Business Intelligence Tutorial

LEAN Business Intelligence

2010-12-08T02:31:00.000-08:00

The need for efficiency

I used to work for the large Danish companies FDB (now COOP) and BG Bank (now Danske Bank) implementing their first datawarehouse and BI systems. Both as a developer and as project manager. I was astonished by the difference in approach to implementing datawarehouse and business intelligence systems (and probably IT systems in general). At FDB we had a scarcity of ressources but accomplished a lot due to a "just do it" culture in our department plus a choice of technologies that made it fairly simple to setup load jobs, create reports, handle large volumes of data and so on. With less than a handful of developers - in one period I even worked the project alone - we succeeded to get a full datawarehouse/BI implementation rolling with data from all +1,000 supermarkets and a comprehensive end-user environment.

At BG Bank by contrast we ended up more than 25 persons on the project and brought out only a draft datawarehouse with no end-user tools in a comparable period of time. Data volumes were somewhat larger but still.

This really got me thinking what a difference our choice of tools and methodology makes. In this comparison I would say at least a factor of 10. If we want this world to grow richer and better we must constantly find ways to do things more efficiently. If the same goal can be achieved with less effort then we are committed to aim for it in order to free up our ressources for better purposes - time for our children for example.

Since back then it has been my primary commitment to develop technologies and methods aimed at making implementation of business intelligence systems that:

require minimum technical skills to work with
are as fast and effortless as possible to implement and maintain
provide the primary benefits of modern BI systems

In my work I have uncovered a range of technologies and methods that will help to achieve these goals. These technologies and methods will in turn allow more people and more companies to exploit the benefits of business intelligence than do traditional BI tools and methods.

Definition of LEAN BI

The main obstacles to efficiency and simplicity in business intelligence systems are:

Datawarehouse, cubes, in-memory storage and other types of systems where data is copied from the operational system(s) to a separate data structure. Some vendors have other names for their offerings. We shall refer to all such structures under the common term Redundant Data Storage (RDS)
The ETL processes required for building a RDS
Complexity of modeling, ETL and end-user reporting tools
Complexity of the source (ERP) systems
Long development cycles
Organizational and motivational issues

I shall restrict myself from dealing with organizational and motivational issues in this book, which has a technical perspective.

Let's have a look at these challenges from the perspective of a smaller company.
What I have found is that most small organizations have:

All their primary data in one system, typically an ERP system
Limited volumes of data
Near-standard ERP systems
Knowledge about the inner workings of their ERP system and SQL databases either in-house or through one of their primary service providers

Thus, in smaller organizations we don’t have the below reasons for creating an RDS:

Integration of data from disparate sources
Handling of large data volumes

It is therefore obvious to aim for elimination of the RDS and ETL processes, which account for typically 75% of the total costs.

We still have the problems with handling operational data schemas however, so in order to be able to eliminate the RDS we need new technologies that allow us to handle the operational data schemes inherent to operational systems.

The LEAN BI concept can be defined as these main areas:

Use as few infrastructure components as possible
Consolidation of end-users tools into as few products as possible
Make development cycles as short as possible. Particularly the first cycle.
Use of standard technologies

Sometimes the above requirements conflict because we are not living in an ideal world. Thus, striking the right balance between the above requirements is one of the most critical planning tasks in BI system development.

In a minimal and thus optimal LEAN scheme the BI system must:

Have no middleware. I.e. it must handle operational data directly from the source systems
Be based on one end-user tool
Be implemented in a first prototype in one day

Is this possible?

Absolutely. Read on.

The case for LEAN BI

Business Intelligence is an extremely useful (read: profitable) discipline for most types of organizations but the costs and complexity of building BI systems is often preventive for smaller organizations.

The main obstacles are:

Cost of implementation
Skill requirements, both technical and business skills
Infrastructure complexity
Cost of tools

The most important factor that contributes to these obstacles is the fact that common technologies and methodologies are aimed at large and complex organizations. Thus, when a vendor glorifies their products with superlatives like scalable then it usually means that its cost of implementation will be preventive in a small organization; it is meant for large organizations. I.e. scalable usually means upwards scalable, not downwards.

LEAN BI provides a flexible framework that allows a BI system to be built to match your organization’s exact needs – not more, not less. If your source systems are simple then so should the BI system be. If you have a well-known source system then you should not have to re-invent the wheel for building a BI system. If your data volumes do not necessitate data replication then you should not have to replicate data. You get the idea.

