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WHAT IS BIG DATA?
Big data is not a single technology but a combination of old and new technologies that helps companies gain
actionable insights. Therefore, big data is the capability to manage a huge volume of disparate data, at the right
speed, and within the right time frame to allow real-time analysis and reaction. It is broken down by the
following characteristics or the 4Vs
Volume – How Much Data
Velocity – How fast that data is processed
Variety – The various types of data
Veracity – How accurate the data is in predicting the business value? Do the results of a big data
analysis make sense?
So “Big Data” can be defined as large amounts of different types of data produced with high velocity from a
high number of various types of resources. Handling today’s highly variable and real-time datasets requires
new tools and methods, such as powerful processors, software and algorithms.
Webopedia defines Big Data as "a massive volume of both unstructured and structured data so large that
it's difficult to process using traditional database and software techniques." The "unstructured" part of that
definition encompasses things like email, video, tweets and Facebook "likes" -- data that doesn't reside in a
database that's accessible to merchants, but is nonetheless very useful.
Structured data, on the other hand, generally refers to databases where specific information is stored based on
a methodology of columns and rows. For e-commerce merchants, this could be customer data like name,
address and ZIP code.”
HOW MUCH DATA IS PRODUCED Every day?
Every day hundreds of millions of people take photos, make videos and send texts. Across the globe businesses
collect data on consumer preferences, purchases and trends. Governments regularly collect all sorts of data
from census data to incident reports in police departments. This deluge of data is growing fast. The total
amount of data in the world was 4.4 zettabytes in 2013. That is set to rise steeply to 44 zettabytes by 2020. To
put that in perspective, one zettabyte is equivalent to 44 trillion gigabytes. This sharp rise in data will be driven
by the rapidly growing daily production of data. But how much data is produced every day today?
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BIG DATA USE CASES
1. Log Analytics
2. Fraud Detection
3. Social Media and Sentiment Analysis
4. Risk Modeling and Management
5. Data Warehouse Optimization
6. Streamlined Data Refinery
7. Customer 360 Degree View
8. Monetize my Data
9. Big Data Exploration
10. Harnessing Machine and Sensor Data
11. Big Data Predictive Analytics
12. Next Generation Appliances
13. On-Demand Big Data Blending
14. Internal Big Data as a Service
15. Improving Science & Research
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WHAT IS BIG DATA ANALYSIS?
The first question that needs to be understood is what problem is being solved by Big Data Analysis? There are
numerous data points available that can give valuable insights from and certainly patterns can emerge from
that data before you understand why they are there. The Analytics can be classified into
Basic Analytics - Basic analytics can be used to explore your data, if you’re not sure what you have, but you
think something is of value. This might include simple visualizations or simple statistics. The basic analysis is
often used when you have large amounts of disparate data. Here are some examples:
Slicing & Dicing: refers to breaking down your data into smaller sets of data that are easier to explore. For
example, you might have a scientific data set of water column data from many different locations that contain
numerous variables captured from multiple sensors. Attributes might include temperature, pressure,
transparency, dissolved oxygen, pH, salinity, and so on, collected over time. You might want some simple
graphs or plots that let you explore your data across different dimensions, such as temperature versus pH or
transparency versus salinity. You might want some basic statistics such as average or range for each attribute,
from each height, for the time.
The point is that you might use this basic type of exploration of the variables to ask specific questions in your
problem space. The difference between this kind of analysis and what happens in a basic business intelligence
system is that you’re dealing with huge volumes of data where you might not know how much query space
you’ll need to examine it and you’re probably going to want to run computations in real time.
Basic Monitoring - You might also want to monitor large volumes of data in real time. For example, you
might want to monitor the water column attributes in the preceding example every second for an extended
period from hundreds of locations and at varying heights in the water column. This would produce a huge data
set. Or, you might be interested in monitoring the buzz associated with your product every minute when you
launch an ad campaign. Whereas the water column data set might produce a large amount of relatively
structured time-sensitive data, the social media campaign is going to produce large amounts of disparate
kinds of data from multiple sources across the Internet.
Anomaly Identification - You might want to identify anomalies, such as an event where the actual
observation differs from what you expected, in your data because that may clue you in that something is going
wrong with your organization, manufacturing process, and so on. For example, you might want to analyze the
records for your manufacturing operation to determine whether one kind of machine, or one operator, has a
higher incidence of a certain kind of problem. This might involve some simple statistics like moving averages
triggered by an alert from the problematic machine.
