Buyer s Guide to Choosing the right Data Science Platform

Transcription

1 Buyer s Guide to Choosing the right Data Science Platform 12 Critical Building Blocks of an Enterprise Data Science Platform

2 Contents 1. Executive Summary 1 2. Introduction What is a Data Science Platform? Data Science Buzz Terms Data Science Platform: The Basics Functions a Data Science Platform performs within an analyticsdriven organization Data Science Platform: A closer look at its functions within an organization Data Processing Journey: Data Mining Cycle fundamentals Transforming Data Insights into Business Value using a Data Science Platform Industry Agnostic Business Applications and Use Cases The Analytics Journey: A path to deriving intelligence and insight from your data Trends Shaping the Future of Data Science Platforms 34 7 Trends shaping the future of Data Science Platforms Critical Building Blocks of an Enterprise Data Science Platform Critical Building Blocks of an Enterprise Data Science Platform How Can Angoss Help? A Comprehensive Data Science Platform that helps institutionalize knowledge and promote collaboration Summary 55

3 1. Executive Summary

4 A Data Science Platform is the most important asset companies acquire as part of their data analytics strategy. Capable of being applied to most business applications pertaining to risk management, customer centricity, and business operations, this Predictive Analytics and Machine Learning workbench is not just a passing trend but is rather quickly becoming a staple across all industries and departments.

5 Growth in the data science platform segment outpaced the overall business intelligence (BI) and analytics software market growth by almost two times. ~ Gartner, Machine Learning: FAQ From Clients, July 2017 When assessing an enterprisewide analytics solution one should take into consideration the following two predictions. The first one being the widespread and analyst recognized adoption of Data Science Platforms capable of performing both Predictive Analytics and Machine Learning functionalities. The rapid acceptance of Data Science Platforms has been mainly due to their visualization capabilities and the innate ability to house multiple functionalities and advanced analytic methods under a single environment. This in turn automates many of the data integration and model building tasks thereby increasing user productivity and overcoming skills gaps. According to Gartner s Priority Matrix for Data Science and Machine Learning, 2017, Predictive Analytics is of high benefit to organizations and it is estimated that it will reach mainstream adoption within 2-5 years. ~Gartner, Hype Cycle for Data Science and Machine Learning, July 2017 I. Executive Summary 3

6 The second prediction revolves around the swift transformation of the data science market as a result of disruptive technologies and data sources. Here are the 7 key technologies and trends shaping the way Data Science Platforms are designed today. 1. Adoption of Big Data Technology 6. IT Centralization 5. Integration with Enterprise Applications & Tools 7. Security and Governance 4. Adoption of Open Standards and Libraries 2. Heterogeneous teams with diverse skills & requirements 3. Use of New Data Sources for Decision Making 4 I. Executive Summary

7 Data Science Platforms that align with these trends will equip users with a single, fully functional platform that In the 2017 Predictive Analytics and Machine is capable of unifying infrastructure, Learning Solutions Wave, technology, and data science and Forrester forecasts a 15% compound annual growth analytics teams. This will simplify data rate (CAGR) for the PAML access and enable CIOs and Data market through Scientists to easily obtain data from ~ The Forrester Wave : Predictive a multitude of new data sources like Analytics and Machine Learning Solutions, Q1 2017, March 2017 social media, Internet of Things (IoT), Hadoop and Amazon S3 buckets, to build actionable strategies. Also, as organizations build more heterogeneous data science and analyst teams, Data Science Platforms will need to cater to different skill levels and requirements. These could range from different visual analytics or custom coding environments (R, Python, Language of SAS, Tableau, Qlik), or out-of-the-box integration with open-source machine learning packages like Spark ML, TensorFlow, or Geotrellis. Having a single, integrated platform that is compliant with current data and technology trends will lead to better and smarter analytic results and provide organizations with greater business value and a competitive edge. I. Executive Summary 5

8 Designed for Small Data and Big Data needs, Angoss Data Science Platform redefines and simplifies data science. In the Q1, 2017 Forrester Wave for Predictive Analytics and Machine Learning Solutions, Angoss achieved Leader status and was recognized as a primary enterprise advanced analytics solution. Features such as multiple language programming flexibility, comprehensive advanced analytics, automatic code generation, direct access to BI tools like Tableau, Spark and Hadoop integration, and numerous deployment options, just to name a few, elevate the Angoss Data Science Platform above all current analytics platforms in the market. Regardless of where an organization resides in the analytics journey, Angoss scalable advanced analytics solutions cater to users of different skill levels, technical requirements, goals and data needs. Gain greater business value from advanced analytics solutions with higher degree of intelligence and insight Angoss Solution: KnowledgeSEEKER Angoss Solution: KnowledgeSTUDIO InsightOPTIMIZER Angoss Solution: KnowledgeENTERPRISE InsightOPTIMIZER Descriptive & Diagnostic Analytics Entry Level Predictive Analytics Advanced Predictive & Prescriptive Analytics Big Data Advanced Predictive & Prescriptive Analytics Business Value & Competitive Advantage Degree of Intelligence & Insight 6 I. Executive Summary

