BIG DATA ANALYTICS BUYER S GUIDE

Transcription

1 BIG DATA ANALYTICS BUYER S GUIDE

2 TABLE OF CONTENTS 02 Why Buy? 03 Steps for Selecting A Big Data Analytics Solution 06 Step 1: Define Decision Criteria 08 Step 2: Agree on Use Cases 16 Step 3: Qualify Solutions 25 Step 4: Validate Solution 36 Key Takeaways (Reducing Time to Value) 01

3 BUILDING A BIG DATA ANALYTIC SOLUTION CAN TAKE OVER A YEAR... BUILD Data loading Transform data and perform data quality Analyze data Visualize data Export data Secure data Monitoring Scheduling and dependencies 14+ months BUY Data loading Scheduling and dependencies 3 months AND COST YOU OVER $1M IN SALARIES ALONE FOR 1 YEAR JOB TITLE BAY AREA NEW YORK IT Project Manager $ 140, $ 126, System Administrator $ 117, $ 105, Network Administrator $ 119, $ 107, Database Administrator $ 125, $ 119, IT Security Manager $ 116, $ 104, Business Intelligence Analyst $ 137, $ 133, Data Scientist $ 138, $ 133, Java Developer $ 136, $ 133, QA Engineer $ 120, $ 114, $ 1,148, $ 1,074,

4 SELECTING A BIG DATA ANALYTICS SOLUTION CAN TAKE MONTHS Define Decision Criteria (.5 month) Agree on Use Case (1 month) Qualify Solutions (1 month) Validate Purchase (1.5 months) (.5 month) AND ALMOST $150K BEFORE YOU START THE PROJECT Salaries based on 4.5 months of yearly average JOB TITLE BAY AREA NEW YORK Project Manager $ 52,500 $ 47,250 Business Intelligence Analyst $ 51,375 $ 49,875 IT Administrator $ 44,645 $ 40,125 $ 148,500 $ 137,250 03

5 THERE ARE MANY ADVANTAGES TO BUYING A BIG DATA ANALYTICS SOLUTION Below are 3 key reasons we wanted to highlight: 1. Fewer steps in the process Instead of hiring developers, ramping them up, defining, developing, testing, deploying and then analyzing, you can go directly to defining and analyzing. 2. One pre-built solution Instead of manually building capabilities for data loading, data parsing, data analytics, visualization, scheduling, dependency management, data synchronization, monitoring API, management UI, and security integration, you can have it in one, purchased platform. 3. Pre-built integration, analytics and visualization functionality Seamless Data Integration Powerful Analytics Business Infographics Structured, semi and unstructured Pre-built connectors Connector plug-in API Interactive spreadsheet UI Built-in analytic functions Macros and function plug-in API Combine anything, WYSIWYG Infographics and dashboards Visualization plug-in API In this guide, we ll discuss how you can expedite the deployment and selection process by: Providing a template for decision criteria Criteria for identifying big data use cases Qualification criteria for the solution Validating and calculating ROI to compare your options 04

6 STEPS FOR SELECTING A BIG DATA ANALYTICS SOLUTION

7 HOW THIS GUIDE WILL HELP YOU SELECT A BIG DATA ANALYTICS SOLUTION FASTER Creating a streamlined buying process is critical. A streamlined buying process paves the way and sets the stage for a streamlined deployment. There are 4 key steps in selecting your big data analytics solution. This guide will go through the things you can do to expedite that buying process. Define Decision Criteria Agree on Use Case Qualify Solutions Validate Define decision criteria: Before you buy something, you will need to agree on your decision criteria. In this guide, we ll share a checklist of the criteria we have seen customers use during their decision making process. Agree on use case(s): Often times companies are looking for a place to begin. This part of the guide will help you identify the ideal big data use cases to go after as well as show you examples of big data use cases companies have implemented. Qualify solutions: What is the functionality you need in a big data analytics solutions? What are the capabilities you should look for? This part of the guide will outline the key capabilities so you can shorten your list of options to evaluate. Validate: In this part of the process, you need to consider how you will validate if a solution meets your requirements. Look for ways to validate that are cost effective. This part of the guide will give you tips on how to expedite the validation process as well as provide an framework to compare your options. 06