Advanced Analytics - Advanced analytics provides algorithms for complex analysis of either structured or
unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text
analytics and other advanced data-mining techniques. Today, advanced analytics is becoming more
mainstream. With increases in computational power, improved data infrastructure, new algorithm
development, and the need to obtain better insight from increasingly vast amounts of data, companies are
pushing toward utilizing advanced analytics as part of their decision-making process. Businesses realize that
better insights can provide a superior competitive position. Some of its applications are
Predictive modeling: Predictive modeling is one of the most popular big data advanced analytics use cases.
A predictive model is a statistical or data mining solution consisting of algorithms and techniques that can be
used on both structured and unstructured data (together or individually) to determine future outcomes. For
example, a telecommunications company might use a predictive model to predict customers who might drop
its service. In the big data world, you might have large numbers of predictive attributes across huge amounts of
observations. Whereas in the past, it might have taken hours (or longer) to run a predictive model, with a
large amount of data on your desktop, you might be able to now run it iteratively hundreds of times if you have
a big data infrastructure in place.
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Text analytics: Unstructured data is such a big part of big data, so text analytics — the process of analyzing
unstructured text, extracting relevant information, and transforming it into structured information that
can then be leveraged in various ways — has become an important component of the big data ecosystem. The
analysis and extraction processes used in text analytics take advantage of techniques that originated in
computational linguistics, statistics, and other computer science disciplines. Text analytics is being used in all
sorts of analysis, from predicting churn, to fraud, and to social media analytics.
Data Mining: Data mining involves exploring and analyzing large amounts of data to find patterns in that
data. The techniques came out of the fields of statistics and artificial intelligence (AI), with a bit of database
management thrown into the mix. Generally, the goal of the data mining is either classification or prediction.
In classification, the idea is to sort data into groups. For example, a marketer might be interested in the
characteristics of those who responded versus who didn’t respond to a promotion. These are two classes. In
prediction, the idea is to predict the value of a continuous (that is, non-discrete) variable. For example, a
marketer might be interested in predicting those who will respond to a promotion. Typical algorithms used in
data mining include the following:
Classification Trees - A popular data mining technique that is used to classify a dependent
categorical variable based on measurements of one or more predictor variables. The result is a tree with
nodes and links between the nodes that can be read to form if-then rules.
Logistic regression: A statistical technique that is a variant of standard regression but extends the
concept to deal with classification. It produces a formula that predicts the probability of the occurrence
as a function of the independent variables.
Neural networks: A software algorithm that is modeled after the parallel architecture of animal
brains. The network consists of input nodes, hidden layers, and output nodes. Each of the units is
assigned a weight. Data is given to the input node, and by a system of trial and error, the algorithm
adjusts the weights until it meets certain stopping criteria. Some people have likened this to a black–
box (you don’t necessarily know what is going on inside) approach.
Clustering techniques like K-nearest neighbors: A technique that identifies groups of similar
records. The K-nearest neighbor technique calculates the distances between the record and points in
the historical (training) data. It then assigns this record to the class of its nearest neighbor in a data set.
Operational Analytics - When you operationalize analytics, you make them part of a business process.
For example, statisticians at an insurance company might build a model that predicts the likelihood of a claim
being fraudulent. The model, along with some decision rules, could be included in the company’s claims-
processing system to flag claims with a high probability of fraud. These claims would be sent to an investigation
unit for further review. In other cases, the model itself might not be as apparent to the end user. For example, a
model could be built to predict customers who are good targets for upselling when they call into a call center.
The call center agent, while on the phone with the customer, would receive a message on specific additional
products to sell to this customer. The agent might not even know that a predictive model was working behind
the scenes to make this recommendation.
Monetized Analytics - Analytics can be used to optimize your business to create better decisions and drive
bottom- and top-line revenue. However, big data analytics can also be used to derive revenue above and
beyond the insights it provides just for your own department or company. You might be able to assemble a
unique data set that is valuable to other companies, as well. For example, credit card providers take the data
they assemble to offer value-added analytics products. Likewise, with financial institutions.
Telecommunications companies are beginning to sell location-based insights to retailers. The idea is that
various sources of data, such as billing data, location data, text messaging data, or web-browsing data can be
used together or separately to make inferences about customer behavior patterns that retailers would find
useful. As a regulated industry, they must do so in compliance with legislation and privacy policies.