9 Read on to learn more about: Data Science buzz terms: What do they all mean? Fundamental technical elements of a Data Science Platform Functions a Data Science Platform performs within an analytics-driven organization Typical Data Science Platform applications & ROI case studies The Analytics Journey: Greater business value with higher degree of technical intelligence The Angoss Enterprise Data Science Platform architecture Latest industry and market trends shaping the future of Data Science Platforms 12 Critical Building Blocks of an Enterprise Data Science Platform

10 2. Introduction 8 2. Introduction

11 2.1 What is a Data Science Platform? A Data Science Platform is a Business Analytics (BA) application that includes but is not limited to advanced analytics techniques such as Predictive Analytics, Prescriptive Analytics, Machine Learning, Deep Learning, and Artificial Intelligence (AI). It provides users with the flexibility to perform data mining, visual analytics, model management, as well as optimization scenarios for complex business needs. Used by companies across a multitude of industries and departments, such as credit risk, fraud, marketing, sales, and CRM analytics, a Data Science Platform enables data insight discovery, strategy planning and communication of actionable insights throughout the organization, all within a single environment. A CLOSER LOOK Insurance and Wealth Management Company saves $15 Million per year via Fraud Detection methods using Angoss Data Science Platform [p28] 9 2. Introduction

12 By 2020, predictive and prescriptive analytics will attract 40% of enterprises' net-new investment in business intelligence and analytics. ~ Gartner, Critical Capabilities for Data Science Platforms, June Introduction

13 2.2 What do you gain from the right Data Science Platform? Perform Perform advanced analytics directly within Big Data lakes without having to move the data Reduce Reduce time spent building models Access Access data from numerous sources such as structured, unstructured, transactional, social or Big Data Enable Enable analytics users of various skill levels to fulfill Predictive Analytics & Machine Learning operations Introduction

14 2.2 What do you gain from the right Data Science Platform?, cont'd Collaborate Increase collaboration between departments and project teams and bridge the gap between analyst and business user Communicate Effectively communicate results within the organization and with Decision Makers Discover Discover patterns and meaningful insights within your data and increase data mining efficiency by more than 40% Design Design strategies for a multitude of industry and departmental needs such as the next best marketing promotion, prevention of fraudulent activity, identification of customers that are most likely to default or churn and many more! 2. Introduction 12

15 2.3 Data Science Buzz Terms Predictive Analytics Gain greater business value and ROI by shifting your business analytics tactics from descriptive and diagnostic methods to Predictive Analytics. Predictive Analytics enables companies to forecast what might happen in the future, through advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) that represent patterns, trends, and the most influential variables and the relationships between them. Prescriptive Analytics A Data Science Platform equipped with optimization functionality will enable users to maximize or minimize any metric or KPI representing a business objective while maintaining relevant business constraints. Being able to handle objective functions and multiple constraints of any complexity allows companies to make the most of their resources while achieving the best ROI and continually balancing the diverse needs and limitations of the business. With Predictive Analytics you can: transition current decisioning methods from reactive to proactive to actionable, understand how customers engage with your products, and identify potential risks to your business Introduction

16 Machine Learning Machine Learning is an application of Artificial Intelligence which automates learning from data. It utilizes similar techniques and models as in Predictive Analytics, such as neural networks and other analytical models, in order to produce effective decisions based on the relationships, patterns, and trends in the historical data. In today s digital-driven era, organization using Data Science platforms that incorporate Machine Learning technologies are able to easily access the wealth of information found within their Big Data lakes. Deep Learning Deep learning algorithms are advanced Machine Learning algorithms based mainly on neural networks that are able to handle complex nonlinear data relationships and perform pattern and classification analysis in a faster and more accurate way. Data Science Platforms that incorporate deep learning algorithms increase your chances of deriving information from data that is of significant value to your business. By 2018, deep learning (deep neural networks) will be a standard component in 80% of data scientists' tool boxes. ~ Gartner, Predicts 2017: Analytics Strategy and Technology, November Introduction 14

17 90% of data has been created in the last two years alone. 1 Angoss Buyer s Guide to Choosing the Right Data Science Platform By 2020 there will be 20x more usable data than today. 3 Global Data Traffic is expected to cross 100 Zettabytes annually by Predictive Analytics is Fueled by Data 4 69% 67% 55% 54% 51% 34% Customer Marketing Product Sales Financial Employee Introduction

