Big Data in Retail Industry

Hey folks!!!! Hope you all are doing great & had a great weekend.

Today we will be discussing the use of Big Data in Retail industry

Retail industry is among the early adopters and innovative users of big data. But they had the challenge of tackling the huge data since 1970s when barcodes were first introduced to scan the products at POS along with surveillance cameras sending huge amount of data to data centers. All these are challenges Retailers face to capture, store, cleanse & analyze all the data they collect. Further, to add to the challenges, consumer’s interaction with social media & internet which generates billions of data points that can be measured via clicks, page views, time spent on per page etc. flood the data centers.

Big Data analytics is helping retailers to capture, store, cleanse, process & analyze data. According to Mckinsey report, Big Data Analytics can raise the operating margins by as much as 60%.

Here is a list of main categories where retailers employ Big Data Analytics –

Merchandizing

1. Assortment Optimization – Deciding which products to carry in which stores based on local demographics, buyer perception, and other big data; this can be termed as assortment optimization that can increase sales materially

2. Placement & Design Optimization – Brick-and-mortar retailers can also gain substantially by optimizing the placement of goods and visual designs by mining sales data at the SKU (stock keeping unit) level

3. Seasonal Optimizations – Consumers make purchases of certain products during certain period or season. This data can be used to maintain minimal availability of particular products during particular season thereby avoiding stock outs

4. Pricing Optimization – Retailers today can take advantage of the increasing granularity of data on pricing and sales and use higher levels of analytical horsepower to take pricing optimization to a new level

5. In-store Staffing Optimization – Depending on the data of consumers visiting a brick & mortar store, staff can be hired optimally to serve the consumers better

 Supply Chain

1. Inventory Management – With the additional detail offered by advanced analytics & mining multiple datasets, big data can continue to improve retailers’ inventory management

2. Supplier Negotiations – Leading retailers can analyze customer preferences and buying behavior to inform their negotiations with suppliers

3. Warehouse Space Optimization – Warehouse space can be utilized optimally with the data available for production, shipment of products & so on

4. Distribution & Logistics Optimization – Leading retailers are also optimizing transportation by using GPS-enabled big data telematics and route optimization to improve their fleet and distribution management

Marketing

1. Sentiment Analysis – Sentiment analysis leverages the voluminous streams of data generated by consumers in the various forms of social media to help inform a variety of business decisions

2. Web Analytics for Online Consumer Behavior – To analyze consumer behavior online, frequent pages visited, products usually viewed etc. specific products can be targeted to every consumer

3. Customer Micro-segmentation – The amount of data available for segmentation has exploded, and the increasing sophistication in analytic tools has enabled the division into ever more granular micro-segments. This is known as Customer Micro-segmentation

4. Advertising & Promotion Campaign Analytics – Companies can analyze data to understand where to spend their advertising & Marketing budget in order to effective

5. Cross Selling – Cross-selling uses all the data that can be known about a customer, including the customer’s demographics, purchase history, preferences, real-time locations, and other facts to increase the average purchase size

6. Location Based Marketing – Location-based marketing relies on the growing adoption of smartphones and other personal location data-enabled mobile devices

7. In-store Behavior Analysis – Analyzing data on in-store behavior can help improve store layout, product mix, and shelf positioning

8. Multichannel Consumer Experience – Usage of big data to integrate promotions and pricing for shoppers seamlessly, whether those consumers are online, in-store, or perusing a catalog. This is known as multichannel consumer experience

9. Loyalty Programs – Loyalty programs are structured marketing efforts that reward and therefore encourage, loyal buying behavior – behavior which is potentially beneficial to the firm

Operations

1. Performance transparency – Retailers can now run daily analyses of performance that they can aggregate and report by store sales, SKU sales, and sales per employee

2. Labor Inputs Optimization – This can create value through reducing costs while maintaining high service levels

3. Demand Based Production to avoid Stock-outs – Depending on the data available for demand of certain products in the past, future predictions/forecasting can be done & accordingly production can be followed to avoid stock-outs & over-stocking

4. Personalized offerings for Consumers – With the availability of micro & minute data available of consumers, personalized offering of products can be possible. This helps in building & maintain relationships

New business models

1. Price Comparison Services – It is common today for third parties to offer real time or near-real-time pricing and related price transparency on products across multiple retailers. This way price comparison can be done

2. Web-Based Markets – Web-based marketplaces, such as those provided by Amazon and eBay, provide searchable product listings from a large number of vendors.