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CHALLENGES FACED – BFSI
1. Not making enough Money - Despite all the headlines about banking profitability, banks
and financial institutions still are not making enough return on investment, or the return on
equity, that shareholders require.
2. Consumer Expectations - These days it’s all about the customer experience, and many
banks are feeling pressure because they are not delivering the level of service that consumers
are demanding, especially in regards to technology.
3. Increasing Competition From FinTech Companies - Financial technology (FinTech)
companies are usually start-up companies based on using software to provide financial
services. The increasing popularity of FinTech companies is disrupting the way traditional
banking has been done. This creates a big challenge for traditional banks because they are not
able to adjust quickly to the changes – not just in technology, but also in operations, culture,
and other facets of the industry.
4. Regulatory Pressure - Regulatory requirements continue to increase, and banks need to
spend a large part of their discretionary budget on being compliant, and on building systems
and processes to keep up with the escalating requirements.
5. Fraud Detection - The more services the BFSI sector offers, the more avenues of investment
open, and it simultaneously increases the risk of fraudulent activity. Various new payment
channels, online payment options including digital wallets have opened new avenues for
customer comfort as well as risks of fraud. With new payment methods, it increases
verification of customers, with an increase of verification details that increases data volume,
requiring big data analytics. Money laundering, fake identity and other fraudulent activities
lead to direct and indirect financial losses for any financial service provider. From reputational
impact to losses of money to address the problem, frauds have a major impact on business.
Now if a customer’s credit or debit card is being misused, with real-time data of geographical
location and time the bank can alert the customer instantly, giving the customer a chance to
take prompt actions. Comparing geographical locations of customers and card usage, spending
patterns and other vital information financial service providers are able to better detect and
take action against frauds. According to EY’s Global Forensic Data Analytics Survey 2014
showed that 72% of respondents believed big data technologies had a role to play in fraud
prevention and detection.
6. Security - The BFSI sector deals with a lot of vulnerable data, making data security a challenge
for this industry. Customers’ personal data, financial data, location and identity are among the
various kinds of data banks and other financial institutions have to delve into and preserve from
security threats. Secure database and stringent data governance provide complete control over
who gets access to which data. Various components of big data are aligned with maintaining data
security in storage and maintaining and upgrading with various compliances including PCI and
PII, Dodd Franks etc. Another important part of this is risk management. Data lakes can serve
as converged regulatory and risk (RDARR) hubs. Thanks to predictive data analytics, it is easier
to sort through customer history and other information to filter out risks and fraudulent activity
before investing. Gartner predicts that by 2018 customer digital assistants will recognize
individuals by face and voice across channels and partners.
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7. IoT Data - Financial services organizations are struggling to understand how to leverage IoT
data. This is the next wave of hype that is grabbing attention in big data, and questions abound
in terms of financial services applications. For some industries (telco, retail, and
manufacturing) it is already a reality, and these segments have driven the need for IoT data
and forced the current conversation. For banks, will IoT data be used more for ATM or mobile
banking? Areas that are worth exploring over the coming year involve multiple streams of
activity in real time. For example, real-time, multi-channel activities can use IoT data to offer
the right offer and advice to retail banking customers at the right time. Or perhaps we should
think about this in reverse, where financial firms could embed their services into the actual
“thing” or device or other client touch points, not unlike trading collocation facilities that then
8. Need For Data Governance - Data governance, lineage, and other compliance aspects are
becoming more deeply integrated with big data platforms. In order to find a more complete
and comprehensive data solution to manage compliance mandates, many banks develop or
purchase point solutions, or they try to use existing legacy platforms that are not able to deal
with the data surge.
9. Digital Shift – Online and mobile banking are disrupting the way traditional banking
transactions happened. According to a Braun Research in the US. 33% of consumers are using
their mobile app more often and 35% are banking online more frequently than a year
ago, while only 16% are stopping by branches more often. In addition, of the more than 1,500
adults surveyed (not confined to Chase Bank customers), 70% of consumers still prefer using a
bank’s website or online portal, compared to the 47% who prefer using the bank’s app on a
mobile phone or tablet. Interestingly, 78% of Millennials use a bank’s website or online portal,
as do 75% of GenX consumers and 67% of Baby Boomers.