18 3. Data Science Platform: The Basics Data Science Platform: The Basics

19 3.1 Functions a Data Science Platform performs within an analytics-driven organization A Data Science Platform is an integral part of your company s analytics structure. It is capable of fulfilling most, if not all, of your analytic processes such as data mining, optimization, governance, visual output for dissemination of results across the company, and deployment of actionable results into a production environment. Having access to many advanced analytics technologies under a single visual environment, such as a Data Science Platform, will: Accelerate business analytics on a large-scale distributed data storage like Hadoop HDFS or other storage systems Eliminate the need for copying and extracting data Reduce expenses associated with utilizing numerous analytics tools Eliminate costs associated with cumbersome and proprietary database warehouse appliances without compromising data access, performance, and ease-of-use Enhance collaboration among users of different skill levels and locations Data Science Platform: The Basics

20 3.2 Data Science Platform: A closer look at its functions within an organization Data Generation Data Storage Data Science Platform Data Processing: Predictive Analytics & Machine Learning Data Optimization Data Governance Data Visualization 3. Data Science Platform: The Basics 18

21 3.2 Data Science Platform: A closer look at its functions within an organization, cont'd. Data Generation Data Storage Data is generated from a variety of sources. These can be structured, unstructured, transactional, social, or Big Data. Regardless of the data source, organizations need to derive reliable insights from their data in order to successfully plan for the growth of their business. Data is stored in data lakes, servers, and databases either on premises or in the Cloud. Being able to access all data and deploy it anywhere provides the flexibility to efficiently deploy models via scoring, automatic code generation (SAS, SQL, SPSS, PMML and Java), in cloud or on-premises, as well as in a physical or virtual environment. DATA SCIENCE PLATFORM Data Processing: Predictive Analytics & Machine Learning Data processing typically relates to the data mining cycle steps which include data understanding, preparation, modeling, evaluation, and deployment. Performing these steps in a Data Science Platform equipped with an easy-to-use GUI, wizard driven features, and an option to code or not to code can significantly increase productivity of data mining projects by reducing the 80% of time spent on data preparation. Optimization Optimization, as a prescriptive analytics approach, helps companies: solve complex business problems and test multiple scenarios, maximize returns and minimize losses while limiting costs, easily formulate optimization problems and automate the decision-making process. A data science platform with intuitive and easy-to-use optimization solutions provides companies with actionable results regardless of the complexity of the business problem and user skill level. Model Governance Model governance functionality within a Data Science Platform should aid in the automation, streamlining, and auditing of model management. This can be done in a secure, compliant, organized, and easily accessible framework that enables the user to store, monitor, compare, and deploy models. Data Visualization Data Visualization functionality within a Data Science Platform is crucial when communicating advanced analytics results company-wide. To provide transparency to complex and technical predictive analytics results, it is best to utilize a Data Science Platform that incorporates native visualization features and integrates with leading visualization platforms such Tableau and Qlik Data Science Platform: The Basics

22 3.3 Data Processing Journey: Data Mining Cycle fundamentals Typically, when analyzing collected and stored data, organizations are likely to take the following data processing journey, which includes: Data Understanding, Data Preparation, Modeling, Evaluation, Optimization (Prescriptive Analytics for more actionable results), Deployment, Model Management, and lastly Visualization. The next page describes some of the vital technical functions associated with each of the data processing steps. The Data Mining Cycle 7 MODEL MANAGEMENT 8 VISUALIZATION 6 DEPLOYMENT 1DATA UNDERSTANDING 5 OPTIMIZATION 2 DATA PREPARATION 4 EVALUATION 3 ADVANCED MODELING 3. Data Science Platform: The Basics 20

23 1 DATA UNDERSTANDING Overview report Dataset chart Segment viewer Crosstabs Correlations 2 DATA PREPARATION Aggregate Join Append De-dupe Expression editor Helpers wizards for generating new fields 3 ADVANCED MODELING Data Profiling Decision Trees Strategy Trees Clustering PCA Linear & Logistic Regression Partial Least Squares Regression (PLS) Constrained Regression Neural Networks Market Basket Analysis Survival Analysis Credit Scorecards and Reject Interference Text Analytics In-memory Execution on Spark In-place Analysis of Data Lakes Asynchronous Execution Automated Scheduled Scoring Regularization 4 EVALUATION Model Analyzer Model Validation Cross Validation Stability and Characteristic Reports 5 OPTIMIZATION Solve complex business problems under multiple constraints Automate problem formulation and decisionmaking process Maximize returns, minimize losses Formulate optimization problems using wizards 6 DEPLOYMENT In-database Code Generation Real-time Big Data Cloud or on-premises deployment Physical or virtual environment Parallel deployment of multiple projects 7 MODEL MANAGEMENT Model governance and auditing Repository for model storage, comparison, monitoring and deployment Model degredation assessment 8 VISUALIZATION Native visualization Integration with BI platforms such as Tableau and Qlik Visual analytics for large-scale distributed data sources Data Science Platform: The Basics

