What is Analytics Report - Applied Data Labs

8/2/2019 What is Analytics Report - Applied Data Labs

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What is

Analytics?


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1What is Analytics?Aid Data a

Preface

The world economy has discovered a resource like few others that is completely renewable and an innite source

of knowledge. The vast amounts of data now available provides insights into anything desiring to be known, and

analytics is the tool that creates that value.

In brief, analytics is a collection of processes and technologies that turn data into usable information and

knowledge. It is also a source of confusion for many because it serves as an umbrella term under which a wide

range of systems and technologies exist. In this report we will introduce the processes and technologies that make

up analytics as well as unpack the most prominent terms such as web analytics, customer analytics, and businessanalytics so that you can understand the effect of these technologies.

What is Applied Data Labs?Life is changing faster than ever with data driving new economic opportunities and transforming the way our

world works. The new eld of Data Science is on the cutting edge of this change, and at Applied Data Labs data

science is what we do. We are a global research and advisory lab delivering the insights necessary to keep our

clients ahead of the curve and discover the trends that will change the world. We guide leaders in IT, marketing,

and strategy through fact-based insight ensuring their business success by helping them understand, strategize,

and act upon opportunities brought by change. Composed of leading voices in analytics, our data scientists have

researched, designed, and deployed analytics projects for many Fortune 500 companies such as Microsoft, Cisco,

Orbitz, Procter & Gamble, and United Airlines. Now we use our knowledge to create a more data driven world.

Jeremy Kolb

Applied Data Labs Senior Data Scientist

2012

All rights reserved. This report or any part thereof may not be reproduced without the written permission of the copyright holder.

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Table of Contents

B

Why Should You Care About Analytics? 3

What Does Analytics Do? 5

What Does Analytics Do Specifically? 6

Where is Analytics Going? 8

Application In Business 9

Technical Specifics About Analytics 10

What to Look for in the Next Generation 11

Term Index 12


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Why Should You Care

About Analytics?Our world is dividing between those with the capability

to take advantage of analytics and those without. The

elites see their data as an ever increasing asset and

competitive advantage, changing the guessing game of

business strategy into a perfectible science. To the rest,

data is a liability stored to satisfy data retention policies

not a resource to be unlocked but a requirement to

be satised. The incredible abilities for discovery and

forecasting are out of reach if they are considered at all.

New technologies and strategies are bridging this gap

by democratizing these abilities, and we are seeing

substantial growth in the data industry as a result.

In this report, we outline the various advances and

important trends that are bringing about this change.

Analytics DefinedAnalytics is the collection of technologies and

processes that turns raw data into usable knowledge in

order to inform decisions and drive action.

Although it provides a clear reference, this denition

does not truly explain the intricacies of the analytics

market and the available tools. In this report, the term

will be examined through rst looking at the problems

analytics solve and also its various manifestations.

How should I understand

Data?Before exploring anything about analytics it is essential

to rst understand the nature of the main resource it

uses: Data. Data is quite similar to traditional resources

such as copper or wool in that someone produces it,

and then someone else buys the raw material and

makes something new from it. However, it is unique

from traditional commodities in that it doesnt get used

up in the process, making it particularly interesting and

uniquely valuable. Data can be produced by anyone,

and with advancements in analytical technology, can

be utilized by everyone. But most companies do not see

their data this wayit is a backend to reports at best

or an expense at worstwhich means this potential

revenue source remains largely untapped.

What is the Problem?The exponential expansion of data in recent years has

resulted in mind numbing amounts of data, making it

virtually impossible for companies to get by using the

old means of analysis. In 2010 Eric Schmidt, then CEO

of Google, said that we now create as much data every

2 days as we did from the dawn of man through 2003.1

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1 http://techcrunch.com/2010/08/04/schmidt-data/

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As the number of devices capable of producing

datasuch as cell phones, tablets, RFID readers, etc

continues to grow, the amount of data being produced

daily is growing with it.

Companies know useful patterns and information

exist in this mountain of data, patterns andinformation that can explain causality and even

predict outcomes. However, without the proper

tools these patterns are difficult to access and the

financial gain they could provide remains buried.

This difculty begins with the reliance on IT

departments to produce the valuable insight needed.

Without proper analytics tools, decision makers must

go to IT for information, which results in IT producing

reports, which typically prompt more questions

and starts the cycle anew. This creates a culture of

scarcity around data analysis due to the difculty of

attaining reports and the limited time available for

report creation.

Adding to the inconvenience of data reporting without

analytics is the inefciency. IT rst has to receive, then

process, and nally return the reports, and because

of the long wait times caused by this process, decision

makers have to know well ahead of time what reports

they need. Reports simply can not be generated easily,

and the amount of time needed to create one demands

that the query be worth the hours used to create it.

