Sunday 24 November 2013

Big Data Analytics touching our lives everyday !!

The world is becoming more and more digital every day. Big Data is one of those megatrends that will impact everyone in one way or another.  

Welcome to the everyday world of "Big Data," the explosions of facts, maps, products, books, calls, references, smartphone apps, trends, videos, advertisements, surveys — all of the sense and nonsense that is literally at your fingertips, 24-7, every day from now on.
Big Data is all that everything we do in our lives to leave a digital trace (or data), which we (and others) can use and analyze for the betterment of our lives.

Here are some real-life examples of how big data is used today right from cradle to grave:
  • In hospitals, pediatric unit looking after premature and sick babies is generating a live steam of every heartbeat. It then combines them with historical data to identify patterns. Based on the analysis the system can detect infections even before the baby would show any visible symptoms, which allows early intervention and treatment.
  • Wearable fitness wristbands from Nike & Fitbit collects daily data about how fast we walk or jog, how many steps we have taken, the calories we have burnt each day, our sleeping patterns and other streams of data which are then combined with our health records by doctors & insurance companies for better wellness programs.
  • In schools & colleges streaming videos courses and data analytics helps teachers track student’s progress, tailor the content to their abilities & predict how a student will perform.
  • SmartThings, a company helps in Smart Homes with installing motion, moisture and other sensors in your home to collect data & keep you posted on what is happening at home & control all the devices via an app on the iPhone while you are away.
  • While we daily drive on the roads, our smartphones send our location information & how fast we are moving, which then combined with real-time traffics to give us optimal routes to avoid traffics. Even combined with our location apps like AroundMe gives you nearby restaurants, banks, gas stations and lot more.
  • In Retail, when we go shopping our loyalty card data is combined with our purchase history & social media data to give us coupons, discounts, and personalized offers.
  • Finally, in IoT, companies like EarlySense are developing wellness & sleep monitoring sensors that go under the bed mattresses & automatically detects, monitors, and records heart rates, breathing rates, motion, and sleep activity as soon as a person gets into bed. The data collected by the sensor is wirelessly sent to smartphones and tablets, where it can be further analyzed.
Some other creative uses of Big data are:
  • Transit Time NYC, an interactive map developed by WNYC, lets New Yorkers click a spot in any of the city's five boroughs for an estimate of subway or train travel times. They pulled data from open source itinerary platform OpentripPlanner & combined it with publicly downloadable subway schedule to create 4 million virtual trips.
  • FluNearYou app developed by American Public Health Association surveys users to get a sense of their symptoms stores and analyzes the vast amount of resulting data and then produces reports to show users the flu activity in their local region.
  • Buildzoom, a “one-stop-shop” for building, remodeling & renovating homes, has information about 2.5 million contractors, 50000+ customer reviews helping 500,000 users bring more objectivity & transparency in decision making.
  • The FBI is combining data from social media, CCTV cameras, phone calls and texts to track down criminals and predict the next terrorist attack.
·      Presidential campaigns of Obama in 2012 used Big data Analytics to collect vast amounts of voter’s data from phone calls & surveys, coupled with top-notch analytical engines allowed him to micro-target the individual voters that were most likely going to vote in his favor.
  • Google’s self-driving car is analyzing a gigantic amount of data from sensor and cameras in real time to stay on the road safely.
·       Smart TVs and set-top-boxes are able to track what you are watching, for how long and even detect how many people sit in front of the TV combined with social sentiments to determine the channel popularity.
  • In Greece, the government is using Google Earth to see who can afford a swimming pool in their backyard, and then matching that against tax records.

Ultimately, you and I are going to benefit from Big data Analytics. Our economies are getting stronger when the banks have a better understanding of risk. Our taxes are lower when the government lowers its fraud expenses. Our communities are becoming healthier when disease outbreaks are pinpointed and treated earlier.

Saturday 17 August 2013

Hadoop Simplified

Today we live in the age of Big data.

Data volumes have outgrown the storage & processing capabilities of a single machine and the different types of data formats required to be analyzed have increased tremendously.  
This brings 2 fundamental challenges: 
  • How to store and work with huge volumes & variety of data
  • How to analyze these vast data points & use it for competitive advantage.

