1. White Paper
Simplifying Analytics Adoption
for Decision Making

2. I believe in God!
For everything else, come with Data
The above statement very aptly emphasises the
importance of data in decision making.
Few decades ago, Managers relied on their
instincts to take business decisions. They could
afford to make mistakes and learn from it. Today,
the scope for learning from mistakes is very
minimal. Instincts should be backed by data to
minimise mistakes.
The probability of “customer centric” decisions
being right could be high, if the top management
makes better use of the end user customer data
(such as point of sale data, voice of customer,
social media buzz etc.) to devise business
strategies.
Technological advancements, in addition to
opening new channels of communication with
customers, have also enabled organizations to
collect vital information about their businesses
with customers. But, have these organizations
fully leveraged this data?
Consider a situation where the organization A
makes use of sales data at the “wholesale” level for
making production planning and inventory
optimisation. Organisation B makes use of sales
data at the “Retail level” for the same tasks.
Organisation B, which has better information
about frequency of product consumption at the
most granular level (retail level) is likely to
forecast better than organisation A.
Today, Organizations make use of data for
business decisions, but the data is not close
enough to the customer to reap maximum benefit.
In many cases, importance is not given to the
granularity of data.
Yes, the organisation's objective is to sell to the
“wholesalers” only but the wholesaler will in turn
place a new order only when his stock gets cleared
and this applies to all downstream levels until the
product gets sold to the end user of the product.
Emphasis on the need to make use of information to help organisation's growth
In a global survey of 1,375 subscribers conducted by Harvard Business Review Analytics
Services in January 2010, 85% of respondents said that information is a key strategic asset,
yet only 36% said their organizations are currently well positioned to use information to help
grow their business.
The disparity at the upper end of the scale was even more dramatic; while almost
half—45%—strongly agreed that information is a key strategic asset, only 7% believed they
are very well positioned to exploit it.
Source: HBR Analytics services: Unlocking the value of the information economy

3. Considerations for data driven decision making
 Unless data gets used in its most granular form (close to customer) at all managerial levels, it is very
difficult to fully benefit from the fruits of analytics adoption. When granularity of information is
compromised, our decisions can't be accurate. Adopting analytics only at select managerial levels can be a
misleading exercise.
 Timing crucial business decisions on changing customer needs is of paramount importance. Granular level
customer data helps point out changing customer needs and behaviours.
Why is it disastrous to use analytics only at select managerial levels?
In the decision value chain, information flows from the bottom level and cascades upwards. Top level
managers make use of this information to take decisions and form strategies. In most organisations, top level
managers have access to BI tools to slice and dice data, but the middle level managers are not equipped with
tools needed to take data backed decisions. They can neither pull granular data dynamically, nor validate the
claims made by the lower level managers. The claims made by the lower management, when cascaded
upwards might paint a wrong picture about crucial metrics. Hence the strategies devised by the top
management could go for a toss. Unfortunately, even today, this problem remains unaddressed in most
organizations!
Emphasis on enterprise wide analytics adoption
“More than 70 percent of the organizations that had deployed analytics throughout their organizations
reported improved financial performance, increased productivity, reduced risks, and faster decision making.”
The Evolution of Decision Making: Harvard Business Review Analytics Services

