Saturday, 19 April 2008

Neeraj Nathani Revenue Assurance


 Revenue Assurance

Revenue assurance (RA) is a business activity most commonly undertaken within businesses that provide telecommunication services. The activity is the use of data quality and process improvement methods that improve profits, revenues and cash flows without influencing demand. This was defined by a TM Forum working group based on research documented in its Revenue Assurance Technical Overview [1]. In many telecommunications service providers, revenue assurance is led by a dedicated revenue assurance department.
Overview
"Revenue assurance" is a broad umbrella term. It is used both to describe an activity performed within telecommunications service providers, and is a common name for a small business unit associated with that activity. Revenue assurance is a practical response to perceived or actual issues with operational underperformance, most commonly relating to billing and collection of revenue. Some of the procedures associated with identifying, remedying or preventing errors may be undertaken by a dedicated Revenue Assurance department, though responsibility for revenue assurance is often diffuse and varies greatly with the organizational structure of the provider. Assuming a provider with a typical organizational split, responsibilities for revenue assurance primarily sit between the Finance and Technology directorates, however, revenue assurance initiatives are often started in a business unit or marketing group.
The relevance to Finance rests with the responsibility for financial control, audit and reporting, whilst the subject matter would be network and IS systems as implemented or operated by the Technology side of the business. Marketing groups and / or business units (e.g. wholesale or retail business lines) will often embark on revenue assurance projects in an effort to improve product line margins. Furthermore, marketing and business units are pivotal in providing input into the "should be" state of customer bills and products.
The sphere of influence described by revenue assurance varies greatly between telecommunications service providers, but is usually closely related to back office functions where small errors may have a disproportionately large impact on revenues or costs. The processing of transaction data in modern telecommunications providers exhibits many attributes akin to a complex system. However, there is significant disagreement about the ultimate aims and legitimate scope of revenue assurance teams. This is in part caused by:
(1) the cross-functional nature of the activity and the consequent need for a variety of skills from IT, marketing, finance, et al.;
(2) the difficulty of generalizing across businesses with different objectives and business models;
(3) political infighting within each telco about responsibility for revenue leakages and assurance; and
(4) the difficulty in reliably measuring the value added by revenue assurance as separable from underlying performance.
There is high-level agreement between practitioners about the goals and methods of revenue assurance, though reaching a consensus on defining the boundaries of revenue assurance has proved elusive so far. The goals relate to improving the financial performance by eliminating mistakes in the processing of transaction data. Some take a more encompassing view of what counts as a mistake, which may extend as far as questioning the policy set by executives even when this has been executed correctly. Others take a more open-ended view of the data that is the subject matter. For example, in decreasing order of frequency, revenue assurance may cover:
(1) revenues from retail and corporate sales;
(2) revenues and costs from interconnect and wholesale contracts; and
(3) margins and profitability of investment in networks and information systems.
Other markets have different or more refined priorities. For example, in the U.S.A. management of wholesale contracts has often been the first objective because of the complexity of the domestic market resulting from the Federal Communications Commission's regulatory framework. In contrast, telcos in developing countries may prioritize management of international interconnect arrangements because of the risks posed by fraud and arbitrage. A cable supplier or internet service provider that predominantly offers retail customers flat monthly charges and no limits on usage may be most interested in assuring the profitability of network investments.
Revenue assurance is often regarded by practitioners as a low-cost mechanism to generate significant financial returns for telecommunications service providers. However, the returns are unpredictable as well as being hard to measure, which encourages many executives to take a sceptical view of its worth. Comparable revenue assurance activities do occur in other industries, such as with billing of utilities or with the licensing of software, and there are many parallels with financial and operational control activities undertaken by most large businesses. The rationale for why revenue assurance has come to be considered particularly important in telecommunications, unlike other industries, is disputed. Reasonable conjectures are that:
(1) the fast pace of change and intense commercial competition increase the likelihood of mistakes;
(2) there is significant complexity in determining the combined effect of interacting systems and processes; and
(3) the high-volume, low-value nature of transactions amplifies the financial implications of "small" errors.