I hope this information helps!!!!

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Big Data in Banking & Financial Services

Hey folks!!!! Hope you all are doing great & hope you all had a great weekend.

Today we will be discussing the use of Big Data in Banking & Financial services industry –

Banking & Financial Service organizations that can harness big data, in the form of transactions, real-time market feeds, customer-service records and social media posts, can derive more insight about their business than ever before and build competitive advantage. Big Data represents a huge opportunity for Banks and Financial Services organizations. The ones who manage to get the best insights from harnessing Big Data, they achieve three critical objectives; creating customer focused enterprise, optimizing enterprise risk management and increasing flexibility & streamline operations. Here is a list of few uses of Big Data which I think would be useful:

  • Segmentation of customers/consumers In Banking & Financial services industry, customer segmentation is a key tool for sales promotion and marketing campaigns. Companies collect data from day-to-day customer transactions to home values, travel records, online buying habits & so on. By collecting and analyzing all this data, banks can group customers into various segments with similar behavior. By doing this, Banking & Financial services organizations can improve relationship with customers, do a bundled offering, personalize targeting, reduce cost & much more.
  • Counterpart credit Risk Management Exploding volumes of data required for counter party risk evaluation, combined with the need for more comprehensive and timely risk metrics, have pushed current infrastructure solutions to their limit. Using Big Data, Banking & Financial service organizations can effectively estimate credit exposures, can get complete & accurate view of aggregated counter party risk position & can move from monitoring to anticipating and managing counter party risk to improve performance and avoid risk of default.
  • Payment Fraud Detection & Investigation – Fighting fraud, financial crimes and security breaches, in all forms, is among the most costly challenges faced by this industry. The key to accurate and non-disruptive fraud detection is to implement emerging technology that allows banks to gain a holistic view of customers. Currently, Big Data helps in detecting frauds. In near future, emerging & evolving new technologies will provide & offer new strategies for fraud detection.
  • Compliance Reporting – To comply with the regulation of compliance reporting, financial & banking service organizations need to implement a system to monitor traders & collect the data. By tracking & collecting all the data, organizations can track deals, prevent accidental trades & identify rogue trades.
  • Personalized/Customized Product Offerings – Organizations collect vast amount of data; internet data, customer transaction data, social media data & so on. By analyzing this data, they come to know about various customers preferences & accordingly can target & offer products to customers.

I hope this information helps!!!!

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‘Drones’ – New Age Bots!!!!

As per Wikipedia, a Drone which is also referred to as unmanned aerial vehicle (UAV) or remotely piloted aircraft (RPA) by the International Civil Aviation organization (ICAO); is an aircraft without a human pilot aboard. It is controlled either autonomously by onboard computers or by the remote control of a pilot on the ground or in another vehicle. The typical launch and recovery method of an unmanned aircraft is by the function of an automatic system or an external operator on the ground.

Conceptualization of Drones!!!

  • The Austrian Balloons

The concept of drones dates back to the mid-1800’s, when Austrians sent off unmanned, bomb-filled balloons as a way to attack Venice on August 22, 1849. Although balloons do not generally meet today’s definition of a UAV, the concept was strong enough that once winged aircraft had been invented, the effort to fly them unmanned for military purposes was not far behind

  • World War I

It continued to be developed during World War I, when the Dayton-Wright Airplane Company came up with a pilot less aerial torpedo that would drop and explode at a particular, preset time. Later, in November 1917, the Automatic Airplane was flown for representatives of the US Army. This led the army to commission a project to build an aerial torpedo (is a naval weapon designed to be dropped into water from an aircraft after which it propels itself to the target), resulting in the Kettering Bug (an experimental, unmanned aerial torpedo, a forerunner of present-day cruise missiles)which first flew in 1918. While the Bug’s revolutionary technology was successful, it was not in time to fight in the war, which ended before it could be fully developed and deployed.