See also:
What is LEAN?

Next post: Comparison of BI architectures

3.4 Definition of Business Intelligence Systems

2010-11-27T06:05:00.000-08:00

This book is about designing and implementing BI systems. But how exactly do we define a BI system?

Most common definitions of business intelligence are quite broad, such as: "The use of data to increase profits and competitiveness".

With this definition you may wonder "is a calculator a BI system"?

We need to restrict ourselves to discussing systems, which aim to make business intelligence processes more efficient by using an architecture and providing functionality that ensures attainment of the previously described benefits of business intelligence.

By contrast we shall use the term business intelligence to describe collection and utilization of data for improving business performance in a broad sense. I.e. it is both the methods, processes and technologies.

Let us define more accurately what we mean by a Business Intelligence System.

The minimal requirements adhere closely to the most important benefits of business intelligence described in the previous chapter.

Let's quickly revise the benefits:

Reduce labor costs
Reduce information bottlenecks
Make data actionable
Better decisions
Faster decisions
Align the organizations towards its business objectives
New insights

To ensure that these goals are met we shall define the following minimal requirements for a Business Intelligence System as follows:

Subject-oriented
Unified, centrally managed subject definitions and targets
System guided data interaction and exploration
Automated data collection and distribution
System supported data documentation and validation

These requirements are explained in more detail below.

3.4.2 Subject-oriented

All data in the BI system must be interfaced using natural terms corresponding to the organization's business' reality. For example, users of the BI system must be able to access data in the BI system using natural terms such as "Customer" and "Sales amount" rather than for example table and field names in the database.

3.4.3 Unified, centrally managed subject definitions and targets

All definitions of business terms and KPIs must exist in one version only and they must be managed from a central point to avoid redundant definitions, reports referring to outdated definitions etc. This requirement implies that application of data and definition of data must be separated by the system into a so-called semantic layer.

3.4.4 System guided data interaction and exploration

Interaction with data and data exploration are two vital features of a BI system that allow users to answer questions fast and autonomously. Many tools offer ways to manipulate data, but it is important to notice the term guided interaction/exploration. A system can only guide the user if it has some knowledge about the data. As an example of what is not meant by guided, consider a query designer: It lets the user draw relations between tables and fields in a database in order to manipulate the output. Since it is up to the user to understand the meaning of tables of fields to obtain the correct results, we do not consider this to be guided interaction/exploration, even if the query designer is implemented using a so-called Guide or Wizard.

As mentioned above, the system must have an understanding of data to truly guide the user. Such an understanding can be implemented using a data model. The two pre-dominant data model frameworks are:

Relational datamodels
Multi-dimensional datamodels

In today's world, relational datamodels are a de facto standard for modelling operational system databases whereas multi-dimensional datamodels have become a de facto standard for analytical reporting systems.

The strength of a relation datamodel is that it allows definition of almost any relation between physical data whereas a multi-dimensional datamodel (MDD) is more limited.

The strength of a MDD is that it is more explicit (less abstract) about the relations it represents, which makes it easier to learn by most people and it also helps systems in guiding users through data interaction and exploration more naturally.

Throughout this book we shall limit ourselves to discussing MDDs as the foundation that enables system guided data interaction and exploration.

3.4.5 Automated data collection and distribution

In order to achieve the benefits of reduced labour costs and faster decisions, all data collection must be automated. Please notice, that if the BI system extracts its data directly from the operational systems then the requirement of automated data collection is implicitly met.

Data collection includes any pre-processing of data that is required for generating the reports. If all processing is performed on the fly when reports are opened, and if this processing does not make report generation slow, then this requirement is implicitly met.
Automatic data distribution refers to reports and other outputs that are generated autonomously by the BI system based on schedules and events and then propagated directly to relevant recipients.

3.4.6 System supported data documentation and validation

Would you use a report if you didn't trust or understand the data it shows?
Stupid question - of course not.

But nevertheless, most business users are fed with reports with no documentation. When data is presented undocumented it is the root of many problems:

The users don't understand the contents of the report and as a result they don't use it
Users think they understand the contents and use the report. But they make the wrong decisions from time to time because the data is not what they think it is.
When data looks wrong, one needs to investigate and validate it. But if there is no documentation of how and when the data was collected and aggregated then it can be impossible for the user to validate the results. As a result the user will resort to other sources of data.