Another conjecture is that revenue assurance is a response to changing market conditions. The thinking is that as markets reach saturation and growth potential falls off, so the value of maximizing returns from existing sales increases. This observation has some merit but does not explain the increasing popularity of revenue assurance in telcos serving growth markets. It also in part contradicts the assumption of a compelling costs versus benefits argument for revenue assurance, which would be enhanced in businesses undergoing rapid change. It is also important to recognize that there is a long history of revenue assurance activities in some telcos that predates the coining of the term "revenue assurance".
The revenue assurance techniques applied in practice cover a broad spectrum, from analysis and implementation of business controls to automated data interrogation. At one end of the spectrum, revenue assurance can appear very similar to the kinds of review and process mapping techniques applied for other financial controlling objectives like accounting integrity, as exemplified by those derived from clause 404 of the Sarbanes-Oxley Act. This form of revenue assurance is most commonly promoted by consultancies. The size of such consultancies covers the entire range; the Big 4 all offer some form of revenue assurance consulting, but there are also niche specialist consultancies. At the other end of the spectrum, revenue assurance is treated as a form of reactive automated data interrogation, seeking to find anomalies in transaction data that may indicate errors and potential revenue loss. This form of revenue assurance is most commonly promoted by software houses that aim to provide databases and configurable tools to extract and interrogate a telco's source data. A less popular form of reactive automated assurance involves using both software and specialised hardware as a means of extracting additional data on transactions, for example by creating actual network events or interfacing directly with network elements to replicate dummy events. As with consultancies, IT-oriented revenue assurance solutions are offered by both large vendors like providers of billing and mediation software, and by specialized niche providers.
There is some debate about the relative merits of the different techniques that can be employed in revenue assurance.
As yet, there is no professional body, no qualifications, and no academic research that would help to drive consensus about the purpose or methods of revenue assurance. In part this is addressed by individuals working in the sector through membership and qualification in related fields such as accountancy and information systems audit. Some scientific research in other fields is also applicable to revenue assurance, though most revenue assurance "facts" rely heavily on anecdotes and oft-repeated truisms. Some of the most helpful and progressive initiatives in addressing the problem of consensus and scientific basis are listed below.
The value of revenue assurance
Revenue assurance is usually understood as a means to identify and remedy, and perhaps also to prevent, problems that result in financial under performance without seeking to generate additional sales. The most common metaphor is that of leaking water from a pipe, where water stands in place of revenues or cash flows, and the leaks represent waste. The value of revenue assurance is hence determined by the size of the leaks "plugged", and possibly also those leaks prevented before they occur, although estimating the value of the latter is very problematic. The value added also includes the recovery of "lost" revenues or costs (through issuing additional bills, chasing uncollected payments, renegotiating with suppliers a refund of costs etc.) after the fact. This last form of reactive revenue assurance is the easiest to put a value to, but is in many ways the least efficient form of revenue assurance; effort is directed towards repeatedly addressing the consequences of known flaws, and not on addressing the flaws themselves. This can lead to a parasitical relationship between a Revenue Assurance department or vendor and the wider business, where the department/vendor finds it easiest to justify its ongoing existence/contract by repeatedly fixing symptoms and not the root causes.
The TM Forum conducted a benchmark survey in 2008 that concluded average leakage, not including losses due to fraud, was 1% of the gross revenues for those telcos that took part.[1] The number of participating telcos was relatively small compared to some other surveys, but the survey technique was more demanding than any comparable survey to date. The survey used the most detailed and prescriptive definition of how to calculate leakage of any survey of its type. The definition was taken from the TM Forum's own standard on how to calculate revenue assurance metrics [2]. To increase confidence that participants calculated their leakages correctly, the TM Forum's benchmark program independently reviewed the results and corroborated them with representatives of the participating companies. The survey's average of 1% leakage of gross revenue, whilst still significant, is notably lower than many other quoted estimates and reported survey findings about average leakage. This may be because the survey used a very strict definition of leakage. The survey measured only actual under-billed and unbilled amounts discovered by the participants; it excluded other types of leakages such as cost leakages and loss of opportunity leakages, and it excluded projected leakage estimations that are commonly used (i.e., what would have been the amount of leakage, if the leakage would not been discovered by revenue assurance activities). It may also reflect a reduction in bias or exaggeration in reported leakages, or at least the exclusion of guesswork. Respondents were given authoritative instructions on how to quantify leakage based on actual data and were instructed to avoid making suppositions in the absence of such data.
