MIS Project The Reporting tools, Decision Support Software tools and different types of Dashboards

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Contents

DECISION SUPPORT SYSTEM (DSS) ......................................................................... 3

Introduction ................................................................................................................................. 5

Theory Development ................................................................................................................... 6

THE STRUCTURE OF DSS ...................................................................................................... 7

KEY DSS FUNCTIONS ............................................................................................................. 9

Types of DSS ............................................................................................................................ 10

DSS UNCERTAINTIES AND LIMITATIONS....................................................................... 12

DASHBOARDS ...................................................................................................................... 14

Types of dashboards .................................................................................................................. 14

Interface design styles ............................................................................................................... 14

History ....................................................................................................................................... 15

Benefits of digital dashboards ................................................................................................... 15

Corporate dashboards ................................................................................................................ 16

Dashboard for CEO's ............................................................................................................. 16

Dashboard for CFO's ............................................................................................................. 17

Dashboard for IT ................................................................................................................... 17

Dashboard for sales and marketing ....................................................................................... 18

Reporting tools ........................................................................................................................ 19

Query plus Data Mining and Reporting .................................................................................... 19

LogiXML by LogiXML ............................................................................................................ 19

Custom CRM Reporting ........................................................................................................... 20

Benefits of custom CRM reporting: ...................................................................................... 20

CONTROL by KCI Computing ................................................................................................ 20

Discover Analytics by On Demand Analysis ............................................................................ 21

Bibliography ............................................................................................................................ 22

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Declaration

We here declaring that report on ―The Reporting tools,

Decision Support Software tools and different types of

Dashboards‖ are original work done by us, submitted in

partial fulfillment of our academic project in Management

Information System of 3rd Term.

This report is correct to the best of our knowledge and it

has not been published anywhere else.

Group num 9

Place: Bangalore Ram Mohan

Shubha M.G

Sourav Pramanik

Monika

Manish

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Acknowledgement

We acknowledge that we are thankful to our respected professor Prof. Anupama for guiding us to

complete the project with honest attention. Without her kind help, the project completion would

not have been possible.

Her presence had a great significance in encouraging us, our warm gratitude to her....

We are thankful to our team members who gave their support and commitment to make this

project successful. As a team, we had a pleasure to work together and share our ideas and efforts

to make the project a remembrance.

We are thankful to the other staff members to provide us the right direction to proceed.

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DECISION SUPPORT SYSTEM (DSS)

Decision support systems are a set of manual or computer-based tools that assist in some

decision-making activity. In today's business environment, however, decision support systems

(DSS) are commonly understood to be computerized management information systems designed

to help business owners, executives, and managers resolve complicated business problems and/or

questions. Good decision support systems can help business people perform a wide variety of

functions, including cash flow analysis, concept ranking, multistage fore-casting, product

performance improvement, and resource allocation analysis. Previously regarded as primarily a

tool for big companies, DSS has in recent years come to be recognized as a potentially valuable

tool for small business enterprises as well.

Introduction

Computerized decision support systems became practical with the development of

minicomputers, timeshare operating systems and distributed computing. The history of the

implementation of such systems begins in the mid-1960s. In a technology field as diverse as

DSS, chronicling history is neither neat nor linear. Different people perceive the field of

Decision Support Systems from various vantage points and report different accounts of what

happened and what was important. As technology evolved new computerized decision support

applications were developed and studied. Researchers used multiple frameworks to help build

and understand these systems. Today one can organize the history of DSS into the five broad

DSS categories explained in Power (2001; 2002; 2004b), including: communications-driven,

data-driven, document driven, knowledge-driven and model-driven decision support systems.

This hypertext document is a starting point in explaining the origins of the various technology

threads that are converging to provide integrated support for managers working alone, in teams

and in organization hierarchies to manage organizations and make more rational decisions.