8 1 STEP 1 DEFINE DECISION CRITERIA

9 DEFINE DECISION CRITERIA DECISION CRITERIA FOR SELECTING A BIG DATA ANALYTICS SOLUTION After working with hundreds of customers, we ve found the following are the top things to look for in a Big Data Analytics Solution. Ease of use Does the business need IT to help? Can a business analyst use the tool to do analysis? Integration with existing IT infrastructure Does the solution support import from and export into other BI systems? Data Integration Does system support native connectors to unstructured and semi-structured data sources (e.g. log files, social, SaaS, machine data)? Does it support flexible partitioning of the data so that it is easy to work with large amounts of data? Does the solution provide data quality functions so that the data can be quickly normalized and transformed? Administration Does the system support flexible security integration with LDAP or ActiveDirectory? Analytics Does the solution provide an intuitive environment (e.g. spreadsheet) that business users can quickly use? Does the solution include pre-built analytic functions? Does the system provide a preview to validate analysis and show data lineage for auditing data flows? Do data models have to be defined before insights can be gained? Do analysts need to know what they want to do before they have had a chance to look at the data? Can analysts look at the data, iterate, make the changes they need and analyze without involving IT? Visualizations Does the solution support complete freeform visualization? Or is it just a combination of reports and dashboards? Extensibility Does the solution support open API s for custom data connections and custom visualizations? Architecture Does the solution run natively on Hadoop? Is a separate cluster required (ideally no separate cluster should be required)? Is the product limited by the availability of the memory within the nodes? The ideal solution should have no memory constraints. Does the solution provide a job planner and optimizer to ensure the lowest number of MapReduce jobs is executed? Vendor requirements Is the system proven, does it have numerous major releases? How much is it in enterprise use, has it analyzed substantial data, (terabytes, petabytes or exabytes)? 09

10 2 STEP 2 AGREE ON USE CASES

11 WHAT DOES A BIG DATA USE CASE HAVE? What does a Big Data Analytics use case look like? This part of the process has taken some companies the longest. First, it is important to understand what a Big Data Analytics use case is not. Second, it is important to understand the characteristics of a Big Data Analtyics use case. What is NOT a Big Data Analytics use case? It s important to draw the distinction between a Big Data Analytics use case and a Business Intelligence use case. Business intelligence use cases are about structured data and lead to reports that aggregate or summarize that data. These use cases require data models that lead to a set of known questions that can be asked of the data. BI use cases involve moderate volumes of structured data, and therefore BI solutions were not designed to handle the big data use cases that require larger data volumes and different types of data that change frequently. On the other hand, the data in Big Data Analytics use cases are of high complexity, meaning they have data in high volumes, high variety and high velocity. The ideal Big Data Analytics solution enables users to integrate, analyze, and visualize data to discover insights. Users get deeper insights across not only transactions but also interactions to reveal more precise insights, predict behavior and even make recommendations of future behavior. In the following pages, you ll see examples of Datameer s customer use cases. These use cases detail the variety, volume and velocity of data. The goal of depicting these use cases is to help you identify potential use cases of your own. Bottom line: faster time to value. 10

12 WHAT IS AN EXAMPLE OF A USE CASE? MARKET BASKET ANALYSIS AND PRICING OPTIMIZATION Analyze pricing and historical data to price and advertise effectively In retail, historical inventory, pricing and transaction data are spread across multiple devices and sources. This data changes on a daily - often hourly basis. Business users need to pull together this information to understand the seasonality of products, come up with competitive pricing, determine which platforms to support so that their online users have optimal performance, and where to target ads. Historical Inventory Pricing Transaction Variety Historical, inventory, pricing, and transaction data Volume 3000 TB Velocity 1M+ rows of additional pricing data added daily 11