24 4. Transforming Data Insights into Business Value using a Data Science Platform

25 4.1 Industry Agnostic The right Data Science should be industry agnostic and enterprise-ready. Governing bodies of departments like Risk, Sales, and Marketing should be able to apply a Data Science Platform to their daily tasks such as Credit Risk Scoring, Fraud and Anomaly Detection, Customer Segmentation, Opportunity Scoring, Complex Optimization, and many more. A CLOSER LOOK A European Telecom Service Provider saves $15 Million by reducing customer churn and improving their customer marketing and onboarding process. [p.30] Transforming Data Insights into Business Value using a Data Science Platform

26 4.1 Industry Agnostic, cont'd. Risk Analytics Credit Risk Scoring for Originations Portfolio & Account Management Fraud & Anomaly Detection Collections & Recovery Claims Management Customer Analytics Customer Segmentation Customer Acquisition Customer Churn Cross-sell/Upsell Customer Lifetime Value Marketing Analytics Product Segmentation Response Prediction Next Best Channel Lead Scoring Text & Social Analytics Sales Analytics Prescrptive Analytics Marketing Insights Risk Management Company Strategy Voice of the Employee Opportunity Scoring Next Best Action Forecasting Complex Optimization Segment Level Optimization 4. Transforming Data Insights into Business Value using a Data Science Platform 24

27 REASONS COMPANIES NEED DATA SCIENCE PLATFORMS EQUIPPED WITH PREDICTIVE ANALYTICS FUNCTIONALITY 1 Predict Trends 2 Understand Customers 4 Improve Business Performance 3 Drive Strategic Decision Making 5 Predict Behavior 6 Access Small & Big Data 7 Unify Infrastructure & Technology 8 Product Dev & Pricing 9 Accessibility to a Wide User Audience 10 Deployment of Strategies Transforming Data Insights into Business Value using a Data Science Platform

28 4.2 Business Applications and Use Cases Credit Risk Scoring: Banks, credit card companies, and financial services institutions can use a Data Science Platform for the development of credit scorecards to screen and monitor their clients at all stages of the credit life cycle. Basel Accords Compliance: Financial institutions use a Data Science Platform to create models and reports required to comply with the requirements of Basel Accords. CRM and Marketing: A Data Science Platform can be used in the development and execution of marketing and customer relationship management (CRM) programs including: customer and market segmentation, cross sell and up-sell, churn and attrition, next product recommendation, customer acquisition and targeting, and propensity modeling. Collections and Debt Recovery: Organizations involved in the collection of debt can use a Data Science Platform to identify clients likely to pay and estimate amounts that can be recovered. These models are used for both the debt recovery operations as well as the evaluation of debt portfolios Transforming Data Insights into Business Value using a Data Science Platform

29 4.2 Business Applications and Use Cases, cont'd. Loyalty Programs: Companies adopting customer loyalty programs can implement a Data Science Platform for the management of retention programs by modeling customer behavior and response to marketing campaigns and promotions. Typical applications are in retail, hospitality and entertainment, and travel programs. Fraud and Money Laundering Detection: Organizations and government agencies concerned with fraudulent transactions can use a Data Science Platform for the development of models that score transactions for the likelihood of fraud. Similar models are also developed in the area of money laundering detection. These models are commonly used by insurance companies, government agencies, customs authorities, internet merchants and credit card operators. Quality Assurance and Six Sigma Programs: A Data Science Platform can be used as a vital component in the tool set of the Six Sigma programs, as implemented in many large organizations across diverse application domains including finance, manufacturing, pharmaceuticals and technology. Other Application Areas: These include human resources applications, B2B channel operations, tax compliance, medical research, and marketing survey analysis, among others. 4. Transforming Data Insights into Business Value using a Data Science Platform 27

30 Customer Case Study Problem: Abuse of Claims Goal: Identify patterns of abuse and fraud among thousands of insurance claims Angoss Solution: Angoss platform was used to improve response to legitimate claims and screen abusive claims through: Insurance and Wealth Management Company Publicly Traded 100+ Years in Business $10+ Billion in Revenue A comprehensive model to detect anomalies across thousands of claims An automated process to identify suspicious claims Integration with the claims processing and payment systems. Customer ROI: $15 million per year in savings through fraud detection Transforming Data Insights into Business Value using a Data Science Platform

