Introducing Big Data
Introducing Big Data
Walmart
Kayak
Coca-Cola
What is Big Data ????????Big data is defined as a collection of
data sets so large and complex that it becomes difficult to process
using hands on database mgmt tools or traditional data processing
tools The challenges include capturing, storing, curating, sharing,
transferring , analyzing and visualizing this data Additionally the
velocity at which the data is generated is also enormous Almost 2.7
zeta bytes of data exist in modern world of which 90% was generated
in the last 2 years Volume , Velocity and Variety are the driving
constructs for Big Data Data Size
Data Visualization Bowling Pattern
Data Visualization EBOLA spread
Example 1 A modern retail store can combine weather,
geographical and social media data with their sales figures
Basically creating interesting correlations to get new insights
into sales over a time period This leads to a challenge to combine
unstructured audio, video, text and graphics over web / social
media into structured rows and columns This is where data science
and technology needs to merge seamlessly Example 2 & 3 A
leading automotive company is combining feeds from social media
platforms with sales analytics , demographic data and vehicle usage
statistics to decide on the features in their next SUV An insurance
company is using customer profile data and macroeconomic feeds to
maximize revenue per customer Result In late September , towards
end of monsoon, a lady walks in a retail store Her mobile beeps a
message. This is the message for special offer on 25 kgs of rice
bag. The lady happily buys it. What all data is combined
???????Season feeds, ladys earlier purchases ( quantity and brand )
, her per purchase value and her location and contact details
The Problem The data being enormous marketers are stuck and get
confused as to how to use the data and implement data insights into
the real time situations
Implementing Big Data Begin with stakeholders and consider the
culture of the organization : Finding the data suppliers and
decision makers in any organization is critical to big data
success. Organizations need to shift their culture if they are a
gut feel driven organization Find your data stewards : Big Data is
a marketing program , but it is wise to keep your CIO on your side
to ensure that the technical aspects are taken care of.
Implementing Big Data Set clear goals : As a departmental store did
a big data pilot , they reported a 10 % increase in sales after
they moved ladies shoes next to dresses section. Following this
success , they rolled out the program across stores and enjoyed a
20 % increase in gross sales Implementing Big Data Create the Plan
: When developing your plan, link the goals to the constructs that
define Big Data (volume, velocity and variety). Also recognize Big
Data is a compliment, not a replacement, to your existing analytics
such as data warehouses, OLAP, and decision support systems (DSS).
And of course no plan is complete without ROI projection, but dont
try to create an overarching Big Data ROI forecast.
Implementing Big Data Establish the Metrics : limit the number
of metrics to only a few high priority measures, rather than a more
exhaustive list. Initially correlate employee demographics and
sales effectiveness to get initial insights into the kind of sales
persons who are successful in your business. Then gradually
escalate to more complex correlations Implementing Big Data Deploy
the technology : A common technology starting point is the open
source Big Data engine, Hadoop. This tool is particularly well
suited for loosely structured or unstructured data as well as high
volume search and discovery. Rather than requiring a separate
application for big data presentations, its far more effective to
include big data insights within existing decision support systems,
or within existing business apps such as CRM, ERP and HCM software
applications. Implementing Big Data Match the Eureka Moment with
commonsense : An employee correlated sales of banana with beer. He
got a real thrashing from his manager. Banana was the highest
selling item in that region and can correlate with any item in the
store practically. So dont forget commonsense.
Implementing Big Data Go for Continuous Process Improvement :
Good decision making is an ongoing process , hence work towards
constant improvement of the system Some Factors used in Big Data
Analytics Consumer Sentiments : Taking this a step further, theres
also an opportunity to correlate customer sentiment analysis with
broad economic factors, specific market indicators, competitor
moves or other factors that may uncover patterns that permit
companies to model changes for improved customer consumption and
company performance.Some Factors used in Big Data Analytics
Customer experience : link this information to demographic,
cultural and other preferences, and leverage the information for
improved customer services delivery (order processing, product
delivery, invoicing, customer support, renewals, etc.), increased
revenue objectives (i.e. up-sell, cross-sell and customer share)
and decreased customer churn. Who can use Big Data ???????Marketers
are using Big Data to better forecast what products to sell to what
customers and when, and how to bundle products to increase
salesSales managers are analyzing website and social media data to
identify products and services frequently viewed (i.e. measured by
volume of page reads, long page durations and low exit points) but
not as frequently purchased in order to uncover the barriers
standing in the way of purchase, and unlock otherwise hidden sales
opportunities. Who can use Big Data ???????Customer service
managers are extracting unstructured data from social networks and
social streams to better predict product defects based on
consumption rates, usage patterns and geographies, and how product
defects occur or accelerate when used with other products. Theyre
also using this information to take proactive actions and implement
remediation measures, sometimes in advance of the defects
occurring. cont.By analyzing customer complaints (tweets, SNS/SMS,
etc.) along with the volume, trending and responses to those
complaints, extracting anomalies or patterns, and comparing the
data to defect signs or product complications, proactive action can
be taken while costs to repair both products and reputation are
low. Some Call Center managers are even tapping into customer
service call recordings (using customer profiles, keywords and
sentiment analysis) to quickly detect product deficiencies early;
not when the period-end reports are compiled and read two weeks
later
Who can use Big Data ???????Human Capital Management
professionals are better optimizing human resource assignments
based on environmental factors, customer market trends and their
employees online social profilesWho can use Big Data ???????Retail
organizations are using store security videos to understand
in-store customer traffic patterns (which can also be done with
IC/RFID tags on carts) and determine how changes to in-store
configuration can impact revenues. They are also correlating this
information with Point of Sale (POS) and weather data to understand
how environmental conditions impact product positioning, promotions
and sales. Who can use Big Data ???????Finance has the ability to
link all other Big Data elements to measures such as customer
profitability, Customer Lifetime Value (CLV), product margins and
other data sets which link to financial outcomes and therefore must
be a part of any Big Data initiative. Pitfalls in Big Data Putting
the technology ahead of the processes, people and specific
outcomesAnother pervasive challenge with big data is data
relevancy. With more data comes more noiseData privacy, information
security, information distribution, data presentation and even data
overload