13 WHAT IS AN EXAMPLE OF A USE CASE? PREDICT SECURITY THREAT Identify where security threats may occur The security landscape is always changing. So changes in behavior can indicate where the next attack may occur. For example, this company used Datameer to follow a virus that started in Russia, moved across Asia, to the US, and forcing Windows upgrades in its path. By seeing where the traffic was generated in particular geographic areas, they could predict where the next security threats would be. Log Files Firewall Feeds Variety 50+ different feeds, MySQL and JSON data from message queues and flat files, and log files Volume As their customer base grew, data volumes outstripped the capacity of their existing RDMBS-based system. Velocity To maintain SLA s within hours, the company had to rapidly analyze data across growing volumes of customer data. 12

14 WHAT IS AN EXAMPLE OF A USE CASE? BEHAVIORAL ANALYTICS Improve game flow and increase number of paying customers The game for gaming companies is to increase customer acquisition, retention and monetization. This means getting more users to play, play more often and longer, and pay. First, analysts use Datameer to identify common characteristics of users. As a result, gaming companies can target these users better with the right advertising placement and content. To increase retention, analysts use Datameer to understand what gets a user to play longer. A user who plays longer and interacts with other players makes the overall gaming experience better. Game Event Logs User Profile Variety Game event logs, user profile data, social interaction data captured during games between players Volume & Velocity 150+ points of data collected from millions of monthly users 13

15 WHAT IS AN EXAMPLE OF A USE CASE? PREDICTIVE SUPPORT Identify operational failure and address them before they are reported A couple of hours of downtime in a store or production environment means lost revenue, sometimes in the millions of dollars. The clues to where downtime may occur are spread across devices around a store or facility including WLAN controllers, mobile devices, routers and firewall devices. For this customer, these devices are used to run operations such as tracking inventory. Each network device generates enormous amounts of machine-generated data. Store X Store Y Store Z Variety Device data from WLAN controllers, mobile devices, routers and firewall devices Volume & Velocity 10B records per week from all the stores 14

16 WHAT IS AN EXAMPLE OF A USE CASE? FRAUD DETECTION Identify potential fraud Credit card fraud has changed. Instead of stealing a credit card and using it to buy big ticket items, some credit card thieves have become more sophisticated. For example, they can now making numerous, small transactions that are seemingly benign. But if Joe is making 100 $5 margarita transactions at various locations, something is wrong. By analyzing point of sale, geolocation, authorization, and transaction data with Datameer, this financial customer was able to identify fraud patterns in historical data. Point of Sale Geo-location Authorization Transaction Variety Point of Sale, Geo-location, Authorization, Transaction data Volume & Velocity 7.5B transactions per month 15

17 WHAT IS AN EXAMPLE OF A USE CASE DEVICE ANALYTICS Enable business analysts or non-technical users through a spreadsheet interface to analyze and do big data discovery This enterprise hardware company was generating and collecting data that was doubling every 15 months. In addition to the rapidly growing data volumes, there were hundreds of different semi-structured and unstructured log formats. Before Datameer, analysts were forced to write ad hoc Perl code to parse a subset of the log files and store data locally. By using Datameer, the company was able to derive valuable insights that helped virtually every group Support, Development, Marketing, and Services. For example, Support was able to send out a replacement part before the component actually failed. Sales was able to look at usage patterns to improve forecasting and renewal negotiations. Log Files Data Store Transaction Data Variety 100s of files, logs and other data sources Volume & Velocity Data volume growing 2x every 15 months 16