History is both a guide to future activity in this field and a record of the ideas and actions of

those who have helped advance our thinking and practice. Historical facts can be sorted out and

better understood, but more information gathering is necessary. This web page is a starting point

in collecting more firsthand accounts and in building a more complete mosaic of what was

occurring in universities, software companies and in organizations to build and use DSS.

This document traces decision support applications and research studies related to model and

data-oriented systems, management expert systems, multidimensional data analysis, query and

reporting tools, online analytical processing (OLAP), Business Intelligence, group DSS,

conferencing and groupware, document management, spatial DSS and Executive Information

Systems as the technologies emerge, converge and diverge. All of these technologies have been

used to support decision making. The study of decision support systems is an applied discipline

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that uses knowledge and especially theory from other disciplines. For this reason, many DSS

research questions have been examined because they were of concern to people who were

building and using specific DSS. Hence much of the broad DSS knowledge base provides

generalizations and directions for building more effective DSS (cf., Baskerville & Myers, 2002;

Keen, 1980).

The next section describes the origins of the field of decision support systems. Section 3

discusses the decision support systems theory development that occurred in the late 1970s and

early 1980s. Section 4 discusses important developments to communications-driven, data-driven,

document driven, knowledge-driven and model-driven DSS (cf., Power, 2002). The final section

briefly discusses how DSS practice, research and technology is continuing to evolve.

Theory Development

In the mid- to late 1970s, both practice and theory issues related to DSS were discussed at

academic conferences including the American Institute for Decision Sciences meetings and the

ACM SIGBDP Conference on Decision Support Systems in San Jose, CA in January 1977 (the

proceeding were included in the journal Database). The first International Conference on

Decision Support Systems was held in Atlanta, Georgia in 1981. Academic conferences provided

forums for idea sharing, theory discussions and information exchange.

At about this same time, Keen and Scott Morton‘s DSS textbook (1978) provided the first broad

behavioral orientation to decision support system analysis, design, implementation, evaluation

and development. This influential text provided a framework for teaching DSS in business

schools. McCosh and Scott-Morton‘s (1978) DSS book was more influential in Europe.

In 1980, Steven Alter published his MIT doctoral dissertation results in an influential book.

Alters‘ research and papers (1975; 1977) expanded the framework for thinking about business

and management DSS. Also, his case studies provided a firm descriptive foundation of decision

support system examples. A number of other MIT dissertations completed in the late 1970s also

dealt with issues related to using models for decision support.

Alter concluded from his research (1980) that decision support systems could be categorized in

terms of the generic operations that can be performed by such systems. These generic operations

extend along a single dimension, ranging from extremely data-oriented to extremely model-

oriented. Alter conducted a field study of 56 DSS that he categorized into seven distinct types of

DSS. His seven types include:

1. File drawer systems that provide access to data items.

2. Data analysis systems that support the manipulation of data by computerized

tools tailored to a specific task and setting or by more general tools and operators.

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3. Analysis information systems that provide access to a series of decision-oriented

databases and small models.

4. Accounting and financial models that calculate the consequences of possible

actions.

5. Representational models that estimate the consequences of actions on the basis

of simulation models.

6. Optimization models that provide guidelines for action by generating an optimal

solution consistent with a series of constraints.

7. Suggestion models that perform the logical processing leading to a specific

suggested decision for a fairly structured or well-understood task.

THE STRUCTURE OF DSS

In order to discuss the support of decisions and what DSS tools can or should do, it is necessary

to have a perspective on the nature of the decision process and the various requirements of

supporting it. One way of looking at a decision is in terms of its key components. The first

component is the data collected by a decision maker to be used in making the decision. The

second component is the process selected by the decision maker to combine this data. Finally,

there is an evaluation or learning component that compares decisions and examines them to see

if there is a need to change either the data being used or the process that combines the data.

These components of a decision interact with the characteristics of the decision that is being

made.

STRUCTURED DECISIONS: Many analysts categorize decisions according to the

degree of structure involved in the decision-making activity. Business analysts describe a

structured decision as one in which all three components of a decision—the data, process, and

evaluation—are determined. Since structured decisions are made on a regular basis in business

environments, it makes sense to place a comparatively rigid framework around the decision and

the people making it.