10. P2P Payments - The opportunity is huge. Globally, the market for peer-to-peer transfers and
remittances is worth well over $1 trillion. Globally, the volume of P2P payments is over $1
trillion and only a sliver of those transactions - just $5 billion in the U.S., for example - are
currently conducted via mobile phones. Peer-to-peer payment apps solve real pain points for
consumers. P2P transactions volume could reach $86 billion in the U.S. by 2018. In emerging
markets, there is especially huge potential for P2P payments made on cell phones, due to a lack
of financial infrastructure. A high proportion of the population in these markets lack access to
checking and savings accounts.
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BIG DATA ANALYTICS IN eCommerce: -
Quantitative Fact Based analysis decision making to gain competitive advantage.
To help build distinctive capabilities in an intensely competitive business environment.
To gain insights to direct, optimize and automate decision making for achieving organizational goals.
Technology and capabilities using analytics lead to value creating actions to improve firm performance
and competitive advantage.
Creates value by creating transparency, discovering needs, exposing variability and improving
Integration of human emotion in data analytics in addition to process automation and optimization.
Focuses on discovering products, features, and value-adding services.
Is used to seek continuously changing patterns, events, and opportunities to generate discovery and
Supports deeper analysis on a wider variety of data types, delivering faster response times driven by
changes in behavior and automating decisions based on analytical models.
Helps organizations better understand and mitigate everything from risky business practices to the big
Application of data and business insights developed through applied analytical disciplines to drive fact-
based planning, decisions, execution, management, measurement, and learning.
CHALLENGES FACED BY DIGITAL COMMERCE PLAYERS: -
1. Product & Market Strategy: eCommerce companies must address issues pertaining to rapidly
evolving customer segments and product portfolios; access information on market intelligence on
growth, size and share; manage multiple customer engagement platforms; focus on expansion into new
geographies, brands and products; and simultaneously tackle a hypercompetitive pricing environment.
2. Customer & Digital Experience: Companies are expected to provide a rich, fresh and simple
customer experience, not geared towards discovery; manage inconsistent brand experience across
platforms; manage the proliferation of technologies; and handle time-to-market pressure for new
applications. In the recent past, social media has become more influential than paid marketing.
3. Payments and transactions: eCommerce companies may face issues around security and privacy
breach and controlling fictitious transactions. Further, RBI restrictions for prepaid instruments or
eWallets act as impediments. From a transactions perspective, cross-border tax and regulatory issues,
and backend service tax and withholding tax can have serious implications.
4. Fulfillment: Companies will need to check if the physical infrastructure gets affected by the internet
speed. Also, the lack of an integrated end-to-end logistics platform and innovation-focused fulfillment
option could cause delivery issues. Challenges around reverse logistics management and third party
logistics interactions could also act as barriers to growth.
5. Organization scaling: eCommerce companies are expected to make sure organization design keeps
pace with the rapidly evolving business strategy, along with fluid governance, strong leadership and
management development. From a growth perspective, identifying acquisition opportunities,
fundraising and IPO readiness becomes necessary. From a technology perspective, it is important to
transform IT as an innovation hub and address the lack of synergy between business, technology and
operations functions of the enterprise.
6. Tax and regulatory structuring: Companies will need to address issues around sub-optimal
warehouse tax planning; imbalance between FDI norms vis-à-vis adequate entity controls; inefficient
holding, IPR or entity structures; and international tax inefficiencies. Future challenges include the new
Companies Act, policy on related-party transaction pricing, and the uncertainty around GST roadmap.
7. Risk, fraud and cyber security: From a risk perspective, eCommerce companies could face issues
around brand risk, insider threats and website uptime. Issues around employee-vendor nexus, bribery
and corruption make companies vulnerable to fines. Cyber security also raises some concerns around
website exploitation by external entities.
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8. Compliance framework: eCommerce companies are to comply with several laws, many of which are
still evolving. Potential issues around cyber law compliance, inefficient anti-corruption framework,
legal exposure in agreements or arrangements, indirect and direct tax compliance framework and
FEMA contraventions and regularization could pose problems. Also, uncertainty around VAT
implications in different states due to peculiar business models could cause issues.