18 3 STEP 3 QUALIFY SOLUTIONS

19 SOLUTIONS QUALIFICATION CRITERIA WHAT FUNCTIONALITY DO I NEED? Now that we ve talked about the criteria you need to make a decision and how to identify the big data use cases, let s talk about the functionality you need or how to qualify that solution will meet your needs. Your big data analytics solution should have the following capabilities: Data Loading Data Loading A software has to be developed to load data from multiple, various data sources. This system needs to deal with the distributed nature of Hadoop on the one side and the non-distributed nature of the data source. The system needs to deal with corrupted records and need to provide monitoring services. Data Parsing Data Parsing Most data sources provide data in a certain format that needs to be parsed into the Hadoop system. For example, let s consider parsing a log file into records. Some formats are complicated to parse like JSON where a record can be on many lines of text and not just one line per record. Data Analytics Data Visualization Scheduling Dependency Management Data Synchronization Monitoring API Management UI Security Integration Data Analytics In order for data to be properly analyzed, a big data analytics solution needs to support rapid iterations. Data Visualization In order for an analyst to see the insights, data needs to be visualized. Integrating visualization is difficult because middleware needs to be built to deliver the data out of Hadoop and into the visualization layer. Scheduling All the items discussed above need to be orchestrated and scheduled. Scheduling needs to be easy to configure. In addition, the scheduling needs have monitoring services to notify administrators of jobs that fail. Dependency Management There are complex dependencies that must be managed. For example, certain data sets have to be loaded before certain jobs in Hadoop can be run. Data synchronization Data often needs to be pushed from Hadoop in to a data store like a database or in-memory system. Monitoring API Every aspect of a big data analytics solution needs to be monitored. Things that need to be monitored include who has access to the system, job health, performance, and data throughput. Management UI A management user interface is critical for ease of configuration and monitoring. Security Integration For security purposes, it is important to be able to integrate with Kerberos and LDAP. These capabilities map to the steps of the big data analytics process, including: integration functional analytics visualization smart analytics We ll walk through the things you ll want to look for to address each of these steps in the following pages of this section. 18

20 SOLUTIONS QUALIFICATION CRITERIA BIG DATA ANALYTICS REQUIRES RAPID INTEGRATION OF ALL TYPES OF DATA To help qualify your solutions (and shorten the list of solutions you evaluate), look for the following integration characteristics. Import any data type Import Jobs Datameer loads all data in raw format directly into Hadoop. The process is optimized and supported with robust sampling, parsing, scheduling and data retention tools that make it simple and efficient for any user to get the data they need quickly. Ensure that data is always current Data Links Some use cases, such as analyzing constantly changing user data, lend themselves to streaming the data into Hadoop as analytics are run. This ensures that user data is always up to date. Datameer provides data links to any data source for just that purpose. An open data platform Data Export The beauty of Datameer s unique integration and analytics capabilities is that their results can be exported to other data stores, such as a databases, remote file servers, data warehouses, or third-party BI (business intelligence) software packages. You can initiate a one-time manual export, configure the job to run each time the workbook is updated, or at a specific time interval. 19

21 SOLUTIONS QUALIFICATION CRITERIA ANALYTICS IS AN ITERATIVE PROCESS Analytics requires an iterative tool that is also easy to use You need a complete solution to analyze structured and unstructured data. Integrate all types of data with pre-built data connectors to databases, SaaS, log files and other unstructured data sources. Prepare and Analyze with point-and-click functions so your analytics are only limited by your imagination. Even the most complex nested joins of a large number of datasets can be performed using an interactive dialog. Visualize and present your analytics on any device and any browser. Prepare and Analyze Visualize Integrate Deploy Pre-built Functions with a Spreadsheet Interface The spreadsheet interface should offer pre-built functions such as currency conversion, date functions etc. 20

22 SOLUTIONS QUALIFICATION CRITERIA ANALYTICS REQUIRES VISUALIZATION THAT END USERS CAN INTERPRET Flexible data visualization You need a library of widgets that includes tables, graphs, charts, diagrams, maps, and tag clouds. This enables users to create simple dashboards or stunning business infographics and visualizations. The end result, data visualizations that communicate data. Beyond static dashboards, your data visualizations should have no built-in constraints. Users need to be able to drag and drop any widget, graphic, text, dashboard or infographic element as needed or desired. Pageviews The most popular pages, represented in the tag cloud by page views this month... l na ter ex busin ess_ The most common click paths followed by visitors news Click paths rum _fo ity un mm co Daily Average Visitors Our stickiest web pages, measured by dwell time w... ment_ne infotain co mm un ity _n ew s New vs. Returning Visitors infotain m ent_hi s... Our stickiest web pages, measured by dwell time 21