The best known estimates of "typical" revenue leakage come from a series of annual surveys conducted by the Analysys consultancy and research business. In these surveys, leakage was commonly estimated as being worth between 5% and 15% of the total revenue of the business. Similar research by other businesses has generated results in the same range, with none concluding leakage of less than 1% of gross revenue, and some suggesting leakage of 20% or more was not uncommon. Reasons to doubt these estimates are as follows:
(1) All the estimates of leakage were derived from the subjective opinions of staff working in service providers;
(2) All the research was conducted by businesses wishing to promote their revenue assurance products;
(3) Increased annual spend on revenue assurance has not resulted in a clear downward trend in estimates;
(4) The estimates were broadly similar even when the criteria for what losses to include varied greatly; and
(5) Genuine losses of this scale should be a severe corporate governance issue in any publicly listed business.
What can be said with some confidence is that revenue assurance practitioners are able to provide a vast number of consistent anecdotes relating to the causes of leakage and means to resolve them. Though there is little objective evidence relating to actual leakages approaching this scale in the public domain as this information per se is highly confidential, there are some indirect measures of data integrity that help give a sense of potential leakage. For example, in reconciling interconnect costs and revenues between telcos, a 5% variance is the common practice to accept before a disputed invoice can lead one party to withhold payment, and a 0.5% variance would be considered industry-leading practice according to best practice advice issued by the UK Revenue Assurance Group.
Revenue Assurance in Telecom
Although Revenue Assurance has always been present in the telecom parlance[citation needed] it has recently been brought at the forefront of the top management[citation needed]. This is due to several factors including
  • Profit: Increasing cost pressures and decreasing margins. The high profit days for most telcos are over[citation needed]. They all need to find alternative means to squeeze higher margins by effectively tracking their revenue.
  • Regulatory: New regulatory structure and compliance requirements[citation needed] which force the telecom operators to report their revenue accurately.
  • Technology Innovation: Ensuring new technologies and products are performing as per perceived plans. Keeping up with release of new technologies along with co-existing of legacy systems.
  • Mergers and Acquisitions: With increase in the number of telco mergers and acquisitions, organizations are finding it very difficult handle multiple BSS systems including Billing, Mediation and Rating together etc.
Revenue Assurance has been a problem for the telecom companies since the very early stages. Tracking of pulses, minutes, counts, bytes etc. has never been more difficult. One would think these would be easy for the tech-savvy telco companies. However, the truth has been just the opposite. In a hurry to release new technologies in the market, the Revenue Assurance systems are always lagging behind. Revenue Assurance in a telco environment covers a wide range of technical and business aspects. An RA operator needs to be aware of both OSS & BSS processes and internal dependencies to accurately decipher the revenue code.
What causes the problem
A telecom organization's revenue chain is usually a very complex set of inter-related technologies and processes providing a seamless set of services to the end consumer. As the set of technologies and business processes grows bigger and more complex, the chance of failure increases in each of its connections. A revenue leakage is typically attributed to when a telco organization is unable to bill correctly for a given service or to receive the correct payment. As the organization grows the probability of revenue leakage only increases.
Where is the problem
The most debated part of revenue assurance is where to start checking, i.e. at the network side, the rating side, the billing side, the interconnect side, the CRM side, etc. However, most surveys and reports state that the maximum leakage happens during the flow of Call Detail Records (CDRs) or Event Detail Records (EDRs) from the Switch to the respective rating / billing engines. Some of the common problem areas are :
  • Network
  * Signaling problems
  * CDRs in Switch not sent to Mediation       
  * CDRs in Mediation not send downstream      
  * CDRs rejected by rating / billing system
  * Wrong duration on the CDRs
  * Incorrect Business rules
  * Subscriber provisioning
  * Incorrect Routing
  • Rating & Billing
  * Incorrect Rejection Logic
  * Duplicate CDRs resulting in double charging
  * Incorrect tariff plans
  * Rating & Billing accuracy errors
  * Late rating / billing
  * Incorrect configurations – rating minutes instead of seconds
  * Incorrect Disconnection
Revenue assurance discipline
Among the disciplines in revenue assurance are:
1. The CORE functions of a revenue assurance group: Monitoring, Baselining, Auditing, Synchronizing, Investigating and Compliance.
2. Decomposing an organization's revenue assurance scope (The Revenue Management Chain).
3. Assessing and minimizing revenue loss risk.
Source: Wikipedia.

