Finance

Finance is often defined simply as the management of money or “funds” management. Modern finance, however, is a family of business activity that includes the origination, marketing, and management of cash and money surrogates through a variety of capital accounts, instruments, and markets created for transacting and trading assets, liabilities, and risks. Finance is conceptualized, structured, and regulated by a complex system of power relations within political economies across state and global markets. Finance is both art (e.g. product development) and science (e.g. measurement), although these activities increasingly converge through the intense technical and institutional focus on measuring and hedging risk-return relationships that underlie shareholder value. Networks of financial businesses exist to create, negotiate, market, and trade in evermore-complex financial products and services for their own as well as their clients’ accounts. Financial performance measures assess the efficiency and profitability of investments, the safety of debtors’ claims against assets, and the likelihood that derivative instruments will protect investors against a variety of market risks.
The financial system consists of public and private interests and the markets that serve them. It provides capital from individual and institutional investors who transfer money directly and through intermediaries (e.g. banks, insurance companies, brokerage and fund management firms) to other individuals, firms, and governments that acquire resources and transact business. With the expectation of reaping profits, investors fund credit in the forms of (1) debt capital (e.g. corporate and government notes and bonds, mortgage securities and other credit instruments), (2) equity capital (e.g. listed and unlisted company shares), and (3) the derivative products of a wide variety of capital investments including debt and equity securities, property, commodities, and insurance products. Although closely related, the disciplines of economics and finance are distinctive. The “economy” is a social institution that organizes a society’s production, distribution, and consumption of goods and services,” all of which must be financed. Economists make a number of abstract assumptions for purposes of their analyses and predictions. They generally regard financial markets that function for the financial system as an efficient mechanism. In practice, however, emerging research is demonstrating that such assumptions are unreliable. Instead, financial markets are subject to human error and emotion. New research discloses the mischaracterization of investment safety and measures of financial products and markets so complex that their effects, especially under conditions of uncertainty, are impossible to predict. The study of finance is subsumed under economics as financial economics, but the scope, speed, power relations and practices of the financial system can uplift or cripple whole economies and the well-being of households, businesses and governing bodies within them—sometimes in a single day.
Three overarching divisions exist within the academic discipline of finance and its related practices: 1) personal finance: the finances of individuals and families concerning household income and expenses, credit and debt management, saving and investing, and income security in later life, 2) corporate finance: the finances of for-profit organizations including corporations, trusts, partnerships and other entities, and 3) public finance: the financial affairs of domestic and international governments and other public entities. Areas of study within (and the interactions among) these three levels affect all dimensions of social life: politics, taxes, art, religion, housing, health care, poverty and wealth, consumption, sports, transportation, labor force participation, media, and education. While each has a vast accumulated literature of its own, the effects of macro and micro level financing that mold and impact these and other domains of human and societal life typically have been treated by researchers as “policy,” “welfare,” “work,” “stratification,” and so forth, or have been largely unexplored. Recent research in "behavioral finance" is promising, albeit a relative newcomer, to the existing body of financial research that focuses primarily on measurement.
Loans have become increasingly packaged for resale, meaning that an investor buys the loan (debt) from a bank or directly from a corporation. Bonds are debt instruments sold to investors for organizations such as companies, governments or charities. The investor can then hold the debt and collect the interest or sell the debt on a secondary market. Banks are the main facilitators of funding through the provision of credit, although private equity, mutual funds, hedge funds, and other organizations have become important as they invest in various forms of debt. Financial assets, known as investments, are financially managed with careful attention to financial risk management to control financial risk. Financial instruments allow many forms of securitized assets to be traded on securities exchanges such as stock exchanges, including debt such as bonds as well as equity in publicly traded corporations.
Central banks, such as the Federal Reserve System banks in the United States and Bank of England in the United Kingdom, are strong players in public finance, acting as lenders of last resort as well as strong influences on monetary and credit conditions in the economy.