What is the Solution?The solution is analyticsit is the tool used by top

companies to leverage data as an asset. It nds

patterns and data trends with data mining tools. It

explains causality through statistical analysis and

quantitative analysis. It tests past decisions using

multivariate testing and a/b testing. it anticipates

future outcomes using predictive modeling and

predictive analytics.

IT Is effIcIenT. Computes processes in minutes

that once took hours.

IT Is profITAble. Automates processes to

convert data into usable information.

IT Is convenIenT. It is mobile and easy to use.

Analytics does the remarkable: it allows companies to

intuitively explore data and automate data discovery. It

opens the door for companies to make fast data driven

decisions and optimize business processes.

I Why Should You Care About Analytics?

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What Does Analytics

Do?Analytics is BI (business intelligence) evolved. Where

BI provides the tools to discover the answer if provided

the proper question, analytics nds the answer to the

question you didnt think to ask. It is the advancement

from deductive data analysis to inductive data analysis.

Ddti Data Aai (bI)Deductive data analysis is one of two means of analyzing

data, it enables users to answer questions such as: What

happened? When? Who? and How Many? Using tools

such as Excel and OLAP, a user rst makes an educated

guess regarding the cause of a particular abnormality

or trend. Then using deductive analysis tools the user is

able to conrm or negate the validity of that hypothesis,

and can form a new hypothesis to test if needed.

Idti Data Aai (Aati)Contrasting deductive is inductive analysis. Instead of

starting with a hypothesis, users are able to start with

a goal and discover the data that informs that goal. Do

you want to know what zip code is most likely to respond

to your offer? Inductive analysis will nd the data most

applicable and give you the answer. Inductive analysis

starts with the data and discovers the best parts of it to

answer whatever question you put to it.

II

bI vs bA busIness InTellIgence busIness AnAlyTIcs

Answers the

questions:

What happened?

When?

Who?

How many?

Why did it happen?

Will it happen again?

What will happen if we change x?

What is the best possible outcome?

What else does the data tell us that we never thought to ask?

Includes: Reporting (KPIs, metrics)

Automated Monitoring/Alerting (thresholds)

Dashboards

Scorecards

OLAP (Cubes, Slice & Dice, Drilling)

Ad hoc query

Multidimensional Queries

Statistical/Quantitative Analysis

Data Mining

Predictive Modeling

Business Resource Planning

Multivariate Testing

Quick response analysis

Sentiment Analysis

Business Planning

Data Visualization

Infographics

Adamt bi ItiSimple comparison of the two technologies:


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What Does Analytics

Do Specifically?

III

Ad Hoc QueryAd Hoc is latin meaning for this, and an Ad Hoc

Query provides reports designed for very specic

situations. Where once business relied upon general

reports meant to service a wide range of needs,

analytic reports focus only on the data needed for

a specic task. In analytics the term has grown to

incorporate speed and ease of use as well. Analytics

has the power to create data reports with minimal

technical skill required and deliver them quickly,

enabling users to make highly informed decisions on

even the most peripheral decisions as well as those

central to their mission.

Predictive Modeling /Trend Projections a society we are growing more accustomed to

computer-generated predictions, and in fact we rely

on them when checking the weather, travel times, or

even typing in a Google search. Behind the interface,

these programs use trend projections and predictive

modeling to get you the information you need or even

predict what you are going to ask.

In the simplest of terms, predictive modeling and trend

projection is precisely what it sounds like. Given a data

set, analytics will project the trends into the future and

predict outcomes. (The term predictive modeling is

an artifact of what happens on the back-end: a data

model is created which describes the data and is then

used to project into the future.) Of course programs

have been able to do this for some time now, but

where analytics sets itself apart is its ability to forecast

the impact changes will have on trends. Instead of

simply knowing where the trend is headed analytics

tell you what you need to know to inuence the trend

in the direction you desire.

Data VisualizationAnother way analytics is breaking down the barriers

to effective data management is through new methods

of data visualization. Data discovery used to rely on

individuals with high levels of technical and statistical

abilities who were able to look at spreadsheets and

understand the implications of that information. With

advancements in data visualisation, those patterns are

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III What Does Analytics Do Specifically?

now accessible to a wider audience. Take for example

the 2010 US census data presented in spreadsheet and

visual form:

Analytics has changed the way we see data. Both

images depict basically the same data, but the bottom

image is designed to be easily understood using

our abilities to recognize patterns visually. What

analytics does is transform the data into an interactive

environment capable of delivering useful knowledge in

a friendly way.

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Where is Analytics

Going?

IV

Cause identificationIn every area of life unexplained things happen. Sudden

drops in revenue, a particular product skyrocketing, or

a plummeting election campaign. Some of these trends

can be identied using currently available analytics

tools, but a great number of the underlying causes

remain hidden due to the lack of necessary data and

context surrounding the incident.

