Business Intelligence
| Site: | Saylor Academy |
| Course: | BUS303: Strategic Information Technology |
| Book: | Business Intelligence |
| Printed by: | Guest user |
| Date: | Friday, April 19, 2024, 6:16 PM |
Description
This chapter gives the practitioner's view of how business intelligence can be used. Can you think of at least one key business process/activity that is well-suited for a business intelligence application?
Business Intelligence
Learning Objectives
- Query sales data to spot meaningful trends
- Distinguish between static reports, dynamic reports, and data mining
- For a given situation, determine what type of business intelligence report is required to solve the problem
Introduction
In order to make strategic decisions about which products to feature in our store, we need to carefully analyze the sales and clickstream data. This type of data analysis is one form of business intelligence.
If there is one thing plentiful in the world today, it is data. At the
heart of every information system is a database that captures
transactional data. For example, who bought what, when, for how much,
and so forth. It is useful to know about the architecture of the
transactional systems so that it is not a complete mystery how the data
is captured.
However, it is critical to know how to distill and analyze the captured
data in order to make managerial decisions. For example, after
summarizing thousands of records we might find a product selling
particularly well with women in a particular age range living in a
particular area. That meaningful information could be actionable in
terms of the supply chain and marketing initiatives.
If anything in the world today there is perhaps too much data.
Distilling that data into meaningful information is a key skill. There
are a number of tools available to perform data analysis. These include
spreadsheet programs such as Excel and database systems such as Access.
Learning to use these tools will enhance your marketability.
Where Are We in the Life Cycle?
Many information systems projects are conceived of in a life cycle that progresses in stages from analysis to implementation. The diagram below shows the stages that we touch in the current chapter:
Kiva: Summarize Data to Produce Information
To illustrate the power of summary data, we will first show how it can be used as a marketing vehicle for a website. Impressive statistics can help encourage repeat business. The same marketing principles operate even for nonprofit organizations.
Kiva is a website that lets you make small loans (typically under $500)
to entrepreneurs in developing countries. The field of small loans is
called microfinance.
Microfinance institutions are an incredibly important resource to help
third world citizens rise out of poverty. Surprisingly, the repayment
rate of the world's poor ranges from 95 to 98%, far higher than the loan
repayment rate in the United States. Over 80% of Kiva's loans are made
to female entrepreneurs. They invest profits back into the businesses
and improve the lives of their families.
Kiva works by pooling resources so that for example 50 people could lend
$10 each to total $500. As part of its marketing effort Kiva maintains
fast facts about their activities to date. For example, they report that
they have nearly half a million lenders who together have lent $161
million dollars over the last three years. These fast facts are gathered
from the website's database after scanning millions of records and
represent business intelligence. Not only does the information serve a
marketing purpose, but it is also an internal scorecard to track the
progress of Kiva's mission and influence decisions.
Kiva's Facts and History page is a business intelligence report. Note
the sentence that appears under "Latest Statistics," which announces
that the statistics are updated nightly (between 1 - 3 am). This is
typical of business intelligence systems. Searching millions of records
puts such a drain on the system that these activities are usually run
during off peak hours.
What Is Business Intelligence?
The Kiva example is a form of business intelligence. Business intelligence (BI) is the delivery of accurate, useful information to the appropriate decision makers within the necessary time frame to support effective decision making.
By this definition all the work we have done with Excel would qualify as
business intelligence since our deliverables contained accurate and
useful information to support effective decision making. However,
business intelligence is commonly understood to include distilling and
analyzing large data sets such as those found in corporate databases.
Extracting and analyzing information stored in databases is the subject
of this chapter. It is very likely that at multiple points in your work
career you will be asked to engage in just this type of analysis.
Business intelligence is part of the big picture information systems
architecture. Most systems in existence can be classified either as
enterprise systems, collaboration systems, or business intelligence
systems. The enterprise systems – taking orders for example – feed their
data to the data warehouse, which in turn is queried to support business
intelligence.
From a managerial standpoint, there are three factors necessary to make an effective decision:
- Construct a set of goals to work toward.
- Determine a way to measure whether a chosen path is moving closer or farther from those goals.
- Present information on those measures to decision makers in a timely fashion
We also need to see performance over time. Is product quality improving or getting progressively worse?
Let's say that our analysis determines that the high rejection rate
comes from just one factory in Southeast Asia. We report the problem to
management. They dispatch a team to review the plant. The review
discovers child labor, abusive conditions, and very low morale at the
plant. The horrible conditions are quickly reversed and the rejection
rate returns to average.
Business Intelligence: Analysis of App Sales Data
The business intelligence portion of the information systems
architecture. Note that business intelligence systems typically operate
off of a data warehouse – a repository of data for the corporation. Each
enterprise system contains one or more databases. The contents of those
databases is routinely copied into the data warehouse to enable the BI
analysis. The process of copying is called extract, transform, and load
(ETL).