  • World War II

Reginald Denny & the Radio Plane Company was evolved in 1930’s which was earlier known as Reginald Hobby Shops. Reginald Denny served with the British Royal Flying Corps during World War I, and after the war, immigrated to the United States to seek his fortunes in Hollywood as an actor. Denny had made a name for himself as an actor, and between acting jobs, he pursued his interest in radio control model aircraft in the 1930s. He and his business partners formed “Reginald Denny Industries” and opened a model plane shop in 1934. The first large-scale production, purpose-built drone was the product of Reginald Denny. Aerial Torpedoes were used extensively

  • Early uses of drones in some or other ways (extensive military use)

1. During Cold War

2. Nuclear Tests were performed using drones to collect radioactive data

3. USSR secret projects were performed using drones

4. Vietnam War

The above mentioned are just a gist of the evolution & history on the concept of drones. The above mentioned facts might be debatable for few, but this is how it is believed to have evolved.

Where are Drones being used currently?

  • The Department of Homeland Security – Back and forth along the U.S. border to monitor for people crossing illegally (USA).
  • Forest Service – To find and map forest fires in California (USA).
  • Some police Departments – Testing them for uses such as photographing accident sites and finding criminal suspects (USA).

The above mentioned are some areas where USA is using drones. This is just to give a gist. The list is exhaustive.

Where these Drones can be seen in near future?

  • Agricultural fields – Drones are moving from battlefield to farmer’s field in USA. Advanced sensors & imaging capabilities are giving farmers new ways to increase yields & reduce crop damage.
  • Amazon & UPS – Drones to be adopted by them to deliver packages.
  • Google & Facebook – Drones will be used by these companies with a motive of delivering internet service from high-altitude balloons to underserved areas. As per the sources, Google is experimenting these drones above New Zealand & Facebook is planning to implement & experiment with Africa. Both have acquired drone manufacturing companies.
  • Real Estate – Real estate companies are beginning to use Unmanned Aircraft Systems, better known as drones, to give potential home-buyers bird’s-eye views of properties with a quick fly-by in USA.
  • Pizza delivery in India – The so called financial capital of India, Mumbai has seen a Pizza restaurant deliver pizza using a drone. This restaurant experimented in a close 2 km range & was successful.

The above mentioned are some areas where drones will be soon implemented.

Don’t you think this invention was crazy? Don’t you think the application of drones for various purposes is trippy? Want to know more about drones? Technicalities? Functionalities?

Stay tuned… We are coming up with this course shortly!!!

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How Big Is Big Data?

What is Big Data?  
Big Data is a term used to describe the availability of data & exponential growth; structured, unstructured & multi structured. It’s playing a vital role for all kinds of businesses irrespective of their size. More accurate data may lead to more accurate analyses, more accurate analysis may lead to more accurate decision making & more accurate decision making may help in improvising operational efficiencies, cost reductions, risk mitigation & so on. Earlier times, decision was taken by an organization on the basis of Gut Feeling but now organizations rely on the historical data to make better decisions. This has given more importance to big data.

In 2001 research report by Gartner, analyst Doug Laney articulated the definition of Big Data as 3vs which is still used to define Big Data –

  •  Volume (increasing amount of data)
  •  Velocity (increasing speed of data in & out)
  •  Variety (different formats of data, range of data types & sources)

Why Big Data Matters to You?

Organizations now have various mediums & sources to collect the data but they don’t know what to do with that data. Even if they know what to do with that data, they don’t know what technology to use for big data processing & analyzing. It is rightly said, that earlier times organization’s problem was how to collect data, then a time came when the problem was how to store that collected data & currently the problem is what to do with the collected and stored data. How to make every bit of data collected count? This is the major question that organizations ask themselves.

Talent pool in India!!!

When we talk about the required talent pool that organizations are looking for, we don’t have a huge no. of people who know Big Data Analytics & the required knowledge of advanced technologies for Big Data. Since, Big Data space is evolving & more and more organizations are practicing it, we expect more requirement will be needed in the near future in this domain.

Source – Analytics India Magazine

The right mix of a professional with excellent analytical skills & hands on experience with advanced technology like Hadoop, R, MongoDB & so on is what organizations are looking for. According to latest McKinsey report, more than 2,00,000 data scientists will be needed by the industry (2014-2016). Also, according to a report published in 2011 by McKinsey & Co., U.S. could face a shortage by 2018 of 140,000 to 190,000 people with “deep analytical talent” and of 1.5 million people capable of analyzing data in ways that enable business decisions. Almost same is the requirement across the globe for this era of Big Data.

This Big is Big Data!!!!!

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