Databases, Business Intelligence, and Competitive Advantage
| Site: | Saylor Academy |
| Course: | BUS303: Strategic Information Technology |
| Book: | Databases, Business Intelligence, and Competitive Advantage |
| Printed by: | Guest user |
| Date: | Saturday, September 14, 2024, 3:52 PM |
Description
Read these sections on how information systems can enable better decision-making. Complete the exercises at the end of each section.
Data, Information, and Knowledge
Learning Objectives
- Understand the difference between data and information.
- Know the key terms and technologies associated with data organization and management.
Data refers simply to raw facts and figures. Alone it tells you nothing. The real goal is to turn data into information. Data becomes information when it's presented in a context so that it can answer a question or support decision making. And it's when this information can be combined with a manager's knowledge – their insight from experience and expertise – that stronger decisions can be made.
Trusting Your Data
The ability to look critically at data and assess its validity is a vital managerial skill. When decision makers are presented with wrong data, the results can be disastrous. And these problems can get amplified if bad data is fed to automated systems. As an example, look at the series of man-made and computer-triggered events that brought about a billion-dollar collapse in United Airlines stock.
In the wee hours one Sunday morning in September 2008, a single reader browsing back stories on the Orlando Sentinel's Web site viewed a 2002 article on the bankruptcy of United Airlines (UAL went bankrupt in 2002 , but emerged from bankruptcy four years later). That lone Web surfer's access of this story during such a low-traffic time was enough for the Sentinel's Web server to briefly list the article as one of the paper's "most popular". Google crawled the site and picked up this "popular" news item, feeding it into Google News.
Early that morning, a worker in a Florida investment firm came across the Google-fed story, assumed United had yet again filed for bankruptcy, then posted a summary on Bloomberg. Investors scanning Bloomberg jumped on what looked like a reputable early warning of another United bankruptcy, dumping UAL stock. Blame the computers again – the rapid plunge from these early trades caused automatic sell systems to kick in (event-triggered, computer-automated trading is responsible for about 30 percent of all stock trades). Once the machines took over, UAL dropped like a rock, falling from twelve to three dollars. That drop represented the vanishing of $1 billion in wealth, and all this because no one checked the date on a news story.
Understanding How Data Is Organized: Key Terms and Technologies
A database is simply a list (or more likely, several related lists) of data. Most organizations have several databases – perhaps even hundreds or thousands. And these various databases might be focused on any combination of functional areas (sales, product returns, inventory, payroll), geographical regions, or business units. Firms often create specialized databases for recording transactions, as well as databases that aggregate data from multiple sources in order to support reporting and analysis.Databases are created, maintained, and manipulated using programs called database management systems (DBMS), sometimes referred to as database software.
DBMS products vary widely in scale and capabilities. They include the
single-user, desktop versions of Microsoft Access or Filemaker Pro,
Web-based offerings like Intuit QuickBase, and industrial strength
products from Oracle, IBM (DB2), Sybase, Microsoft (SQL Server), and
others. Oracle is the world's largest database software vendor, and
database software has meant big bucks for Oracle cofounder and CEO Larry
Ellison. Ellison perennially ranks in the Top 10 of the Forbes 400 list of wealthiest Americans.
The acronym SQL (often pronounced sequel) also shows up a lot when talking about databases. Structured query language (SQL)
is by far the most common language for creating and manipulating
databases. You'll find variants of SQL inhabiting everything from lowly
desktop software, to high-powered enterprise products. Microsoft's
high-end database is even called SQL Server. And of course there's also
the open source MySQL (whose stewardship now sits with Oracle as part of
the firm's purchase of Sun Microsystems). Given this popularity, if
you're going to learn one language for database use, SQL's a pretty good
choice. And for a little inspiration, visit Monster.com or another job
site and search for jobs mentioning SQL. You'll find page after page of
listings, suggesting that while database systems have been good for
Ellison, learning more about them might be pretty good for you, too.