 Techniques and sectors of the financial industry



An entity whose income exceeds its expenditure can lend or invest the excess income. On the other hand, an entity whose income is less than its expenditure can raise capital by borrowing or selling equity claims, decreasing its expenses, or increasing its income. The lender can find a borrower, a financial intermediary such as a bank, or buy notes or bonds in the bond market. The lender receives interest, the borrower pays a higher interest than the lender receives, and the financial intermediary earns the difference for arranging the loan.
A bank aggregates the activities of many borrowers and lenders. A bank accepts deposits from lenders, on which it pays interest. The bank then lends these deposits to borrowers. Banks allow borrowers and lenders, of different sizes, to coordinate their activity.
Finance is used by individuals (personal finance), by governments (public finance), by businesses (corporate finance) and by a wide variety of other organizations, including schools and non-profit organizations. In general, the goals of each of the above activities are achieved through the use of appropriate financial instruments and methodologies, with consideration to their institutional setting.
Finance is one of the most important aspects of business management and includes decisions related to the use and acquisition of funds for the enterprise.
In corporate finance, a company's capital structure is the total mix of financing methods it uses to raise funds. One method is debt financing, which includes bank loans and bond sales. Another method is equity financing - the sale of stock by a company to investors, the original shareholders of a share. Ownership of a share gives the shareholder certain contractual rights and powers, which typically include the right to receive declared dividends and to vote the proxy on important matters (e.g., board elections). The owners of both bonds and stock, may be institutional investors - financial institutions such as investment banks and pension funds — or private individuals, called private investors or retail investors.

Personal finance

Questions in personal finance revolve around

• How can people protect themselves against unforeseen personal events, as well as those in the external economy?
• How can family assets best be transferred across generations (bequests and inheritance)?
• How does tax policy (tax subsidies or penalties) affect personal financial decisions?
• How does credit affect an individual's financial standing?
• How can one plan for a secure financial future in an environment of economic instability?

Personal financial decisions may involve paying for education, financing durable goods such as real estate and cars, buying insurance, e.g. health and property insurance, investing and saving for retirement.
Personal financial decisions may also involve paying for a loan, or debt obligations.

Corporate finance

Managerial or corporate finance is the task of providing the funds for a corporation's activities (for small business, this is referred to as SME finance). Corporate finance generally involves balancing risk and profitability, while attempting to maximize an entity's wealth and the value of its stock, and generically entails three interrelated decisions. In the first, "the investment decision", management must decide which "projects" (if any) to undertake. The discipline of capital budgeting is devoted to this question, and may employ standard business valuation techniques or even extend to real options valuation; see Financial modeling. The second, "the financing decision" relates to how these investments are to be funded: capital here is provided by shareholders, in the form of equity (privately or via an initial public offering), creditors, often in the form of bonds, and the firm's operations (cash flow). Short-term funding or working capital is mostly provided by banks extending a line of credit. The balance between these elements forms the company's capital structure. The third, "the dividend decision", requires management to determine whether any unappropriated profit is to be retained for future investment / operational requirements, or instead to be distributed to shareholders, and if so in what form. Short term financial management is often termed "working capital management", and relates to cash-, inventory- and debtors management. These areas often overlap with the firm's accounting function, however, financial accounting is more concerned with the reporting of historical financial information, while these financial decisions are directed toward the future of the firm.

Finance of public entities

Public finance describes finance as related to sovereign states and sub-national entities (states/provinces, counties, municipalities, etc.) and related public entities (e.g. school districts) or agencies. It is concerned with:

• Identification of required expenditure of a public sector entity
• Source(s) of that entity's revenue
• The budgeting process
• Debt issuance (municipal bonds) for public works project

Financial risk management

Financial risk management is the practice of creating and protecting economic value in a firm by using financial instruments to manage exposure to risk, particularly credit risk and market risk. (Other risk types include Foreign exchange, Shape, Volatility, Sector, Liquidity, Inflation risks, etc.) It focuses on when and how to hedge using financial instruments; in this sense it overlaps with financial engineering. Similar to general risk management, financial risk management requires identifying its sources, measuring it (see: Risk measure: Well known risk measures), and formulating plans to address these, and can be qualitative and quantitative. In the banking sector worldwide, the Basel Accords are generally adopted by internationally active banks for tracking, reporting and exposing operational, credit and market risks.

Financial economics

Financial economics is the branch of economics studying the interrelation of financial variables, such as prices, interest rates and shares, as opposed to those concerning the real economy. Financial economics concentrates on influences of real economic variables on financial ones, in contrast to pure finance. It centers on decision making under uncertainty in the context of the financial markets, and the resultant economic and financial models. It essentially explores how rational investors would apply decision theory to the problem of investment. Here, the twin assumptions of rationality and market efficiency lead to modern portfolio theory (the CAPM), and to the Black Scholes theory for option valuation; it further studies phenomena and models where these assumptions do not hold, or are extended. "Financial economics", at least formally, also considers investment under "certainty" (Fisher separation theorem, "theory of investment value", Modigliani-Miller theorem) and hence also contributes to corporate finance theory. Financial Econometrics is the branch of Financial Economics that uses econometric techniques to parametrize the relationships suggested.

