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What is Business Intelligence
In today’s world data is spread across the inside as well as outside of the organization and doesn’t provide any insight about the organization. Due to this, business executives, managers and senior officials used to make strategic decisions based on their gut feeling rather than fact; which is not good for any organization in the long term.
Business Intelligence (BI) helps business owners/officials to make important decisions based on data or more precisely facts. It is a method to make the right information available at the right time to the right people.
BI is a set of processes, tools, technologies and methodologies that combine data from various data sources and make the single source of data available at right time so that timely, better informed and strategic decisions can be made. BI also provides different tools to quickly analyze the consolidated information in a variety of data presenting tools.
Benefit of Business Intelligence
Organizations always deal with multiple departments and these departments also deal with various applications to meet the department need. With this approach, an organization’s data is always segregated and maintained in heterogeneous sources. BI provides the way to handle this scattered data and help in strategic decision making. A BI provides a method to consolidate data from all available sources into a central repository in a consistent format, not only tp help to business owners but other users as well with:
- Manage all type of risk well in advance
- Understand an organization’s current position and it’s position over a period of time
- Fact based strategic, informed decision making at the right time
- Find the problem areas and resolve them to improve the revenue
- Identify all selling opportunities, i.e. cross and up for the business
- Understanding customer behavior to serve them better and win more deals
- Understand customer segmentation, product analysis, cost, and operations improvements
BI benefits are not limited to the above list but it’s necessary to know how a BI system can become a game changer for any organization.
Business Intelligence – Broader Perspective
BI is not only a reporting solution, although reporting is one of the important areas, which pulls data from a consolidated data repository or data warehouse, whereas BI is a platform and covers the data warehouse (data integration), analysis (OLAP), different ways of reporting (information delivery) and data mining (predictive analysis).
Today, I’m not discussing how BI has evolved and who has contributed but I’ll touch upon the methodologies that are very much needed for a successful BI system implementation.
A Data Warehouse (DW) is the central database repository in which current and historical data gets consolidated from various heterogeneous data sources in order to facilitate historical and trend analysis reporting. There are different methodologies for designing a data warehouse. I will discuss these approaches later in this article.
A Data Mart (DM) is a consolidation of data for one business area whereas a Data Warehouse is a collection of one or more Data Marts, providing a central data repository for the overall organization.
Once data is available in the DW or DM, information can be visualized by many ways like decision making reports, online queries, notifications, predictive analysis and web users.
Predictive analysis or data mining is focused on predicting the future using mathematical analysis and based on identified trends or patterns available in the data warehouse. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data.
DW Design Methodology
Data warehouse (DW) is a backbone for any BI system and is important for collecting, storing, and delivering decision support data for an organization.
Fundamentally, there are two approaches available to design a DW; Inmon’s “Top down” and Kimball’s “Bottom up” approach.
As per Inmon’s top-down approach, data should be integrated from various sources into a centralized repository (called a data warehouse). This approach provides a consistent dimensional view of data across data marts as all data marts are loaded from the centralized repository (Data Warehouse). This approach uses a normalized data model. Inmon envisions a data warehouse at the center of the “Corporate Information Factory”, which provides a logical framework for delivering BI, business analytics and business management capabilities.
As per Kimball’s bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes and later on these data marts can eventually be unioned together to create a comprehensive data warehouse. This approach is also known as dimensional modeling.
Most of the time business owners or technical officials get into the situation where they have to choose one approach as both approaches have their own pros and cons and set of parameters to know before it starts. The table below will give you more clarity to understand the approach and their differences.
|
Inmon theory |
Kimball theory |
|
Top-down approach |
Bottom-up approach |
|
DW is Central repository of data also known as “Corporate Information Factory” |
Data mart (DM) has business function specific data and all business function(s) DMs union together and form a DW |
|
DW holds data in Normalized form (3NF) |
DMs/DW holds data in Dimension model (Star or Snowflake) |
|
Hub and Spoke architecture |
Bus architecture |
|
Need enough time to build the data warehouse |
Immediate need for a data warehouse |
|
Business requirements are very ad hoc and don’t have enough clarity |
Business requirements don’t change that much and have enough clarity |
|
Scope is not limited |
Limited scope |
|
Supports strategic decisions |
Support tactical decisions |
With the above fundamental approaches there are new design approaches developing; Hybrid design, Data vault and Anchor modeling.
Here, I’m not discussing all of the above approaches in detail but for any successful DW/BI system implementation you need to undergo a thorough study before making any decision as success or failure is all yours.
BIG Data – An Industry Buzzword
We know how a BI system can help any organization in faster and advanced decision making, but day by day the nature and size of data is changing and challenges are growing. BI systems provide a more structured user experience, which runs reports and analysis over data stores whereas its very difficult to deal unstructured and huge volumes of data.
Big Data helps to overcome traditional BI challenges; Big Data is high-volume (petabyte/exabyte data), high-velocity (data from social Web, sensors, etc.) and high-variety ((Un)structutred) information assets that require new methods of information processing for better insight and decision making. Big Data generally refers to mining and analyzing large sets of unstructured information obtained from the social Web, sensors and other sources.
BIG Data – Benefit Over BI
Some extend Big Data is an extension of traditional BI; Big Data deals unstructured/semi-structured along with structured data and does the predictive analysis.
Big Data is large volume unstructured data, which can’t be handled by standard database management systems like DBMS, RDBMS or ORDBMS. The benefit of Big Data is not only evident in consumption of unstructured data, which is almost no use in traditional BI, but a source for big data. Also, uses unrelated data to produce a related output. By combining different data sources hidden patterns, links and trends can be identified to derive value.
Overall, Big Data offers value from very large volumes of a wide variety of data with high-velocity.
Summary
In this article we talked how BI platforms enable organizations to understand their business. It is not only for top level executives, business decision makers, business analysts for analyzing trends and helping middle level management, line managers or workers for improving operational efficiency. BI can also be used by external customers to understand the business and gain confidence.
Also, with the shift in the data paradigm Big Data is emerging to support huge volume, different variety of data with high velocity.
I’ll discuss more about BI & Big Data components in upcoming articles.
References
http://en.wikipedia.org/wiki/Business_intelligence
http://en.wikipedia.org/wiki/Big_data
http://www.gartner.com/technology/reprints.do?id=1-1QYUTPJ&ct=140220&st=sb
Author
Anoop worked for Microsoft for almost six and half years and has 12+ years of IT experience. Currently, he is working as an ETL Architect in one of the top Fortune Companies. He has worked on end to end delivery of enterprise scale BI/DW projects. He carries strong knowledge on database, data warehouse and business intelligence application design and development. Also, e worked extensively on SQL Server, designing of ETL using SSIS, SSAS, SSRS and SQL Azure. Anoop is a Microsoft Certified IT Professional (MCITP) in Microsoft SQL Server – Database Development 2008, Business Intelligence 2005 and Microsoft Certified Technology Specialist (MCTS) in Microsoft SQL Server 2008 – Implementation and Maintenance.
