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BUYER’S GUIDEBIG DATA ANALYTICS

01

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)

TABLE OF CONTENTS

02Big Data Analytics Buyer’s Guide

Data loading

Data loading

Analyze data Visualize data Export data Secure data MonitoringTransform data andperform data quality

Scheduling anddependencies

Scheduling anddependencies

BUILD

BUY14+ months

3 months

BUILDING A BIG DATA ANALYTICSOLUTION CAN TAKE OVER A YEAR...

AND COST YOU OVER $1M IN SALARIESALONE FOR 1 YEAR

JOB TITLE

IT Project Manager

System Administrator

Network Administrator

Database Administrator

IT Security Manager

Business Intelligence Analyst

Data Scientist

Java Developer

QA Engineer

BAY AREA

$ 140,000.00

$ 117,000.00

$ 119,000.00

$ 125,000.00

$ 116,000.00

$ 137,000.00

$ 138,000.00

$ 136,000.00

$ 120,000.00

$ 1,148,000.00

NEW YORK

$ 126,000.00

$ 105,000.00

$ 107,000.00

$ 119,000.00

$ 104,000.00

$ 133,000.00

$ 133,000.00

$ 133,000.00

$ 114,000.00

$ 1,074,000.00

Big Data Analytics Buyer’s Guide

03

SELECTING A BIG DATA ANALYTICSSOLUTION CAN TAKE MONTHS

DefineDecisionCriteria(.5 month)

Agree onUse Case(1 month)

QualifySolutions(1 month)

Validate(1.5 months)

Purchase(.5 month)

AND ALMOST $150K BEFORE YOUSTART THE PROJECT

JOB TITLE

Project Manager

Business Intelligence Analyst

IT Administrator

BAY AREA

$ 52,500

$ 51,375

$ 44,645

$ 148,500

NEW YORK

$ 47,250

$ 49,875

$ 40,125

$ 137,250

Salaries based on 4.5 months of yearly average


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

THERE ARE MANY ADVANTAGES TOBUYING A BIG DATA ANALYTICS SOLUTION

Seamless Data Integration

• Structured, semi and unstructured• Pre-built connectors• Connector plug-in API

Powerful Analytics

• Interactive spreadsheet UI• Built-in analytic functions• Macros and function plug-in API

Business Infographics

• Combine anything, WYSIWYG• Infographics and dashboards• Visualization plug-in API

Below are 3 key reasons we wanted to highlight:

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


STEPS FOR SELECTINGA BIG DATA ANALYTICSSOLUTION

HOW THIS GUIDE WILL HELP YOUSELECT A BIG DATA ANALYTICSSOLUTION FASTER

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.

DefineDecisionCriteria

Agree onUse Case

QualifySolutions Validate

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 DECISIONCRITERIA

STEP 1

1

Ease of use

Does the business need IT to help?

Can a business analyst use the tool to do analysis?

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?

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?

Integration with existing IT infrastructure

Does the solution support import from and export into

other BI systems?

Administration

Does the system support flexible security integration

with LDAP or ActiveDirectory?

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)?

DEFINE DECISION CRITERIA

After working with hundreds of customers, we’ve found the following are the top things to look for in a Big Data Analytics Solution.

DECISION CRITERIA FOR SELECTINGA BIG DATA ANALYTICS SOLUTION


2

STEP 2

AGREE ONUSE CASES

WHAT DOES A BIG DATA USE CASE HAVE?What does a Big Data Analytics use case look like?

10

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.


WHAT IS AN EXAMPLE OF A USE CASE?

MARKET BASKET ANALYSIS ANDPRICING OPTIMIZATION

Historical

Inventory

Pricing

Transaction

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.

11

Variety Historical, inventory, pricing, and transaction data

Volume 3000 TB

Velocity 1M+ rows of additional pricing data added daily



PREDICT SECURITY THREATIdentify 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.

Feeds

Log Files

Firewall

12

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.


13


BEHAVIORAL ANALYTICSImprove game flow and increase number of paying customersThe 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.