Structured decision support systems may simply use a checklist or form to ensure that all

necessary data is collected and that the decision making process is not skewed by the absence of

necessary data. If the choice is also to support the procedural or process component of the

decision, then it is quite possible to develop a program either as part of the checklist or form. In

fact, it is also possible and desirable to develop computer programs that collect and combine the

data, thus giving the process a high degree of consistency or structure. When there is a desire to

make a decision more structured, the support system for that decision is designed to ensure

consistency. Many firms that hire individuals without a great deal of experience provide them

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with detailed guidelines on their decision making activities and support them by giving them

little flexibility. One interesting consequence of making a decision more structured is that the

liability for inappropriate decisions is shifted from individual decision makers to the larger

company or organization.

UNSTRUCTURED DECISIONS: At the other end of the continuum are unstructured

decisions. While these decisions have the same components as structured ones—data, process,

and evaluation—there is little agreement on their nature. With unstructured decisions, for

example, each decision maker may use different data and processes to reach a conclusion. In

addition, because of the nature of the decision there may only a limited number of people within

the organization that are even qualified to evaluate the decision.

Generally, unstructured decisions are made in instances in which all elements of the business

environment—customer expectations, competitor response, cost of securing raw materials, etc.—

are not completely understood (new product and marketing strategy decisions commonly fit into

this category). Unstructured decision systems typically focus on the individual or team that will

make the decision. These decision makers are usually entrusted with decisions that are

unstructured because of their experience or expertise, and therefore it is their individual ability

that is of value. One approach to support systems in this area is to construct a program that

simulates the process used by a particular individual. In essence, these systems—commonly

referred to as "expert systems"—prompt the user with a series of questions regarding a decision

situation. "Once the expert system has sufficient information about the decision scenario, it uses

an inference engine which draws upon a data base of expertise in this decision area to provide

the manager with the best possible alternative for the problem," explained Jatinder N.D. Gupta

and Thomas M. Harris in the Journal of Systems Management. "The purported advantage of this

decision aid is that it allows the manager the use of the collective knowledge of experts in this

decision realm. Some of the current DSS applications have included long-range and strategic

planning policy setting, new product planning, market planning, cash flow management,

operational planning and budgeting, and portfolio management."

Another approach is to monitor and document the process that was used so that the decision

maker(s) can readily review what has already been examined and concluded. An even more

novel approach used to support these decisions is to provide environments that are specially

designed to give these decision makers an atmosphere that is conducive to their particular tastes.

The key to support of unstructured decisions is to understand the role that individuals experience

or expertise plays in the decision and to allow for individual approaches.

SEMI-STRUCTURED DECISIONS: In the middle of the continuum are semi-

structured decisions, and this is where most of what are considered to be true decision support

systems are focused. Decisions of this type are characterized as having some agreement on the

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data, process, and/or evaluation to be used, but are also typified by efforts to retain some level of

human judgment in the decision making process. An initial step in analyzing which support

system is required is to understand where the limitations of the decision maker may be

manifested (i.e., the data acquisition portion, the process component, or the evaluation of

outcomes).

Grappling with the latter two types of decisions—unstructured and semi-structured—can be

particularly problematic for small businesses, which often have limited technological or work

force resources. As Gupta and Harris indicated, "many decision situations faced by executives in

small business are one-of-a-kind, one-shot occurrences requiring specifically tailored solution

approaches without the benefit of any previously available rules or procedures. This unstructured

or semi-structured nature of these decisions situations aggravates the problem of limited

resources and staff expertise available to a small business executive to analyze important

decisions appropriately. Faced with this difficulty, an executive in a small business must seek

tools and techniques that do not demand too much of his time and resources and are useful to

make his life easier." Subsequently, small businesses have increasingly turned to DSS to provide

them with assistance in business guidance and management.

