Home > Articles > Business & Management

This chapter is from the book

Challenges with Business Analytics

Optimization modeling and heuristic tools have yet to make the transition into the Big Data era. In an October 2013 issue of the Wall Street Journal, John Jordan of Penn State University describes several challenges involved when implementing business analytics.17 He notes that there is “a greater potential for privacy invasion, greater financial exposure in fast-moving markets, greater potential for mistaking noise for true insight, and a greater risk of spending lots of money and time chasing poorly defined problems or opportunities.” This section discusses some of the challenges associated with prescriptive analytics and offers some practical recommendations on how to avoid them.

Lack of Management Science Experts

The everyday use of mathematical modeling and other techniques requires that business managers or other practitioners have a good understanding of numeracy and mathematical skills. However, there is a lack of such skills, especially for medium-sized or small organizations. It is estimated that by 2018, U.S. universities and other educational institutions will need to produce between 140,000 and 190,000 more graduates for deep analytical talent positions and 1.5 million more data-savvy managers.18

Business analytics, in general, and prescriptive analytics, in particular, can become more “popular” with the use of spreadsheet modeling. Spreadsheet modeling is widely used in colleges and universities for teaching mathematical programming. Instead of heavy modeling, which seeks optimal solutions, spreadsheet modeling techniques include simpler formulations, which seek practical solutions. However, the spreadsheets have limitations in the amount of data they can store. They cannot store data about millions of transactions in a bank or the details of federal spending on transportation projects, even for a week. There is a time for organizations to introduce more advanced tools.

Analytics Brings Change in the Decision-Making Process

The goal of prescriptive analytics is to bring business value through better strategic and operational decisions. At the strategic level, those who make decisions about what models to implement and what needs to be measured will accrue more power. At the operational level, the implementation of such models brings a power shift in the decision-making process. Information-based decisions across organizational boundaries can upset traditional power relationships.

The story of the Oberweis Dairy 19 is an excellent example of how data analytics can transform organizations. The company started as an Illinois farmer selling his surplus milk to neighbors in 1915. Today, the company has “three distribution channels: home delivery, with thousands of customers; retail, with 47 corporate and franchise stores; and wholesale, to regional and national grocery chains like Target.” The usual approach to decision making at the company is by asking executives to figure out the best configuration for future changes.

In 2012, the company was trying to expand its operations geographically, and the chief executive officer (CEO) asked a data analytics executive with only three years of experience to join the strategy table. Bringing analytics to the table changed the preconceived notion about the customers. Although there were current customers who benefited from the offerings of the company, data analysis indicated that the company had spent a lot of money to acquire customers who should not have been approached in the first place. Contrary to the company’s conventional wisdom, data from the customer sales indicated that “the so-called Beamer and Birkenstock group—liberal, high-income, BMW-driving, established couples living leisurely lifestyles” was not a good fit for the dairy farm. So, the meeting changed from a tactical meeting with a focus on “how many trucks and transfer centers would be required” into a strategic “define the target market” meeting. The change is cultural, and it has grown to a point now where people want to acquire a better understanding of analytics tools because they can see that there is real benefit.

Big Data Leads to Incorrect Information

Modeling with business analytics is more an art than a science. One fundamental step when building mathematical models is the process of abstraction. Through this process, the modeler eliminates or suppresses any unnecessary details and allows only the relevant information to enter the model. When good information goes in the model, a good model produces good results. The opposite is known as GIGO (garbage in, garbage out). In the era of Big Data, it is significantly more difficult for the data analyst to mine in the mountains of information and find the relevant pieces.

Very often, valid models produce poor results, which lead to the wrong decisions. In the era of Big Data, this happens very often. A recent story 20 reports how ten volunteers checked the accuracy of their information on AboutTheData.com and they each found inaccuracies. In one specific case, a volunteer found that “she had two teens, at 26.” Interestingly, a CNN team found that Acxiom, the company that runs the database, was more accurate specifying the interests and less accurate in demographic data (marriage status, number of children). Wrong assumptions can lead to wrong decisions. If you are a company purchasing this database, you know the interests of your future customers, but very likely you may be sending out 2 million direct mails pieces on baby products to people who may not even have children.

Big Data Demands Big Thinking

Business organizations are just entering the new paradigm of Big Data. They have been using standard databases for more than three decades and have accumulated experience and knowledge. However, Big Data demands new techniques and many of them are still in the developmental stages. Acquiring the new tools requires a radical change in underlying beliefs or theory—they require a new way of thinking. It requires, for example, that more people think probabilistically rather than anecdotally. It also requires that managers learn to focus on the signals and do not get lost in the noise.21 This way, organizations will be able to better understand the factors behind customers, products, services, and how to make analytical decisions.

What is Big Data? Big Data is a combination of structured, in-house operational databases with external databases, with automatically captured and often nonstructured data from social networks, web server logs, banking transactions, content of web pages, financial market data, and so on. All this data, coming from a wide variety of sources is combined into non-normalized data warehouse schema. Big Data is usually characterized by three Vs: volume, velocity, and variety:1 22

  • Volume—Today, the high volume of business transactions is automatically captured by advanced enterprise information systems. Nonstructured and external databases also produce large amounts of data. These sources are then combined into denormalized data warehouses. Unnormalized (or denormalized) data means high-volume data with intentional redundancy. The volume of Big Data is larger than the volume processed by conventional relational databases.

    Descriptive and predictive analytics benefit from the high volume of data. After all, statistical analysis and reliability of predictions is better when the population size increases. A forecasting method with hundreds of factors can predict better than the one with only a few input factors. Prescriptive models also benefit from Big Data. They are based on aggregated inputs, which are the result of descriptive analytics: contribution coefficients, average processing times, mean of distributions, and so on. The validity of these aggregate values improves with high-volume data.

    Stochastic models can benefit from high-volume data, as well. Statistical distributions are more reliable when fitted with a large number of data points. A prescriptive model, which assumes a normal distribution for processing times, is more reliable when the mean and standard deviation of the normal distribution is based on thousands or millions of data points as compared with only hundreds of data points.

  • Velocity—Velocity refers to the rate at which data flows into an organization. Online sales, mobile computing, smartphones, and social networks have significantly increased the information flow for the organization. Organizations can analyze customer behavior, sales history, and buying patterns. They are able to quickly produce operational business intelligence and recommend additional purchases or customized marketing strategies. The velocity of system output is also important. The recommendations must be delivered in a timely manner and must be included as part of business operations. A loan officer, for example, could compare the information in a loan application against business rules and mining models, and make a recommendation to the applicant or make a decision about the loan.

    Prescriptive modeling techniques can take advantage of velocity. They can be modeled to run in the background and take data from input to make optimal or near-optimal decisions.

  • Variety—Variety of data refers to the mix of different data sources in different formats. As mentioned earlier, Big Data input may arrive in the form of a text from social networks or an image from a camera sensor. Even when the data source is structured, the format can be different. Different browsers generate different data. Different users may withhold information, or different vendors may send different information based on the type of software they use. Of course, every time humans are involved, there may be errors, redundancy, and inconsistency. Management science models require the input data to be uniform. As such, the implementation of these models in the era of Big Data normally requires an additional layer between the source data and the prescriptive model.

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020