From the above we see that if users do not trust or understand the data they see in reports, then it is impossible to enforce the requirement that subject definitions must be unified and centrally managed. The BI system can only centrally manage data definitions if users actually use the BI system. And if users don't understand the system's data definitions then one can hardly claim that the definitions are "managed".

As a minimum, the BI system must support documentation and validation these ways:

Allow and encourange system designers to write good, readable descriptions on all system objects.
Give users easy access to object descriptions, e.g. directly inside the reports they view
Allow users to perform so-called "drill-through" operations on data to see the source transactions. If the reports use replicated data (e.g. a datawarehouse, cube etc.) then the drill-through operation must also show the user when the displayed source transactions were replicated from the source system(s).

Some important points need to be made here in order to fully understand the implications of the above requirements. If documentation must be provided on every business term in the BI system, then it must be a requirement that those terms are actually implemented and interpreted as objects by the BI system. This is an important aspect because many reports are programmed, i.e. they are generated using SQL, JavaScript, C++, Visual Basic, Pascal - or more commonly, some vendor-specific programming language inside the ERP system. Converting program code to user documentation is difficult and the result is not good. By contrast, a model is usually composed of objects that can be easily described individually by system designers, and since the logic is based on a model it is also far easier for the system to convert object definitions into human language descriptions.

Because a modelled reporting approach has the above important and distinct advantages over a programmed reporting approach, we shall require a BISS system to be model-based.

The BI model must contain these parts:

Datamodel. A set of subject definitions and interrelations that describe the name, purpose, construction and inter-relation of all relevant business data terms. E.g. "Customer", "Item group", "Sales Amount" etc. This part corresponds to a MDD and its components, Dimensions and Metrics.
Rule model. A set of rules that describe thresholds of KPIs, potential business actions and business events. For example a rule can describe under which threshold a KPI - say Profit Margin Percent - is not acceptable and what the user could/should do about it. Rules can be associated with highlighting information such as colors and icons plus a descriptive text that is displayed in the viewing context, i.e. inside reports.
Process model. The process model relates reports to users and reports with other reports. The process model ensures relevance by giving each user access to exactly the data that user needs in any context, not less and not more. For example, the process model describes which report/dashboard must be opened automatically when the user connects to the system. And it describes how data in one report can be linked to other reports.

3.4.7 Conclusion

In this chapter we have made a distinction between business intelligence as an overall concept of business processes and business intelligence systems as computer software applications that support the processes of business intelligence.

LEAN Business Intelligence >>

3.3 What are the benefits of business intelligence?

2010-07-10T07:08:00.000-07:00

You may be asking yourself: Should we implement a BI system?

Good question.

Although an increasingly popular and accepted technology for improving business performance, BI - like any other system - must be carefully considered before deciding to implement it. Benefits must be clear and understood and they must justify the investments.

So let's elaborate on the exact benefits of implementing a BI system in your organization.

First, a bit of introduction to my perspective on this subject. If you ask companies which benefits they want from their BI systems you will get one answer. If you ask a BI expert you will get another. In my experience, most companies only grasp the benefits in tiny pieces and they either can't express the benefits they are getting or they are not aware that they can get them. Oftentimes both.

For the purpose of giving the most complete picture of Business Intelligence benefits I have chosen to describe them from an expert point of view. I.e. from the perspective of a professional who has been working with multiple BI systems at multiple organizations for the past 15 years.

3.3.1 Reduced labor costs

The most tangible benefit of BI is the time and effort saved with manually producing the standard reports for the organization. It is rarely the largest benefit though. However, because it is so tangible it is often part of the equation when a decision must be made about implementing BI, and if it turns out that these savings alone can justify the BI system, then it is the easiest way to justify it.

BI systems reduce labor costs for generating reports by:

automating data collection and aggregation
automating report generation
providing report design tools that make programming of new reports much simpler
reducing training needed for developing and maintaining reports

3.3.2 Reduce information bottlenecks

The BI system allows end-users to extract reports when they need them rather than depending on people in the IT or financial department to prepare them. The BI system will even allow authorized users to design new reports to match their requirements.

BI systems reduce information bottlenecks by:

providing individualized, role-based dashboards that collect the most important data for daily operations
letting the user open and run reports autonomously
providing documentation of KPIs and other information
allowing users to analyze and validate the data without involving IT specialists
allowing users to create new views of data as needed

3.3.3 Make data actionable

What happens when employees in an organization get too much data, too little data, too old data, too detailed data or just irrelevant data?