Even if you don't become a database programmer or database administrator (DBA), you're almost surely going to be called upon to dive in and use a database. You may even be asked to help identify your firm's data requirements. It's quite common for nontech employees to work on development teams with technical staff, defining business problems, outlining processes, setting requirements, and determining the kinds of data the firm will need to leverage. Database systems are powerful stuff, and can't be avoided, so a bit of understanding will serve you well.
A complete discourse on technical concepts associated with database
systems is beyond the scope of our managerial introduction, but here are
some key concepts to help get you oriented, and that all managers
should know.
- A table or file refers to a list of data.
- A database is either a single table or a collection of related tables. The course registration database above depicts five tables.
- A column or field defines the data that a table can hold. The "Students" table above shows columns for STUDENT_ID, FIRST_NAME, LAST_NAME, CAMPU.S._ADDR (the "…" symbols above are meant to indicate that in practice there may be more columns or rows than are shown in this simplified diagram).
- A row or record represents a single instance of whatever the table keeps track of. In the example above, each row of the "Students" table represents a student, each row of the "Enrollment" table represents the enrollment of a student in a particular course, and each row of the "Course List" represents a given section of each course offered by the University.
- A key is the field used to relate tables in a database. Look at how the STUDENT_ID key is used above. There is one unique STUDENT_ID for each student, but the STUDENT_ID may appear many times in the "Enrollment" table, indicating that each student may be enrolled in many classes. The "1" and "M" in the diagram above indicate the one to many relationships among the keys in these tables.
We've just scratched the surface for a very basic introduction. Expect
that a formal class in database systems will offer you far more detail
and better design principles than are conveyed in the elementary example
above. But you're already well on your way!
Key Takeaways
- Data includes raw facts that must be turned into information in order to be useful and valuable.
- Databases are created, maintained, and manipulated using programs called database management systems (DBMS), sometimes referred to as database software.
- All data fields in the same database have unique names, several data fields make up a data record, multiple data records make up a table or data file, and one or more tables or data files make up a database.
- Relational databases are the most common database format.
Questions and Exercises
- Define the following terms: table, record, field. Provide another name for each term along with your definition.
-
Answer the following questions using the course registration database system, diagramed above:
- Imagine you also want to keep track of student majors. How would you do this? Would you modify an existing table? Would you add new tables? Why or why not?
- Why do you suppose the system needs a "Course Title" table?
- This database is simplified for our brief introduction. What additional data would you need to keep track of if this were a real course registration system? What changes would you make in the database above to account for these needs?
- Research to find additional examples of organizations that made bad decisions based on bad data. Report your examples to your class. What was the end result of the examples you're citing (e.g., loss, damage, or other outcome)? What could managers have done to prevent problems in the cases that you cited? What role did technology play in the examples that you cite? What role did people or procedural issues play?
- Why is an understanding of database terms and technologies important, even for nontechnical managers and staff? Consider factors associated with both system use and system development. What other skills, beyond technology, may be important when engaged in data-driven decision making?
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.
Where Does Data Come From
Learning Objectives
- Understand various internal and external sources for enterprise data.
- Recognize the function and role of data aggregators, the potential for leveraging third-party data, the strategic implications of relying on externally purchased data, and key issues associated with aggregators and firms that leverage externally sourced data.
Organizations can pull together data from a variety of sources. While the examples that follow aren't meant to be an encyclopedic listing of possibilities, they will give you a sense of the diversity of options available for data gathering.
Transaction Processing Systems
For most organizations that sell directly to their customers, transaction processing systems (TPS) represent a fountain of potentially insightful data. Every time a consumer uses a point-of-sale system, an ATM, or a service desk, there's a transaction (some kind of business exchange) occurring, representing an event that's likely worth tracking.
