Neeraj Nathani Smart Bridge Trading Solutions 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.

Neeraj Nathani Smart Bridge Trading Solutions

RETAILING

Retail is the sale of goods and services from individuals or businesses to the end-user. Retailers are part of an integrated system called the supply chain. A retailer purchases goods or products in large quantities from manufacturers directly or through a wholesale, and then sells smaller quantities to the consumer for a profit. Retailing can be done in either fixed locations like stores or markets, door-to-door or by delivery. Retailing includes subordinated services, such as delivery. The term "retailer" is also applied where a service provider services the needs of a large number of individuals, such as a public. Shops may be on residential streets, streets with few or no houses or in a shopping mall. Shopping streets may be for pedestrians only. Sometimes a shopping street has a partial or full roof to protect customers from precipitation. Online retailing, a type of electronic commerce used for business-to-consumer (B2C) transactions and mail order, are forms of non-shop retailing.
Shopping generally refers to the act of buying products. Sometimes this is done to obtain necessities such as food and clothing; sometimes it is done as a recreational activity. Recreational shopping often involves window shopping (just looking, not buying) and browsing and does not always result in a purchase

Types of retail outlets

A marketplace is a location where goods and services are exchanged. The traditional market square is a city square where traders set up stalls and buyers browse the merchandise. This kind of market is very old, and countless such markets are still in operation around the whole world.

In some parts of the world, the retail business is still dominated by small family-run stores, but this market is increasingly being taken over by large retail chains.

Retail is usually classified by type of products as follows:

• Food products
• Hard goods or durable goods ("hardline retailers") - appliances, electronics, furniture, sporting goods, etc. Goods that do not quickly wear out and provide utility over time.
• Soft goods or consumables - clothing, apparel, and other fabrics. Goods that are consumed after one use or have a limited period (typically under three years) in which you may use them.

There are the following types of retailers by marketing strategy:

• Department stores - very large stores offering a huge assortment of "soft" and "hard goods; often bear a resemblance to a collection of specialty stores. A retailer of such store carries variety of categories and has broad assortment at average price. They offer considerable customer service.
• Discount stores - tend to offer a wide array of products and services, but they compete mainly on price offers extensive assortment of merchandise at affordable and cut-rate prices. Normally retailers sell less fashion-oriented brands.
• Warehouse stores - warehouses that offer low-cost, often high-quantity goods piled on pallets or steel shelves; warehouse clubs charge a membership fee;
• Variety stores - these offer extremely low-cost goods, with limited selection;
• Demographic - retailers that aim at one particular segment (e.g., high-end retailers focusing on wealthy individuals).
• Mom-And-Pop : is a retail outlet that is owned and operated by individuals. The range of products are very selective and few in numbers. These stores are seen in local community often are family-run businesses. The square feet area of the store depends on the store holder.
• Specialty stores: A typical speciality store gives attention to a particular category and provides high level of service to the customers. A pet store that specializes in selling dog food would be regarded as a specialty store. However, branded stores also come under this format. For example if a customer visits a Reebok or Gap store then they find just Reebok and Gap products in the respective stores.
• General store - a rural store that supplies the main needs for the local community;
• Convenience stores: is essentially found in residential areas. They provide limited amount of merchandise at more than average prices with a speedy checkout. This store is ideal for emergency and immediate purchases as it often works with extended hours, stocking everyday;
• Hypermarkets: provides variety and huge volumes of exclusive merchandise at low margins. The operating cost is comparatively less than other retail formats.
• Supermarkets: is a self-service store consisting mainly of grocery and limited products on non food items. They may adopt a Hi-Lo or an EDLP strategy for pricing. The supermarkets can be anywhere between 20,000 and 40,000 square feet (3,700 m²). Example: SPAR supermarket.
• Malls: has a range of retail shops at a single outlet. They endow with products, food and entertainment under a roof.
• Category killers or Category Specialist: By supplying wide assortment in a single category for lower prices a retailer can "kill" that category for other retailers. For few categories, such as electronics, the products are displayed at the centre of the store and sales person will be available to address customer queries and give suggestions when required. Other retail format stores are forced to reduce the prices if a category specialist retail store is present in the vicinity.
• E-tailers: The customer can shop and order through internet and the merchandise are dropped at the customer's doorstep. Here the retailers use drop shipping technique. They accept the payment for the product but the customer receives the product directly from the manufacturer or a wholesaler. This format is ideal for customers who do not want to travel to retail stores and are interested in home shopping. However it is important for the customer to be wary about defective products and non secure credit card transaction. Example: Amazon, Pennyful and eBay.
• Vending Machines: This is an automated piece of equipment wherein customers can drop the money in the machine and acquire the products.

Some stores take a no frills approach, while others are "mid-range" or "high end", depending on what income level they target.

Other types of retail store include:

• Automated Retail stores are self-service, robotic kiosks located in airports, malls and grocery stores. The stores accept credit cards and are usually open 24/7. Examples include ZoomShops and Redbox.
• Big-box stores encompass larger department, discount, general merchandise, and warehouse stores.