Hadoop fills this gap by overcoming both the challenges. Hadoop is based on research papers from Google & it was created by Doug Cutting, who named the framework after his son’s yellow stuffed toy elephant.

So What is Hadoop? It is a framework made up of:
  • HDFS – Hadoop distributed file system
  • Distributed computation tier using programming of MapReduce
  • Sits on the low-cost commodity servers connected together called Cluster
  • Consists of a Master Node or NameNode to control the processing
  • Data Nodes to store & process the data
  • JobTracker & TaskTracker to manage & monitor the jobs

Let us see why Hadoop has become so much popular now.
  • Over the last decade, all the data computations were done by increasing the computing power of a single machine by adding the no of processors & increasing the RAM but they had physical limitations. 
  • As the data started growing beyond these capabilities, an alternative was required to handle these storage requirements for eBay (10 PB), Facebook (30 PB), Yahoo (170 PB), JPMC (150 PB) and increasing
  • With a typical 75 MB/Sec disk data transfer rate, it was impossible to process such humongous data
  • Scalability was limited by physical size & no or limited fault tolerance
  • Additionally, various formats of data are being added to the organizations for analysis, which is not possible with traditional databases

How Hadoop addresses these challenges?
  • Data is split into small blocks of 64 or 128MB and stored onto minimum 3 machines at a time to ensure data availability & reliability
  • Many machines connected in cluster work parallel for the faster crunching of data
  • If anyone machine fails, the work is assigned to other automatically
  • MapReduce breaks complex tasks into smaller chunks to be executed in parallel

Benefits of using Hadoop as Big data platform are:
  • Cheap storage – commodity servers to decrease the cost per terabyte
  • Virtually unlimited scalability – new nodes can be added without any changes to existing data gives the ability to process any amount of data, so no archival necessary
  • The speed of processing – tremendous parallel processing to reduce processing time
  • Flexibility – schema-less, can store any data format – structured & unstructured ( audio, video, texts, csv, pdf, images, logs, clickstream data, social media)
  • Fault tolerant – any node failure is covered by another node automatically

Later multiple products & components are added to Hadoop so it is now called an eco-system.
  • Hive – SQL like interface
  • Pig – data management language like commercial tools AbInitio, Informatica
  • HBase – column-oriented database on top of HDFS
  • Flume – real-time data streaming such as credit card transaction, videos
  • Sqoop – SQL interface to RDBMS and HDFS
  • Zookeeper – a DBA management for Hadoop

 And multiple such products are getting added all the time from various companies like Cloudera, Hortonworks, Yahoo, etc.

How some of the world leaders are using Hadoop:
  • Chevron collects large amounts of seismic data to find where they can get more oil resources
  • JPMC uses it for storing more than 150 PB of data, over 3.5 Billion user log-ins for Credit scoringFraud detection
  • eBay using it for real-time analysis and search of 9 PB data with 97 million active buyers, over 200 million items for Cross-Sell
  • Nokia uses it to store data from phone, service logs to analyze how people interact with apps and usage patterns to address customer churn
  • Walmart uses it to analyze customer behavior of over 200 million customer visits in a week
  • UC Irvine Health hospitals are storing 9 million patients records over 22 years to build patients surveillance algorithms
  • Manufacturers are using it for warranty analytics

Hadoop may not replace the existing data warehouses but it is becoming no 1 choice for Big data platform with the price/performance ratio.

Saturday 11 May 2013

Big data Analytics in Retail

All the industry leaders like  Wal-Mart, Axa, Citibank, Humana, GE and several others are exploring how Big Data analytics can be used to better understand customer needs, pinpoint risk, improve marketing, enhance the customer experience, combat fraud, and drive profitability.  

Companies are seeking ways to rebuild their customer relationships in this time of extremely high customer expectations.

The retail industry is among the early adopters and innovative users of big data. But they have the challenge of tackling the huge data since the 1970s when barcodes were first introduced to scan the products at POS.  All sorts of supply chain data came into effect later in 1980-90s while RFID and other sources such as surveillance video cameras started sending humongous data to data centers recently.  These have challenged Retailers to capture, store, cleanse & analyze all the data they collect.

