FMCG Market Smart Bridge Neeraj Nathani

                                                                FMCG Market

Fast-moving consumer goods (FMCG) or consumer packaged goods (CPG) are products that are sold quickly and at relatively low cost. Examples include non-durable goods such as soft drinks, toiletries, and grocery items.^[1][2] Though the absolute profit made on FMCG products is relatively small, they are generally sold in large quantities, and so the cumulative profit on such products can be substantial.
Fast-moving consumer electronics are a type of FMCG and are typically low priced generic or easily substitutable consumer electronics, including lower end mobile phones, MP3 players, game players, and digital cameras, which have a short usage life, typically a year or less, and as such are disposable. Cheap FMCG electronics are often retained even after immediate failure, as the purchaser rationalizes the decision to not return the goods on the basis that the goods were cheap to begin with, and that the cost of return relative to the low cost of purchase is high. Thus low-quality electronic FMCG goods can be highly profitable for the vendors.
The term FMCGs refers to those retail goods that are generally replaced or fully used up over a short period of days, weeks, or months, and within one year. This contrasts with durable goods or major appliances such as kitchen appliances, which are generally replaced over a period of several years
FMCG have a short shelf life, either as a result of high consumer demand or because the product deteriorates rapidly. Some FMCGs—such as meat, fruits and vegetables, dairy products, and baked goods—are highly perishable. Other goods such as alcohol, toiletries, pre-packaged foods, soft drinks, and cleaning products have high turnover rates. An excellent example is a newspaper—every day's newspaper carries different content, making one useless just one day later, necessitating a new purchase every day.

The following are the main characteristics of FMCGs:^[1]

• From the consumers' perspective:
• Frequent purchase
• Low involvement (little or no effort to choose the item – products with strong brand loyalty are exceptions to this rule)
• Low price
• From the marketers' angle:
• High volumes
• Low contribution margins
• Extensive distribution networks
• High stock turnover

Source: Wikipedia.

Neeraj Nathani TELECOM BILLING

                                                TELECOM BILLING

Billing is the aggregation of all non-recurring, periodic, and chargeable events, on an account by account basis. It is also the calculation of all outstanding charges and available discounts and bonuses.

The output from billing process is a stream of tagged bill data that can be used to create a bill on paper, disk, or any other media. Billing Engine which is part of the Billing System performs creates invoices.

Bill Process:

The following diagram shows the basic diagram of the Billing Engine and associated functions:

Billing Engine picks up an account due to generate a billing and following associated information to generate invoice data:

• All the rated CDRs for the customer with-in the month of invoice.
• All type of charges ( initiation, installation, periodic, suspension, termination etc.) applicable for the customer's product and services.
• If there is any refund or any other charges applicable.
• Total outstanding from previous bills.
• Total payments made by the customer in the given month.
• Total adjustment passed in favor of the customer or against the customer.
• Total discount given to the customer.
• Total taxes applicable on customer usage and rental charges.
• Billing configuration parameter required to run the Billing Engine, for example payment due date etc.

Above mentioned information is just an indicative and may vary from billing system to billing system and operator to operator.

Billing Engine produce raw data having all the information required to generate a final bill and this raw data can be used to generate a final invoice to be sent to the end customer.

Bill Cycles:

When a customer is added into the Billing System, system assigns the customer a predefined Bill Cycle. A bill cycle is a date on while Billing Engine runs and produces bill for a set of customers.

If there are many customers, then they are divided in to different billing cycles. For example, a group of customers can have billing data as 1st of every month; another can have the bill date of 15th of every month.

If customer is assigned to run a bill on 1st of the month, this would be called customer's nominal bill date. But because of various reasons, many times bill run becomes belay and actual bill gets generated on a later date, this would be called actual bill date.

Bill Types:

There could be various types of bill available for a user. Few of them may not be supported by some Billing System.