Nothing.

Nothing happens. Everybody is just wasting time and resources.

Most organizations use extensive amounts of resources putting together piles of standard reports that are delegated throughout the company. To make sure everyone has every information they need, all kinds of reports are sent to employees - usually on a very detailed level. As a result employees feel overwhelmed by the amounts of information that don't give a clear picture of the overall situation. And moreover, since so much effort is required to assemble the reports they usually arrive at the employees' desktop days or weeks after they were most relevant.

All put together this means that the potential corrective and opportunistic actions that these data could have led to, are missed due to either being too late or because the employees overlooked or were out of time to find the relevant trends in the myriads of information.

When employees try to find head and tail of the data they even often find that the numbers are not comparable between different reports and end up analyzing the differences instead of interpreting the actual numbers. And since trust in data is lost, nobody dares to make a decision based on the numbers.

But worse yet: Many employees don't have the training and knowledge to interpret the numbers in order to identify threats and opportunities.

BI systems make information actionable by:

providing information through unified views of data where KPIs are assembled and calculated using a central repository of definitions - a data model - to prevent conflicting definitions and incomparable report data
providing to-the-minute information in real-time reports that show the state of the business in this very moment - not a historical view of how it looked days or weeks ago
allowing users to search and design reports autonomously instead of being dependent on specialists in the IT department
showing data in a context, e.g. by benchmarking KPI values against comparable values (e.g. averages, budgets/target and last period) to let the user interpret whether the KPI value is acceptable or needs corrective action
using rules to highlight KPI thresholds as "good" or "bad"
providing integrated documentation to help the user understand the meaning and definition of the KPIs
providing links back to the operational systems that make it easy for the user to carry out corrective actions (closed loop)
making data collaborative, e.g. let the user forward and share selected data with other users and assign targets and responsible persons to KPIs
only showing data relevant to the specific user in a role-based environment to avoid "Information overload"
showing data on a high, aggregated level where overall trends can be easily spotted and then let the user drill-down to detail data in a top-down manner
using intuitive visualizations that enhance on the nature of the data such as graphs/charts and gauges
forwarding relevant information based on the occurence of predefined events, i.e. only sending certain reports when specific business events occur, such as too high stock levels, customer churn etc.
shortening the analyze-decision loop to avoid losing the train of thought

3.3.4 Better decisions

Decisions need to be made every day and, as we all know, decisions have varying quality. Good decisions can provide tremendous benefits. Bad decisions provide no benefits - they may even cause you losses.

BI systems help make better decisions by:

providing decision makers with rich, exact and up-to-date information
letting users dive into data for further investigation

In this context the term decision maker needs to be seen in a broad perspective; it is not only management that makes decisions. In fact, the decisions that affect an organization the most are those made by people all over the organization, from the sales person who decides to give a customer a discount to the procurement assistant who decides to buy certain products for inventory.

3.3.5 Faster decisions

A decision can be made the moment you have all the relevant information at your hands. In other words, the faster the relevant information gets into your hands the faster you can make a decision. Fast decisions are important for two reasons:

It makes the organization more responsive to threats and opportunities
It shortens the time between thought and action. Most people will lose their train of thought if they need to wait a long time for further information about the problem they are dealing with.

BI systems enable fast decisions by:

combining multiple data sources in common reports, thus saving the user from manually combining data in spreadsheets etc.
providing analytical and ad-hoc reporting capabilities that allow users to quickly retrieve new or different combinations of data as needed instead of having to request new reports in the IT or financial departments.
providing reduced system response times by using pre-aggregated data or other techniques for fast data aggregation

3.3.6 Align the organization towards its business objectives

The most successful organizations are those that succeed to make every person in the organization work towards a common goal.

BI systems help organizations align all parts of the organization towards common business objectives by:

centralizing KPI definitions. BI reports don't calculate KPIs using autonomous queries and scripts. They retrieve KPI values and definitions through a central repository and thus prevent conflicting KPI definitions and values
guiding information presentation using advanced visualisations, benchmarks and KPI thresholds thus ensuring a common interpretation of the KPIs
providing a single source of information. All reports collect their data from one source - the BI system.
"pushing" selected information throughout the organization. By enabling organizations to push KPIs and other information to the end-users the BI system helps focus employees' attention on the most critical success factors.
assigning targets to KPI values for each organizational unit to be used for measuring the ability to achieve the goals set forth and thus pushing the organization towards the defined goals

These uses of BI systems are sometimes referred to as Performance Management.