The cash register is the data generation workhorse of most physical
retailers, and the primary source that feeds data to the TPS. But while
TPS can generate a lot of bits, it's sometimes tough to match this data
with a specific customer. For example, if you pay a retailer in cash,
you're likely to remain a mystery to your merchant because your name
isn't attached to your money. Grocers and retailers can tie you to cash
transactions if they can convince you to use a loyalty card.
Use one of these cards and you're in effect giving up information about
yourself in exchange for some kind of financial incentive. The
explosion in retailer cards is directly related to each firm's desire to
learn more about you and to turn you into a more loyal and satisfied
customer.
Some cards provide an instant discount (e.g., the CVS Pharmacy ExtraCare
card), while others allow you to build up points over time (Best Buy's
Reward Zone). The latter has the additional benefit of acting as a
switching cost. A customer may think "I could get the same thing at
Target, but at Best Buy, it'll increase my existing points balance and
soon I'll get a cash back coupon".
Tesco: Tracked Transactions, Increased Insights, and Surging Sales
UK grocery giant Tesco, the planet's third-largest retailer, is envied worldwide for what analysts say is the firm's unrivaled ability to collect vast amounts of retail data and translate this into sales.
Tesco's data collection relies heavily on its ClubCard loyalty program,
an effort pioneered back in 1995. But Tesco isn't just a physical
retailer. As the world's largest Internet grocer, the firm gains
additional data from Web site visits, too. Remove products from your
virtual shopping cart? Tesco can track this. Visited a product
comparison page? Tesco watches which product you've chosen to go with
and which you've passed over. Done your research online, then traveled
to a store to make a purchase? Tesco sees this, too.
Tesco then mines all this data to understand how consumers respond to
factors such as product mix, pricing, marketing campaigns, store layout,
and Web design. Consumer-level targeting allows the firm to tailor its
marketing messages to specific subgroups, promoting the right offer
through the right channel at the right time and the right price. To get a
sense of Tesco's laser-focused targeting possibilities, consider that
the firm sends out close to ten million different, targeted offers each
quarter.
The firm's data-driven management is clearly delivering results. Even
while operating in the teeth of a global recession, Tesco repeatedly
posted record corporate profits and the highest earnings ever for a
British retailer.
Enterprise Software (CRM, SCM, and ERP)
Firms increasingly set up systems to gather additional data beyond conventional purchase transactions or Web site monitoring. CRM or customer relationship management systems are often used to empower employees to track and record data at nearly every point of customer contact. Someone calls for a quote? Brings a return back to a store? Writes a complaint e-mail? A well-designed CRM system can capture all these events for subsequent analysis or for triggering follow-up events.
Enterprise software includes not just CRM systems but also categories
that touch every aspect of the value chain, including supply chain
management (SCM) and enterprise resource planning (ERP) systems. More
importantly, enterprise software tends to be more integrated and
standardized than the prior era of proprietary systems that many firms
developed themselves. This integration helps in combining data across
business units and functions, and in getting that data into a form where
it can be turned into information.
Surveys
Sometimes firms supplement operational data with additional input from surveys and focus groups. Oftentimes, direct surveys can tell you what your cash register can't. Zara store managers informally survey customers in order to help shape designs and product mix. Online grocer FreshDirect surveys customers weekly and has used this feedback to drive initiatives from reducing packaging size to including star ratings on produce.
Can Technology "Cure" U.S. Health Care?
The U.S. health care system is broken. It's costly, inefficient, and problems seem to be getting worse. Estimates suggest that health care spending makes up a whopping 18 percent of U.S. gross domestic product. U.S. automakers spend more on health care than they do on steel. Even more disturbing, it's believed that medical errors cause as many as ninety-eight thousand unnecessary deaths in the United States each year, more than motor vehicle accidents, breast cancer, or AIDS.