KEY DSS FUNCTIONS

Gupta and Harris observed that DSS is predicated on the effective performance of three

functions: information management, data quantification, and model manipulation: "Information

management refers to the storage, retrieval, and reporting of information in a structured format

convenient to the user. Data quantification is the process by which large amounts of information

are condensed and analytically manipulated into a few core indicators that extract the essence of

data. Model manipulation refers to the construction and resolution of various scenarios to answer

'what if' questions. It includes the processes of model formulation, alternatives generation and

solution of the proposed models, often through the use of several operations

research/management science approaches."

Entrepreneurs and owners of established enterprises are urged to make certain that their business

needs a DSS before buying the various computer systems and software necessary to create one.

Some small businesses, of course, have no need of a DSS. The owner of a car washing

establishment, for instance, would be highly unlikely to make such an investment. But for those

business owners who are guiding a complex operation, a decision support system can be a

valuable tool. Another key consideration is whether the business's key personnel will ensure that

the necessary time and effort is spent to incorporate DSS into the establishment's operations.

After all, even the best decision support system is of little use if the business does not possess the

training and knowledge necessary to use it effectively. If, after careful study of questions of DSS

utility, the small business owner decides that DSS can help his or her company, the necessary

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investment can be made, and the key managers of the business can begin the process of

developing their own DSS applications using available spreadsheet software.

Types of DSS

Since the definition of Decision Support Systems can be stretched to include almost any

application that processes data there is some confusion as to exactly what constitutes a DSS. In

an effort to clarify the term, DS systems can be separated into seven broad categories, each

aiding decision making by different methods.

* Communications Driven DSS

A C-D DSS is a type of DSS that enhances decision-making by enabling communication and

sharing of information between groups of people. At its most basic level a C-D DSS could be a

simple threaded e-mail. At its most complexes it could be a web-conferencing application or

interactive video.

Communication-Driven DSS will exhibit at least one of the following characteristics:

1. Supports coordination and collaboration between two or more people;

2. Facilitates information sharing; Enables communication between groups of people;

3. Supports group decisions.

* Data-Driven DSS

Data-driven DSS are a form of support system that focuses on the provision of internal (and

sometimes external) data to aid decision making. Most often this will come in the form of a data

warehouse – a database designed to store data in such a way as to allow for its querying and

analysis by users.

Another example of a data-driven DSS would be a Geographic Information System (GIS),

which can be used to visually represent geographically dependant data using maps.

* Document-Driven DSS

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Document-driven DSS are support systems designed to convert documents into valuable

business data. While data-driven DSS rely on data that is already in a standardized format that

lends it to database storage and analysis, document-driven DSS makes use of data that cannot

easily be standardized and stored.

The three primary forms of data used in document driven DSS are:

1. Oral (i.e. transcribed conversations);

2. Written (i.e. reports, memos, e-mail and other correspondence);

3. Video (i.e. TV commercials and news reports).

None of these formats lend themselves easily to standardized database storage and analysis, so

managers require DSS tools to convert them into data that can be valuable in the decision making

process.

Document-driven DSS is the newest field of study in Decision Support Systems. Examples of

document-driven tools can be found in Internet search engines, designed to sift through vast

volumes of unsorted data through the use of keyword searches.

Knowledge-Driven DSS

Knowledge-driven DSS are systems designed to recommend actions to users. Typically,

knowledge-driven systems are designed to sift through large volumes of data, identify

hidden patterns in that data and present recommendations based on those patterns.

Model-Driven DSS

Model-driven support systems incorporate the ability to manipulate data to generate statistical

and financial reports, as well as simulation models, to aid decision-makers. Model-based

decision support systems can be extremely useful in forecasting the effects of changes in

business processes, as they can use past data to answer complex ‗what-if‘ questions for decision

makers.

In addition to these basic types of DSS there are also two additional factors: whether the DSS is

spreadsheet-based, web-based or something else entirely.