User Profile

Game Event Logs

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


14


PREDICTIVE SUPPORTIdentify 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



FRAUD DETECTIONIdentify 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.

15

Point of Sale

Geo-location

Authorization

Transaction

Variety Point of Sale, Geo-location, Authorization, Transaction data

Volume & Velocity 7.5B transactions per month


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.

WHAT IS AN EXAMPLE OF A USE CASE

DEVICE ANALYTICSEnable business analysts or non-technical users through a spreadsheet interface to analyze and do big data discovery

16

Log Files

Data Store

Transaction Data

Variety 100s of files, logs and other data sources

Volume & Velocity Data volume growing 2x every 15 months


3

STEP 3

QUALIFYSOLUTIONS

18

Data LoadingA 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 Loading

Dependency ManagementThere are complex dependencies that must be managed. For example, certain data sets have to be loaded before certain jobs in Hadoop can be run.

DependencyManagement

Data synchronizationData often needs to be pushed from Hadoop in to a data store like a database or in-memory system. Data

Synchronization

Monitoring APIEvery 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.

Monitoring API

Management UIA management user interface is critical for ease of configuration and monitoring.Management UI

Security Integration For security purposes, it is important to be able to integrate with Kerberos and LDAP.Security

Integration

Data ParsingMost 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 Parsing

Data AnalyticsIn order for data to be properly analyzed, a big data analytics solution needs to support rapid iterations. Data Analytics

SchedulingAll 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.

Scheduling

Data VisualizationIn 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.

DataVisualization

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:

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.


19

Import any data type

Import JobsDatameer 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 LinksSome 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 ExportThe 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.


BIG DATA ANALYTICS REQUIRES RAPIDINTEGRATION OF ALL TYPES OF DATATo help qualify your solutions (and shorten the list of solutions you evaluate), look for the following integration characteristics.


20


ANALYTICS IS AN ITERATIVE PROCESSAnalytics requires an iterative tool that is also easy to useYou 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.

Pre-built Functions with a Spreadsheet InterfaceThe spreadsheet interface should offer pre-built functions such as currency conversion, date functions etc.

Prepare and Analyze

Integrate

Deploy

Visualize


21


ANALYTICS REQUIRES VISUALIZATIONTHAT END USERS CAN INTERPRETFlexible data visualizationYou 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

Daily Average Visitors

New vs. Returning Visitors

Click pathsThe most common click paths followed by visitors

The most popular pages, represented in the tag cloud by page views this month...

Our stickiest web pages, measured by dwell time

Our stickiest web pages, measured by dwell time

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260

5.05.56.06.57.07.58.08.59.09.5

10.010.511.0

busi

ness

_new

s

community_forum

community_news

info

tain

men

t_hi

s...

infotainment_new...

external


22

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.

Automatically find groups and relationships hidden in your data

SMART ANALYTICS FUNCTIONALITY

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



23

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:

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

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

...continued SMART ANALYTICS FUNCTIONALITY


24


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.

SUMMARY ON HOW TO QUALIFY ABIG DATA ANALYTIC SOLUTION

1. Ease of Use — Does the Solution:

Provide an intuitive user interface that can be used by anyone?

Have a web based user interface?

Support all popular browsers?

Support HTML5 for usage on desktops, tablet and mobile devices?

Offer an undo capability?

3. Analytics – Does the solution support:

A comprehensive set of analytic and transformation functions?

Special functions for unstructured data?

Instant feedback and preview to validate analysis?

Data lineage to audit data flows?

Grouping of analytics into logical groups?

Analysis of structured and unstructured data?

Sentiment analysis?

4. Visualizations – Does the solution:

Provide an extensive library of graphic widgets including tables, graphs, charts, diagrams, maps, and tag clouds?

Provide a free form canvas layout with no built in placement constraints?

Support for network diagram relationships

Include fonts, color, shapes and clipart?

Support multiple pages of visualizations?

Importing of images for backgrounds?

Support annotations?

Include comparing current and historical data?

Enable content sharing?

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

The ability to work with dirty data?