For years it's been claimed that technology has the potential to reduce
errors, improve health care quality, and save costs. Now pioneering
hospital networks and technology companies are partnering to help tackle
cost and quality issues. For a look at possibilities for leveraging
data throughout the doctor-patient value chain, consider the
"event-driven medicine" system built by Dr. John Halamka and his team at
Boston's Beth Israel Deaconess Medical Center (part of the Harvard
Medical School network).
When docs using Halamka's system encounter a patient with a chronic
disease, they generate a decision support "screening sheet". Each event
in the system: an office visit, a lab results report (think the medical
equivalent of transactions and customer interactions), updates the
patient database. Combine that electronic medical record information
with artificial intelligence
on best practice, and the system can offer recommendations for care,
such as, "Patient is past due for an eye exam" or, "Patient should
receive pneumovax [a vaccine against infection] this season".
The systems don't replace decision making by doctors and nurses, but
they do help to ensure that key issues are on a provider's radar.
More efficiencies and error checks show up when prescribing drugs. Docs
are presented with a list of medications covered by that patient's
insurance, allowing them to choose quality options while controlling
costs. Safety issues, guidelines, and best practices are also displayed.
When correct, safe medication in the right dose is selected, the
electronic prescription is routed to the patients' pharmacy of choice.
As Halamka puts it, going from "doctor's brain to patients vein" without
any of that messy physician handwriting, all while squeezing out layers
where errors from human interpretation or data entry might occur.
President Obama believes technology initiatives can save health care as
much as $120 billion a year, or roughly two thousand five hundred
dollars per family.
An aggressive number, to be sure. But with such a large target to aim
at, it's no wonder that nearly every major technology company now has a
health solutions group. Microsoft and Google even offer competing
systems for electronically storing and managing patient health records.
If systems like Halamka's and others realize their promise, big benefits
may be just around the corner.
External Sources
Sometimes it makes sense to combine a firm's data with bits brought in from the outside. Many firms, for example, don't sell directly to consumers (this includes most drug companies and packaged goods firms). If your firm has partners that sell products for you, then you'll likely rely heavily on data collected by others.
Data bought from sources available to all might not yield competitive advantage on its own, but it can provide key operational insight for increased efficiency and cost savings. And when combined with a firm's unique data assets, it may give firms a high-impact edge.Consider restaurant chain Brinker, a firm that runs seventeen hundred eateries in twenty-seven countries under the Chili's, On The Border, and Maggiano's brands. Brinker (whose ticker symbol is EAT), supplements their own data with external feeds on weather, employment statistics, gas prices, and other factors, and uses this in predictive models that help the firm in everything from determining staffing levels to switching around menu items.
In another example, Carnival Cruise Lines combines its own customer data with third-party information tracking household income and other key measures. This data plays a key role in a recession, since it helps the firm target limited marketing dollars on those past customers that are more likely to be able to afford to go on a cruise. So far it's been a winning approach. For three years in a row, the firm has experienced double-digit increases in bookings by repeat customers.
Who's Collecting Data about You?
There's a thriving industry collecting data about you. Buy from a catalog, fill out a warranty card, or have a baby, and there's a very good chance that this event will be recorded in a database somewhere, added to a growing digital dossier that's made available for sale to others. If you've ever gotten catalogs, coupons, or special offers from firms you've never dealt with before, this was almost certainly a direct result of a behind-the-scenes trafficking in the "digital you".
Firms that trawl for data and package them up for resale are known as data aggregators.
They include Acxiom, a $1.3 billion a year business that combines
public source data on real estate, criminal records, and census reports,
with private information from credit card applications, warranty card
surveys, and magazine subscriptions. The firm holds data profiling some
two hundred million Americans.
Or maybe you've heard of Lexis-Nexis. Many large universities subscribe
to the firm's electronic newspaper, journal, and magazine databases. But
the firm's parent, Reed Elsevier, is a data sales giant, with divisions
packaging criminal records, housing information, and additional data
used to uncover corporate fraud and other risks. In February, 2008, the
firm got even more data rich, acquiring Acxiom competitor ChoicePoint
for $4.1 billion. With that kind of money involved, it's clear that data
aggregation is very big business.