31 Customer Case Study Problem: Credit Card Debt Collection Goal: Develop an optimization strategy for collections in order to increase collection rates and improve efficiencies Angoss Solution: Angoss platform was used to develop the overall collection strategy with: North American Multinational Bank Publicly Traded 100+ Years in Business $20+ Billion in Revenue An extensive model to predict customers' propensity to pay An optimization strategy for collections channels with daily scoring Customer ROI: $24 million per year in additional collections from Single Metro Region Transforming Data Insights into Business Value using a Data Science Platform

32 Customer Case Study Problem: High Customer Churn Goal: Develop an accurate risk scoring process for customer applications to prevent churn as a result of inability to pay Angoss Solution: Angoss platform was used to develop an improved customer marketing and onboarding process with: European Telecom Service Provider Publicly Traded Cell Phone, Landline Internet Service Provider Response model to improve response rate for targeted marketing (doormat campaigns) Accurate application scoring to identify ideal customers 60-Churn model for all customers to predict customers who will struggle to pay bills on time Customer ROI: $15 million per year in Savings through improved onboarding and churn prevention 4. Transforming Data Insights into Business Value using a Data Science Platform 30

33 4.3 The Analytics Journey: A path to deriving intelligence and insight from your data 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. Predictive Analytics Benefits 68% 55% 52% 45% 44% Achieve Competitive Advantage Find New Revenue Opportunities Increase Profitability Increase Customer Service Gain Operational Efficiencies Transforming Data Insights into Business Value using a Data Science Platform

34 4.3 The Analytics Journey: A path to deriving intelligence and insight from your data, cont'd. With the growing need for more dynamic solutions, organizations are seeking analytics tools that automate and ease current decision making processes, increase productivity, and consequently transition current decisioning methods from reactive to proactive to actionable. Regardless of where your organization resides in the analytics journey, the right Data Science Platform should be capable of addressing your business analytics needs in order to meet your predefined departmental or enterprise-wide goals. It is vital that analytics solutions, whether it be in the form of software or services, are able to easily access data, unify infrastructure and technology, simplify the user experience, and be capable of deploying actionable strategies. 4. Transforming Data Insights into Business Value using a Data Science Platform 32

35 Gain greater business value from advanced analytics solutions with higher degree of intelligence and insight Angoss Buyer s Guide to Choosing the Right Data Science Platform Are you currently deriving enough business value from your analytics solution? Business Value & Competitive Advantage Degree of Intelligence & Insight DESCRIPTIVE & DIAGNOSTIC ANALYTICS Reporting: At this stage of the analytics journey companies can create alerts, queries, ad hoc, and standard reports. ENTRY LEVEL PREDICTIVE ANALYTICS Prediction of future value of measure: At this stage companies can facilitate fundamental data mining and predictive analytics functions such as data preparation, profiling, visualization, segmentation, strategy design, and deployment. Angoss Solution: KnowledgeSEEKER ADVANCED PREDICTIVE & PRESCRIPTIVE ANALYTICS Advanced Predictive Modeling & Optimization: At this stage companies have access to Advanced Modeling & Machine Learning techniques for all stages of the data mining cycle. Geared with optimization functionality, organizations are able to: progress beyond future predictions; automate their decisioning processes for complex business problems, and transition to deploying actionable results. Angoss Solution: KnowledgeSTUDIO InsightOPTIMIZER BIG DATA ADVANCED PREDICTIVE & PRESCRIPTIVE ANALYTICS Advanced Analytics on Big Data Framework: At this stage companies rely on a large-scale data set framework for distributed storage and processing. They are seeking Visual Advanced Predictive and Prescriptive Analytics Platforms to reveal meaningful insights about their company and customers. It is via these visual Data Science Platforms, integrated with Machine Learning libraries such as SparkML, that they are able to gain unprecedented access to analyze data within Big Data repositories like Hadoop HDFS, Amazon S3, and Cassandra. Angoss Solution: KnowledgeENTERPRISE InsightOPTIMIZER 4. Transforming Data Insights into Business Value using a Data Science Platform 33

36 5. Trends Shaping the Future of Data Science Platforms

37 7 Trends shaping the future of Data Science Platforms There are several key Predictive Analytics and Machine Learning marketplace trends that are shaping the way Data Scientists, Business Analysts, and IT users interact with data science tools. In order to effectively execute on pre-defined business requirements and goals let's examine the 7 technology trends that must be considered when choosing your enterprise-wide analytics solution. 1 Adoption of Big Data Technologies Most companies we have spoken to have either adopted or plan to adopt Big Data technologies. More specifically, they use either Hortonworks or Cloudera to store their data. The impact to the Data Science Platform is that it now needs to access data within HDFS and Hive Trends Shaping the Future of Data Science Platforms