Retailers can opt for a format as each provides different retail mix to its customers based on their customer demographics, lifestyle and purchase behaviour. A good format will lend a hand to display products well and entice the target customers to spawn sales.

Global Top Five Retailers

Worldwide Top Five Retailers[2]
Retail Sales Rank	Company	Country of Origin	2010 group revenue (US $mil)
1	Walmart	US	$421,849
2	Carrefour	France	$121,519
3	Tesco	UK	$94,244
4	Metro AG	Germany	$89,311
5	Kroger	US	$82,189

Operations

Retail pricing

The pricing technique used by most retailers is cost-plus pricing. This involves adding a markup amount (or percentage) to the retailer's cost. Another common technique is suggested retail pricing. This simply involves charging the amount suggested by the manufacturer and usually printed on the product by the manufacturer.
In Western countries, retail prices are often called psychological prices or odd prices. Often prices are fixed and displayed on signs or labels. Alternatively, when prices are not clearly displayed, there can be price discrimination, where the sale price is dependent upon who the customer is. For example, a customer may have to pay more if the seller determines that he or she is willing and/or able to. Another example would be the practice of discounting for youths, students, or senior citizens..

Staffing

Because patronage at a retail outlet varies flexibility in scheduling is desirable. Employee scheduling software is sold which, using known patterns of customer patronage, more or less reliably predicts the need for staffing for various functions at times of the year, day of the month or week, and time of day. Usually needs vary widely. Conforming staff utilization to staffing needs requires a flexible workforce which is available when needed but does not have to be paid when they are not, part-time workers; as of 2012 70% of retail workers in the United States were part-time. This may result in financial problems for the workers, who while they are required to be available at all times if their work hours are to be maximized, may not have sufficient income to meet their family and other obligations.^[3]

Transfer mechanisms

There are several ways in which consumers can receive goods from a retailer:

• Counter service, where goods are out of reach of buyers and must be obtained from the seller. This type of retail is common for small expensive items (e.g. jewelry) and controlled items like medicine and liquor. It was common before the 1900s in the United States and is more common in certain countries like India.^[which?]
• Delivery, where goods are shipped directly to consumer's homes or workplaces. Mail order from a printed catalog was invented in 1744 and was common in the late 19th and early 20th centuries. Ordering by telephone is now common, either from a catalog, newspaper, television advertisement or a local restaurant menu, for immediate service (especially for pizza delivery). Direct marketing, including telemarketing and television shopping channels, are also used to generate telephone orders. started gaining significant market share in developed countries in the 2000s.
• Door-to-door sales, where the salesperson sometimes travels with the goods for sale.
• Self-service, where goods may be handled and examined prior to purchase

Source: Wikipedia.

Picking Winners In Big Data Neeraj Nathani Smart bridge

Picking Winners In Big Data

Big data solutions are picking up speed in the IT industry. There’s a Cambrian explosion of interesting start-ups, and all the database and business intelligence incumbents have moved to create big data offerings, or rebrand into the new universe.

The key to seeing the value of big data is understanding that it’s a business problem, not a matter of picking the right tools and waiting for the magic to happen. That said, technology choices still need to be made in an increasingly crowded and confused market.

The biggest question for anybody wanting to invest or adopt technology in this area is how to pick the winner? Glancing through marketing materials will do little to help you: everybody claims relevance to big data.

As I am often asked my opinion about big data companies, I thought I’d share some of the principles I use to help me think about the industry.

Where’s the value?

We need to understand where the actual value is in the data world. For the most part, this value doesn’t lie purely in the software. Over the past decade we have seen a rising tide of commoditization of the software stack: from operating system, to relational databases, to Hadoop itself. In fact, without this, we wouldn’t have the big data revolution as we know it.

As Hadoop has become a de facto standard, so has the notion of building on top of it with open source. There is some advantage in software innovation, but it is momentary. Once something is known to be possible, there are enough smart programmers out there that reproducing it becomes straightforward. (Because of this, I gloomily predict no shortage of patent battles in the not-too-distant future.)

Despite the flux in the software world, two things about big data remain constant: the need for compute and storage, and data itself. It’s ultimately to ownership of one or both of these factors that IT industry value will gravitate.

Compute and storage

The ever growing need for computing power and storage bodes well for companies providing the basics. These fall into two categories: hardware manufacturers, and cloud infrastructure providers. Not that these two markets are immune to their own fluctuations, thanks to standardization and commoditization, but fundamentally, getting paid for use of metal is the name of the game.

In this respect, it’s not too much of a mystery why storage company EMC has plowed so early and so deep into the big data world. Neither is it hard to see the reasoning behind Intel creating its own optimized Hadoop distribution.

Data

Value resting in data is the more subtle of the two axes of big data success.

Big data is ultimately about the smart use of data to drive a business. There are two kinds of data: data about your business, and data external to your business that you can create value from.