The Internet also allows for easy access to data that had been public
but otherwise difficult to access. For one example, consider home sale
prices and home value assessments. While technically in the public
record, someone wanting this information previously had to traipse down
to their Town Hall and speak to a clerk, who would hand over a printed
log book. Not exactly a Google-speed query. Contrast this with a visit
to Zillow.com. The free site lets you pull up a map of your town and
instantly peek at how much your neighbors paid for their homes. And it
lets them see how much you paid for yours, too.
Computerworld's Robert Mitchell uncovered a more disturbing issue when public record information is made available online. His New Hampshire municipality had digitized and made available some of his old public documents without obscuring that holy grail for identity thieves, his Social Security number.
Then there are accuracy concerns. A record incorrectly identifying you as a cat lover is one thing, but being incorrectly named to the terrorist watch list is quite another. During a five-week period airline agents tried to block a particularly high profile U.S. citizen from boarding airplanes on five separate occasions because his name resembled an alias used by a suspected terrorist. That citizen? The late Ted Kennedy, who at the time was the senior U.S. senator from Massachusetts.For the data trade to continue, firms will have to treat customer data as the sacred asset it is. Step over that "creep-out" line, and customers will push back, increasingly pressing for tighter privacy laws. Data aggregator Intellius used to track cell phone customers, but backed off in the face of customer outrage and threatened legislation.
Another concern – sometimes data aggregators are just plain sloppy, committing errors that can be costly for the firm and potentially devastating for victimized users. For example, in 2005, ChoicePoint accidentally sold records on 145,000 individuals to a cybercrime identity theft ring. The ChoicePoint case resulted in a $15 million fine from the Federal Trade Commission. In 2011, hackers stole at least 60 million e-mail addresses from marketing firm Epsilon, prompting firms as diverse as Best Buy, Citi, Hilton, and the College Board to go through the time-consuming, costly, and potentially brand-damaging process of warning customers of the breach. Epsilon faces liabilities charges of almost a quarter of a billion dollars, but some estimate that the total price tag for the breach could top $4 billion. Just because you can gather data and traffic in bits doesn't mean that you should. Any data-centric effort should involve input not only from business and technical staff, but from the firm's legal team as well.
Privacy Regulation: A Moving Target
New methods for tracking and gathering user information appear daily, testing user comfort levels. For example, the firm Umbria uses software to analyze millions of blog and forum posts every day, using sentence structure, word choice, and quirks in punctuation to determine a blogger's gender, age, interests, and opinions. While Google refused to include facial recognition as an image search product ("too creepy," said its chairman), Facebook, with great controversy, turned on facial recognition by default. It's quite possible that in the future, someone will be able to upload a photo to a service and direct it to find all the accessible photos and video on the Internet that match that person's features. And while targeting is getting easier, a Carnegie Mellon study showed that it doesn't take much to find someone with a minimum of data. Simply by knowing gender, birth date, and postal zip code, 87 percent of people in the United States could be pinpointed by name. Another study showed that publicly available data on state and date of birth could be used to predict U.S. Social Security numbers – a potential gateway to identity theft.
Some feel that Moore's Law, the falling cost of storage, and the
increasing reach of the Internet have us on the cusp of a privacy train
wreck. And that may inevitably lead to more legislation that restricts
data-use possibilities. Noting this, strategists and technologists need
to be fully aware of the legal environment their systems face and consider how such environments may
change in the future. Many industries have strict guidelines on what
kind of information can be collected and shared.
For example, HIPAA (the U.S. Health Insurance Portability and
Accountability Act) includes provisions governing data use and privacy
among health care providers, insurers, and employers. The financial
industry has strict requirements for recording and sharing
communications between firm and client (among many other restrictions).