38 Heterogeneous Teams As organizations build more heterogeneous data science teams, Data Science Platforms will need to cater to different skill levels and requirements. These could range from different visual analytics or custom coding environments (R, Python, Language of SAS, Tableau, Qlik), or out-of-the-box integration with opensource machine learning packages like Spark ML. 2 3 Increase in the usage of New Data Sources for Decision Making This is where data access becomes absolutely crucial. Since customer information is streamed from a variety of sources, your Data Science Platform must be able to support structured and unstructured data sources such as social media feeds, audio, transactional, IoT, as well as data from Big Data environments such as Amazon S3, Hadoop, and Teradata. 5. Trends Shaping the Future of Data Science Platforms 36

39 Data scientists who are coders are increasingly using more than one programming language because of open-source add-on libraries such as CRAN for R and scikit learn for Python. ~ The Forrester Wave : Predictive Analytics And Machine Learning Solutions, Q Adoption of Open Standards and Libraries Businesses are constantly trying to evolve by yielding value from their data and so they are adopting opensource Machine Learning libraries like SparkML, TensorFlow, among others. As organizations make the shift towards Big Data, Data Science Platforms should provide tight integration with Machine Learning libraries for large scale data analytics processes. Integration with Enterprise Applications and Tools 5 With the mainstream adoption of advanced analytics, it is vital for Data Science Platforms to integrate with enterprise applications and BI tools such as SalesForce and Tableau. Direct access to these tools, from within the analytics platform, enables collaboration between users and eases communication of data insights with decision makers. This in turn provides transparency to advanced analytics results Trends Shaping the Future of Data Science Platforms

40 IT Centralization Many organizations 6 7 Security and Governance have made the shift With the implementation of IT towards centralized Centralization, the importance IT in order to reduce of governance is magnified hardware and storage for both the models and data. overheads. Furthermore, Data Science Platforms must IT centralization can possess a project and model drastically improve management framework with efficiencies and reduce documentation capabilities, costs thus requiring the logging, auditing, as well as Data Science Platform to access to streamlined reporting support a multi-tenant/ (historical comparison results, shared environment. validation reports, and model validation charts and tables) for model comparison and testing. 5. Trends Shaping the Future of Data Science Platforms 38

41 6. Critical Building Blocks of an Enterprise Data Science Platform

42 12 CRITICAL BUILDING BLOCKS OF AN ENTERPRISE DATA SCIENCE PLATFORM 1 User-Friendly and Interactive Interface 2 5 Comprehensive Advanced Analytic Flexibility 9 Integration with Big Data Technologies Data Access 6 Integration & Extensibility with Open-Source Machine Learning Libraries 10 Automation 3 Data Preparation 7 Governance 4 Data Exploration, Profiling, Visualization 8 Collaboration 11 Deployment Flexibility 12 Scalability, Performance Critical Building Blocks of an Enterprise Data Science Platform 40

43 12 Critical Building Blocks of an Enterprise Data Science Platform As demand for Data Science Platforms adapted to Big Data needs increases, organizations are looking for analytics workbenches to: Satisfy the requirements of the modern day Data Scientist Ease current decision making precesses without sacrificing functionality When evaluating current legacy applications or acquiring an enterprise-wide data science solution, organizations should be looking for the following 12 Critical Building blocks that comprise a comprehensive end-to-end Data Science Platform. 1 USER-FRIENDLY AND INTERACTIVE INTERFACE Data Science Platforms with a consistent look-andfeel, wizard-driven functionality, and an automated workflow minimize the learning curve, ease navigation, optimize model development, and enable users to easily construct workflows in a fraction of the time. Additionally, an easy-to-use platform will influence Critical Building Blocks of an Enterprise Data Science Platform

44 user interaction with the software and result in a pleasant user experience consequently contributing to significant time savings. Factors to consider that make up an easy-to-use Data Science Platform: 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. Interactive Graphical User Interface (GUI) Workflows should possess essential components which minimize the learning curve. These include wizarddriven features guided by wizard-step and point-andclick 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 enable users to easily construct workflows in a fraction of the time. Programming Capabilities Not all users are alike! Having a GUI that interacts with users of all skill levels and expertise is key. A Data Critical Building Blocks of an Enterprise Data Science Platform