Spreadsheet-based DSS

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Model- and Data-driven DS systems can be built using spreadsheets. Spreadsheets offer

decision-makers easy to understand representations of large amounts of data. Additionally,

spreadsheet data is arranged in such a way as to make it easy to convert the data into

visualizations to further aid decision-makers.

Web-based DSS

Any type of DSS can be web-based. The term simply describes any decision support system that

is operated through the interface of a web browser, even if the data used for decision support

remains confined to a legacy system such as a data warehouse.In addition to these basic types of

Decision Support System there are also two separate categories used to define systems.

Enterprise-wide DSS

Enterprise-wide DS systems are systems that are linked into large data warehouses, and offer

decision support to managers at all levels of an enterprise. Enterprise-wide systems will typically

be basic, general use systems that can perform a wide variety of functions.

Desktop DSS

Desktop DS systems are much smaller applications designed to be run from a desktop PC. While

these systems may well be linked into a data warehouse or other large volume of data, they will

typically be more limited in scope. An example of a desktop DSS is Microsoft Excel, the desktop

spreadsheet application.

DSS UNCERTAINTIES AND LIMITATIONS

While decision support systems have been embraced by small business operators in a wide range

of industries in recent years, entrepreneurs, programmers, and business consultants all agree that

such systems are not perfect.

LEVEL OF "USER-FRIENDLINESS": Some observers contend that although

decision support systems have become much more user-friendly in recent years, it remains an

issue, especially for small business operations that do not have significant resources in terms of

technological knowledge.

HARD-TO-QUANTIFY FACTORS: Another limitation that decision makers confront

has to do with combining or processing the information that they obtain. In many cases these

limitations are due to the number of mathematical calculations required. For instance, a

manufacturer pondering the introduction of a new product cannot do so without first deciding on

a price for the product. In order to make this decision, the effect of different variables (including

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price) on demand for the product and the subsequent profit must be evaluated. The

manufacturer's perceptions of the demand for the product can be captured in a mathematical

formula that portrays the relationship between profit, price, and other variables considered

important. Once the relationships have been expressed, the decision maker may now want to

change the values for different variables and see what the effect on profits would be. The ability

to save mathematical relationships and then obtain results for different values is a feature of

many decision support systems. This is called "what-if" analysis, and today's spreadsheet

software packages are fully equipped to support this decision-making activity. Of course,

additional factors must be taken into consideration as well when making business decisions.

Hard-to-quantify factors such as future interest rates, new legislation, and hunches about product

shelf life may all be considered. So even though the calculations may indicate that a certain

demand for the product will be achieved at a certain price, the decision maker must use his or her

judgment in making the final decision.

If the decision maker simply follows the output of a process model, then the decision is being

moved toward the structured end of the continuum. In certain corporate environments, it may be

easier for the decision maker to follow the prescriptions of the DSS; users of support systems are

usually aware of the risks associated with certain choices. If decision makers feel that there is

more risk associated with exercising judgment and opposing the suggestion of the DSS than

there is in simply supporting the process, the DSS is moving the decision more toward the

structured end of the spectrum. Therefore, the way in which a DSS will be used must be

considered within the decision-making environment.

PROCESSING MODEL LIMITATIONS: Another problem with the use of support

systems that perform calculations is that the user/decision maker may not be fully aware of the

limitations or assumptions of the particular processing model. There may be instances in which

the decision maker has an idea of the knowledge that is desired, but not necessarily the best way

to get that knowledge. This problem may be seen in the use of statistical analysis to support a

decision. Most statistical packages provide a variety of tests and will perform them on whatever

data is presented, regardless of whether or not it is appropriate. This problem has been

recognized by designers of support systems and has resulted in the development of DSS that

support the choice of the type of analysis.

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DASHBOARDS

In management information systems, a dashboard is an executive information system user

interface that (similar to an automobile's dashboard) is designed to be easy to read. For example,

a product might obtain information from the local operating system in a computer, from one or

more applications that may be running, and from one or more remote sites on the Web and

present it as though it all came from the same source.