4. Is it really difficult to adopt analytics at
an enterprise level?
What should the solution look like for
enterprise wide analytics adoption?
Rapid advancement in computer hardware
capabilities has certainly ensured that it is now
possible to scale up the usage of Analytics at an
enterprise level, but it does not facilitate analytics
adoption at the enterprise level, because it is not
that easy!
A solution suitable for enterprise wide adoption
must have the following characteristics
 No significant changes to the existing
organizational processes
 No need for special coding skills
 A basic understanding of statistics needed to
comprehend results
 An easy to use consumer app like tool to
perform statistical analysis
 Very short learning curve
 Zero or Minimal dependency on external
agencies
 Lower cost of adoption and implementation
Let us talk about the pain points involved in
implementing analytics/ usage of analytics tools
at an Organisation level:
 Need for special coding skills for performing
Advanced Analytics.
Inherent complexities of the Analytics tools
Strong understanding of statistics
Huge training and prohibitive licensing costs.
Significant change to existing organizational
processes
Huge time for implementation
Resistance from the not-so –techy managers
towards analytics implementation
Few decades back, such a solution was a distant
reality. Today it is well within reach. It is now
possible to pack the power of complex analytics
behind a simple consumer friendly user interface.
This can also significantly reduce the learning
curve, training costs and time period for
implementation.
Obviously you can't expect even modern day
managers to start using Analytics tools for
d e c i s i o n m a k i n g ! N o w o n d e r, i n m o s t
organisations such costly analytics tools go unused. Given the prohibitive licensing cost,
“Analytics adoption” becomes a distant dream for
smaller organisations as well.
“Cloud computing” now gives access to high end
hardware at minimal costs. This advantage can
encourage organizations to think about enterprise
wide analytics adoption. Add to this, a “consumer
app” like tool that lets managers dynamically
validate granular data and perform advanced
analytics using simple drag drop operations.
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Why invest in cloud based solutions ?
In a new global survey of nearly 1,500 business and technology leaders conducted by Harvard Business
Review Analytic Services, the majority — 85% — said their organizations will be using cloud tools moderately
to extensively over the next three years. They cited the cloud's ability to increase business speed and agility,
lower costs, and enable new means of growth, innovation, and collaboration as the drivers for this fairly
aggressive rate of adoption.
HBR Analytics services -How the Cloud Looks from the Top: Achieving Competitive Advantage In the Age of Cloud Computing

5. Case
“Surveys” are a commonly used by organizations today to collect data that is closest to the customer. There
are many tools in the market today that can execute surveys and perform first level analysis. Such tools
however don't deliver advanced analytics and significant statistical interpretations. On the other hand, the
Analytics tools that have advanced analytics capabilities, are not easy enough to be used by managers and
moreover, demand significant long term investments for licensing and training.
Solution
Imagine a manager being provided with a tool that performs all the complex survey data analytics in the
background and delivers insights such as - “The respondents have given a low recommendation score for our
company's products and services due to the reasons “Difficult to access”, “High turnaround time”, “High cost
of servicing” etc. and the following are the respondents' opinion “Poor IVR”, “CSR's lack technical
knowledge”, “Poor After sales service” etc.”
In technical terms, we call the reasoning part of it as “Key driver Analysis” and the opinion part as “Verbatim
analysis” or “Unstructured data” analysis. Today, in the survey analytics space, performing advanced analytics
with “drag & drop tools” and getting actionable insights (such as the one mentioned above!) within hours, is a
reality. It empowers middle level managers in their decision making and helps them validate the data. It also
serves as an assistant for data scientists. The bottom line is “timely decision making” and “affordability”!
A live example of an “easy-to-adopt” solution.
Surveyi2i is the perfect example of a manager friendly tool which enables dynamic insight generation from
survey data. Surveyi2i is a cloud-based one-stop shop for all analysis and reporting needs from survey data.
Surveyi2i enhances productivity for analysts and researchers. Business managers can derive insights quickly
without help from data scientists. Surveyi2i enables financial institutions to very easily implement datadriven customer and employee engagement strategies by understanding customer needs and experiences
better at negligible costs.”
To know more about Surveyi2i visit http://www.bridgei2i.com/surveyi2i.html
About BRIDGEi2i
BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve
accelerated business impact harnessing the power of data. These analytics services and technology
solutions enable business managers to consume more meaningful information from big data, generate
actionable insights from complex business problems and make data driven decisions across panenterprise processes to create sustainable business impact. BRIDGEi2i has featured among the top 10
analytics and big data start-ups in several coveted publications.
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