There are laws limiting the kinds of information that can be gathered on
younger Web surfers. And there are several laws operating at the state
level as well.
International laws also differ from those in the United States. Europe,
in particular, has a strict European Privacy Directive. The directive
includes governing provisions that limit data collection, require notice
and approval of many types of data collection, and require firms to
make data available to customers with mechanisms for stopping collection
efforts and correcting inaccuracies at customer request. Data-dependent
efforts plotted for one region may not fully translate in another
effort if the law limits key components of technology use. The
constantly changing legal landscape also means that what works today
might not be allowed in the future.
Firms beware – the public will almost certainly demand tighter controls if
the industry is perceived as behaving recklessly or inappropriately
with customer data.
Key Takeaways
- For organizations that sell directly to their customers, transaction processing systems (TPS) represent a source of potentially useful data.
- Grocers and retailers can link you to cash transactions if they can convince you to use a loyalty card which, in turn, requires you to give up information about yourself in exchange for some kind of financial incentive such as points or discounts.
- Enterprise software (CRM, SCM, and ERP) is a source for customer, supply chain, and enterprise data.
- Survey data can be used to supplement a firm's operational data.
- Data obtained from outside sources, when combined with a firm's internal data assets, can give the firm a competitive edge.
- Data aggregators are part of a multibillion-dollar industry that provides genuinely helpful data to a wide variety of organizations.
- Data that can be purchased from aggregators may not in and of itself yield sustainable competitive advantage since others may have access to this data, too. However, when combined with a firm's proprietary data or integrated with a firm's proprietary procedures or other assets, third-party data can be a key tool for enhancing organizational performance.
- Data aggregators can also be quite controversial. Among other things, they represent a big target for identity thieves, are a method for spreading potentially incorrect data, and raise privacy concerns.
- Firms that mismanage their customer data assets risk lawsuits, brand damage, lower sales, fleeing customers, and can prompt more restrictive legislation.
- Further raising privacy issues and identity theft concerns, recent studies have shown that in many cases it is possible to pinpoint users through allegedly anonymous data, and to guess Social Security numbers from public data.
- New methods for tracking and gathering user information are raising privacy issues which possibly will be addressed through legislation that restricts data use.
Questions and Exercises
- Why would a firm use a loyalty card? What is the incentive for the firm? What is the incentive for consumers to opt in and use loyalty cards? What kinds of strategic assets can these systems create?
- In what ways does Tesco gather data? Can other firms match this effort? What other assets does Tesco leverage that helps the firm remain among top performing retailers worldwide?
- Make a list of the kind of data you might give up when using a cash register, a Web site, or a loyalty card, or when calling a firm's customer support line. How might firms leverage this data to better serve you and improve their performance?
- Are you concerned by any of the data-use possibilities that you outlined in prior questions, discussed in this chapter, or that you've otherwise read about or encountered? If you are concerned, why? If not, why not? What might firms, governments, and consumers do to better protect consumers?
- What are some of the sources data aggregators tap to collect information?
- Privacy laws are in a near constant state of flux. Conduct research to identify the current state of privacy law. Has major legislation recently been proposed or approved? What are the implications for firms operating in effected industries? What are the potential benefits to consumers? Do consumers lose anything from this legislation?
- Self-regulation is often proposed as an alternative to legislative efforts. What kinds of efforts would provide "teeth" to self-regulation. Are there steps firms could do to make you believe in their ability to self-regulate? Why or why not?
- What is HIPPA? What industry does it impact?
- How do international privacy laws differ from U.S. privacy laws?
Data Rich, Information Poor
Learning Objectives
- Know and be able to list the reasons why many organizations have data that can't be converted to actionable information.
- Understand why transactional databases can't always be queried and what needs to be done to facilitate effective data use for analytics and business intelligence.
- Recognize key issues surrounding data and privacy legislation.
