Business Intelligence (BI) Maturity Model.pdf

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Business Intelligence Dec 08, 2010 1 Comment

BI Maturity Model Overview

The six levels of the Business Intelligence (BI) Maturity Model are measured by the value provided to the business vs the

sophistication of the tool suite. The lowest level of business intelligence maturity (level 0) is characterized by fractured

reporting at different times using different data sources and rules for defining metrics within an organization. Thus creating a

disjointed and somewhat inaccurate view of an enterprise. While the highest level of business intelligence maturity (level 5)

is characterized by strategic, tactical, and operational decision-making in situations where numerous factors and variables are

included. Organizations utilizing level 5 tools are able to effectively model their business model and accurately project future

results.

Level 0 Limited BI / Spreadsheets

Example Spreadsheet

A spreadsheet is a computer application that simulates a paper, accounting worksheet. It displays multiple cells that together

make up a grid consisting of rows and columns, each cell containing alphanumeric text, numeric values or formulas.

Because spreadsheets are easy to work with and are widely available, many businesses rely on them to take the place of a

more sophisticated business intelligence tool. Instead of providing a consistent view of the organizational data, spreadsheets

ess Intelligence (BI) Maturity Model http://bi-insider.com/portfolio/bi-maturi

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ave ecentra ze v ews o m te amounts o ata. n a t on, sprea s eets are typ ca y eve ope y an n v ua user,

consist of little or no organizational standards, and encompass limited system analysis and quality assurance before being

used as an official reporting source. It is typically up to the spreadsheet developer to decide what metrics are important,

what data needs to be included, how the data is formatted, and what level of aggregation is necessary. Spreadsheets become

isolated and inconsistent data silos, and are difficult for analysts to extract, transform and load data into a central database to

be interpreted at an enterprise level.

Characteristics of Spreadsheets

Widely used personal desktop application

Reporting conducted in rows and columns High degree of individual use

High degree of inaccuracy and variability

Limited security

Limited collaboration

Level 1 Operational Reports

Sample Operational Report

Operational reports are pre-designed business reports that focus on listings of data at the detailed level with data presented

within a highly-structured format. Also known as canned reports, operational reports enable organizations to present datain a logical format and are designed to support the day-to-day activities of an organization at the transaction level. They are

typically developed by information technology (IT) departments and/or advanced report users that have a good

understanding of reporting tools, business rules, and database concepts. In addition, operational reports can be scheduled,

refreshed, and distributed on a regular basis. Fundamentally operational reports are used by people with the responsibility

for improving their organizations operations. The tools provide task-oriented line-item information on individual transactions

at the very granular level of detail required for operational management. (source: information management magazine)

Characteristics of Operational Reports

Presents data in a logical format

Data is distributed in s highly-formatted manner

Reports can be published on a regular schedule

Distribution of organized listings of data Enables users to understand transactional and/or detailed level data

Typically developed by information technology (IT) personnel and/or knowledgeable power users

Level 2 Query & Analysis Solutions & Environments


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Sample Adhoc Query and Analysis Solution

Query and analysis solutions enable business users and analysts to rapidly generate business queries and reports from

enterprise data based upon business question of the data. Also known as ad-hoc querying, these tools typically provide

intuitive, graphical interfaces that shields users from technical complexities and allows users to leverage business

terminology instead of the more technical database names. The business focus of these tools allows nontechnical

professionals to be comfortable with their data and allows them to quickly and efficiently satisfy their own information needs

in real time with minimal assistance from system developers. Commonly, query and analysis environments include a

middleware layer that converts database conventions into business nomenclature that is more intuitive and understanding to

end-users. Moreover, query & analysis environments give users the capability to access and analyze data in a unique and

personal manner. Utilized primarily by business users, query and analysis solutions provide an environment that enables

interactive methods to query data, present data in an ad-hoc manner, and find information on an as-needed basis.

Characteristics of Query & Analysis Environments

Utilized primarily by business users

Drag-n-drop interfaces to create queries and simple reports

Rapidly and intuitively generate queries with minimal help from IT professionals

Highly-interactive methods to query data

Users ask business questions to develop queries

To produce queries, users only need to understand their own business terms

Users can independently dive into the details of their data

Level 3 Dashboard Management

Dashboard management systems are intended to facilitate and support the information and decision-making needs of

management by providing easy access to key business information in a highly graphical and intuitive format. Fundamentally,

a dashboard is a graphical business tool that displays a set of KPIs (key performance indicators), metrics, and any other

relevant information to a business user, manager, or key decision-maker in a single consolidated view and allows for

organizational performance to be easily measured and monitored

Sample Dashboard


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Dashboard data is often displayed as aggregate information and contains data that consolidated from multiple data sources

scattered throughout an organization. Dashboards are commonly interactive and provide users users the ability to drill into

particular aspects of the display and/or rapidly switch between views of the data. To make this possible, dashboards are

typically composed of advanced data visualization tools including charts, grids, gauges, and maps that allow for various ways

of presenting information and interacting with the data. Moreover, dashboard management systems are characterized by

providing users with graphical methods to view and interact with key data elements and get a snapshot of organizational

performance.

Characteristics of Dashboard Management

Presents a number of organizational metrics in a single consolidated view Utilizes graphics, charts, grids, gauges, and maps

Monitors organizational metrics and key performance indicators (KPI)

Enables real-time visibility into data

Primarily utilized by senior management and key decision-makers

Rapidly displays a snapshot of organizational performance

Level 4 On-line Analytical Analysis (OLAP)

On-line analytical processing (OLAP) is a technique for rapidly visualizing and analyzing business metrics across different

points of view. OLAP is a term used to generically refer to software and applications that provide users with the ability to

store and access data in OLAP cubes (also called a multidimensional cube or a hypercube) with this cube being made up

of numeric facts, called measures, and text values, called dimensions.

Example of On-line Analytical Analysis (OLAP)

Moreover, OLAP systems provide users with insight into past performance and they enable a deep understanding of the

reasons behind why previous events have occurred. Fundamentally, OLAP systems allow users to rapidly view and analyze

data from many perspectives or dimensions and allows the users to conduct advanced What-If analysis.

Characteristics of OLAP

Queries from OLAP cubes rather than database tables

Enables advanced insight into past performance

Provides accurate and precise What-If analysis

Queries perform extremely rapidly

Primarily used by business areas concerned with financial and resource planning

Level 5 Data Mining


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Sample Data Mining Application

According to data-mining-guide.net, data mining is the process of analyzing large data sets in order to find patterns that can

help to isolate key variables to build predictive models for management decision making. In addition, data mining

applications help discover hidden patterns and relationships in data in order to effectively project and predict future results.

In order to accomplish this goal, data mining application utilize statistics, algorithms, advanced mathematical techniques, and

sophisticated data search capabilities.

Moreover, these sophisticated tools provide answers to questions that may never have been asked and these tools are

effectively able to determine relative amounts of correlation between data elements. Further, the predictive features of these

data mining tools enable organizations to exploit useful patterns in data that may have otherwise been difficult to determine.

Characteristics of Data Mining

Leverages statistics, advanced techniques, algorithms, and sophisticated data search capabilities

Extrapolate past performance to forecast future events

Provides answers to questions that may never have been asked

Calculates levels of correlation between data elements

Implementers are experts of statistic analysis and/or processing of large queries


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