Types of dashboards

Dashboard of Sustainability screen shot illustrating example dashboard layout.

Digital dashboards may be laid out to track the flows inherent in the business processes that they

monitor. Graphically, users may see the high-level processes and then drill down into low level

data. This level of detail is often buried deep within the corporate enterprise and otherwise

unavailable to the senior executives.

Three main types of digital dashboard dominate the market today: stand alone software

applications, web-browser based applications, and desktop applications also known as desktop

widgets. The last are driven by a widget engine.

Specialized dashboards may track all corporate functions. Examples include human resources,

recruiting, sales, operations, security, information technology, project management, customer

relationship management and many more departmental dashboards.

Digital dashboard projects involve business units as the driver and the information technology

department as the enabler. The success of digital dashboard projects often depends on the metrics

that were chosen for monitoring. Key performance indicators, balanced scorecards, and sales

performance figures are some of the content appropriate on business dashboards.

Interface design styles

Like a car's dashboard (or control panel), a software dashboard provides decision makers with

the input necessary to "drive" the business. Thus, a graphical user interface may be designed to

display summaries, graphics (e.g., bar charts, pie charts, bullet graphs, "spark lines," etc.), and

gauges (with colors similar to traffic lights) in a portal-like framework to highlight important

information.

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History

The idea of digital dashboards followed the study of decision support systems in the 1970s. With

the surge of the web in the late 1990s, digital dashboards as we know them today began

appearing. Many systems were developed in-house by organizations to consolidate and display

data already being gathered in various information systems throughout the organization. Today,

digital dashboard technology is available "out-of-the-box" from many software providers. Some

companies however continue to do in-house development and maintenance of dashboard

applications. For example, GE Aviation has developed a proprietary software/portal called

"Digital Cockpit" to monitor the trends in aircraft spare parts business.

In the late 1990s, Microsoft promoted a concept known as the Digital Nervous System and

"digital dashboards" were described as being one leg of that concept.

Benefits of digital dashboards

Digital dashboards allow managers to monitor the contribution of the various departments in

their organization. To gauge exactly how well an organization is performing overall, digital

dashboards allow you to capture and report specific data points from each department within the

organization, thus providing a "snapshot" of performance.

Benefits of using digital dashboards include:

Visual presentation of performance measures

Ability to identify and correct negative trends

Measure efficiencies/inefficiencies

Ability to generate detailed reports showing new trends

Ability to make more informed decisions based on collected business intelligence

Align strategies and organizational goals

Save time over running multiple reports

Gain total visibility of all systems instantly

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Corporate dashboards

Dashboard for CEO's

Dashboard for CFO's

Dashboard for sales and marketing

Dashboard for IT

Dashboard for retail

Dashboard for CEO's

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Dashboard for CFO's

Dashboard for IT

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Dashboard for sales and marketing

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Reporting tools

Query plus Data Mining and Reporting

Query Plus by QualCorp is a state-of-the-art data extraction tool designed to give you instant

access to the critical information you need to make timely business decisions. With Query Plus

you are in the driver‘s seat.

Designed specifically for insurance agencies and built with the novice user in mind, Query Plus

is the key to the information locked away inside your software. Combine fields from any section

of your management software to create custom hybrid datasets never before possible and do it on

the fly. With Query Plus you have the ability set up your own data filters, define custom data

groups, decide the export file format, and much more—all quickly and easily without writing one

single line of code.

For those agencies with advanced reporting needs, there is a customized report designer shipped

along with Query Plus which allows you to create even the most complex reports using your

custom dataset. Though the preferred format for data processing may often be that of Excel

spreadsheets, Query Plus is also the tool of choice for those who want the most robust reporting

features available. By providing a friendly, intuitive, visually-based, wizard-driven report design

environment, Query Plus allows for possibilities that will stretch the imagination of even the

most advanced report users.