9. Competitive Pricing: Online merchants often compete on price. For example, dozens or even
hundreds of sellers may list identical products on marketplaces. These companies are vying to make a
sale with nothing to differentiate their stores except price. This sort of price competition has hurt some
small retailers, which may not have the buying power to compete with mid-sized or large competitors.
Add to this the pressure to offer free shipping on nearly every order, and price and shipping
competition is a real problem for some stores. In 2016, this problem will grow worse for the smallest
businesses in the industry. Large online sellers like Amazon and Walmart have shipping facilities
around the country. These distributed warehouses allow large e-commerce businesses to ship orders
from the nearest facility, which for as many as 60 percents of orders is in the same metropolitan area
the customer is in. When orders are shipped from these nearby warehouses, the cost to send the order
can be substantially less and the order will frequently arrive with a day or two. Bottom line, online
shoppers will increasingly expect fast, free shipping — on top of the lowest price.
10.Financial Health: Managing an eCommerce business comes with a lot of financial risks. One of the
biggest challenges is cash liquidity. Accurate data on margins and revenue is key to sustainability. So,
the leaders must go beyond and above what is required in terms of regulation and vulnerability.
11. Data Management and accounting Issues: E-commerce companies have a huge transaction base
based on the order book, but what we don’t anticipate are the multiple layers of each transaction. Every
customer order generates seller order, logistics order and much more internal reconciliation
transactions. Each of these transactions has a financial impact and any lapse even in a single layer could
result in a huge financial loss, which may go unnoticed until the same is reconciled on a regular
12. Inventory Accounting: One of the key challenges for operational success is the company's inventory
management process and ability to effectively manage a lean working capital. Additionally, to ensure
that inventory is available at the right time and at the traceable location, these companies need to
manage inventory records in a comprehensive manner. Error in maintaining these records gets
converted into financial loss due to inventory loss and non-fulfilment to the customer.
13. Managing Seller Registration and Settlement: The most important aspect of e-commerce
business, especially in the marketplace, is managing seller/vendors who are selling their products on
the platform. Some of the key areas of concern are: Seller and Vendor registration, catalog update, and
blocking, Reconciliation of seller settlement, and Seller return, refund and collection.
14. Customer Data: In some countries marketers are getting tired of hearing about the importance of
data. In the last 5 years, it was all about big data, they say. But consumers don’t shop the way they did
before. And their behavior is still changing. Therefore, data to measure consumers’ paths to
purchase (and post-purchase) are still relevant, and will be even more in 2016. They offer an ability to
understand the evolving customer preferences.
Understanding customers’ preferences will also make it possible to offer a successful pre-
purchase touch point. For instance, L’Oréal’s app “Make Up Genius” let potential customers make a
picture of themselves, and try the L’Oréal’s products. More than 65 million consumers asked for a
Analyzing and understanding consumers’ data also will make it possible to make successful assortment
choices in digital and offline stores. Fashion retailers can offer specific clothes only in their digital store
or only in the physical store. Not only popularity (online or offline), but also the relevance to feel and try
it on, are some of the reasons to offer different collections. Knowing and understanding the modern
customer, will fill possible gaps in the customer journey, and will make it possible to offer what is
truly relevant for the customer.
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15. Availability: eCommerce is far more than just offering products online. On the contrary, more
important is the availability of the goods. Retailers lose more than $600 billion worldwide to sales
returns annually. The largest chunk of the total is lost by retailers in North America, per a report, with
retailers in Europe and the Middle East not far behind in losses. Retailers need to minimize their
returns. But on the consumer side, easy returns are key to a comfortable shopping experience. That is
why nearly half of retailers worldwide offer the option to return or exchange purchases in-store.
In 2016 it is to be expected that real-time visibility of the stock, and the handling of the delivery
and returns will be crucial. This will be influenced by the new ways consumers are shopping online, and
the (high) expectations of home delivery and seamless cross-overs between devices used on the go, at
home, at work, and in stores. eCommerce organizations need to look beyond traditional fulfillment
strategies such as make-to-stock, make-to-forecast and make-to-order.
2016 will be the year of solving issues regarding the last mile of eCommerce fulfillment: the final leg
of products’ journeys reaching the spot where the customer is available. Already 2015 was important for
the logistic part of eCommerce with many specialized startups coming up in the space. Instant and daily
delivery at customers’ doorsteps, pickup points, or fulfillment centers; in 2016 every retailer or e-tailer
will find the best way to service its customers to perfect its last-mile delivery process. In 2016 no
retailer or e-tailer can afford to make its customers wait due to last-mile issues anymore. Improvements
also include real-time order tracking, to let customers keep an eye on the product’s journey. And on
top of that packages innovations are being expected.