45 Science Platform that provides its users with access to multiple coding languages like R, Python, SQL, PMML, and the language of SAS (LOS) 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 analytics capabilities. 2 DATA ACCESS Customer information is streamed from a variety of sources. A Data Science Platform must be able to support data from structured sources like Excel as well as data from unstructured sources such as social media feeds, Internet of Things, Amazon S3, Hadoop, and Teradata. 3 DATA PREPARATION Having access to fundamental data preparation functionalities, advanced data preparation in the language of SAS, and pre-built functional nodes for automatic coding can significantly increase productivity. A Data Science Platform that provides users with the option to code or not to code increases efficiencies of data mining projects by reducing the 80% of time spent on data preparation. 4 DATA EXPLORATION, PROFILING, VISUALIZATION More often than not, high-quality statistical or analytical projects don t take flight when results lack effective interpretation. The right Data Science Critical Building Blocks of an Enterprise Data Science Platform 43

46 Platform should be equipped with a high-performance visual environment which provides users with an intuitive workflow and wizard-driven GUI to help build effective models without having to code while still providing you with an option to custom code. A wide range of exploratory features such as cross tabulations, segmentation, characteristics analysis, and the segment viewer, help Data Scientists quickly assess data quality, derive insights, and detect patterns and trends making every stage of the data mining process easily interpretable and presentable. Additionally, platforms that support BI tools like Tableau and Qlik dashboards provide transparency to advanced analytic results and ease communication of data insights with decision makers. 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! 5 COMPREHENSIVE ADVANCED ANALYTIC FLEXIBILITY 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 Critical Building Blocks of an Enterprise Data Science Platform

47 future outcomes with Predictive Analytics, and provide a preferred course of action using Prescriptive Analytics. To meet the rising demands of your business, the right Data Science Platform should be multi-functional and scalable. It should provide users with access to all BA requirements, whether they are descriptive, predictive, or prescriptive. Having access to a complete Data Science Platform that addresses all elements of the Data Mining Process, as discussed earlier, 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. Modeling techniques such as advanced scorecard development, regression analysis, optimization, and Machine Learning methods are a critical component of a platform when building data science solutions for an extensive variety of business applications. Data Science Platforms that extend analytical capabilities beyond their own, such as programming in multiple languages like R, Python, SQL, and PMML, offer immense flexibility and collaboration potential for diverse data science teams. A CLOSER LOOK Top Tier North American Bank generates $24 Million per year in additional revenue using Angoss Collection Optimization Strategy [p29] Critical Building Blocks of an Enterprise Data Science Platform 45

48 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 analytics needs thereby avoiding future costs. 6 INTEGRATION & EXTENSIBILITY WITH OPEN-SOURCE MACHINE LEARNING LIBRARIES Enterprises with Big Data needs require Data Science Platforms integrated with Machine Learning libraries such as Spark ML, Tensorflow, or Geotrellis, for large scale data analytics The data science and machine processes. This learning market is one of the provides users with most vibrant and collaborative technology markets that ample flexibility to strongly embraces open-source take advantage of technologies. open-source analytics ~ Gartner, Hype Cycle for libraries and packages Data Science and Machine Learning, July 2017 as well as participate in open-source communities. Integrating open-source technologies in a Data Science Platform ensures that both engineered and innovative approaches are supported Critical Building Blocks of an Enterprise Data Science Platform

49 7 GOVERNANCE To satisfy security, governance, and compliance regulations, Data Science Platforms must possess a project and model management framework with documentation capabilities, logging, auditing, as well as access to streamlined reporting (historical comparison results, validation reports, and model validation charts and tables) for model comparison and testing. Additionally, managing predictive 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 Critical Building Blocks of an Enterprise Data Science Platform 47

50 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. 8 COLLABORATION In order to provide transparency at every step of the modeling process, enable communications across all modeling steps, and facilitate project communications between multiple stakeholders, Data Science Platforms must be equipped with features like self documentation, model documentation, and model sharing. These features, among others, help create a continuous and simultaneous teamwork across different locations and teams. 9 INTEGRATION WITH BIG DATA TECHNOLOGIES Many companies have either adopted or plan to adopt Big Data technologies. More specifically, they use either Hortonworks or Cloudera as their distributed storage tier for enterprise data. The impact to current Data Science Platforms is that they now need to access data within HDFS and Hive, and provide visual analytics for Hadoop. Most importantly, Data Science Critical Building Blocks of an Enterprise Data Science Platform

51 Platforms must integrate with Spark for model training as well as scoring. 10 AUTOMATION Automation is inherent in Data Science Platforms adapted for Big Data Frameworks. Features such as a visual canvas for building automated workflows - data collection, data prep, transformations, scoring, report generation - can ease the process of deployment on Hadoop clusters. Gartner predicts that by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists. ~ Gartner, Hype Cycle for Data Science and Machine Learning, July DEPLOYMENT FLEXIBILITY A platform s ability to efficiently deploy models via scoring, automatic code generation (SAS, SQL, SPSS, PMML and Java), in cloud or on-premises, as well as in a physical or virtual environment, enables Big Data insights dissemination and is crucial when dealing with a magnitude of business scenarios. Additionally, in order to turn data into actionable insights, Data Science Platforms require API s (REST API and Web Services) to integrate with production applications that call for Real-Time Analytics Critical Building Blocks of an Enterprise Data Science Platform 49