Business Intelligence Process
We will look at three types of business intelligence – static reports, dynamic reports, and data mining.
Static reports
are by far the most common form of business intelligence. Most
businesses have summarized standard reports already laid out and printed
to assist in managerial decision making. For example, universities use
enrollment reports to gauge which departments might need to hire more
faculty. Credit card companies will request reports of persons with high
credit scores to target credit card promotions. Similarly, the
companies might target college students with good future earning
potential. Marketers might look at sales figures for different stores
and regions to determine where there are opportunities to run a sales
promotion.
Dynamic reports look similar to static reports but online and interactive. A manager curious as to where a certain summary number on his dashboard comes from can drill down
to expose the detail that contributed to that number. In essence it is a
fact finding tour where information discovered in each step gives clues
on where to search next for information. For example, if sales in North
America are down, then drill down to discover a problem in the Midwest
region. Then drill down farther to discover a problem in the Cleveland,
Ohio plant.
Data mining
uses computer programs and statistical analyses to search for unexpected
patterns, correlations, trends, and clustering in the data. In essence,
it is fishing through the data to see if there are patterns of
interest. One often cited example of data mining was the discovery that
beer and diapers are frequently purchased on the same trip to the
grocery store. Upon further inquiry marketers discovered that Dad picks
up some beer on his trip to the grocery store to buy diapers. Marketers
can use this information to place the two items in close proximity in
the store.
The business intelligence process for dynamic reports is depicted here.
The top half of the diagram shows how data finds it way into the data
warehouse through the extract, transform, and load process. The dynamic
report begins with an executive dashboard providing a high level view of
the business. The dashed red arrows represent drilling down to find a
reason for a pattern in the data. In this example, a downturn in North
American sales is traced all the way back to a Cleveland, Ohio plant.
Key Takeaways
- Business intelligence is a way of uncovering trends and patterns in corporate data that might have strategic or operational significance.
- Most corporations already have the data that they need for business intelligence. However analyzing the data, presenting the results, and then following through on where the data leads, separates the winners from the losers in a competitive environment.
- Static reports, dynamic reports, and data mining are three different forms of business intelligence.
Questions and Exercises
- Managers are often most interested in exceptions – data that does not fit pre-established expectations. Describe how business intelligence can aid in this process.
- Why do lower level managers require higher level detail in their information?
- In what ways does fantasy football rely on business intelligence?
This text was adapted by Saylor Academy under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License without attribution as requested by the work's original creator or licensor.
Databases
Learning Objectives
- Determine which tables and fields in a database are needed to complete a query
- Explain how data is captured in our Class App store
- Explain how the Class App store data can be used for business intelligence
Introduction
In all of the forms of BI described above, you must actually store data to analyze. Organizations store their data in databases connected to their production systems. Here are some examples:
- Banking transaction systems store data in databases containing information about customers, accounts, and transactions against those accounts.
- University enrollment systems store data in databases containing information about students, faculty, courses, and enrollment in those courses.
- Cell phone billing systems store data in databases containing information about customers, rate plans, and calls made.
- Credit card billing systems store data in databases containing information about customers, credit plans, and items charged.
- Supermarket checkout systems store data in databases containing information about customers, products, and buying habits of their customers. The loyalty card that you have swiped at the checkout ties all your purchases back to your name.
What do these databases actually look like? They consist of tables of data that are related to each other. This is called a relational database. Each table must have a unique identifier that is called a primary key. The database is organized into parent and child tables to avoid duplicating data. Data common to each child is stored in the parent table. Diagrammatically a parent table points to its child tables. Each parent record can have zero or more child records. To logically link the tables together simply repeat the primary key as a foreign key in each corresponding record of the child table. To get information in and out of a relational database requires a relational database management system (RDBMS) such as Microsoft Access. The goal of the system is to facilitate transactions while safe guarding the integrity of the data.
The theory behind database design is one of the most elegant areas in all of information systems. If you continue in information systems, you will see it in detail. However, for our purposes all we need to know is that data is typically stored in multiple files even if the report that we get is contained in a single file. Why? The simple answer is that we want to avoid duplicate data by storing information common to each child in the parent table. Why do we care? Because duplicate data opens up the possibility that one of the duplicates will be different in an important way. For example you would not want your bank balance to be sometimes one number, sometimes another depending on which record happens to be called up by the database.
The data from the Class App store is stored in a relational database consisting of two tables – an APP table and a SALES table. The primary key of the APP table is App name. The primary key of the SALES table is the combination of Timestamp and App name. App name in the SALES table is also a foreign key linking each sale with its corresponding App.
Architecture of Class App Store
The Class App store created for this course has at its heart a simple database. Nonetheless, that database supports some fairly sophisticated functionality. The beauty of the Class App store is that it was created almost entirely without writing code, by using Google Sites and Google Docs.