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Benefits of BDA (Big Data Analytics) for
A. Differentiating pricing strategies at the customer-product level and optimization of
pricing. McKinsey found that 75% of a typical company’s revenue comes from its standard
products and that 30% of the thousands of pricing decisions companies make every year fail to
deliver the best price. With a 1% price increase translating into an 8.7% increase in operating
profits, assuming there is no loss of volume, pricing has significant upside potential for improving
B. Customer Insights & Responsiveness - Forrester study found that 44% of B2C marketers are
using big data and analytics to improve responsiveness to 36% are actively using analytics and data
mining to gain greater insights to plan more relationship-driven strategies.
C. Incremental Customer Acquisition and Revenue per customer, Reduction of Churn –
According to a study by Datameer Customer Analytics (48%), Operational Analytics (21%), Fraud
and Compliance (12%) New Product & Service Innovation (10%) and Enterprise Data Warehouse
Optimization (10%) are among the most popular big data use cases in sales and marketing.
D. Embedded intelligence into Contextual Marketing - The marketing platform stack in many
companies is growing fast based on evolving customer, sales, service and channel needs not met
with existing systems today. As a result, many marketing stacks aren’t completely integrated at the
data and process levels. Big data analytics provides the foundation for creating scalable Systems of
Insight to help alleviate this problem.
E. Cementing customer Relationships - By using big data analytics to define and guide customer
development, marketers increase the potential of creating greater customer loyalty and improving
F. Optimization of Sales & GTM Strategies - McKinsey found that biopharma companies
typically spend 20% to 30% of their revenues on selling, general, and administrative If these
companies could more accurately align their selling and go-to-market strategies with regions and
territories that had the greatest sales potential, go-to-market costs would be immediately reduced.
G. Long Term - 58% of Chief Marketing Officers (CMOs) say search engine optimization (SEO) and
marketing, email marketing, and mobile is where big data is having the largest impact on their
marketing programs today.
H. Greater Customer Engagement and Loyalty – According to a Forbes study with market
leaders across ten industries department-specific analytics and Big Data expertise were sufficient to
get strategies off the ground and successful; enterprise-wide expertise and massive culture change
were accomplished after pilot programs delivered positive results.
I. Enhancing Profitability – According to Deloitte stats Generating revenue, reducing costs and
reducing working capital are three core areas where Big Data is delivering business value today. An
enterprises’ value drivers scale more efficiently when managed using advanced analytics and Big
J. Omnichannel Experience - CVA is emerging as a viable series of Big Data-based technologies
that accelerate sales cycles while retaining and scaling the personalized nature of customer
relationships. The bottom line is that CVA is now a viable series of technologies for orchestrating
excellent omnichannel customer experiences across a selling network.
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THE MARKET (GLOBAL)
Global Growth in eCommerce and Big Data Analytics: -
Year Growth in Number of eCommerce
Consumers (In Millions)
Growth in eCommerce sales
per customer worldwide
Growth in Big Data
Worldwide (In Bn. US$)
2011 792.6 1162 7.3
2012 903.6 1243 11.8
2013 1015.8 1318 18.6
2014 1124.3 1399 28.5
2015 1228.5 1459 38.4
2016 1321.4 1513 45.3
Source: Google. Adapted from Springer
The big data and analytics market are expected to be worth more than $187 billion in 2019, up from
$122 billion in 2015, per IDC.
Over five years, the big data market is expected to grow at about a 50 percent clip, said IDC.
Services will represent more than half of all big data and analytics revenue with software representing
the second largest category. Big data and analytics software will be a $55 billion market in 2019.
Hardware will be about $28 billion in 2019.
Most of the software revenue will revolve around query, reporting, analysis and data warehouse
By industry, IDC said that discrete manufacturing will be the biggest industry to chase big data followed
by banking and process manufacturing. Government, services, telecom and retail will also be large
Utilities, resource industries, healthcare and banking will show the biggest data and analytics revenue
growth over the next five years.
Large enterprises will drive spending and account for $140 billion in big data analytics revenue in 2019.
Smaller businesses (less than 500 employees) will be a quarter of big data revenue.