Despite being awash in data, many organizations are data rich but information poor. A survey by consulting firm Accenture found 57 percent of companies reporting that they didn't have a beneficial, consistently updated, companywide analytical capability. Among major decisions, only 60 percent were backed by analytics – 40 percent were made by intuition and gut instinct. The big culprit limiting BI initiatives is getting data into a form where it can be used, analyzed, and turned into information. Here's a look at some factors holding back information advantages.
Incompatible Systems
Just because data is collected doesn't mean it can be used. This limit is a big problem for large firms that have legacy systems, outdated information systems that were not designed to share data, aren't compatible with newer technologies, and aren't aligned with the firm's current business needs. The problem can be made worse by mergers and acquisitions, especially if a firm depends on operational systems that are incompatible with its partner. And the elimination of incompatible systems isn't just a technical issue. Firms might be under extended agreement with different vendors or outsourcers, and breaking a contract or invoking an escape clause may be costly. Folks working in M&A (the area of investment banking focused on valuing and facilitating mergers and acquisitions) beware – it's critical to uncover these hidden costs of technology integration before deciding if a deal makes financial sense.
Legacy Systems: A Prison for Strategic Assets
The experience of one Fortune 100 firm that your author has worked with illustrates how incompatible information systems can actually hold back strategy. This firm was the largest in its category, and sold identical commodity products sourced from its many plants worldwide. Being the biggest should have given the firm scale advantages. But many of the firm's manufacturing facilities and international locations developed or purchased separate, incompatible systems. Still more plants were acquired through acquisition, each coming with its own legacy systems.
The plants with different information systems used different part numbers and naming conventions even though they sold identical
products. As a result, the firm had no timely information on how much
of a particular item was sold to which worldwide customers. The company
was essentially operating as a collection of smaller, regional
businesses, rather than as the worldwide behemoth that it was.
After the firm developed an information system that standardized data
across these plants, it was, for the first time, able to get a single
view of worldwide sales. The firm then used this data to approach their
biggest customers, negotiating lower prices in exchange for increased
commitments in worldwide purchasing. This trade let the firm take share
from regional rivals. It also gave the firm the ability to shift
manufacturing capacity globally, as currency prices, labor conditions,
disaster, and other factors impacted sourcing. The new information
system in effect liberated the latent strategic asset of scale,
increasing sales by well over a billion and a half dollars in the four
years following implementation.
Operational Data Can't Always Be Queried
Another problem when turning data into information is that most transactional databases aren't set up to be simultaneously accessed for reporting and analysis. When a customer buys something from a cash register, that action may post a sales record and deduct an item from the firm's inventory. In most TPS systems, requests made to the database can usually be performed pretty quickly – the system adds or modifies the few records involved and it's done – in and out in a flash.
But if a manager asks a database to analyze historic sales trends
showing the most and least profitable products over time, they may be
asking a computer to look at thousands of transaction records, comparing
results, and neatly ordering findings. That's not a quick in-and-out
task, and it may very well require significant processing to come up
with the request. Do this against the very databases you're using to
record your transactions, and you might grind your computers to a halt.
Getting data into systems that can support analytics is where data
warehouses and data marts come in, the topic of our next section.
Key Takeaways
- A major factor limiting business intelligence initiatives is getting data into a form where it can be used (i.e., analyzed and turned into information).
- Legacy systems often limit data utilization because they were not designed to share data, aren't compatible with newer technologies, and aren't aligned with the firm's current business needs.
- Most transactional databases aren't set up to be simultaneously accessed for reporting and analysis. In order to run analytics the data must first be ported to a data warehouse or data mart.
Questions and Exercises
- How might information systems impact mergers and acquisitions? What are the key issues to consider?
- Discuss the possible consequences of a company having multiple plants, each with a different information system using different part numbers and naming conventions for identical products.
- Why does it take longer, and require more processing power, to analyze sales trends by region and product, as opposed to posting a sales transaction?