Further to flood the data centers are consumer’s interaction with social media & internet which is generating billions of data points that can be measured via clicks, page views, time spent on per page and path traversed from landing to conversion.
Big data analytics is helping retailers to collect and analyze this fine-grained shopper visit data and optimize page designs, placements and tailor promotional messages.

McKinsey report says that using big data analytics can raise the operating margins by as much as 60%

Some of the questions Retailers have are:
·   How to drive critical decision around market segmentation, personalization & merchandising?
·  How to avoid lost revenues due to stock-outs, lower online sales per visit, a lower visit to buy ratios?

Here is a glimpse of what retailers can do in big data analytics:

·        Enhancing customer experience across all the channels such as calls, emails, campaigns, catalogs, mobile offers, brick & mortar stores
·        Customer sentiment analysis to know the market pulse and market dynamics
·        Call center data analysis for customer feedback
·        Build loyalty programs based on purchase data & customer segmentation
·   Staffing optimization based on weather forecasts & promotional campaigns for better customer experience

·        Optimizing the product placements and layouts based on video data
·        Price optimization based on competitor pricing
·        Market basket analysis for revenue growth
·        Optimizing seasonal markdowns
·        Store analysis for best location & better effectiveness
·  Improve in-store sales by leveraging past data with current economic, weather & season/holiday data

·        Consumer segmentation, cross-selling
·        Campaign analytics to channelize advertising dollars in an optimal medium for highest ROI
·        Sentiment analysis from social media, call centers, surveys, blogs, product reviews
·   Identify new products, service & market opportunities by real-time monitoring of these customer sentiments
·        Location-based personalized offers on smartphones, tablets
·        Web log analytics for customer behavior analysis & next best offer

Supply Chain:
·        Inventory optimization to avoid stock outs
·        Demand-driven forecasting fueled by structured and unstructured data
·        Route optimization for cost reductions
·        Warehouse space optimization
·        Vendor performance analysis for better competitive prices

Ultimately, the goal of big data analytics is to develop an effective Omni-channel experience that integrates many different factors of supply chain including supplier effectiveness, warehouse optimization, and inventory/logistics optimization for real-time customer engagement.
Big data analytics provides the required ammunition & tools to accelerate growth, boost profits, control risks and meet regulatory & competitive demands.

Saturday 12 January 2013

Enhancing Customer experience with Big Data Analytics

The era of Big Data is upon us.

Today, most organizations are still struggling to unlock the full value of this Big data that is available to them. From internet to mobile and social, the amount of customer data is continuously growing.

Company’s ability to extract value from big data through smart analytics will be the key to their business success.

Big data by definition is:
  • Volume - Unmanageable volumes by traditional databases
  • Variety - Combination of all the internal structured business data (CRM, ERP, POS and all the internal system data) and external unstructured data ( Social media data, feedback surveys, Audios, Videos, streaming data, Call center data, images)
  • Velocity - enormous speed at which it comes into the organization.

Channel based marketing is the least priority now. The increased amount of data available at individual customer level has allowed companies to do a personal marketing.  But all this customer data out there is worthless if you can’t process it & turn it into actionable intelligence.

With Big data platforms helping in collection, integration, and transformation of large volumes of data, companies can conduct complex and varied analysis on much larger data sets and reduce the time to action and reaction to customer needs.

Organizations can now impact the entire customer lifecycle and every interaction by being well prepared for each interaction, shaping the interaction in real time as it happens and driving the huge improvements across all the channels for next interaction.

By listening to the data as a signal from customers and working to personalize the experience for the customer, creates the value for the customer as well as business.

Some examples of enhancing the customer experience using Big data Analytics:
  • Retail giants are using Big data to personalize the offers to enhance the customer experience
  • Healthcare companies are using it for improving the patient care in hospitals
  • Banks are using it for cross-up selling and bringing the new products to market
  • Insurance companies are making tailor-made policies for their customers in real time
  • Manufacturing companies are using Big data to improve their products, predict the failures in their product lines ahead of time to make every customer interaction very smooth

Using Big data to address customer inputs before they become problems is extremely important to ensure they stay loyal and more profitable to the company.

Customers expect to have the best possible experience from their vendors/service providers. They want to be recognized as individuals and not as a part of a segment.

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