Financial mathematics

Financial mathematics is a field of applied mathematics, concerned with financial markets. The subject has a close relationship with the discipline of financial economics, which is concerned with much of the underlying theory. Generally, mathematical finance will derive, and extend, the mathematical or numerical models suggested by financial economics. In terms of practice, mathematical finance also overlaps heavily with the field of computational finance (also known as financial engineering). Arguably, these are largely synonymous, although the latter focuses on application, while the former focuses on modeling and derivation (see: Quantitative analyst). The field is largely focused on the modelling of derivatives, although other important sub fields include insurance mathematics and quantitative portfolio problems. See Outline of finance: Mathematical tools; Outline of finance: Derivatives pricing.

Experimental finance 

Experimental finance aims to establish different market settings and environments to observe experimentally and provide a lens through which science can analyze agents' behavior and the resulting characteristics of trading flows, information diffusion and aggregation, price setting mechanisms, and returns processes. Researchers in experimental finance can study to what extent existing financial economics theory makes valid predictions, and attempt to discover new principles on which such theory can be extended. Research may proceed by conducting trading simulations or by establishing and studying the behavior of people in artificial competitive market-like settings. 

Behavioral Finance

Behavioral Finance studies how the psychology of investors or managers affects financial decisions and markets. Behavioral finance has grown over the last few decades to become central to finance.
Behavioral finance includes such topics as:

1. Empirical studies that demonstrate significant deviations from classical theories.
2. Models of how psychology affects trading and prices
3. Forecasting based on these methods.
4. Studies of experimental asset markets and use of models to forecast experiments.

A strand of behavioral finance has been dubbed Quantitative Behavioral Finance, which uses mathematical and statistical methodology to understand behavioral biases in conjunction with valuation. Some of this endeavor has been led by Gunduz Caginalp (Professor of Mathematics and Editor of Journal of Behavioral Finance during 2001-2004) and collaborators including Vernon Smith (2002 Nobel Laureate in Economics), David Porter, Don Balenovich, Vladimira Ilieva, Ahmet Duran). Studies by Jeff Madura, Ray Sturm and others have demonstrated significant behavioral effects in stocks and exchange traded funds. Among other topics, quantitative behavioral finance studies behavioral effects together with the non-classical assumption of the finiteness of assets.

Intangible asset finance

Intangible asset finance is the area of finance that deals with intangible assets such as patents, trademarks, goodwill, reputation, etc.

 

Business Intelligence

Business intelligence (BI) mainly refers to computer-based techniques used in identifying, extracting, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and predictive analytics.
Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS). Though the term business intelligence is sometimes used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. Business intelligence understood broadly can include the subset of competitive intelligence.
In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."
Business intelligence as it is understood today is said to have evolved from the decision support systems which began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.
In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems." It was not until the late 1990s that this usage was widespread.

Business intelligence and data warehousing

Often BI applications use data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse.
In order to distinguish between concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways:
Using a broad definition: "Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making." When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack.
Forrester defines the latter, narrower business intelligence market as "referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."

Business intelligence and business analytics

Thomas Davenport has argued that business intelligence should be divided into querying, reporting, OLAP, an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI based on statistics, prediction, and optimization.

Applications in an enterprise

Business intelligence can be applied to the following business purposes, in order to drive business value.

1. Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management).
2. Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, complex event processing.
3. Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.
4. Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.
5. Knowledge management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance/compliance.

In addition to above, business intelligence also can provide a pro-active approach, such as ALARM function to alert immediately to end-user. There are many types of alerts, for example if some business value exceeds the threshold value the color of that amount in the report will turn RED and the business analyst is alerted. Sometimes an alert mail will be sent to the user as well. This end to end process requires data governance, which should be handled by the expert

 

Prioritization of business intelligence projects

It is often difficult to provide a positive business case for business intelligence initiatives and often the projects will need to be prioritized through strategic initiatives. Here are some hints to increase the benefits for a BI project.

• As described by Kimball you must determine the tangible benefits such as eliminated cost of producing legacy reports.
• Enforce access to data for the entire organization. In this way even a small benefit, such as a few minutes saved, will make a difference when it is multiplied by the number of employees in the entire organization.
• As described by Ross, Weil & Roberson for Enterprise Architecture, consider letting the BI project be driven by other business initiatives with excellent business cases. To support this approach, the organization must have Enterprise Architects, which will be able to detect suitable business projects.

Success factors of implementation

Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:

1. The level of commitment and sponsorship of the project from senior management
2. The level of business need for creating a BI implementation
3. The amount and quality of business data available.

