Business Intelligence purpose is to support better business decision making for the management on the whole. It would also give end-users the ability to do more with the data without necessarily having technical skills.
According to Forrester Research, 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.
Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack
Business Intelligence (BI)
The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).
Business Intelligence systems provide historical, current, and predictive view of operational and most importantly financial view of a business. Currently organisations are starting to see that data and content should not be considered separate aspects of information management, but instead should be managed in an integrated enterprise approach. Enterprise information management brings Business Intelligence and Enterprise Content Management together. Currently organisations are moving towards Operational Business Intelligence which is currently under served and uncontested by vendors.
Target Audience For BI
Target Audience will be CEO , COOs and C-Level management down to the personnel down the line in the organizational hierarchical such as General, Regional or Operational managers.
Many of the business intelligence tools in the market today offer similar features and functionality. But, what sets one business intelligence implementation apart from another is the way in which the solution is applied to support decision-making.
Below is an overview of some of the most popular ways on which business intelligence tools can be built upon.
Business analysts and other power users – those that are responsible for uncovering the most sophisticated trends in corporate data – must be able to analyse information down to the finest detail. Most business intelligence tools offer in-depth Online Analytical Processing (OLAP) capabilities, allowing users to instantly manipulate data in an unlimited number of ways, so it can be reviewed from multiple perspectives.
Ad Hoc Reporting
In many cases, simply refreshing the content in an existing report will be enough to satisfy a worker’s specific information need. However, often times, employees will need to rapidly create a new report to answer an urgent question, make an “on the fly” decision, or address a pending issue. Many of today’s most popular business intelligence tools provide features that allow even non-technical users to quickly and easily build and generate their own custom reports.
Executive Scorecards and Dashboards
A company’s top-level managers don’t have time to generate ad-hoc reports. That’s why many business intelligence tools enable the deployment of graphical dashboards that allow senior executives to monitor key performance indicators and critical metrics in real-time, providing them with an “at a glance” view of the overall state of the business.
Every day, in every business, thousands of operational tasks are executed. Businesses run on these activities, and any inefficiencies or errors can significantly impact performance. Business intelligence tools can be used to support the kind of operational reporting that enables real-time monitoring of day-to-day events. So, problems can be instantly identified and corrected.
A company’s ability to anticipate trends is critical to maintaining organisational flexibility and agility. Additionally, historical data must be leveraged to predict future events in order to ensure effective strategic planning. That’s why many business intelligence tools include predictive analytics that allow for rapid and highly accurate forecasting.
Companies today maintain massive volumes of information. And sometimes, sorting through that data to find what’s most relevant can be a daunting task – particularly for business users who aren’t technically-savvy. The data mining features within many business intelligence tools can help locate and extract the most important information from large data sets, making it much easier for users to access and leverage the information they really need, instead of wasting time browsing through the data.
As organisations strive to become more customer-centric, they are turning to business intelligence tools to gather and consolidate the client data that resides in various systems such as CRM, accounting, and help desk applications. This provides a complete, 360-degree view of all customer interactions, so businesses can better understand needs, behaviours, and preferences and use that knowledge to implement more successful loyalty programmes.
When companies centralise and consolidate their data into one database or program, it is called data warehousing. With a data warehouse, an organisation may spin off segments of the data for specific users to analyse and utilise. However, in other cases, analysts may start with the type of data they want and create a data warehouse based on those specs. Regardless of how businesses and other entities organise their data, they use it to support management’s decision-making processes.
The purpose of gathering corporate information together in a single structure, typically an organisation’s data warehouse, is to facilitate analysis so that information that has been collected from a variety of different business activities may be used to enhance the understanding of underlying trends in their business. Analysis of the data can include simple query and reporting functions, statistical analysis, more complex multidimensional analysis, and data mining.
Data Warehousing Approaches
There are two approaches to data warehousing
- Top-Down approach spins off data marts for specific groups of users after the complete data warehouse has been created
- Bottom-Up approach builds the data marts first and then combines them into a single, all-encompassing data warehouse.
Data mining is the computing process of discovering patterns in large data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
Obviously we are not going to discuss data mining in details, but to give you an overview of this key factor in line with developing BI, have a look at the 4 stages process of data mining below:
Extract, Transform, Load (ETL)
Extract, Transform, Load (ETL) refers to three separate functions combined into a single programming tool. First, the extract function reads data from a specified source database and extracts a desired subset of data. Next, the transform function works with the acquired data – using rules or lookup tables, or creating combinations with other data – to convert it to the desired state. Finally, the load function is used to write the resulting data (either all of the subset or just the changes) to a target database, which may or may not previously exist.
ETL vs. Data Mining
ETL used to bring data from diverse sources together in a single, accessible structure, and load it into the data mart or data warehouse. Whereas, data mining which use a variety of techniques, including neural networks, and advanced statistics to locate patterns within the data and develop hypotheses.
For a layman’s understanding a Meta Data is nothing but a database layer of log files just like the index section of a book serves as a metadata for the contents in the book. Simply . this is data about data.
To partition data in order to impose access control strategies, to speed up the queries by reducing the volume of data to be scanned and to structure data in a form suitable for a user access tools are some of the reason for using data mart concept.
BI vs. Advanced Analytics
Business intelligence (BI) solutions are among the most valuable data management tools available. BI solutions seek to collect and analyse current, actionable data with the purpose of improving business operations. Meanwhile, Advanced Analytics software is primarily used to analyse historical data to predict business trends, usually with an eye toward improvement, and, often, preparation for change.
Know The Difference Business Intelligence (BI) and Business Analytics (BA)
the real difference between Business Intelligence (BI) and Business Analytics (BA) then here are few key differences.
We have tried to give you a quick look with short description on all relevant technologies and methodologies associated with Business Intelligence. In future , we will cover some of these topics in details specially Data Warehousing.