Query Plus represents the next generation in data extraction. Taking your input, QueryPlus

dynamically writes complex SQL queries behind the scenes and then displays the results in a

simple, familiar, and flexible grid format where data can be easily manipulated by simply

dragging and dropping. From customer ranking reports to complex accounting reports, Query

Plus allows you to get the critical business data you need to make timely decisions.

LogiXML by LogiXML

LogiXML is the smart choice for Business Intelligence, giving you more power and more choice

with less effort. It's simple to start, easy to use and our no user fees licensing model enables you

to empower your entire organization to get the most value from their data. Our solutions include

Logi Info for developers, Logi Ad Hoc for end users, and the Logi 9 Platform which includes

both plus data integration

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Custom CRM Reporting

Every business has unique processes, requirements, and information tracking. Configure custom

CRM reporting for your business metrics by creating your own reports or extending the pre-built

report templates included in Maximizer CRM with industry-leading Crystal Reports® and/or

Microsoft SQL Reporting Services (SRS)i.

Benefits of custom CRM reporting:

Create and edit your own custom CRM reports to gain further insight into customer

behaviours, new business opportunities and operational inefficiencies

Use the industry-leading Crystal Reports report designer for easy creation of custom

reports

Quickly and easily produce visual summary and drill-down reports with calculations and

conditional formatting

Automatically send updated reports directly to decision makers on a regular basisii

Uncover potential issues (recurrent service problems or poor sales performance), by

setting up automatic alerts when metrics meet certain criteria

Compare and analyze with data in other applications, such as accounting systems, to get a

complete view of relationship health and customer value

CONTROL by KCI Computing

Unified end-to-end solution for all aspects of performance management - budgeting, modeling,

forecasting, OLAP analysis, consolidations & reporting. Excel based front end; industry standard

relational database back-end; dynamic read-write to myriad data sources; drill-thru to transaction

detail and more

Key reporting features include:

―On-the-fly‖ multi-dimensional financial and operational report generation

User defined report formatting

Shareable re-usable formatting templates

Embedded hyperlinks in context

Granular security: Users only see data and reports they have been granted access to

Anyone who can see data can comment on it

Prototype Reports

Navigable reports

Snapshot ―point in time‖ VIEWS

Scheduled reports

Zero effort web publishing requiring only the user‘s internet browser for access

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Discover Analytics by On Demand Analysis

Discover Analytics is a powerful business intelligence and analytics reporting solution integrated

with the Microsoft BI platform. It can deliver your organizations business intelligence data into a

custom Microsoft Excel based application. Can connect to any data source or ERP system,

including SQL Server, DB2, Oracle, SAP and many others. Regardless of your database

structure, this tool can connect to your data. Free Demo with our business intelligence

experts.Customers demand reporting flexibility more than ever before. They aren‘t willing to

settle for static reports or take the time to learn new tools. Leap ahead of the competition by

offering rigorous analytics alongside easy-to-use visibility, data trends, charting, dashboards and

key performance indicators. Complement your software with great Business Intelligence and

watch new customer sales grow.

No code integration necessary

Requires no additional software (other than Excel)

Can be configured locally or in the ―cloud‖ (SaaS)

Rapid Implementation at low cost

One-stop access to user set up, customer deployment, version control and desktop install

ODA has built multiple, full-platform collaborative solutions to handle all aspects of

transactional and reporting systems, from web interfaces to cloud-based SaaS solutions. ODA

has delivered solutions to your industry, whether you‘re in oil, hi-tech, lodging, transportation,

manufacturing, distribution or retail.

Automate manual processes

Improve business transactional flow

Provide rich analytics on your data

Streamline workflows to save time and money

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Bibliography

http://www.business.com/directory/management/operations_management/software/report

ing_software/m/mis-reporting/

http://www.kcicorp.com/html/reporting.html

http://www.ondemandanalysis.com/why-choose.html

http://www.logixml.com/products/managed-reporting.html

http://www.maximizer.com/crm/business-intelligence.html