Business sponsorship

The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment. This is because having strong management backing will help overcome shortcomings elsewhere in the project. But as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”. It is very important that the management personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. It is imperative that there is support from multiple members of the management so the project will not fail if one person leaves the steering group. However, having many managers that work together on the project can also mean that the there are several different interests that attempt to pull the project in different directions. For instance if different departments want to put more emphasis on their usage of the implementation. This issue can be countered by an early and specific analysis of the different business areas that will benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground between them. Another management problem that should be encountered before start of implementation is if the Business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data will most likely add many months to the original plan, it is probably a good idea to make sure that the person from management is aware of his actions.

Implementation should be driven by clear business needs

Because of the close relationship with senior management, another critical thing that needs to be assessed before the project is implemented is whether or not there actually is a business need and whether there is a clear business benefit by doing the implementation. The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.

The amount and quality of the available data

This ought to be the most important factor, since without good data – it does not really matter how good your management sponsorship or your business-driven motivation is. If you do not have the data, or the data does not have sufficient quality, any BI implementation will fail. Before implementation it is a very good idea to do data profiling; this analysis will be able to describe the “content, consistency and structure of the data. This should be done as early as possible in the process and if the analysis shows that your data is lacking, it is a good idea to put the project on the shelf temporarily while the IT department figures out how to do proper data collection.

User aspect

Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization. If the of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to really use the system. If the system does not add value to the users´ mission, they will simply not use it.
In order to increase the user acceptance of a BI system, it may be advisable to consult the business users at an early stage of the DW/BI life cycle, for example at the requirements gathering phase. This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions.
When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.
Taking on a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.
Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball suggests implementing a function on the business intelligence portal website where reports on system usage can be found. By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more.
In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive. Watson describes how a large call center has implemented performance dashboards for all the call agents and that monthly incentive bonuses have been tied up to the performance metrics. Furthermore the agents can see how their own performance compares to the other team members. The implementation of this type of performance measurement and competition significantly improved the performance of the agents.
Other elements which may increase the success of BI can be by involving senior management in order to make BI a part of the organizational culture and also by providing the users with the necessary tools, training and support. By offering user training, more people may actually use the BI application.
Providing user support is necessary in order to maintain the BI system and assist users who run into problems. User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, help desk support can be used. The help desk can be manned by e.g. power users or the DW/BI project team.

Marketplace

There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and the consolidated "mega vendors" which have entered the market through a recent trend of acquisitions in the BI industry.
Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (full-service).

Industry-specific

Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations. The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing laws.

Semi-structured or unstructured data

Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news. According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once.
The management of semi-structured data is recognized as a major unsolved problem in the information technology industry. According to projections from Gartner (2003), white collar workers will spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making. Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project. This can ultimately lead to poorly-informed decision making.
Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data.

Unstructured data vs. semi-structured data

Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, it refers to data that cannot be stored in columns and rows. It must be stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems.
But many of these data types, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of meta data. Meta data can include information such as author and time of creation, and this can be stored in a relational database. Therefore it may be more accurate to talk about this as semi-structured documents or data, but no specific consensus seems to have been reached.

Problems with semi-structured or unstructured data

There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are:

1. Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats.
2. Terminology – Among researchers and analysts, there is a need to develop a standardized terminology.
3. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis.
4. Search ability of unstructured textual data – A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: “a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies.”

The use of metadata

To solve problems with search ability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata. Many systems already capture some metadata (e.g. file name, author, size, etc.), but more useful would be metadata about the actual content – e.g. summaries, topics, people or companies mentioned. Two technologies designed for generating metadata about content are automatic categorization and information extraction.

Future

A 2009 Gartner paper predicted these developments in the business intelligence market:

• Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.
• By 2012, business units will control at least 40 percent of the total budget for business intelligence.
• By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.

A 2009 Information Management special report predicted the top BI trends: "green computing, social networking, data visualization, mobile BI, predictive analytics, composite applications, cloud computing and multi touch."
Other business intelligence trends include the following:

• Third party SOA-BI products increasingly address ETL issues of volume and throughput.
• Cloud computing and Software-as-a-Service (SaaS) are ubiquitous.
• Companies embrace in-memory processing, 64-bit processing, and pre-packaged analytic BI applications.
• Operational applications have callable BI components, with improvements in response time, scaling, and concurrency.
• Near or real time BI analytic s is a baseline expectation.
• Open source BI software replaces vendor offerings.

Other lines of research include the combined study of business intelligence and uncertain data. In this context, the data used is not assumed to be precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by BI.
According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago – 15% in 2009 compared to 7% in 2008. An article by Info World Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.