Bill Type	Description
Initiation bill	Normally only requested as the first bill on an account. Includes product charges and adjustments, but no events.
Periodic bill	Produced at regular intervals. Includes all periodic charges, events, and adjustments.
Interim bill	An extra bill that contains charges due to events processed for the account since the last bill. Includes all events and adjustments, but no periodic charges.
Suspension bill	Sent when an account has been suspended. Includes all periodic charges, events, and adjustments.
Final bill	Sent when an account has been terminated to bill all outstanding charges that are due. Includes all periodic charges, events, and adjustments, along with any refunds, for example, the return of a deposit.
Post-final bill	Sent when a terminated account has receivables outstanding after the production of a final bill. Includes any post-termination events and adjustments, but no periodic charges.
Credit note	An extra bill that contains all adjustments in the customer.s favor generated since the last bill.
Summary Statements	A summary statement can be produced for a customer-driven billing hierarchy. It can summarize all bills produced by all accounts below the customer it is associated with. Optionally, they can also concatenate all the bills into a single statement.

Bills are produced either automatically, or on request from a customer.

Billing Modes:

A Billing System can generate bills in two modes, for example:

• Test (what if?) billing mode . This mode if used to produce formatted test bills whilst leaving the database unchanged. These bills are useful to make sure that system is working fine and test after making changes to bill templates or tariffs.

When running the Billing Engine in test mode, commits are not made to the database. So there would not be any impact on customer's profile even after running test billing many number of time.

Test bills are usually run for a sample set of customers. If you are satisfied with the test bills then you can proceed for production bills.

• Production (live) billing mode . This mode is used to produce normal production bills. Most of the time, this is the default mode for the Billing Engine.

Once a production bill is generated, Billing Engine updates customer's profile in the database with the total outstanding balance to be paid by the customer, and next bill date etc.

Billing Engine assigns different invoice numbers to all the production bills which helps in keeping track of different payments made against the invoice.

Bill Suppression:

There may be a situation when it is not worth to generate a bill, and better to suppress the bill. Following are such type of situations:

• Suppressing bills for accounts with zero (zero activity bills) or very little value (small bills).
• A particular type of bill can also suppressed if multiple bill types are requested/scheduled at the same time and therefore preventing unnecessary bills from being sent to the customer.

A small bill is a bill that falls between the range defined by the minimum positive bill amount and the maximum negative bill amount exceptional bill conditions. Small valued bills are produced and then removed from the billing process, so that they are not sent out to customers.

Exceptional Bills:

Examples of possible exceptional bills are unusually high bills or bills which exceed the account's credit limit by a set multiplier. The Billing Engine performs some basic checks on the bill data that it produces. These involve testing the total being billed to ensure that the following conditions are met:

• The bill total is greater than the minimum negative bill amount.
• The bill total is less than the maximum positive bill amount.
• The bill total is less than account's credit limit multiplied by the credit limit multiplier.

All the above conditions vary from billing system to billing system and operator to operator and they are called exceptional bills conditions.

Bill Itemization:

By default, all the invoices provide a detail summary of product and services charges along with usage charges. But it does not provide a detail on all the calls made by the customer.

An itemized bill means giving complete detail of all the calls made by the customer. This needs more number of papers to be printed. Recent trend is to send itemized bill through electronic email and summary statement is sent using a physical copy of the bill.

Bill Formatting:

There are Billing Systems who provide Billing Formatting utilities which can be used to generate final formatted bills.

Bill formatters take the output data produced by the Billing Engine and usually generate either Post Script file or a PDF file which can be used by the Bill Printing Company.

If Billing System is not capable enough to generate formatted bills then system generates a set of tags files alongwith billing information and any external Bill Formatter can use those tagged information to generate a well formatted invoice.

No matter, if Billing System generates formatted invoice or we use any external tool to generate these formatted invoice using raw data generated by the billing engine, finally these invoice are sent to the bill printing company who takes care of generating final copy of generating invoice. I will explain it in detail in subsequent chapter "Invoice Generation".

Source: Wikipedia.

Big data Neeraj Nathani

Big data

Big data^[1][2] is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage,^[3] search, sharing, transfer, analysis,^[4] and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions."^[5][6][7]

As of 2012^[update], limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.^[8][9] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,^[10] connectomics, complex physics simulations,^[11] and biological and environmental research.^[12] The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.^[13][14] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;^[15] as of 2012^[update], every day 2.5 quintillion (2.5×10¹⁸) bytes of data were created.^[16] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.^[17]

Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".^[18] What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."^[19].

