can the Data Science Platform handle large
volumes of data?

9. 9
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
4. Does the Data Science Platform support
and blend both structured and unstructured data?
Seeing as data is streamed from a variety of sources, it is important
for a Data Science Platform to be able to support structured data
that arrives in various formats like Excel, CSV, HTML, and
unstructured text sources such as social media feeds or call center
notes. In addition, advanced users should have the ability to blend
unstructured and structured data, in a single view, to perform data
mining and analysis thus improving the predictive and exploratory
power of models.
Consequently, this will enable users to build better predictive
strategies for customer acquisition and retention, cross sell and
upsell, next-best-offer, claims fraud reduction, customer service
routing, warranty analysis and more. Ultimately, the valuable
insights gained from combining both structured and unstructured
data in predictive modeling can result in business decisions that
can truly enhance your customer’s experience with your brand.
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10. 10
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
5. Does the Data Science Platform address
problems that require real-time scoring?
Data continues to come at us with increasing velocity and so there
are many business applications that require timely decision making
and speed to action.
Businesses are now looking for Data Science Platforms with
real-time scoring engines that easily integrate into their business
processes to provide on-demand scores, decisions,
recommendations, real-time actions and treatments.
The ability to deploy predictive models and strategies in real-time,
automates the decision making process, and helps companies
improve agility and speed-to-action for improved business
performance.

11. 11
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
6. Does that Data Science Platform support
optimization functions?
Optimization - a prescriptive analytics approach used in solving
complex decision making problems - is a crucial component of a
Data Science Platform. It can help you determine your next
strategic move, for each customer, such as the next best channel,
best credit limit increase, most profitable product offering and so
on.
A Data Science Platform with optimization functionality will
eliminate the need to experiment with different deployment
scenarios of various options by optimizing a merit function subject
to real business constraints. It will also remove the challenge of
combining the deployment of several models competing on the
same resource to help you minimize loss and achieve maximum
results.
Essentially, by meeting constraints and maximizing an objective,
companies will be able to utilize limited resources to their fullest
potential.

12. 12
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
Optimization functions will help you
minimize loss and achieve maximum results!

13. 13
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
Working with models and knowing which models should be used
for production, when done manually, can be an error-prone, and
time-consuming task. With the ongoing pressure for companies to
be at the cutting edge faster than ever before, delays and errors in
the decision making process can have costly consequences.
In order to automate and streamline the management of models,
the right Data Science Platform needs to have a secure,
compliant, organized and easily accessible framework for model
storage, comparison, monitoring, and deployment. This will
enable organizations to: facilitate accurate business projections by
ensuring that only the best performing models are used; minimize
compliance risk with comprehensive audit trails; and save time and
money by automating the decision making process.
7. Does the Data Science Platform have the capability
to manage models?

14. 14
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
What good is your predictive analysis if you cannot effectively
communicate its analytic insights company-wide? More often than
not, high-quality statistical or analytical projects don’t take flight
when results lack effective interpretation.
A great Data Science Platform should provide you with the ability
to express your advanced model results through a visual analytic
tool, such as Tableau, that is fast, easy-to-use, integrates with big
data infrastructure, and enables easy digestion of results by various
members of the organization.
Quick and effortless capabilities such as being able to easily explain
advanced models by simply cutting and pasting the results into
Microsoft Office and generating reports with the click of a button
are a bonus!
8. Does the Data Science Platform provide you with the ability to effectively
communicate your analytic insights company-wide?

15. 15
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
The Angoss Data Science Platform Edge!
Over the years, Angoss’ focus has been on providing customers with cost effective and user friendly predictive analytics tools that improve
performance across multiple business applications, increase data mining efficiencies by more than 40%, help deliver valuable data insights in a
fraction of the time, and enable smart and fast communication of analyst reports.
Reduce Cost, Resources, and Maintenance by Running all
Analytics Tasks in a Single Platform
• Advanced data preparation, profiling and transformation
• Advanced predictive modeling techniques and algorithms to support
hundreds of business applications
• Advanced visual analytics available via Angoss native charts and reports and
Tableau integration directly within the Angoss workflow
• Easily connect to many data formats and databases from virtually
any data source
Improve Performance and Automate Decision Making
• In-database analytics drivers allow organizations to analyze data directly
within their database without having to export it in order to quickly and effectively
apply analytics against massive amounts of customer and business data
• Optimization solves complex business problems to find the Best Solution from a
set of feasible solutions, making the best use of limited resources
• Real-Time Scoring engine easily integrates into business process to provide
on-demand scores, decisions or recommendations
Minimize the Learning Curve and Improve Collaboration
Amongst Users of all Skill-levels
• Automatic code generation functions reduce coding time by more than 30%
• Custom code in multiple languages such as the language of SAS, R, SQL and Python
• Automated and easy-to-use workflows further enhance the user experience
• Intuitive GUI reduces manual intervention and enables users of all skill levels to easily
navigate through the software
Angoss Unique Differentiators
• Easy-to-use workflow and interface bridges the gap between the data scientist
and the business user
• Best-in-Class Decision Trees have been recognized by leading industry analysts as
“Best tool for Decision Trees”
• Angoss unique Strategy Trees enable easy and visual deployment of prescriptive
strategies at a segment level
• Programming and analytical flexibility provides the ability to code in multiple
languages in a single platform thus extending capabilities beyond Angoss
• Data Science Platform designed for users of all skill levels
ANGOSS DATA
SCIENCE PLATFORM

16. 16
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
Explore the Angoss
Data Science Platform
Contact Sales
1.866.687.8838

17. 17
8 Guidelines for choosing the right Data Science Platform
for your business analytics needs
Angoss is a global leader in delivering predictive analytics to businesses looking to improve performance across risk, marketing and sales. With a
suite of big data analytics software solutions and consulting services, Angoss delivers powerful approaches that provide you with a competitive
advantage by turning your information into actionable business decisions.
Many of the world’s leading organizations in financial services, insurance, retail and high tech rely on Angoss to grow revenue, increase sales
productivity and improve marketing effectiveness while reducing risk and cost. Headquartered in Toronto, Canada, with offices in the United States,
United Kingdom and Singapore, Angoss serves customers in over 30 countries worldwide. For more information, visit www.angoss.com.
Corporate Headquarters
111 George Street, Suite 200
Toronto, Ontario M5A 2N4
Canada
Tel: 416-593-1122
European Headquarters
Enigma House
30b Alan Turing Road
The Surrey Research Park
Guildford, Surrey GU2 7AA
Tel: +44 (0) 1483-661-661
ABOUT ANGOSS