Parallel loading of data into Hadoop?

No size limitations on data?

Support data retention policies and management

Offer flexible data partitioning?

Data links to data sources that streams the data into Hadoop as analytics are run?

Ingestion of data in raw format directly into Hadoop with robust parsing and sampling?

Offer REST API to start and monitor import orexport of data?

Exception reporting on data?

An API to create custom connectors for proprietary/ custom/legacy applications?

next page...


The following are things to look for when selecting a Big Data analytics solution:

25

...continued HOW TO QUALIFY A BIG DATA ANALYTIC SOLUTION

6. Administration – Does the solution provide:

Role based security?

Authentication through LDAP or Active Directory?

Data retention policy management?

Multi-tenancy within cluster and other apps?

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?

8. System Architecture – Describe how the solution:

Provides linear scalability

Run natively on Hadoop without a separate layer of integration

Support virtualized environments

Allow import and export of data from other Hadoop clusters

Is architected from the ground up for Big Data and not just an add on or connector

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?

5. Integration with existing IT infrastructure – Does the solution:

Provide integration with HIVE?

Support all major Hadoop distributions?

Provide a REST API for orchestrating integration

7. Extensibility – Does the solution provide:

An SDK to add custom functions?

Support for Java, R, Ruby, Python?


4

STEP 4

VALIDATESOLUTION

27

Identify Fraud

Lower CustomerAcquisition Costs

Increase Retention

IncreaseConversion Rate

Lower IT Costs

Hardware

Software

Integration

People

Operations

Logistics

$$$

Time

Flexibility

VALIDATE AND CALCULATE ROIVALIDATE SOLUTION

TCO$$$ Return

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).


28

per month / quarter

BUSINESS VALUE AND ROIVALIDATE SOLUTION

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.

EstimateROI

MeasureROI

UpdateModel

Step 1 Step 2 Step 3


29

MEASURE ROIVALIDATE SOLUTION

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.

HardwareSavings

SoftwareSavings Productivity Business

Benefit

Project 3

Project 2

Project 1


30

HOW HAS BIG ANALYTICS HELPED?VALIDATE SOLUTION

Optimize Funnel Conversion

Behavioral Analytics

Customer Segmentation

Predictive Support

Market Basket Analysis and

Pricing Optimization

Predict Security Threat

Fraud Detection

Increased customer conversion by 3x

2X Revenue

Decreased customer acquisition costs by 30%

Decreased customer churn

Reduced time to insight from 12 weeks to 3 days

Predict security threats within hours

Prevented $2B in potential fraud

Software Security

Online Gaming

Financial Services

Enterprise Storage

Retail

Software Security

Financial Services

USE CASE INDUSTRY BENEFIT

In this section, you’ll see how Datameer customers have calculated ROI from using Datameer

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.


VALIDATE SOLUTION

OPTIMIZE FUNNEL CONVERSIONIdentify 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.

Customer conversion increased by 60%


Conversion60%

VALIDATE SOLUTION

BEHAVIORAL ANALYTICSImprove game flow and increase number of paying customersThe 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.

User Profile

Game Event Logs


Increase revenue by 2x

Revenue2x

VALIDATE SOLUTION

PREDICTIVE SUPPORTIdentify 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


Reduced network failure by 30%

Failure30%

34

VALIDATION CRITERIA

MARKET BASKET ANALYSIS ANDPRICING OPTIMIZATION

Historical

Inventory

Pricing

Transaction

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.


Report time from 12 weeks to 3 days

12 weeksto

3 days

35

VALIDATE SOLUTION

PREDICT SECURITY THREATIdentify 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.

Feeds

Log Files

Firewall


Predict security threats within hours

Predictthreat

36

VALIDATE SOLUTION

FRAUD DETECTIONIdentify 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


Prevented $2B in fraud

Preventfraud

37

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?


3. Have qualified the solutions so that you have only one or two solutions to validate?


4. Have validated the solution and created the ROI/TCO to compare the solution?


KEY TAKEAWAYS

If you have any questions or comments, please contact us at [email protected]


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