23 SOLUTIONS QUALIFICATION CRITERIA SMART ANALYTICS FUNCTIONALITY Automatically find groups and relationships hidden in your data Clustering With clustering (a K-means algorithm) Datameer automatically finds non-obvious but related groups within your data by automating the process of identifying and measuring common attributes within the dataset. The obvious benefit is that if you can segment your data into groups, you can treat the groups differently. For example, automatically identify groups in: Customer databases Text documents Product libraries POS data Weblogs Health records Social media Online gaming logs Clickpaths Decision Trees Datameer s decision trees (random forest algorithm) help you understand the different combinations of data attributes that result in a desired outcome. Decision trees are often used when enriching a dataset with additional data sources to optimize a process for a better outcome. The structure of the decision tree reflects the structure that is possibly hidden in your data. For example, find out what common attributes influence: Disease risk Fraud risk Customer churn Purchases Online signups Root-cause Product conversions 22

24 ...continued SMART ANALYTICS FUNCTIONALITY Column Dependencies Want to know how strongly a single data attribute like age, location, or gender, relates to other data attributes like income, college degree, or credit score? The column dependency algorithm automatically compares every possible data attribute combination and visually ranks the strengths of those relationships so you can instantly see where to focus further. Those relationships are important themselves and is often used to help target further analysis. For example, see the relationship between: Title and purchase amount Transaction type and frequency Location and product selection Average session length and virtual goods purchase Age and disease type Account age and product type Age and number of SMS messages Recommendation Engine Datameer s recommendation engine automatically predicts interests of a person based on historical observations of similar people s interests so you can increase engagement, recommend more relevant choices, increase customer satisfaction, and more. For example, predict interest in: Music Movies Content Services Products Documents Applications 23

25 SOLUTIONS QUALIFICATION CRITERIA SUMMARY ON HOW TO QUALIFY A BIG DATA ANALYTIC SOLUTION To summarize a comprehensive big data analytic solution consists of an end-to-end, scalable platform that enables any user to integrate, analyze and visualize all types of data. The following are things to look for when selecting a Big Data analytics solution: 1. Ease of Use Does the Solution: 3. Analytics Does the solution support: Provide an intuitive user interface that can be used by anyone? A comprehensive set of analytic and transformation functions? Have a web based user interface? Special functions for unstructured data? Support all popular browsers? Instant feedback and preview to validate analysis? Support HTML5 for usage on desktops, tablet and mobile devices? Data lineage to audit data flows? Grouping of analytics into logical groups? Offer an undo capability? Analysis of structured and unstructured data? Sentiment analysis? 2. Data Integration Does the solution support: A large set of native connectors for the integration of all types of disparate data (databases, file systems, social media, mainframe, SaaS applications)? Support for structured and unstructured data Self service data integration without IT 4. Visualizations Does the solution: Provide an extensive library of graphic widgets including tables, graphs, charts, diagrams, maps, and tag clouds? The ability to work with dirty data? Provide a free form canvas layout with no built in placement constraints? Parallel loading of data into Hadoop? Support for network diagram relationships No size limitations on data? Include fonts, color, shapes and clipart? Support data retention policies and management Support multiple pages of visualizations? Offer flexible data partitioning? Importing of images for backgrounds? Data links to data sources that streams the data into Hadoop as analytics are run? Support annotations? Ingestion of data in raw format directly into Hadoop with robust parsing and sampling? Enable content sharing? Offer REST API to start and monitor import or export of data? Include comparing current and historical data? next page... Exception reporting on data? An API to create custom connectors for proprietary/ custom/legacy applications? 24