Where once companies attempted to explain all causes

using only in-house data, analytics is now trending

towards the inclusion of more data than just what the

customer provides, from sources both public and private.

This augmentation provides users with much deeper

understanding of causality by not only analyzing the

business but also the physical surroundings and industry

trends. For example you may see that customers in a

certain area are buying more product, but you may not be

able to understand why until you add the data that tells you

that customers in that area tend to have larger families. This

insight will then allow you to advertise more effectively in

that area by understanding the demographics.

So when you hold a focus group, not only will you have

the data they generate, but you will be able to augment

that data with information about the focus group. Arethey a good representation of local demographics? What

percentage of their answers are likely to be in line with

the rest of the market? Did they just tell me what I want

to hear? Would they likely say they are happy? The right

data can answer any question and analytics will soon

have all of the contextual data needed.

For more information on this trend read, Applied Data

Labs Fusion Project

ConsumerizationData discovery and information manipulation was once

the sole domain of the precious few with high levels

of technical and statistical abilities. But the world of

analytics is quickly transforming this elitist state. Many

programs now focus on usability for people of all levels

and the ease of training--called consumerization.

However, certain barriers still endure. Overwhelming

interfaces and limited data dexterity still pester those

unfamiliar with the territory. At Applied Data Labs

we see that advancements in this area will soon make

analytics so accessible that anyone and everyone will

feel condent in their data.

MobilityAnalytics has recently started breaking away from

the traditional desktop and laptop interface through

smartphone and tablet applications, but for most

software programs these are simply additional features

to augment the desktop version of the program. This

often makes these applications rather dicey and

unable to perform at the high level of their desktopcounterparts. Several analytics projects are changing

this, and the trend is headed towards increased

mobile ability and mobile interfaces designed to take

advantage of all the tools and new interfaces available

on smartphones and tablets.


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Application In Business

V

Business AnalyticsCustomer, sales, ERP, and web analytics all are within

the broad eld of business and have several shared

features. Each in their own way seek to explore

and investigate past performance data in order to

facilitate planning and optimization. Regardless ofthe particular business or specic need, all analytics

programs enable users to make better use of the

resources they have.

ERP (Enterprise Resource

Planning)Useful information about everything from team

effectiveness to issue frequency and cost, is hidden

within company data. ERP programs use data tools

to manage these areas and others such as human

resource data, resource usage, resource cost data,

trend measurement, and project data. In general ERP

facilitates the sharing of information throughout the

entirety of an organization, and manages the ow of

data to stakeholders outside the company.

Customer analyticsCustomer analytics enables businesses to take

information gained about consumers gathered either

internally or externally and use predictive modeling

in order to gain useful insight about customer

behavior. The information gained is often used for

marketing purposes as well as customer relationship

management. Simply put, it enables you to know more

about your customers so you can more effectively

meet their needs and sell more product.

Sales analyticsSales analytics is often a subset of customer analytics,

It focuses primarily on data from marketing efforts,

customer analytics, and customer feedback among

other sources. Its goal is to discover sales ideas, tell

you product life cycle information, and enable you to

capitalize on opportunities and maximize product return.

Web AnalyticsThe purpose of web analytics is to increase your

knowledge about your websites performance in a wide

range of areas. Web analytics does this by providing users

with incredible amounts of data concerning their website.

It shows how well user websites retain viewers, where

viewers are lost, whether demographics plays a role in

retention, and how long they linger on a page. This type of

information enables users to address problems with their

website to optimize performance.

Beyond addressing website problems, web analytics also

enables users to capitalize on high performing sections

through identication and optimization. Web analytics

will quickly identify how effective landing pages are at

creating conversions and show users the process typical

of a user after nding the website. It will examine where

trafc is coming from, what browsers are most heavily

used to access their content, and even what mobile

devices are used.


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Technical Specifics

About Analytics

VI

Horizontally-scalable

columnar data storesThese are newly-popular types of data stores which store

data in a much simpler, atter, and non-relational manner

which allows data repositories to be scaled up by addingmore servers, typically in on-demand computing clouds

like Amazon Cloud. In the past (with relational databases)

scaling up involved complex clustering congurations and

replication. The drawback to these columnar data stores

is that they do very little for you as a programmer aside

from providing a place to put your data, which means that

you have to spend much more time upfront to use them

because their schemas have to be pretty much hard-

coded (and I do mean coded), and programming for them

is not a simple as writing simple SQL queries (althoughthis is slowly changing). Popular examples include Apache

Cassandra, MongoDB, and the new Amazon DynamoDB,

but there are many others.

