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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2What is Analytics?Aid Data a
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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3What is Analytics?Aid Data a
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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4What is Analytics?Aid Data a
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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Aati i th t d t
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a at.
1 http://www.emc.com/leadership/programs/digital-universe.htm
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ditd that th amt
data i th wd wi w 50X i th xt dad.1
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5What is Analytics?Aid Data a
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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6What is Analytics?Aid Data a
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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7What is Analytics?Aid Data a
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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8What is Analytics?Aid Data a
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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9What is Analytics?Aid Data a
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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10What is Analytics?Aid Data a
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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11What is Analytics?Aid Data a
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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di hw t di data.
Th xt wi a aati
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12What is Analytics?Aid Data a
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