It’s easy to see who might get success from the latter, external data. We can expect that massive data owners such as Google, Facebook, Thomson Reuters, Bloomberg will experience ongoing success for as long as they are able to create product from their data.

Who owns your data, though? The obvious answer, you, isn’t the only answer. In fact, your data is locked up inside the platform choices you make, at both the hardware and software level. If your systems are based on Oracle, Microsoft, you are very unlikely to move in a hurry. Data likes to stay where it is, and tends to attract more data as you build systems around it. Production systems are expensive to replace.

So, the vendors of your software platforms of choice also get long term value from your data. For this reason, it’s hard to bet against existing enterprise application platform incumbents in the big data world. Big data, for most of today’s organizations, is an additive phenomenon, not a challenge to the core of IT.

We’ll see more large platforms ensuring nobody needs to move away. Examples include SAP adding HANA, in order to enable their existing customers for big data, or Amazon Web Services’ addition of their data warehouse Redshift, to ensure that the entire data needs of a company can be met on their platform.

Finally, it would be lunacy to bet against either Oracle, who have been portentously quiet in the big data world over the last year, or Microsoft, who in Excel have the world’s most popular data manipulation environment.

It is all business as usual?

I’m not saying there is no new opportunity in the big data industry. What I am saying is that, as an additive technology, big data is unlikely to enable anybody to challenge Oracle or Microsoft for the throne.

There will be change, though, and it will bring both winners and losers.

The area of value we haven’t yet looked at yet is the point where data interacts with the actual mechanism of a business. That’s where it transfers value to you and enables you to leverage data to get ahead. This breaks down into several areas of opportunity for big data innovators.

• Domain specific: tools that enable the manipulation and exploitation of data in a way that’s specific to a business segment. We see initial evidence of this market opportunity in the evolution of web and customer analytics products.
• Machine learning: more data means more to understand, and the only way an organization can realistically do this is with the aid of computers. Machine learning helps automate many parts of the data wrangling process. Furthermore, cross-company data sharing can significantly boost the effectiveness of machine-learning, creating the opportunity for companies gaining early market share. A recent example of this is Sift Science, a fraud detection application.
• Tools for exploration: human interaction with data is a requirement that’s hard to abstract away. Tableau has a head start in this market for big data, filling the role of “Microsoft Office for Data”, but the field is ripe for new innovation, especially with the increasing power of graphical capabilities and new device formats.
• Data agility: speed-to-decision is a critical factor in business competitiveness. Any solution that removes laborious steps has an advantage: a particularly problematic area here is data integration, the loading of both internal and external data sources ready for analytics. Most of today’s big data solutions are frankensteined combinations of layers: there’s a great opportunity for vertically integrated solutions that removes needless impedance to data manipulation.

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Areas of risk

What are those riskier big data options? If a solution isn’t scoring high in the categories above, it’s not likely to be around for the long run.

The biggest risk is with solutions that address only a single horizontal part of the data architecture. Time is against companies in this game. By selling just part of a complete solution, they’re working against the rising tide of commoditization. Customers will demand standardization (e.g. Hadoop compatibility) in order to feel safe adopting such solutions, but that prevents the lock-in that will protect that business. It’s not Oracle’s relational database that cements their position: it’s their vertical position up and down the application stack.

Therefore, it’s not surprising to see the pure Hadoop distribution companies making partnerships, and branching out into other vertical layers. In the long term, it’s a tough road they’ve chosen.

One company working actively to solve this problem is DataStax, who have pivoted from being seen as the corporate backer for the Cassandra NoSQL database—a risky horizontal play—to selling an integrated stack of Cassandra, Hadoop and Solr, intended as a complete platform for building enterprise applications. They’re going after some of the platform business: being involved in the actual use of data to drive business value.

For some start-ups, not having long term big data viability might be just fine as a strategy. As the bigger enterprise companies lumber into the arena, they’ll prove handy acquisitions. But given the crowded space and progressive commoditization, this isn’t a certain future. And certainly for their customers, the risks are growing.

More controversially, another area at risk is that of traditional ETL. Or in its broader sense, data integration. This is a hard, hard, problem. It’s not easy to integrate data retrospectively, and it’s not all certain that the integration players from the data warehouse world will be able to translate that success into the big data world. Many early adopters of Hadoop were motivated by the fact that existing ETL solutions couldn’t meet their needs.

In the long term, data integration is likely to be best served by entire new architectures that don’t try to separate and tame the data in the first place. It’s a lot easier to build truly integrated data infrastructure as greenfield.

For those who crack the integration problem, the potential rewards are high. But so is the risk.

Conclusion

In the long term, the additive nature of big data, combined with inertia, makes it a safe bet that current enterprise IT incumbents will continue their reign, as long as they move to embrace big data in their architectures.

There is plenty of opportunity though, especially in greenfield and cloud scenarios. Expect to see increasing returns for those who provide integrated solutions and do a good job of equipping human decision makers.

There’s long term value in metal, and value in data. About everything else, it’s worth thinking carefully.

Source: Wikipedia./Forbes