Distributed data analysisThe ability to analyze data as it comes in, and distribute

that analysis across a cluster, is quite different from the

traditional ETL process used by data warehouses. This

is where Hadoop is getting popular, because it allows

you to take each chunk of data you receive and send it

to a cluster for detailed analysis. Being able to break up

complex queries and run them across a cluster is much

more efcient than running it in a single process, and

this becomes very important if you need to analyze very

large amounts of data. (Hadoop is not the only game in

town for distributed analysisfor example Stormproject).

Data synergy and

AugmentationData synergy and augmentation is the idea that the

more data you add to your stockpile, the more valuable

your existing data becomes. If you have the means tocombine and overlay multiple data sets so that they feed

off one another, the value of your data pool as a whole

grows exponentially and the insights you can derive from

it become much richer and more valuable.

An improved ability to

recognize patternsThe ability to store multiple data sets in one place

and use distributed processes to analyze the whole

allows you to do some interesting things with pattern

recognition. Often patterns and trends only emerge as

you add more and more data sets to the pool, which is

why the ability to add an exponentially-growing amount

of data to the equation is important in the rst place. By

teasing out the patterns in the cumulative data sets you

begin to expose the real value in the datathe insights

and revelations that werent possible before.


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What to Look for in the

Next Generation

VII

This report claries where analytics is today, but the

culture around data is ever evolving and advancement

in various technologies necessitate advancements

in analytics.The trouble with our current business

analytics solutions is that they rely on old methods of

manipulating data and outdated interfaces. Googles Eric

Schmidt said it well:

I actually think most people dont want Google to

answer their questions. They want Google to tell them

what they should be doing next.

As it stands now, people need to discover how to

discover data. The next gen will enable analytics to tell

you what to do next.

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Term Index

AppendIx A

Ad H Q

A data query issued in response to an immediate need

requiring instant feedback.

Aat data

Data combined from multiple sources.

bi Data

Umbrella buzzword under which a wide range of

advancements in data management reside.

cd cmti

One form of computing as a service, often providing

analytics services without requiring on site installation.

crM (ctm ratihi Maamt)

CRM software provides basic BI abilities to small

businesses

Data cmizati

The process of making data easy to use.

Data Di

The analytics driven ability to play with data and nd

unique and valuable information

Data DiiBreaking data into its component parts in order to gain

greater insight. (days to hours, hours to minutes, etc.)

Data Kwd

Clear usable information gathered through data analysis.

Data Mii

Designing of new processes for creating useful data

knowledge.

Data Q

The information (a question) sent to analytics

software in order to gather data knowledge (an

answer).

Data rti

The task of turning a data query into data knowledge

that is now performed by analytics.

Data st

A collection of facts and gures, commonly in

spreadsheet form, submitted to a program for

analysis.

Data st

The idea that, when understood properly, data tells

useful stories.

Data viaizati

An emerging trend in analytics that enables easier

proportional and relational analysis through the use of

charts, graphs, and infographics.

Data Wahi

The storing and managing of large amounts of data.

Data xati

See Data Discovery


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Dahad

an old means of keeping track of data that presented

groups of important data selected by the user.

Dii Atmati

New technology that enables analytics systems to

make changes to optimize performance.

erp (eti r pai)

Company wide uniform data management system

providing real time data tracing and often automated

decision making tools.

eTl (extat Tam lad)The process of taking data from an outside source,

converting it to t current standardization, and adding

it to current data.

HolAp (Hid oi Aatia pi)

Combination of ROLAP (relational) and MOL AP

(multidimensional) enabling higher degrees of control

and data manipulation for the user.

Itati rti

Data reporting tools with high levels of data discovery

easily accessible.

KpI (K pma Idiat)

User selected data streams that indicate overall

success. Typically a key component of dashboards.

KsI (K s Idiat)

See KPI

MolAp (Mutidimensina onine Anaytica prcessing)

The more traditional form means of data storage for

OLAP, faster processing but less data storage ability.

MtaData

The concept of data about data, most easily understood

as reference tools.

Mtidimia Aai

Data visualization demonstrating multiple factors

of importance. (volume and time) (Prot margin,

expenses, revenue, time, etc.)

Mtiaiat Tti

Hypothesis testing on complex multi-variable systems.

olAp oi Aatia pi

A technical term referring to specic background

structures of analytics within cloud computing.

sad

a data report tracking KPIs and comparing currentlevel with set goals. Does not provide information on

how to attain the goals however.

rDbMs (ratia Dataa Maamt stm)

The technology enabling more rational organization of

data.

rolAp (ratia oi Aatia pi)

A means of data storage that enables far greater

amounts of data storage.

rt ca Aai

The process by which analytics identies the initial

cause of a statistical anomaly.

Thtia Aati

The branch of analytical science focused on the

expansion of analytical computing abilities.

AppendIx A Term Index

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