A particularly well-known scheme for implementing strategy is The Balanced Scorecard developed by Robert Kaplan and David Norton.

3.3.7 New insights

Traditional reporting systems aim to give users data according to a fixed and predefined structure. This rigid approach gives the organization answers to exactly the questions it is able to specify in advance. And no more. Modern business intelligence systems on the other hand provide ad hoc query capabilities that allow users to poke randomly around in data to get answers to any question that comes to their mind. This allows users to strengthen there understanding of the underlying patterns of the business and thus to gain new insights into the dynamics that lead to success or failure.

Such analysis is often referred to as OLAP: Online Analytical Processing.

http://www.allbusiness.com/company-activities-management/sales-selling-sales/11496096-1.html

In another application, some BI systems provide special mathematical algorithms for finding hitherto unknown patterns in data - so-called data mining. Such algorithtms comprise Cluster Analysis, Decision Trees, Neural Networks and Rule Induction.

Data mining algorithms are advanced statistical methods that attempt to uncover patterns automatically and thus help the organization answer questions such as "which variable is most important in determining customer churn?" and help discover rules such as "92% of all Bear Peanutbutter is sold with Forest Bread". Most Data mining techniques require a deep understanding of mathematics to make their results actionable and they require large amounts of data in order to be statistically significant. Thus, data mining is not for everyone.

Data mining techniques

Conclusion

The benefits of implementing business intelligence are numerous and solid. Even if most of the benefits must be categorized as soft benefits it is evident that few other systems offer the unique strategic benefits that business intelligence does.

Related information and links:

Our definition of business intelligence >>

3.2 Who needs business intelligence?

2010-06-29T03:59:00.000-07:00

Any organization, that needs to get into a better competitive position and needs to share and analyze business information between multiple interested parties, needs BI.

But of course some need it more than others and the factors that drive organizations into actual business intelligence efforts are usually those listed below. The factors can be categorized into external and internal factors. External factors are forces and circumstances outside the organization’s control, e.g. competitors, customers, environment and government. Internal factors are circumstances and processes that are under the organization’s control such as procurement, logistics, sales, employees etc.

3.2.1 External factors driving organizations towards BI

Competitive pressure. Some companies are under great competitive pressure. This applies especially to products and services that have high volume and low barriers for replacement. Tele-companies are known for massive campaigns that aim to grab customers from competitors and in most countries it is fairly easy to change operator.
Market volatility. In markets with quickly changing customer demands and/or quickly changing goods (e.g. computers and accessories) it can be a question of life or dead to be ahead of the situation and always have the right products in the right quantities at the right prices. And these factors may virtually change overnight in some businesses.
Legal requirements. Some companies are under restrictions by law to be able to deliver certain sorts of reports to external entities. Especially well-known is the Sarbanes-Oxley regulations that governs large companies in USA and Basle 2, which applies to the European banking sector. Such regulations force companies to have well-defined report delivery systems.

3.2.2 Internal factors driving organizations towards BI

Volumes of data. The more data an organization collects the more likely it is to establish formalized systems for extracting this data and utilizing it because the sheer volume of data makes it difficult to keep a detailed understanding of the dynamics of the organization’s processes. Tools are needed to quickly aggregate, dive into and analyze data to keep an overview of the business situation.
Single version of the truth. Too often, different report that supposedly show the same information show different results. More often than not this is caused by different definitions of the KPIs. As a result, time is wasted discussing and analyzing these differences instead of building the business. And trust in the data evaporates.
Information overload. Perhaps surprisingly, organizations aim for BI systems because they have too many reports. But it is no surprise actually. BI tools let organizations reduce the number of reports by:

Combining multiple information types in single views (dashboards)
Show data on a higher (more aggregated) level while giving users access to detail data through analytic capabilities such as drill-down. Thus, users need only deal with detail data when it is relevant.