The database consists of two tables – an App table and a Sales table. The App table captures registration information about each app. The Sales table captures sales information – who bought what and when.
Conceptually the tables are linked by what is called a one to many relationship. One app has many sales. Every database has one to many links of this sort. The relationships are formed by the primary key to foreign key correspondence.
Once the architecture is established the next step is to get data in and out of the database. Data is entered into a database using forms. For the App table, use the Register App form. For the Sales table, use the Purchase App form.
Data is extracted from the database using reports. The listing of apps on the Class App store home page is a report.
When the reports involve summary data, we would characterize that as meaningful information. For example, listing the best selling apps and the top rated apps qualifies as information. The number of apps purchased by each student is also information – it reveals how many students have completed the assignment.
And there are a variety of reports that can come out of even a simple database such as this. For example, a report might list the best selling apps for men who are freshmen. One can be quite specific as to the information extracted for analysis.
Architecture of the Class App store. Even this simple database requires two forms and four reports.
Group and Summarize Data
We will analyze the sales data for our own app store to find trends in buying patterns for the class. Distilling that data and finding meaningful patterns is a form of business intelligence.
The important concepts here are to group and summarize data, and then to order and compare groups. For example, showing a list of the best selling apps. Creating this list requires counting total sales for each app and then listing those totals in descending order.
To do this in real time requires sending a query to the store typically written in a language called Structured Query Language (SQL). This is how we were able to get the store to display tables of best selling and top rated apps. The query looks similar to this:
select App, count(Timestamp)
group by App
order by count(Timestamp) desc, App asc
Translation: select the app name and count the number of records
(timestamps) for that app. Produce a subtotal (group by) for each App
name. Then order the subtotals in descending order. If two apps have the
same subtotal, then order them alphabetically.
However, SQL is beyond the scope of this course. What is within the
scope of the course is to download and analyze the data in a
spreadsheet. Database data can be downloaded and then analyzed using
Excel pivot tables. A pivot table is a visual query tool that allows you to answer sophisticated questions without writing any SQL code.
Data is sorted by timestamp above left and by app above right. However,
neither sorting produces useful information. Left we download and then
group, summarize and sort the data by sales in descending order to
reveal the top selling apps. This is meaningful information. "Count of
Email" means that we are counting the number of email addresses
registered for each app. We count emails since they are unique whereas
names might not be. This analysis is performed using an Excel pivot
table on the downloaded data.
Multi-Table Databases
The problem with one table databases is that we are limited to querying the data that happens to be in that table. For example, there is no way to see which developers bought their own apps. The sales data here shows only the buyer not the seller. The seller data is stored in a different table. What we need is a way to join information between the two tables. While joining information between tables is possible to do with a spreadsheet (using the Vlookup operation), it is rather difficult and is error prone. The best practice way to accomplish a join is using a database system such as Microsoft Access.
The magic of database systems is that they are able to make data that
lives in separate tables appear to reside in the same table. Once the
data appears to reside in a single table, then all of the query
techniques that apply to one table databases become tools for analysis.
The APP table above and the SALES table below. A relational database is able to integrate information between the two tables.
Data Warehouse
As with many subjects in the course, it is more complicated than that. It would be relatively rare to pull business intelligence data from a live database. The drain on the system might slow down the entire business and thereby frustrate customers. Instead, corporations typically copy data from their databases into a repository called a data warehouse. The warehouse can then be queried repeatedly without affecting the production system.
Periodically, perhaps once a day, data is copied from the company’s many databases to a very large database called the data warehouse. The process of copying the data is called extract, transform, and load (ETL).
- Extract – Copies data from one or more databases systems.
- Transform – Cleans the data so that related records in different databases appear in a consistent format.
- Load – Inserts the cleansed data into the data warehouse.
It is the data warehouse that is analyzed to produce management reports.
Note the role of the data warehouse as the central repository for all the business intelligence data.
Latency is the
amount of time between the occurrence of a transaction and the loading
of that transaction’s information into the business intelligence system.
In other words it is the amount of time that passes before a manager
has a distilled report in hand analyzing the operation. Some mangers are
content to get a monthly update, others need daily or even hourly
updates. It depends on the nature of the job. Ironically, lower level
managers tend to need more up to the minute data. This is because they
control the systems in real time. Upper level managers, by contrast,
tend to focus on the big picture over a larger time horizon.
Key Takeaways
- Multiple corporate databases feed into a large data warehouse that is used for querying the data.
- The greatest sin in database design is allowing duplicate data. Duplicate data has the potential to become inconsistent – sometimes one value, sometimes another.
- The higher up a manager is in the organization, the less detail he or she needs to see in the data. In fact, detail only becomes important to an upper manager when it is needed to explain an unexpected trend.
Questions and Exercises
- The transform step in the ETL process can be quite involved. Research and find an example of data that needs to be cleaned.
- Explain why databases beyond one table require relationships among the tables.