52 Businesses are now also looking for Data Science Platforms with real-time scoring engines that can easily integrate into their business process to provide ondemand scores, decisions models or recommendations as well as apply real-time actions or 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. 12 SCALABILITY, PERFORMANCE, AND IT CENTRALIZATION Data Science Platforms adapted for Big Data functionality will be equipped to support large datasets, in-memory execution on Spark, and deployment of multiple projects in parallel. Furthermore, platforms that support a multi-tenant/shared environment will improve IT efficiency and lower costs associated with hardware and storage overhead Critical Building Blocks of an Enterprise Data Science Platform

53 7. How Can Angoss Help?

54 7.1 A Comprehensive Data Science Platform that helps institutionalize knowledge and promote collaboration In today s high-speed commercialized market, organizations with similar goals in areas of profitability, growth, customer service, and retention compete to secure a successful future. The vast majority have instinctively determined that data is one of their most precious assets. In fact, it is generalized that data accumulation helps to infer findings that positively shape the direction of the business. For instance, with access to more customer data, financial institutions are able to decrease the margin of error in their predictions thereby reducing risk of exposure to factors like fraud, bankruptcy, and default. Our customers choose Angoss for the Easy Implementation and Usability of our software which in turn provides Smart Analytics results! Consequently, if data is so valuable it only makes sense for companies to adopt the most integrated Data Science Platform, equipped with Predictive Analytics, Prescriptive Analytics, Machine Learning, and Visualization functionality, in order to adapt to changing data sources, business needs, and user expectations How Can Angoss Help?

55 7.1 A Comprehensive Data Science Platform that Helps Institutionalize Knowledge and Promote Collaboration cont'd. Recognized by leading industry analysts and a Leader in the 2017 Forrester Wave for Predictive Analytics and Machine Learning Solutions, Angoss Data Science Platform offers businesses a comprehensive Data Science Platform that helps institutionalize knowledge and promote collaboration across heterogeneous teams of different skills and tools requirements. Designed for Small Data and Big Data needs, Angoss Data Science Platform provides customers with a cost effective, collaborative, and user-friendly advanced analytics platform that improves performance across multiple business applications, increases data mining efficiencies by more than 40%, helps deliver valuable data insights in a fraction of the time, and enables smart and fast communication of analyst reports. Angoss Data Science Platform is Enterprise Ready! It is capable of performing advanced analytics directly within Big Data environments thereby eliminating data duplication, minimizing security breaches, and automating results which consequently lead to insights. With Angoss Enterprise Solution, businesses will be able to reduce infrastructure costs by leveraging centralized infrastructure for data science across the organization. Angoss enables companies to truly live up to the Big Data philosophy by being able to continue to accrue vast amounts of data without compromising data access, performance, and analytics. 7. How Can Angoss Help? 53

56 7.1 A Comprehensive Data Science Platform that Helps Institutionalize Knowledge and Promote Collaboration cont'd. The Angoss Data Science Platform is redefining and simplifying data science with fully-integrated advanced analytics solutions for organizations that are ready to progress to the next step of their analytics journey. Angoss delivers an Advanced Analytics Platform that unifies infrastructure and technology to simplify Data Science How Can Angoss Help?

57 7.2 Summary Integrated with Big Data & Open-Source Tight integration with Spark and Hadoop Organization- Wide Solution Visual Building Blocks for open-source packages like Spark ML, H2O, etc. Ready for Centralized IT Infrastructure Scalable, Performant Platform Extensive capabilities for all industries Small and Big Data enabled All analytics tasks performed under a single platform Integrated Model Management, Security & Governance Capabilities Collaboration Capabilities for users of different skill levels and locations Support for Public, Private, Hybrid Clouds Deploy models via scoring, automatic code generation, in cloud or onpremises, as well as in a physical or virtual environment Ideal for Diverse Analytics Teams Programming, analytical flexibility, and visual development in the languages of R, Python, and Language of SAS Automatic code generation for Citizen Data Scientists and Business Analysts Intuitive and user-friendly GUI for Business Analysts Direct access to BI tools like Tableau and Qlik Develop on Desktop, Shared Server, or Spark Ingest data from large variety of Data Sources 7. How Can Angoss Help? 55

58 About Angoss Angoss is a global leader in delivering advanced 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. sales@angoss.com Angoss Software Corporation 7. How Can All Angoss rights Help? reserved. 56