Definition

Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012^[update] ranging from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, new platforms of "big data" tools are being developed to handle various aspects of large quantities of data.
In a 2001 research report^[20] and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.^[21] In 2012, Gartner updated its definition as follows: "Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization."^[22

Examples

Examples include Big Science, web logs, RFID, sensor networks, social networks, social data (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, forecasting drive times for new home buyers, medical records, photography archives, video archives, and large-scale e-commerce.

Big science

The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.999% of these streams, there are 100 collisions of interest per second.^[23][24][25]

• As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication.
• If all sensor data were to be recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×10²⁰) bytes per day, almost 200 times higher than all the other sources combined in the world.

Science and research

• When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2016 it is anticipated to acquire that amount of data every five days.^[5]
• Decoding the human genome originally took 10 years to process; now it can be achieved in one week.^[5]
• Computational social science — Tobias Preis et al. used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators.^[26][27][28] The authors of the study examined Google queries logs made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’), which they call the ‘future orientation index’.^[29] They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. The results hint that there may potentially be a relationship between the economic success of a country and the information-seeking behavior of its citizens captured in big data.

Government

• In 2012, the Obama administration announced the Big Data Research and Development Initiative, which explored how big data could be used to address important problems facing the government.^[30] The initiative was composed of 84 different big data programs spread across six departments.^[31]
• Big data analysis played a large role in Barack Obama's successful 2012 re-election campaign.^[32]
• The United States Federal Government owns six of the ten most powerful supercomputers in the world.^[33]
• The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster.^[34]
• The Utah Data Center is a data center currently being constructed by the United States National Security Agency. When finished, the facility will be able to handle yottabytes information collected by the NSA over the Internet.^[35][36]

Private sector

• Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux-based and as of 2005 they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.^[37]
• Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes (2560 terabytes) of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress.^[5]
• Facebook handles 50 billion photos from its user base.
• FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide.^[38]
• The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates.^[39]
• Infosys has also launched the BigDataEdge to analyse the Big data.^[40][41]
• Windermere Real Estate uses anonymous GPS signals from nearly 100 million drivers to help new home buyers determine their typical drive times to and from work throughout various times of the day ^[42]

International development

Following decades of work in the area of the effective usage of information and communication technologies for development (or ICT4D), it has been suggested that Big Data can make important contributions to international development.^[43][44] On the one hand, the advent of Big Data delivers the cost-effective prospect to improve decision-making in critical development areas such as health care, employment, economic productivity, crime and security, and natural disaster and resource management.^[45] On the other hand, all the well-known concerns of the Big Data debate, such as privacy, interoperability challenges, and the almighty power of imperfect algorithms, are aggravated in developing countries by long-standing development challenges like lacking technological infrastructure and economic and human resource scarcity. "This has the potential to result in a new kind of digital divide: a divide in data-based intelligence to inform decision-making."^[45]

Technologies

File:DARPA’s Topological Data Analysis program seeks the fundamental structure of massive data sets and is developing the tools to exploit that knowledge.tiff

DARPA’s Topological Data Analysis program seeks the fundamental structure of massive data sets.

Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. A 2011 McKinsey report^[49] suggests suitable technologies include A/B testing, association rule learning, classification, cluster analysis, crowdsourcing, data fusion and integration, ensemble learning, genetic algorithms, machine learning, natural language processing, neural networks, pattern recognition, anomaly detection, predictive modelling, regression, sentiment analysis, signal processing, supervised and unsupervised learning, simulation, time series analysis and visualisation. Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation,^[50] such as multilinear subspace learning.^[51] Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data-mining grids, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and computing resources) and the Internet.^{[citation needed]}

Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS.^[52]

DARPA’s Topological Data Analysis program seeks the fundamental structure of massive data sets and in 2008 the technology went public with the launch of a company called Ayasdi.

The practitioners of big data analytics processes are generally hostile to slower shared storage^{[citation needed]}, preferring direct-attached storage (DAS) in its various forms from solid state disk (SSD) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—SAN and NAS—is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost.

Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.

There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of 2011^[update] did not favour it.^[53]

Source: Wikipedia.