26 ...continued 5. HOW TO QUALIFY A BIG DATA ANALYTIC SOLUTION Integration with existing IT infrastructure Does the solution: 7. Extensibility Does the solution provide: An SDK to add custom functions? Provide integration with HIVE? Support for Java, R, Ruby, Python? Support all major Hadoop distributions? Provide a REST API for orchestrating integration 8. System Architecture Describe how the solution: Provides linear scalability 6. Administration Does the solution provide: Role based security? Run natively on Hadoop without a separate layer of integration Authentication through LDAP or Active Directory? Support virtualized environments Data retention policy management? Allow import and export of data from other Hadoop clusters Multi-tenancy within cluster and other apps? Is architected from the ground up for Big Data and not just an add on or connector Ad-hoc and scheduling of import jobs? Web based UI for quick deployment from any browser/desktop? Centralized management through a web based interface? Log file viewer to audit logs? 9. Vendor Requirements Is/Does the vendor: Built upon years of deep Hadoop experience? Well financed/stable? Have customer support globally? Have a strong customer base? 25

27 4 STEP 4 VALIDATE SOLUTION

28 VALIDATE SOLUTION VALIDATE AND CALCULATE ROI Now you need to consider how you will validate if a solution will meet your requirements. Look for ways to validate that are cost effective. An extensive validation process can take many resources including people, equipment and time. In many cases you could end up spending more money on the resources to evaluate than on the purchase price of the solution you chose. Include: references a solution provider can offer as a way to validate ROI and TCO analysis based on actual customer experiences This section will go into the ROI and TCO framework in more detail. We ll also walk through some examples of ROI so that you have an idea of the ROI that can be achieved with Big Data use cases. Below is a framework for measuring return. Return is simply the benefits (which are business benefits and IT savings) the total costs (which are related to hardware, software, integration, etc). $$$ TCO Return Identify Fraud Hardware $$$ Lower Customer Acquisition Costs Software Time Increase Retention Integration Flexibility Increase Conversion Rate People Lower IT Costs Operations Logistics 27

29 VALIDATE SOLUTION BUSINESS VALUE AND ROI Measuring ROI is a process. Step 1: You first need to estimate ROI or business value to provide justification and set goals for your project. Step 2: Then you measure the actual ROI. Step 3: With the ROI metrics (e.g. % increase in customer conversion), you can update the model after each project. With your updates and your model, you can map out future projects. Step 1 Step 2 Step 3 Estimate ROI Measure ROI Update Model per month / quarter 28

30 VALIDATE SOLUTION MEASURE ROI Typical TCO (total cost of ownership) and ROI (return on investment) analyses show hardware savings, software savings, and productivity gains. In addition, the Datameer analysis provides business benefits such as the increased customer conversion rate that leads to $20M in new revenue. This business benefit is magnitudes larger than the IT savings. Take the time to find the business benefits and become the big data hero. IT savings are signifcant, but business benefits are greater. Datameer can help with this. Have a big vision. Start small. Iterate. Build on your successes. Once you ve measured the ROI for one project you can reuse that ROI metric (e.g. % increase in customer conversion rate) to estimate ROI gains for other projects within your organization that have similar use cases. Project 3 Project 2 Project 1 Hardware Savings Software Savings Productivity Business Benefit 29

31 VALIDATE SOLUTION HOW HAS BIG ANALYTICS HELPED? In this section, you ll see how Datameer customers have calculated ROI from using Datameer USE CASE INDUSTRY BENEFIT Optimize Funnel Conversion Software Security Increased customer conversion by 3x Behavioral Analytics Online Gaming 2X Revenue Customer Segmentation Financial Services Decreased customer acquisition costs by 30% Predictive Support Enterprise Storage Decreased customer churn Market Basket Analysis and Retail Reduced time to insight from 12 weeks to 3 days Predict Security Threat Software Security Predict security threats within hours Fraud Detection Financial Services Prevented $2B in potential fraud Pricing Optimization You can use these returns in your estimation of return from a big data analytics project. In the following pages you will see examples of our previous use cases and how Datameer customers have captured ROI. 30

32 VALIDATE SOLUTION OPTIMIZE FUNNEL CONVERSION Identify roadblocks in funnel conversion Generating traffic is good. But generating traffic that leads to sales is better. Datameer has helped companies identify which Google AdWords lead to sales. By analyzing Google AdWords, Salesforce, and Marketo data, this company was able to track a lead from AdWord click through to transaction. As a result, this company was able to improve funnel conversion by 3x, leading to $20M in additional revenue. Conversion 60% Customer conversion increased by 60% 31