The U.S. will be the biggest market for big data and analytics tools and represent $98 billion by 2019.
The U.S. will be followed by Western Europe, Asia Pacific and Latin America.
Key Growth Drivers: -
Adoption of advanced predictive analytics by the Big industry giants.
The growth of M-Commerce, Smartphone Proliferation and Mobile Analytics.
Integration of IoT
Adoption of Omni-Channel Data Integration.
Modernization of Retail Marketing Mix.
Real Time insights is a “MUST” to succeed.
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THE MARKET (INDIA)
Big data analytics to reach $16 billion industry by 2025
Big data analytics sector in India is expected to witness eight-fold growth reach $16 billion by 2025
from the current level of $2 billion, the National Association of Software and Services Companies said
India is currently among top 10 big data analytics markets in the world.
The sector has huge growth potential and by 2025, India will have 32 percent share in the global
There are about 600 analytics firms in the country, out of which 400 are start-ups. About 100
companies were added during 2015.
There is approximate $700 million worth of start-up funding over the last two-and-half years.
Analytics Market in India currently stands at $1.64 Billion annually in revenues, growing at a healthy
rate of 28.8% CAGR.
In terms of Sector type, Finance & Banking form the largest sector being served by analytics in India. Of
the total revenue earned by analytics industry in India, 35% or$575 Million comes from Finance &
Marketing comes second at 25%, followed by E-commerce sector at 17% of analytics revenues in India.
Almost 70% of all analytics related work done in India is in the space of BI/ Reporting/ Dashboard or
analytics code deployment & maintenance.
Just 7% of analytics is in advanced model building and prediction.
22% of the professionals are involved in Big Data Management.
29% or $472 Million in market size for analytics industry comes from Delhi/ NCR. This is followed
by Bengaluru at 26%.
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BUSINESS DEVELOPMENT APPROACH
(B2B Institutional): -
1. Find the decision makers from the marketing departments of the universe mentioned above.
Typical levels of people will be CDOs/CMOs/GM/VP/CMOs via online search, official websites,
LinkedIn and personal contacts.
2. Messaging via EDM’s, Telephone, Social Media.
3. Filtering the prospects based on the conversations into an ABC list
4. Have discovery meetings.
5. Understand their current setup, the solution in place and explore possibilities of what to pitch.
6. Follow up on the leads generated and filter out the ones that are not going to be suitable for
many reasons such as they do not want to switch, do not have the budget, longer gestation
periods and categorize them as per ABC analysis.
7. Line up 30-50 active conversations coupled with POCs within the first nine months of working
and work on building an effective and healthy pipeline for the next 6-8 quarters.
8. Strive for at least 3-5 closures within this time frame.
9. Consolidate the existing pipeline.
10. Collaborate with relevant internal stakeholders for the solution design as per the client brief
and plan final delivery as desired.
11. Work closely with Marketing to design effective online and offline strategies. Streamline the
current marketing efforts and infrastructure.
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First 30 Days (LEARN): -
Understand the organization, its business philosophy, overall culture and the answers to the
questions WHY, HOW, WHAT in that order.
Identify and analyze (RCA-Root Cause Analysis) the gaps, based on the data which is available
Training, mastering product knowledge, learning corporate systems.
Travelling to learn if required to do so.
Understand the target audience and the industry.
A skeletal framework to be designed about the short term and long term expectations from the
role and plan the desired milestones.
Develop an S.M.A.R.T goals plan for the success in the role broadly including answers to the
questions like why are we here? Where are, we going? What are the performance requirements
and objectives? How to achieve the desired goals? What are the values that guide decisions?
31 – 60 Days (CLARIFY): -
Review the tasks accomplished in the first 30 days and assess the milestones reached.
Upgrade and fine-tune the knowledge of the company’s products, systems and customers.
Design and plan the tactical and strategic approach for business development for the whole
year or a three-year plan if needed.
Map and track what is getting done and tweak the approach wherever necessary
60 – 90 Days (ALIGN): -
Review with the management as to the activity accomplished and what has been missing. What
needs to be done to stay on course?
Start executing the deliveries.
Start networking with known prospects within the target audience industry.
Review and determine if all the short-term plans were met or gaps identified. Execute to
address the gaps if any.
Plan a progress map for the whole year. Set a timeline for periodic review.
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