Size of the organization. The larger the organization is the more people will need reports about the organization's operations. Larger companies can usually better justify the investment in a BI system with the time saved for creating reports and improving information quality.
Skilled personnel. The internal availability of skilled personnel has a big influence on whether an organization decides to implement BI systems and how much they invest in it. An organization that has people with the technical skills to build a BI system and people who have experience defining, deploying and using it is much more likely to go this way than an organization that does not hold these competences.
Culture. Some companies have a culture to use data pro-actively and to make detailed measurements of everything they do whereas others are not comfortable with the openness about data that is required to get the full benefits of BI. Organization culture may thus be an enabler or barrier to successful implementation of BI. One may say that being “result oriented” goes hand in hand with using BI.
Shrinking response time. The faster an organization can derive insights and take proper action from data, the more competitive it will be. Managers are in a constant need to get to up-to-the-minute information as fast as possible thus driving the implementation of systems that can support such requirements.

Links

What are the benefits of business intelligence? >>

3.1 Why is business intelligence needed?

2010-06-24T17:08:00.000-07:00

BI is about analyzing an organization’s performance in order to improve its income and competitiveness. Thus, in a sense BI is something organizations have always been involved with BI on some level. With today’s modern IT systems however, organizations are collecting ever increasing volumes of data and it is no longer possible to analyze such data without specialized tools and methods. The data will not fit inside a person’s head.

Business Intelligence tools let users sip efficiently through huge amounts in a matter of seconds. Such impressive response times are not simply given by modern hardware and software. Special architectures and methods for building such systems are required.

Links:

Who needs business intelligence? >>

2. Preface

2010-06-22T09:37:00.000-07:00

2.1 Learn business intelligence

Business intelligence is a growing area of business optimization focused on utilization of an organization's data. Business intelligence projects have consistently proved to increase profits and competitiveness of the organizations that implement it.

The understanding, methodology and available technologies for developing business intelligence systems have reached a level of maturity where even smaller organizations can reap the benefits. In fact, with recent evolution in hardware and software, smaller organizations even have distinct advantages over larger organizations that allow them to develop BI systems faster, with less costs and with significant functional advantages.

This is the right time to learn business intelligence and with this book the subject is as easily accessible as ever before.

2.2 About the author

This book is written by Nikolaj Henrichsen, co-founder of Acinta Intelligence Technology and inventor of Acinta’s business intelligence products. Before starting Acinta Intelligence Technology, Nikolaj built a solid experience within business intelligence in positions as developer, architect and project manager respectively of data warehouse initiatives at large Danish companies, FDB (now Coop Danmark) and BG Bank (later merged with Danske Bank).

At Acinta Intelligence Technology Nikolaj Henrichsen has designed and implemented more than 40 business intelligence systems at various small and medium sized organizations and has achieved a unique experience and insight into the characteristics of business intelligence solutions in smaller organizations.

With this book Nikolaj shares with us the knowledge, techniques and insights that he has accumulated over the years that are the secrets of successful design and implementation of business intelligence systems in small organizations.

My LinkedIn profile.

2.3 Intended audience

This book is aimed at practitioners within the field of business intelligence, more specifically technical consultants and advisors who need to design and implement business intelligence solutions. The book gives a grounds-up introduction to business intelligence so the reader is not required to have prior experience with business intelligence solutions.

The book takes its offspring in Lean Business Intelligence, which is a set of technologies and methods aimed at making business intelligence solutions as simple and effortless as possible to implement and maintain. Thus, the primary audience is BI practitioners within smaller organizations and departments in larger organizations.

One particular audience that this book aims at is technical consultants studying for certification in Acinta’s business intelligence software and as such the book is based on the Acinta Intelligence Suite set of BI applications. The book also serves for self-studying and for preparing exams in basic Acinta certifications.

Next >>

3. What is business intelligence?

2010-06-21T19:20:00.001-07:00

The term Business Intelligence – often referred to simply as BI – was originally coined by Gartner Group who in 1996 wrote:

By 2000, Information Democracy will emerge in forward-thinking enterprises, with Business Intelligence information and applications available broadly to employees, consultants, customers, suppliers, and the public. The key to thriving in a competitive marketplace is staying ahead of the competition. Making sound business decisions based on accurate and current information takes more than intuition. Data analysis, reporting, and query tools can help business users wade through a sea of data to synthesize valuable information from it - today these tools collectively fall into a category called "Business Intelligence."

This definition of BI is quite broad: Data analysis, reporting and query tools. And since then the category has become even broader as it often happens with popular new terms.

Why is business intelligence needed? >>