How BI Evolved
Key to understanding how BI analyzes business is understanding how data is processed into information (via different technologies) and how it is analyzed.
Knowing these processes and how it fits into BI’s architecture, tools, and applications will also provide clarity about BI.
BI doesn’t produce any data, but it uses data produced by other business applications like enterprise resource planning (ERP), customer relationship
management (CRM), supply chain management (SCM) etc. Over the past two decades, and especially in nineties, organizations have stored huge amounts of
data by building online transaction processing (OLTP) systems and ERP systems, call centers, and the Internet. In pursuit of better data management
enterprises build data warehouses (DW), data marts, and installed extract transform load (ETL) tools to work with data warehouses. But very few of these data
were processed into information, and even less was used for decision support systems, largely because of the lack of tools to access and analyze the data for
business users.
In seventies and eighties, accessing information systems was very tedious and it was rarely permitted to end users. Query and reporting was cumbersome and
analytical reporting was spreadsheet based. The whole process of accessing information was time consuming, and delayed reporting didn’t achieve results.
The advent of technology and the increasing demand from companies to have better information, pushed BI systems to evolve.
Why BI
BI has a tremendous impact on business once installed. It produces the right information at the right time, which is key element for the success of any business
enterprise. BI is the art of knowing and gaining the business advantage from data. Whether it is marketing competition, customer retention, inventory control,
financial modeling, or even in national security, BI is the answer. BI can answer a company’s critical questions such as, why market shares are going to
competitors; which products contribute the most to profit; how can business become more profitable; why some divisions are not profitable; which plants
produce at the lowest cost; how can productivity improve; which parts of the world are the most profitable; who are best and worst customers; where is money
being lost or made, etc.
BI answers these questions by analyzing and comparing business historical data. Data is created by business activities or data from outside sources like
environmental, demographic, immigration data, etc. to study a particular group of people or customers. Such information is used by businesses to understand
their business trends, their strengths and weakness, and to analyze competitors and the market situation. The information can also be used by government or
secret agencies, especially like US Homeland Security, that need to have access to financial, immigration, transportation, and any kind of related data that can
be analyzed to determine probable attacks on its citizens or property.
In addition to determining trends, another push to implement BI comes from, the Sarbanes-Oxley legislation, which affects corporate financial reporting, and
accounting rules for publicly-held companies. To be in compliance with Sarbanes-Oxley, BI systems will be needed to insure the timely and accurate analysis of
business data. Thus real time BI is not only relevant but key to achieving compliance.
Business Intelligence Activities and Tools
As mentioned earlier, BI is a combination of technologies and architectures. Some important BI tools are data warehouses (DW) and data mart, extract
transfer load (ETL), reporting and query tools, data visualization, balanced scorecards, dashboards, OLTP, OLAP, data mining, alerting and
notification systems, and analytics. Under the BI umbrella all these tools are combined within a special architecture.
Data Warehouses and Data Mart
Because data can come from various sources, like OLTP, ERP, CRM, legacy applications, and external data sources, data can be stored in a diversified
database, in different formats and structures. As a result, a data warehouse (DW), is the most important and expensive player in the whole BI system because it
captures data from these diverse sources, and unifies them. The data is then ready to be accessed by BI system. As a central repository of business, DW
contains data used for decision support systems (DSS) which focuses on the lower and middle management and makes it possible to look at and analyze data
in different ways. Such data also used for executive information systems (EIS). Data extracted from the DW by departments are collected and put into smaller
repository for easy and fast access are called data mart. Like data mart for marketing data, sales, production etc.
Extract Transfer Load (ETL)
The process of populating data into data warehouse is done through extract transfer load or ETL, which is a set of three separate functions—extract, transfer,
and load. First, the extract function reads data from a specified source 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 the extracted information to the desired state. Finally, the load
function is used to write the resulting data to a target database, conversion from one database type to another, and the migration of data from one database or
platform to another.
Sometimes DW and BI functions are confused. The difference between the two is that DW does not require BI for functionality. For example, reports can be
generated from DW through reporting tools. However, the vice versa is not true. BI needs DW to access accurate and selective data, however, there are
exceptions to that. In recent days some vendors designed their architecture to access data directly from the source as opposed to DW. Also, cost-wise, BI is
more expensive, but with BI architecture, there is flexibility to select from different tools. The high cost of BI can be offset by companies if they take advantage of
building BI infrastructure using tools they already have installed.
Reporting and Query Tools
Reporting is the process of accessing data, formatting it, and delivering it as information. Report and query enables users to issue structured query language
(SQL) queries to the warehouse. It allows users to view and report specific information they require. Reporting is one of the main functions of BI.
Data Visualization
Data visualization tools help users to see data clearly. It is the graphical representations of data, including complex three-dimensional data pictures. Data
visualization tools interpret information and data relationships. It combines representations of multiple data sets simultaneously, or gives multiple views of a
single data set, even a set that encompasses millions of data points. Tools include of data visualization include charts and graphs, dashboards, and scorecards.
Balanced Scorecards
Balanced scorecards (BSC) are a concept that help users translate strategy into action. It is a performance measurement system, derived from vision and
strategy, and reflects the most important aspects of the business, such as customer knowledge, financial performance, internal business processes, etc. BSC is
a central list of pre-defined numbers, which show each key part of an organization’s success. BSC focus on strategic views of management goals, and then hold
people accountable for what management has set. Through BSC, managers at all levels monitor results in their key business areas.
Dashboard
A BI dashboard is a user interface that somewhat resembles an automobile’s dashboard and organizes and presents information in a way that is easy to read.
However, a computer dashboard is more likely to be interactive than an automobile dashboard. To some extent, most graphical user interfaces (GUI) resemble
a dashboard.
In years, millions of people will use BI visual tools and analytics every day. Visualization tools will be used by producers, retailers, government and special
agencies. More and more industry specific Analytical tools will flood the market to do any kind of analysis and help to make informed decision making from top
level to user level.
Another potential trend involving BI is its possible merger with artificial intelligence (AI). AI has been used in business applications since eighties, and it is
widely used for complex problem-solving and decision-support techniques in real-time business applications. It will not be long before AI applications are merged
with BI, bringing in a new era in business. It’s also not unlikely that one day BI may adopt new name of artificial business intelligence (ABI).
Business intelligence is spreading it’s wings to cover everyone, from small, medium, and to large companies. As large BI players are for large enterprises, small,
niche players service mid-size or small companies. Analytics tools also penetrating into the market for very specialized functions, which will help some
companies to go for analytics instead of full BI implementation. Moreover, the argument to replacing traditional BI with analytic applications is not an immediate
threat. According to industry analysts, this will take years that to happen.
BI takes the advantage of already developed and installed components of IT technologies to helps companies leverage their current IT investments and use
valuable data stored in legacy and transactional systems. For many large-size companies that have already spent millions of dollars building data warehouse
and data marts, now is the right time to build BI as next step to get full benefit of their investment which will directly impact return in investment (ROI).