33 VALIDATE SOLUTION BEHAVIORAL ANALYTICS Improve game flow and increase number of paying customers The game for gaming companies is to increase customer acquisition, retention and monetization. This means getting more users to play, play more often and longer, and pay. First, analysts use Datameer to identify common characteristics of users. As a result, gaming companies can target these users better with the right advertising placement and content. To increase retention, analysts use Datameer to understand what gets a user to play longer. A user who plays longer and interacts with other players makes the overall gaming experience better. To increase monetization, analysts use Datameer to identify the group of users most likely to pay based on common characteristics. As a result of this analysis the company was able to double their revenue to over $100M. Game Event Logs User Profile Revenue 2x Increase revenue by 2x 32

34 VALIDATE SOLUTION PREDICTIVE SUPPORT Identify operational failure and address them before they are reported A couple of hours of downtime in a store or production environment means lost revenue, sometimes in the millions of dollars. The clues to where downtime may occur are spread across devices around a store or facility including WLAN controllers, mobile devices, routers and firewall devices. For this customer, these devices are used to run operations such as tracking inventory. Each network device generates enormous amounts of machine-generated data. By using Datameer to analyze all network data, this company was able to detect potential network failures faster. As a result, they were able to reduce the number of network failures by 30%. Store X Store Y Store Z Failure 30% Reduced network failure by 30% 33

35 VALIDATION CRITERIA MARKET BASKET ANALYSIS AND PRICING OPTIMIZATION Analyze pricing and historical data to price and advertise effectively In retail, historical inventory, pricing and transaction data are spread across multiple devices and sources. Business users need to pull together this information to understand seasonality of products, come up with competitive pricing, determine which platforms to support so that their online users would have optimal performance, and where to target ads. With Datameer, these business users could do their analysis in 3 days instead of 12 weeks with their traditional tools and heavy IT involvement. Historical Inventory Pricing Transaction 12 weeks to 3 days Report time from 12 weeks to 3 days 34

36 VALIDATE SOLUTION PREDICT SECURITY THREAT Identify where security threats may occur The security landscape is always changing. So changes in behavior can indicate where the next attack may occur. For example, this company used Datameer to follow a virus that started in Russia, moved across Asia, to the US, and forcing Windows upgrades in its path. By seeing where the traffic was generated in particular geographic areas, they could predict where the next security threats would be. This enabled them to proactively go after the security threats and reduce the risk of a data breach, which on average costs organizations $5.5M. Log Files Firewall Feeds Predict threat Predict security threats within hours 35

37 VALIDATE SOLUTION FRAUD DETECTION Identify potential fraud Credit card fraud has changed. Instead of stealing a credit card and using it to buy big ticket items, some credit card thieves have become more sophisticated. For example, they can now making numerous, small transactions that are seemingly benign. But if Joe is making 100 $5 margarita transactions at various locations, something is wrong. By analyzing point of sale, geolocation, authorization, and transaction data with Datameer, this financial customer was able to identify fraud patterns in historical data. This analysis helped the firm identify $2B in fraud. By applying the fraud model to new transactions, the company was able to identify potential fraud and proactively notify customers. Point of Sale Geo-location Authorization Transaction Prevent fraud Prevented $2B in fraud 36

38 KEY TAKEAWAYS 1. Have you defined your decision criteria? (See Decision Criteria for selecting a big data analytics solution p.6) 2. Have you defined and identified your big data use cases? (See Decision Criteria for selecting a big data analytics solution p.8) 3. Have qualified the solutions so that you have only one or two solutions to validate? (See Decision Criteria for selecting a big data analytics solution p.16) 4. Have validated the solution and created the ROI/TCO to compare the solution? (See Decision Criteria for selecting a big data analytics solution p.8) If you have any questions or comments, please contact us at marketing@datameer.com 37