Business Management information System

We will provide Business Management information System which is needed to manage organizations professionally and effectively. Management information systems engross three primary resources: people, technology, and information or decision making. Management information systems are distinct from other information systems in that they are used to examine operational activities in the organization.  Academically, the term is commonly used to refer to the group of information management methods attached to the automation or support of human decision making, e.g. decision support systems, expert systems, and executive information systems.

To begin within businesses and other organizations, internal reporting was produced manually and only occasionally, as a by-product of the accounting system and with some additional statistic(s), and gave limited and late information on management performance. Data was organized manually according to the requirements and necessity of the organization. As computational technology developed, information began to be illustrious from data and systems were developed to produce and organize abstractions, summaries, relationships and generalizations based on the data.
Early business computers were used for simple operations such as tracking sales or payroll data, with little detail or structure. Over time, these computer applications became more complex, hardware storage capacities grew, and technologies better for connecting previously isolated applications. As more and more data was stored and linked, managers sought greater detail as well as greater concept with the aim of creating entire management reports from the raw, stored data. The term "MIS" arose to describe such applications providing managers with information about sales, inventories, and other data that would help in managing the enterprise. Today, the term is used broadly in a number of contexts and includes (but is not limited to): decision support systems, resource and people management applications, enterprise resource planning (ERP), enterprise performance management (EPM), supply chain management (SCM), customer relationship management (CRM), project management and database retrieval applications.
The successful MIS supports a business's long range plans, providing reports based upon performance analysis in areas critical to those plans, with feedback loops that allow for titivation of every aspect of the enterprise, including recruitment and training regimens. MIS not only indicate how things are going, but why and where performance is failing to meet the plan. These reports include near-real-time performance of cost centers and projects with detail sufficient for individual accountability.

Types

Most management information systems specialize in particular commercial and industrial sectors, aspects of the enterprise, or management substructure.

• Management information systems (MIS), per se, produce fixed, regularly scheduled reports based on data extracted and summarized from the firm’s underlying transaction processing systems^[4] to middle and operational level managers to identify and inform structured and semi-structured decision problems.
• Decision support systems (DSS) are computer program applications used by middle management to compile information from a wide range of sources to support problem solving and decision making.
• Executive information systems (EIS) is a reporting tool that provides quick access to summarized reports coming from all company levels and departments such as accounting, human resources and operations.
• Marketing information systems are MIS designed specifically for managing the marketing aspects of the business.
• Office automation systems (OAS) support communication and productivity in the enterprise by automating work flow and eliminating bottlenecks. OAS may be implemented at any and all levels of management.
• School management information systems (MIS) cover school administration, often including teaching and learning materials. 

Advantages

The following are some of the benefits that can be attained for different types of management information systems

• Companies are able to highlight their strengths and weaknesses due to the presence of revenue reports, employees' performance record etc. The identification of these aspects can help the company improve their business processes and operations.
• Giving an overall picture of the company and acting as a communication and planning tool.
• The availability of the customer data and feedback can help the company to align their business processes according to the needs of the customers. The effective management of customer data can help the company to perform direct marketing and promotion activities.
• Information is considered to be an important asset for any company in the modern competitive world. The consumer buying trends and behaviors can be predicted by the analysis of sales and revenue reports from each operating region of the company..

Enterprise applications

• Enterprise systems, also known as enterprise resource planning (ERP) systems provide an organization with integrated software modules and a unified database which enable efficient planning, managing, and controlling of all core business processes across multiple locations. Modules of ERP systems may include finance, accounting, marketing, human resources, production, inventory management and distribution.

• Supply chain management (SCM) systems enable more efficient management of the supply chain by integrating the links in a supply chain. This may include suppliers, manufacturer, wholesalers, retailers and final customers.

• Customer relationship management (CRM) systems help businesses manage relationships with potential and current customers and business partners across marketing, sales, and service.

• Knowledge management system (KMS) helps organizations facilitate the collection, recording, organization, retrieval, and dissemination of knowledge. This may include documents, accounting records, and unrecorded procedures, practices and skills.

Developing Information Systems

The actions that are taken to create an information system that solves an organizational problem are called system development. These include system analysis, system design, programming/implementation, testing, conversion, production and finally maintenance. These actions usually take place in that specified order but some may need to repeat or be accomplished concurrently.

Conversion is the process of changing or converting the old system into the new. This can be done in four ways:

• Direct cut-over – The new system replaces the old at an appointed time.
• Pilot study – Introducing the new system to a small portion of the operation to see how it fares. If good then the new system expands to the rest of the company.
• Phased approach – New system is introduced in stages.