Thursday, 17 April 2008

Neeraj Nathani RETAILING


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
US
$421,849
2
France
$121,519
3
UK
$94,244
4
Germany
$89,311
5
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.

Saturday, 5 April 2008

Neeraj Nathani smart Bridge PERFORMANCE BOOSTERS FOR ETL PROGRAMS:



PERFORMANCE BOOSTERS FOR ETL PROGRAMS:

Challenges
ETL processes can involve considerable complexity, and significant operational problems can occur with improperly designed ETL systems.
The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that will need to be managed by transform rules specifications. This will lead to an amendment of validation rules explicitly and implicitly implemented in the ETL process.
Data warehouses are typically assembled from a variety of data sources with different formats and purposes. As such, ETL is a key process to bring all the data together in a standard, homogeneous environment.
Design analysts should establish the scalability of an ETL system across the lifetime of its usage. This includes understanding the volumes of data that will have to be processed within service level agreements. The time available to extract from source systems may change, which may mean the same amount of data may have to be processed in less time. Some ETL systems have to scale to process terabytes of data to update data warehouses with tens of terabytes of data. Increasing volumes of data may require designs that can scale from daily batch to multiple-day microbatch to integration with message queues or real-time change-data capture for continuous transformation and update
Performance
ETL vendors benchmark their record-systems at multiple TB (terabytes) per hour (or ~1 GB per second) using powerful servers with multiple CPUs, multiple hard drives, multiple gigabit-network connections, and lots of memory. The fastest ETL record is currently held by Syncsort,[1] Vertica and HP at 5.4TB in under an hour which is more than twice as fast as the earlier record held by Microsoft and Unisys.
In real life, the slowest part of an ETL process usually occurs in the database load phase. Databases may perform slowly because they have to take care of concurrency, integrity maintenance, and indices. Thus, for better performance, it may make sense to employ:
  • Direct Path Extract method or bulk unload whenever is possible (instead of querying the database) to reduce the load on source system while getting high speed extract
  • most of the transformation processing outside of the database
  • bulk load operations whenever possible.
Still, even using bulk operations, database access is usually the bottleneck in the ETL process. Some common methods used to increase performance are:
  • Partition tables (and indices). Try to keep partitions similar in size (watch for null values which can skew the partitioning).
  • Do all validation in the ETL layer before the load. Disable integrity checking (disable constraint ...) in the target database tables during the load.
  • Disable triggers (disable trigger ...) in the target database tables during the load. Simulate their effect as a separate step.
  • Generate IDs in the ETL layer (not in the database).
  • Drop the indices (on a table or partition) before the load - and recreate them after the load (SQL: drop index ...; create index ...).
  • Use parallel bulk load when possible — works well when the table is partitioned or there are no indices. Note: attempt to do parallel loads into the same table (partition) usually causes locks — if not on the data rows, then on indices.
  • If a requirement exists to do insertions, updates, or deletions, find out which rows should be processed in which way in the ETL layer, and then process these three operations in the database separately. You often can do bulk load for inserts, but updates and deletes commonly go through an API (using SQL).
Whether to do certain operations in the database or outside may involve a trade-off. For example, removing duplicates using distinct may be slow in the database; thus, it makes sense to do it outside. On the other side, if using distinct will significantly (x100) decrease the number of rows to be extracted, then it makes sense to remove duplications as early as possible in the database before unloading data.
A common source of problems in ETL is a big number of dependencies among ETL jobs. For example, job "B" cannot start while job "A" is not finished. You can usually achieve better performance by visualizing all processes on a graph, and trying to reduce the graph making maximum use of parallelism, and making "chains" of consecutive processing as short as possible. Again, partitioning of big tables and of their indices can really help.
Another common issue occurs when the data is spread between several databases, and processing is done in those databases sequentially. Sometimes database replication may be involved as a method of copying data between databases - and this can significantly slow down the whole process. The common solution is to reduce the processing graph to only three layers:
  • Sources
  • Central ETL layer
  • Targets
This allows processing to take maximum advantage of parallel processing. For example, if you need to load data into two databases, you can run the loads in parallel (instead of loading into 1st - and then replicating into the 2nd).
Of course, sometimes processing must take place sequentially. For example, you usually need to get dimensional (reference) data before you can get and validate the rows for main "fact" tables.
Parallel processing
A recent[update] development in ETL software is the implementation of parallel processing. This has enabled a number of methods to improve overall performance of ETL processes when dealing with large volumes of data.
ETL applications implement three main types of parallelism:
  • Data: By splitting a single sequential file into smaller data files to provide parallel access.
  • Pipeline: Allowing the simultaneous running of several components on the same data stream. For example: looking up a value on record 1 at the same time as adding two fields on record 2.
  • Component: The simultaneous running of multiple processes on different data streams in the same job, for example, sorting one input file while removing duplicates on another file.
All three types of parallelism usually operate combined in a single job.
An additional difficulty comes with making sure that the data being uploaded is relatively consistent. Because multiple source databases may have different update cycles (some may be updated every few minutes, while others may take days or weeks), an ETL system may be required to hold back certain data until all sources are synchronized. Likewise, where a warehouse may have to be reconciled to the contents in a source system or with the general ledger, establishing synchronization and reconciliation points becomes necessary.

Source: Wikipedia.